Trends – Artificial Intelligence (AI)

May 30, 2025

Mary Meeker / Jay Simons / Daegwon Chae / Alexander Krey

Context

We set out to compile foundational trends related to AI. A starting collection of several disparate datapoints turned into this beast.

As soon as we updated one chart, we often had to update another – a data game of whack-a-mole…

a pattern that shows no sign of stopping…and will grow more complex as competition

among tech incumbents, emerging attackers and sovereigns accelerates.

Vint Cerf, one of the ‘Founders of the Internet,’ said in 1999, ‘…they say a year in the Internet business is like a dog year –

equivalent to seven years in a regular person's life.’ At the time, the pace of change catalyzed by the internet was unprecedented.

Consider now that AI user and usage trending is ramping materially faster…and the machines can outpace us.

The pace and scope of change related to the artificial intelligence technology evolution is indeed unprecedented,

as supported by the data. This document is filled with user, usage and revenue charts that go up-and-to-the-right…

often supported by spending charts that also go up-and-to-the right.

Creators / bettors / consumers are taking advantage of global internet rails that are accessible to 5.5B citizens via

connected devices; ever-growing digital datasets that have been in the making for over three decades;

breakthrough large language models (LLMs) that – in effect – found freedom with the November 2022 launch of

OpenAI’s ChatGPT with its extremely easy-to-use / speedy user interface.

In addition, relatively new AI company founders have been especially aggressive about innovation / product releases / investments / acquisitions / cash burn and capital raises. At the same time, more traditional tech companies (often with founder involvement) have

increasingly directed more of their hefty free cash flows toward AI in efforts to drive growth and fend off attackers.

And global competition – especially related to China and USA tech developments – is acute.

The outline for our document is on the next page, followed by eleven charts that help illustrate observations that follow.

We hope this compilation adds to the discussion of the breadth of change at play – technical / financial / social / physical / geopolitical.

No doubt, people (and machines) will improve on the points as we all aim to adapt to this evolving journey

as knowledge – and its distribution – get leveled up rapidly in new ways.

Special thanks to Grant Watson and Keeyan Sanjasaz and BOND colleagues who helped steer ideas and bring this report to life.

And, to the many friends and technology builders who helped, directly or via your work, and are driving technology forward.

2

Outline

#

1 • Seem Like Change Happening Faster Than Ever? 9-51 Yes, It Is

2 • AI User + Usage + CapEx Growth = 52-128 Unprecedented

3 • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = 129-152

Performance Converging + Developer Usage Rising

4 • AI Usage + Cost + Loss Growth = 153-247 Unprecedented

5 • AI Monetization Threats =

Rising Competition + Open-Source Momentum + China’s Rise 248-298

6 • AI & Physical World Ramps = 299-307 Fast + Data-Driven

7 • Global Internet User Ramps Powered by AI from Get-Go =

Growth We Have Not Seen Likes of Before 308-322

8 • AI & Work Evolution =

Real + Rapid 323-336

3

Charts Paint Thousands of Words…

1 2

Seem Like Change Happening Faster Than Ever? AI User + Usage + CapEx Growth =

Yes, It Is Unprecedented

Developers in Leading Chipmaker’s Ecosystem Leading USA-Based LLM Users

M M M 6MM 800MM M

rs, s, e

lop

User

ve

f cti Deve

A

e kly r o

b ee Num W 2005 2025 10/22 4/25

Details on Details on

Source: Leading Chipmaker Page 38 Source: Company disclosures Page 55

2.1 2.2

AI User + Usage + CapEx Growth = AI User + Usage + CapEx Growth =

Unprecedented Unprecedented

Internet vs. Leading USA-Based LLM: Big Six* USA Technology Company CapEx

Total Current Users Outside North America

100% $212B

% 90%

s, +63% User B t n @ Year 3 90% $, x re @ Year 23 E Cur 50%

l Cap ta

o

T

f

o

re

a

h

S 0%

0 33 Internet LLM 2014 2024

Years In Years In

Note: LLM data is for monthly active mobile app users. App not available in select countries, including

China and Russia, as of 5/25. Details on *Apple, NVIDIA, Microsoft, Alphabet, Amazon (AWS only), & Meta Platforms Details on Source: United Nations / International Telecommunications Union (3/25), Sensor Tower (5/25) Page 56 Source: Capital IQ (3/25), Morgan Stanley Page 97

4

…Charts Paint Thousands of Words…

3 4

AI Model Compute Costs High / Rising + AI Usage + Cost + Loss Growth =

Inference Costs Per Token Falling =

Performance Converging + Developer Usage Rising Unprecedented

Cost of Key Technologies Relative to Launch Year Leading USA-Based AI LLM Revenue vs. Compute Expense

r Electric Power +$3.7B a ) e

y Y 0 Computer Memory )

r ) & Red

P o ( p l t ex a d u e in n E xte ig e ve dup Or f ce e u AI Inference sen ri Y Ble B a e ( (In Re

o

Com -$5B

%

2022 2023 2024

0 Years 72 Years

Note: Per-token inference costs shown. Details on Note: Figures are estimates. Details on Source: Richard Hirsh; John McCallum; OpenAI Page 138 Source: The Information, public estimates Page 173

5 5.1

AI Monetization Threats = AI Monetization Threats =

Rising Competition + Rising Competition +

Open-Source Momentum + China’s Rise Open-Source Momentum + China’s Rise

Leading USA LLMs vs. China LLM China vs. USA vs. Rest of World Industrial Robots Installed

Desktop User Share

d

, lle re 2/24 2/25 4/25 China a sta h % 75%

60% In Rest of World

r S ts (excl. China & USA) o

Use Rob pl oia

Deskt 21% strud 10% 15%In

0% USA

USA – LLM #1 China USA – LLM #2 2014 2023

Note: Data is non-deduped. Share is relative, measured across six leading global LLMs. Details on Note: Data as of 2023. Details on Source: YipitData (5/25) Page 293 Source: International Federation of Robotics Page 289

5

…Charts Paint Thousands of Words

6 7

AI & Physical World Ramps = Global Internet User Ramps Powered by AI from Get-Go =

Fast + Data-Driven Growth We Have Not Seen Likes of Before

A Ride Share vs. Autonomous Taxi Provider, Leading USA-Based LLM App Users by Region San Francisco Operating Zone Market Share

34% M Sub-Saharan Africa

M South Asia s,

ci g p nA User Middle East & North Africa ki 27% ran sc p s North America o F o ile ve o b Latin America & Caribbean cti B o

an M 19% S oss ly A

th Europe & Central Asia

of Gr no % 0%M East Asia & Pacific

5/23 4/25

8/23 Ride Share Autonomous Taxi 4/25

Details on Note: Region definitions per World Bank definitions. China not included in East Asia figures. Details on

Source: YipitData (4/25) Page 302 Data for standalone app only. Source: Sensor Tower (5/25) Page 315

8

AI & Work Evolution =

Real + Rapid

USA IT Jobs – AI vs. Non-AI

s,

g )

n n

sti ee

o +448%

Jo 1 -A T to I d A eNon x b /1 =I P 8 Gr

US e , d nlue i In e B g= nI

(A -9%

Cha

1/18 Non-AI IT Jobs AI IT Jobs 4/25

Source: University of Maryland’s UMD-LinkUp AIMaps Details on (in collaboration with Outrigger Group) (5/25) Page 332

6

Overview…

To say the world is changing at unprecedented rates is an understatement.

Rapid and transformative technology innovation / adoption represent key underpinnings of these changes.

As does leadership evolution for the global powers.

Google’s founding mission (1998) was to ‘organize the world’s information and make it universally accessible and useful.’

Alibaba’s founding mission (1999) was to ‘make it easy to do business anywhere.’

Facebook’s founding mission (2004) was ‘to give people the power to share and make the world more open and connected.’

Fast forward to today with the world’s organized, connected and accessible information being supercharged by

artificial intelligence, accelerating computing power, and semi-borderless capital…all driving massive change.

Sport provides a good analogy for AI’s constant improvements. As athletes continue to wow us and break records,

their talent is increasingly enhanced by better data / inputs / training.

The same is true for businesses, where computers are ingesting massive datasets to get smarter and more competitive.

Breakthroughs in large models, cost-per-token declines, open-source proliferation and chip performance improvements

are making new tech advances increasingly more powerful, accessible, and economically viable.

OpenAI’s ChatGPT – based on user / usage / monetization metrics – is history’s biggest ‘overnight’ success

(nine years post-founding). AI usage is surging among consumers, developers, enterprises and governments.

And unlike the Internet 1.0 revolution – where technology started in the USA and steadily diffused globally –

ChatGPT hit the world stage all at once, growing in most global regions simultaneously.

Meanwhile, platform incumbents and emerging challengers are racing to build and deploy the next layers of AI infrastructure:

agentic interfaces, enterprise copilots, real-world autonomous systems, and sovereign models.

Rapid advances in artificial intelligence, compute infrastructure, and global connectivity are fundamentally reshaping how

work gets done, how capital is deployed, and how leadership is defined – across both companies and countries.

At the same time, we have leadership evolution among the global powers, each of whom is challenging the other’s

competitive and comparative advantage. We see the world’s most powerful countries

revved up by varying degrees of economic / societal / territorial aspiration…

7

…Overview

…Increasingly, two hefty forces – technological and geopolitical – are intertwining.

Andrew Bosworth (Meta Platforms CTO), on a recent ‘Possible’ podcast described the

current state of AI as our space race and the people we’re discussing, especially China, are highly capable…

there’s very few secrets. And there’s just progress. And you want to make sure that you’re never behind.

The reality is AI leadership could beget geopolitical leadership – and not vice-versa.

This state of affairs brings tremendous uncertainty…yet it leads us back to one of our favorite quotes –

Statistically speaking, the world doesn’t end that often, from former T. Rowe Price Chairman and CEO Brian Rogers.

As investors, we always assume everything can go wrong, but the exciting part is the consideration of what can go right.

Time and time again, the case for optimism is one of the best bets one can make.

The magic of watching AI do your work for you feels like the early days of email and web search –

technologies that fundamentally changed our world. The better / faster / cheaper impacts of

AI seem just as magical, but even quicker.

No doubt, these are also dangerous and uncertain times.

But a long-term case for optimism for artificial intelligence is based on the idea that intense competition and innovation…

increasingly-accessible compute…rapidly-rising global adoption of AI-infused technology…and thoughtful and

calculated leadership can foster sufficient trepidation and respect, that in turn, could lead to Mutually Assured Deterrence.

For some, the evolution of AI will create a race to the bottom; for others, it will create a race to the top.

The speculative and frenetic forces of capitalism and creative destruction are tectonic.

It’s undeniable that it’s ‘game on,’ especially with the USA and China and the tech powerhouses charging ahead.

In this document, we share data / research / benchmarks from third parties that use methodologies they deem to be effective –

we are thankful for the hard work so many are doing to illustrate trending during this uniquely dynamic time.

Our goal is to add to the discussion.

8

Outline

1 • Seem Like Change Happening Faster Than Ever?

Yes, It Is

2 • AI User + Usage + CapEx Growth =

Unprecedented

3 • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling =

Performance Converging + Developer Usage Rising

4 • AI Usage + Cost + Loss Growth =

Unprecedented

5 • AI Monetization Threats =

Rising Competition + Open-Source Momentum + China’s Rise

6 • AI & Physical World Ramps =

Fast + Data-Driven

7 • Global Internet User Ramps Powered by AI from Get-Go =

Growth We Have Not Seen Likes of Before

8 • AI & Work Evolution =

Real + Rapid

9

Technology Compounding =

Numbers Behind The Momentum

10

Technology Compounding Over Thousand-Plus Years =

Better + Faster + Cheaper → More…

Global GDP – Last 1,000+ Years, per Maddison Project

Cloud

Smartphones

Internet

PCs

Transistors

Synthetic Fertilizer

Flight

Internal Combustion Engine

Mass Production & Assembly Lines

Mass Steel Production

Electrification

Telegraph

Steam Engines

Printing Press

1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000

Note: Chart expressed in trillions of real GDP as measured by 2011 ‘GK$’ on a logarithmic scale. GK$ (Gross Knowledge Dollars) is an informal term used to estimate the potential business value of a specific insight, idea, or proprietary knowledge. It reflects how much that knowledge could be worth if applied effectively, even if it hasn’t yet generated revenue. Source: Microsoft, ‘Governing AI: A Blueprint for the Future,’ Microsoft Report (5/23); Data via Maddison Project & Our World in Data

Technology Compounding = Numbers Behind The Momentum 11 …Technology Compounding Over Fifty-Plus Years =

Better + Faster + Cheaper → More

Computing Cycles Over Time – 1960s-2020s, per Morgan Stanley

AI Era

MM Units in Log Scale Mobile

Internet

1,000,000

Desktop

Internet

Note: Axis is Tens of Billions

logarithmic; 10,000 of Units

i.e., there are ~4B+ PC

expected to Units ~1B+

be tens of Minicomputer Units / Users thousands 100 ~300MM+

more AI Era Units Mainframe ~10MM+

devices than

Mainframe Units

1

devices ~1MM+

Units

1960 1970 1980 1990 2000 2010 2020 2030

CPUs

Enabling

Infrastructure Big Data / Cloud

GPUs

Note: PC units as of 2000. Desktop internet users as of 2005, installed base as of 2010. Mobile internet units are the installed based of smartphones & tablets in 2020. Cloud & data center capex includes Google, Amazon, Microsoft, Meta, Alibaba, Apple, IBM, Oracle, Tencent, & Baidu for ten years ending 2022. ‘Tens of billions of units’ refers to the potential device & user base that could end up using AI technology; this includes smartphones, IOT devices, robotics, etc. Source: Weiss et al . ‘AI Index: Mapping the $4 Trillion Enterprise Impact’ via Morgan Stanley (10/23)

Technology Compounding = Numbers Behind The Momentum 12

AI Technology Compounding =

Numbers Behind The Momentum

13

260% Annual Growth Over Fifteen Years of…

Data to Train AI Models Led To…

Training Dataset Size (Number of Words) for Key AI Models – 1950-2025, per Epoch AI

rds

o

W

f

o

r

e

b +260%

/ Year

Num

–

e

iz

S

t

se

a

Dat

ing

rain

T

Note: Only “notable” language models shown (per Epoch AI, includes state of the art improvement on a recognized benchmark, >1K citations, historically relevant, with significant use). Source: Epoch AI (5/25)

AI Technology Compounding = Numbers Behind The Momentum 14 …360% Annual Growth Over Fifteen Years of…

Compute to Train AI Models Led To…

Training Compute (FLOP) for Key AI Models – 1950-2025, per Epoch AI

Grok 3

+360%

* / Year OP

FL

–

te

u

p

Com

ing

rain

T

*A FLOP (floating point operation) is a basic unit of computation used to measure processing power, representing a single arithmetic calculation involving decimal numbers. In AI, total FLOPs are often used to estimate the computational cost of training or running a model.

Note: Only language models shown (per Epoch AI, includes state of the art improvement on a recognized benchmark, >1K citations, historically relevant, with significant use). Source: Epoch AI (5/25)

AI Technology Compounding = Numbers Behind The Momentum 15 …200% Annual Growth Over Nine Years of…

Compute Gains from Better Algorithms Led To…

Impact of Improved Algorithms on AI Model Performance – 2014-2023, per Epoch AI

)

4

1

0

2

to

e

iv

e +200% / lat

(R Year te

u

p

Com

e

ctiv

e

ff

E

Note: Estimates how much progress comes from bigger models versus smarter algorithms, based on how much computing power you'd need to reach top performance without any improvements. Source: Epoch AI (3/24)

AI Technology Compounding = Numbers Behind The Momentum 16 …150% Annual Growth Over Six Years of…

Performance Gains from Better AI Supercomputers Led To…

Performance of Leading AI Supercomputers (FLOP/s) – 2019-2025, per Epoch AI

Enabled by 1.6x annual

growth in chips per cluster

and 1.6x annual growth in

performance per chip

/s

OP

FL

it

-b

6

1

,

ce +150% / n a Year rm

o

rf

e

P

Source: Epoch AI (4/25)

AI Technology Compounding = Numbers Behind The Momentum 17 …167% Annual Growth Over Four Years in…

Number of Powerful AI Models

Number of New Large-Scale AI Models (Larger than 10 23 FLOP*) – 2017-2024,

per Epoch AI

100

r

a

e

Y

ch Includes models from a

E • xAI d • Anthropic se • a Meta

+167% / • NVIDIA

Rele • Mistral Year 50 ls • Arc Institute e d • & Others…

Mo

New

o Both models from f

e DeepMind (AlphaGo r

b

Zero & Master)

Num

0

2017 2018 2019 2020 2021 2022 2023 2024

Publication Date

*As of 4/25, ‘Large-Scale AI Models’ are generally defined as those with a training compute of 1023 FLOPs or greater, per Epoch AI. Source: Epoch AI (5/25)

AI Technology Compounding = Numbers Behind The Momentum 18 ChatGPT AI User + Subscriber + Revenue Growth Ramps =

Hard to Match, Ever

ChatGPT User + Subscriber + Revenue Growth – 10/22-4/25,

per OpenAI & The Information

Users (MM) Subscribers (MM) Revenue ($B)

800 $4 20

rs, MM

Use

e B ctiv , , MM $ A u rs e kly 400 be 10 n $2 e

e bscri Rev e

W Su T P

tG

Cha

0 0 $0

10/22 8/23 6/24 4/25 10/22 8/23 6/24 4/25 2022 2023 2024

Note: 4/25 user count estimate from OpenAI CEO Sam Altman’s 4/11/25 TED Talk disclosure. Revenue figures are estimates based off OpenAI disclosures. Source: OpenAI

disclosures (as of 4/25), The Information (4/25) (link, link, link & link)

AI Technology Compounding = Numbers Behind The Momentum 19 Time to 365B Annual Searches =

ChatGPT 5.5x Faster vs. Google

Annual Searches by Year (B) Since Public Launches of Google & ChatGPT – 1998-2025,

per Google & OpenAI

5,000

B

s,

rche

a

S 2,500 l e

a

u

n ChatGPT Hit 365B Annual n

A Searches in 2 Years (2024) vs.

Google’s 11 Years (2009)

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Google Search ChatGPT

Years Since Public Launch (Google = 9/98, ChatGPT = 11/22)

Note: Dashed-line bars are for years where Google did not disclose annual search volumes. Source: Google public disclosures, OpenAI (12/24). ChatGPT figures are estimates per company disclosures of ~1B daily queries

AI Technology Compounding = Numbers Behind The Momentum 20

In 1998, tapping emerging Internet access, Google set out to

‘organize the world’s information and make it

universally accessible and useful.’

Nearly three decades later

– after some of the fastest change humankind has seen –

a lot of information is indeed digitized / accessible / useful.

The AI-driven evolution of how we

access and move information is happening much faster…

…AI is a compounder – on internet infrastructure, which allows

for wicked-fast adoption of easy-to-use broad-interest services.

AI Technology Compounding = Numbers Behind The Momentum 21

Knowledge Distribution Evolution =

Over ~Six Centuries

22

Knowledge Distribution – 1440-1992 =

Static + Physical Delivery…

Printing Press – Invented 1440

Source: Wikimedia Commons

Knowledge Distribution Evolution = Over ~Six Centuries 23 …Knowledge Distribution – 1993-2021 =

Active + Digital Delivery…

Internet – Public Release 1993*

*The internet is widely agreed to have been ‘publicly released’ in 1993 with release of the World Wide Web (WWW) into the public domain, which allowed users to create websites; however, Tim Berners-Lee invented the World Wide Web in 1989, per CERN.

Source: Google, USA Department of Defense, CERN

Knowledge Distribution Evolution = Over ~Six Centuries 24 …Knowledge Distribution – 2022+ =

Active + Digital + Generative Delivery

Generative AI – Public Launch of ChatGPT 2022*

*We define the public launch of ChatGPT in November 2022 as the public release of Generative AI which we see as AI’s ‘iPhone Moment.’ ChatGPT saw the fastest user ramp ever for a standalone product (5 days to secure 1MM users). Generative AI = AI that can create content – text, images, audio, or code – based on learned patterns. Source: OpenAI

Knowledge Distribution Evolution = Over ~Six Centuries 25

Knowledge is a process of piling up facts;

wisdom lies in their simplification.

Martin H. Fischer, German-born American Physician / Teacher / Author (1879-1962)

Knowledge Distribution Evolution = Over ~Six Centuries 26

AI =

Many Years Before Lift-Off

27

AI Milestone Timeline – 1950-2022, per Stanford University…

10/50: 1/62: 5/97: 10/05: 6/14: 6/20:

Alan Turing Arthur Samuel, Deep Blue, A Stanford Eugene OpenAI creates his an IBM computer IBM’s chess- team build a Goostman, a releases GPT-

Turing Test to scientist, creates playing driverless car chatbot, 3, an AI tool

measure AI a self-learning computer, named Stanley; passes the for automated

computer program that 1 ‘Winter’ defeats Garry it completes a Turing Test, conversations;

intelligence, proves capable (1967-1996) Kasparov, 132-mile with 1/3 of Microsoft positing that of defeating a the world course, winning judges exclusively

computers top USA chess the DARPA believing that licenses the

could think like checkers champion at Grand Eugene is model

humans champion the time Challenge human

6/56: 1/66: 9/02: 4/10: 6/18: 11/22:

Stanford Stanford Roomba, the Apple OpenAI OpenAI

computer researchers first mass- acquires releases releases

scientist John deploy produced Siri voice GPT-1, the ChatGPT

McCarthy Shakey, the autonomous assistant & first of their to the

convenes the first general- robotic integrates large public

Dartmouth purpose vacuum it into language

Conference on mobile robot cleaner that iPhone 4S models

‘Artificial that can can navigate model one

Intelligence,’ a reason about homes, is year later term he coined its own actions launched

1: AI ‘Winter’ was a term used by Nils J. Nilsson, the Kumagai Professor of Engineering in computer science at Stanford University, to describe the period during which AI continued to make conceptual progress but could boast no significant practical successes. This subsequently led to a drop in AI interest and funding. Includes data from sources beyond Stanford. Source: Stanford University & Stanford Law School sources, iRobot, TechCrunch, BBC, OpenAI. Data aggregated by BOND.

AI = Many Years Before Lift-Off 28

…AI Milestone Timeline – 2023-2025, per Stanford University

3/23: 3/23: 11/23: 4/24: 5/24: 9/24: 1/25: 2/25:

OpenAI Google 28 countries, Meta Google Alibaba DeepSeek OpenAI

releases releases including USA, Platforms introduces AI releases 100 releases its releases

GPT-4, a Bard, its EU members & releases its overviews to open-source R1 & R1- GPT-4.5,

multimodal* ChatGPT China, sign open- augment its Qwen 2.5 Zero open- Anthropic

model capable competitor Bletchley source** search models, with source releases

of processing Declaration on Llama 3 functions performance in reasoning Claude 3.7

both text & AI Safety model with line with models Sonnet, &

images 70B Western xAI

parameters competitors releases

Grok 3

3/23: 3/23: 3/24: 5/24: 7/24: 12/24: 1/25: 4/25:

Microsoft Anthropic USA OpenAI Apple OpenAI Alibaba unveils ChatGPT

Integrates releases Department releases releases announces Qwen2.5-Max, reaches

Copilot into Claude, its AI of Homeland GPT-4o, Apple o3, its which 800MM

its 365 assistant Security which has full Intelligence, highest-ever surpasses the weekly

product suite 1 focused on unveils its AI multimodality an AI system performing performance of users

safety & inter- Roadmap across audio, integrated model other leading

pretability Strategy visual, & text into its models (GPT-

inputs devices, for 4o, Claude 3.5)

developers on some

reasoning tests

*Multimodal = AI that can understand and process multiple data types (e.g., text, images, audio) together. **Open-source = AI models and tools made publicly available for use, modification, and redistribution. 1) 4/25 estimate from OpenAI CEO Sam Altman’s 4/11/25 TED Talk disclosure.

Source: Aggregated by BOND from OpenAI, Microsoft, Google, Anthropic, Meta, Apple, Alibaba, Deepseek, UK Government, US Department of Homeland Security. China data may be subject to informational limitations due to government restrictions.

AI = Many Years Before Lift-Off 29

AI =

Circa Q2:25

30

Top Ten Things AI Can Do Today, per ChatGPT

Source: ChatGPT (5/15/25)

AI = Circa Q2:25 31

AI =

Circa 2030?

32

Top Ten Things AI Will Likely Do in Five Years, per ChatGPT

Source: ChatGPT (5/15/25)

AI = Circa 2030? 33

AI =

Circa 2035?

34

Top Ten Things AI Will Likely Do in Ten Years, per ChatGPT

Source: ChatGPT (5/15/25)

AI = Circa 2035? 35

AI Development Trending =

Unprecedented

36

Machine-Learning Model* Trending = In 2015...

Industry Surpassed Academia as Data + Compute + Financial Needs Rose

Global Notable Machine Learning Models by Sector – 2003-2024, per Stanford HAI

e 2015-today: ls

o Industry Era d

M

g

rnin

a

e

-L

ine

ch

a 2003-2014:

M Academia Era le b a

Not

New

l

a

u

n

n

A

*Machine Learning = A subset of AI where machines learn from patterns in data without being explicitly programmed. Note: Academia includes models developed by one or more institutions, including government agencies. Industry-academia collaboration excludes government partnerships and only captures partnerships between academic institutions and industry. Industry excludes models developed in partnership with any entity other than another company. Epoch AI, an AI Index data provider, uses the term ‘notable machine learning models’ to designate particularly influential models within the AI/machine learning ecosystem. Epoch maintains a database of 900 AI models released since the 1950s, selecting entries based on criteria such as state-of-the-art advancements, historical significance, or high citation rates. Since Epoch manually curates the data, some models considered notable by some may not be included. A count of zero academic models does not mean that no notable models were produced by academic institutions in 2023, but rather that Epoch AI has not identified any as notable. Additionally, academic publications often take longer to gain recognition, as highly cited papers introducing significant architectures may take years to achieve prominence. China data may be subject to informational limitations due to government restrictions. Source: Nestor Maslej et al., ‘The AI Index 2025 Annual Report,’ AI Index Steering Committee, Stanford HAI (4/25)

AI Development Trending = Unprecedented 37 AI Developer Growth (NVIDIA Ecosystem as Proxy) =

+6x to 6MM Developers Over Seven Years

Global Developers in NVIDIA Ecosystem (MM) – 2005-2025, Per NVIDIA

6

M

M

rs,

e

lop +6x e

3

Dev

f

o

r

e

b

Num

0

2005 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025

Note: We assume negligible developers in NVIDIA’s ecosystem in 2005 per this text from an 8/20 blog post titled ‘2 Million Registered Developers, Countless Breakthroughs’: ‘It took 13 years to reach 1 million registered developers, and less than two more to reach 2 million.’ Source: NVIDIA blog posts, press releases, & company overviews

AI Development Trending = Unprecedented 38 AI Developer Growth (Google Ecosystem as Proxy) =

+5x to 7MM Developers Y/Y

Estimated Global Developers in Google Ecosystem (MM) – 5/24-5/25, Per Google

10

M

M

ini, 7.0MM m

Ge

ith

w

5

ing

ild +5x u B

rs

e

lop

e

1.4MM

Dev

0

1/24 5/24 1/25 5/25

Note: Per Google in 5/25, ‘Over 7 million developers are building with Gemini, five times more than this time last year.’ Source: Google, ‘Google I/O 2025: From research to reality’ (5/25)

AI Development Trending = Unprecedented 39 Computing-Related Patent Grants, USA = Exploded…

Post-Netscape IPO (1995)...Again + Faster Post-ChatGPT Public Launch (2022)

USA Computing-Related* Patents Granted Annually – 1960-2024, per USPTO

+6,000

more patents granted

15,000

in 2024 vs. 2023

(1 year)

r

g Yr +1,000 tin e u- ea

p P more patents granted d in 2022 vs. 2004

te 10,000

n (18 years)…

Com

Gra +6,300

o te r a e 1995 (8 years)… b f n granted in 2003 vs. USA ts more patents

d 5,000 P

te

Num

Rela

0

1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016 2020 2024

*Uses Cooperative Patent Classification (CPC) code G06, which corresponds to computing, calculating or counting patents. Google patents data changes somewhat between each query so numbers are rounded and should be viewed as directionally accurate. Source: USA Patent & Trademark Office (USPTO) via Google Patents (4/25)

AI Development Trending = Unprecedented 40 AI Performance = In 2024…

Surpassed Human Levels of Accuracy & Realism, per Stanford HAI

AI System Performance on MMLU Benchmark Test – 2019-2024, per Stanford HAI

Note: The MMLU (Massive Multitask Language Understanding) benchmark evaluates a language model's performance across 57 academic and professional subjects, such as math, law, medicine, and history. It measures both factual recall and reasoning ability, making it a standard for assessing general knowledge and problem-solving in large language models. 89.8% is the generally-accepted benchmark for human performance. Stats above show average accuracy of top-performing AI models in each calendar year. Source: Papers With Code via Nestor Maslej et al., ‘The AI Index 2025 Annual Report,’ AI Index Steering Committee, Stanford HAI (4/25)

AI Development Trending = Unprecedented 41 AI Performance = In Q1:25…

73% of Responses & Rising Mistaken as Human by Testers

% of Testers Who Mistake AI Responses as Human-Generated – 3/25,

per Cameron Jones / Benjamin Bergen

Date Released

5/24

1/25

2/25

AI system performance

consistently improving

over time

Note: The Turing test, introduced in 1950, measures a machine’s ability to mimic human conversation. In this study, ~500 participants engaged in a three-party test format, interacting with both a human and an AI. Most discussions leaned on emotional resonance and day-to-day topics over factual knowledge. Eliza was developed in the mid-1960s by MIT professor Joseph Weizenbaum, It is considered the world's first chatbot. In January 2025, researchers successfully revived Eliza using its original code. Source: Cameron Jones and Benjamin Bergen, ‘Large Language Models Pass the Turing Test’ (3/25) via UC San Diego

AI Development Trending = Unprecedented 42 AI Performance =

Increasingly Realistic Conversations Simulating Human Behaviors

Turing Test Conversation with GPT-4.5 – 3/25, per Cameron Jones / Benjamin Bergen

What Was Tested:

The Turing Test is a concept introduced by Alan

Turing in 1950 to evaluate a machine’s ability to

exhibit intelligent behavior indistinguishable from that

of a human. In the test, if a human evaluator cannot

reliably tell whether responses are coming from a

human or a machine during a conversation, the

machine is said to have passed. Here, participants

had to guess whether Witness A or Witness B was an

AI system.

Results:

The conversation on the left is an example Turing Test

carried out in 3/25 using GPT-4.5. During the test,

participants incorrectly identified the left image

(Witness A) as human with 87% certainty, saying ‘A

had human vibes. B had human imitation vibes.’ A

was actually AI-generated; B was human.

Source: Cameron Jones and Benjamin Bergen, ‘Large Language Models Pass the Turing Test’ (3/25) via UC San Diego

AI Development Trending = Unprecedented 43 AI Performance =

Increasingly Realistic Image Generation…

AI-Generated Image: ‘Women’s Necklace with a Sunflower Pendant’ – 2/22-4/25,

per Midjourney / Gold Penguin

Model v1 (2/22) Model v7 (4/25)

Notes: Dates shown are the release dates of each Midjourney model. Source: Midjourney (4/25) & Gold Penguin, ‘How Midjourney Evolved Over Time (Comparing V1 to V6.1 Outputs)’ (9/24)

AI Development Trending = Unprecedented 44 …AI Performance =

Increasingly Realistic Image Generation

AI-Generated vs. Real Image – 2024

AI-Generated Image (2024) Real Image

Source: Left – StyleGAN2 via ‘The New York Times,’ ‘Test Yourself: Which Faces Were Made by A.I.?’ (1/24); Right – Creative Commons

AI Development Trending = Unprecedented 45 AI Performance =

Increasingly Realistic Audio Translation / Generation…

ElevenLabs AI Voice Generator – 1/23-4/25, per ElevenLabs & Similarweb

ElevenLabs Monthly Global Site Visits (MM),

per Similarweb – 1/23-4/25

20

M

M

ts,

isi

V

site

b

e

When you create a new dubbing project, Dubbing Studio W

p

automatically transcribes your content, translates it into the 10

new language, and generates a new audio track in that skto

language. Each speaker’s original voice is isolated and De

cloned before generating the translation to make sure they &

sound the same in every language. ile

b

o

- ElevenLabs Press Release, 1/24 M

l

a

b

In just two years, ElevenLabs’ millions of users have Glo

generated 1,000 years of audio content and the company’s

tools have been adopted by employees at over 60% of 0

1/23 4/23 7/23 10/23 1/24 4/24 7/24 10/24 1/25 4/25

Fortune 500 companies.

- ElevenLabs Press Release, 1/25

Note: China data may be subject to informational limitations due to government restrictions. Source: ElevenLabs (1/24 & 1/25), Similarweb (5/25)

AI Development Trending = Unprecedented 46 …AI Performance =

Evolving to Mainstream Realistic Audio Translation / Generation

AI-Powered Audio Translation – 5/25, per Spotify

2/25:

Spotify begins accepting

audiobooks AI-translated into

29 languages from ElevenLabs

Imagine if you’re a creator and you’re the world expert at something…but you happen to be Indonesian.

Today, there’s a language barrier and it will be very hard if you don’t know English to be able to get to a world stage.

But with AI, it might be possible in the future where you speak in your native language,

and the AI will understand it and will actually real-time translate…

…What will that do for creativity? For knowledge sharing? For entertainment?

I think we’re in the very early innings of figuring that out…

…We want Spotify to be the place for all voices.

- Spotify Co-Founder & CEO Daniel Ek (5/25)

In Q1:25, Spotify had 678MM Monthly Active Users and 268MM Subscribers and supported

€16.8B in annualized revenue while hosting 100MM+ tracks, ~7MM podcast titles and ~1MM creative artists.

Note: Revenue annualized using Q1:25 results. Source: Spotify, ‘The New York Post,’ ‘Inside Spotify: CEO Daniel Ek on AI, Free Speech & the Future of Music’ (5/2/25); Spotify earnings releases; eMarketer, ‘Spotify dominates Apple and Amazon in digital audio’ (4/25)

AI Development Trending = Unprecedented 47 AI Performance =

Emerging Applications Accelerating

Emerging AI Applications – 11/24, per Morgan Stanley

Source: Morgan Stanley, ‘GenAI: Where are We Seeing Adoption and What Matters for ‘25?’ (11/24)

AI Development Trending = Unprecedented 48

AI =

Benefits & Risks

49

AI Development =

Benefits & Risks

The widely-discussed benefits and risks of AI – top-of-mind for many – generate warranted excitement and trepidation,

further fueled by uncertainty over the rapid pace of change and intensifying global competition and saber rattling.

The pros Stuart Russell and Peter Norvig went deep on these topics in the

Fourth Edition (2020) of their 1,116-page classic ‘Artificial Intelligence: A Modern Approach’ (link here),

and their views still hold true.

Highlights follow…

…the benefits: put simply, our entire civilization is the product of our human intelligence.

If we have access to substantially greater machine intelligence, the [ceiling of our] ambitions is raised substantially.

The potential for AI and robotics to free humanity from menial repetitive work and to dramatically

increase the production of goods and services could presage an era of peace and plenty.

The capacity to accelerate scientific research could result in cures for disease and

solutions for climate change and resource shortages.

As Demis Hassabis, CEO of Google DeepMind, has suggested: ‘First we solve AI, then use AI to solve everything else.’

Long before we have an opportunity to ‘solve AI,’ however, we will incur risks from the misuse of AI,

inadvertent or otherwise.

Some of these are already apparent, while others seem likely based on current trends including

lethal autonomous weapons…surveillance and persuasion…biased decision making…

impact on employment…safety-critical applications…cybersecurity…

Source: Stuart Russell and Peter Norvig, ‘Artificial Intelligence: A Modern Approach’

AI = Benefits & Risks 50

Success in creating AI could be the biggest event in the

history of our civilization. But it could also be the last –

unless we learn how to avoid the risks.

Stephen Hawking, Theoretical Physicist / Cosmologist (1942-2018)

Source: University of Cambridge, Centre for the Future of Intelligence

AI = Benefits & Risks 51

Outline

1 • Seem Like Change Happening Faster Than Ever?

Yes, It Is

2 • AI User + Usage + CapEx Growth =

Unprecedented

3 • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling =

Performance Converging + Developer Usage Rising

4 • AI Usage + Cost + Loss Growth =

Unprecedented

5 • AI Monetization Threats =

Rising Competition + Open-Source Momentum + China’s Rise

6 • AI & Physical World Ramps =

Fast + Data-Driven

7 • Global Internet User Ramps Powered by AI from Get-Go =

Growth We Have Not Seen Likes of Before

8 • AI & Work Evolution =

Real + Rapid

52

AI User + Usage + CapEx Growth =

Unprecedented

53

Consumer / User AI Adoption =

Unprecedented

54

AI User Growth (ChatGPT as Foundational Indicator) =

+8x to 800MM in Seventeen Months

ChatGPT User Growth (MM) – 10/22-4/25, per OpenAI

800

rs, MM

Use

e

ctiv +8x A

kly 400

e

e

W

T

P

tG

Cha

0

Note:OpenAI reports Weekly Active Users which are represented above. 4/25 estimate from OpenAI CEO Sam Altman’s 4/11/25 TED Talk disclosure. Source: OpenAI disclosures

Consumer / User AI Adoption = Unprecedented 55 AI Global Adoption (ChatGPT as Foundational Indicator) =

Have Not Seen Likes of This Around-the-World Spread Before

Internet vs. ChatGPT Users – Percent Outside North America (1990-2025),

Per ITU & Sensor Tower

100%

90%

@ Year 3

% 90%

rs, @ Year 23 75%

Use

t

n

rre

50%

Cu

l

ta

o

T

f

o 25%

re

a

h

S

0%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

Internet ChatGPT App

Indexed Years (Internet @ 1 = 1990, ChatGPT App @ 1 = 2023)

Note: Year 1 for Internet = 1990; year 33 = 2022. Year 1 for ChatGPT app = 5/23; year 3 for ChatGPT app = 5/25. ChatGPT app monthly active users (MAUs) shown. Note that ChatGPT is not available in China, Russia and select other countries as of 5/25. China data may be subject to informational limitations due to government restrictions. Includes only Android, iPhone & iPad users. Figures may understate true ChatGPT user base (e.g., desktop or mobile webpage users). Regions per United Nations definitions. Figures show % of total current users in that year – note that as year 3 for ChatGPT has not yet finished, percentages could move in coming months. Data for standalone ChatGPT app only. Country-level data may be missing for select years, as per ITU. Source: United Nations / International Telecommunications Union (3/25), Sensor Tower (5/25)

Consumer / User AI Adoption = Unprecedented 56 AI User Adoption (ChatGPT as Proxy) =

Materially Faster vs. Internet Comparables …

Years to Reach 100MM Users – 2000-2023

ChatGPT 0.2

TikTok

Fortnite

Disney+

Instagram

WhatsApp

Snapchat

YouTube

Facebook 4.5

Spotify

Telegram

Twitter

Uber

Pinterest

LinkedIn

Netflix 10.3

0 2 4 6 8 10 12

Year Launched: ’00-’05 ’05-’10 ’10-’15 ’15-’20 ’20+

Note: Netflix represents streaming business. Source: BOND, ‘AI & Universities’ (2024) via company filings, press

Consumer / User AI Adoption = Unprecedented 57 …AI User Adoption (ChatGPT as Proxy) =

Materially Faster + Cheaper vs. Other Foundational Technology Products

Days to Reach 1MM Customers / Users – 1908-2022

rs 3,000

Use ~2,500

/

rs

e

o ~1,680 m

st

1,500

MM Cu

1

ch

a

Re

to 74 5 s 0 y

Da Ford Model T TiVo iPhone ChatGPT*

(1908) (1999) (2007) (2022)

Purchase $29,330 $945 $756 $0

Price (2024 $)

*Public launch of ChatGPT = first release to the public as a free research preview (11/22). Note: Per Ford Corporate, the Model T could be sold for between $260 and $850. We use $850 in 1908 dollars for our figures above. For TiVo, we use the launch of consumer sales on 3/31/99, when TiVo charged $499 for its 14-hour box set. We do not count TiVo subscription costs. We also use the iPhone 1’s 4GB entry level price of $499 in 2007. Source: Heartcore Capital, CNBC, Museum of American Speed, World Bank, Ford Corporate, Gizmodo, Apple, Encyclopedia Britannica, Federal Reserve Bank of St. Louis, Wikimedia Commons, UBS

Consumer / User AI Adoption = Unprecedented 58 AI User Adoption – Time to 50% Household Penetration =

Each Cycle Ramps in ~Half-the-Time…AI Following Pattern

Years to 50% Adoption of Household Technologies in USA, per Morgan Stanley

AI Era 3 Years?

Mobile Internet Era 6 Years

Desktop Internet Era 12 Years

PC Era 20 Years

Second Industrial Revolution 42 Years

0 25 50

Years

Note: 3 years for AI Era implies that the time to 50% USA Household Adoption is similarly cut in half from the previous cycle. Source: Morgan Stanley, ‘Google and Meta: AI vs. Fundamental 2H Debates’ (7/23), Our World in Data, other web sources per MS

Consumer / User AI Adoption = Unprecedented 59

Technology Ecosystem AI Adoption =

Impressive

60

NVIDIA AI Ecosystem Tells Over Four Years =

>100% Growth in Developers / Startups / Apps

NVIDIA Computing Ecosystem – 2021-2025, per NVIDIA

Number of Applications

Number of Developers (MM) Number of AI Startups (K) Using GPUs (K)

6 6MM 30 5

27K

4K

+2.4x

+2.4x

+3.9x

3 15 2.5

2.5MM

1.7K

7K

0 0 0

2021 2025

Note: GPU = Graphics Processing Unit. Source: NVIDIA (2021 & 2025)

Technology Ecosystem AI Adoption = Impressive 61

Tech Incumbent AI Adoption =

Top Priority

62

Tech Incumbent AI Focus =

Talking-the-Talk…

Mentions of ‘AI’ in Corporate Earnings Transcripts – Q1:20-Q1:24, per Uptrends

Source: Uptrends, ‘Top 15 Companies Mentioning AI on Earnings Calls’ (6/24), company earnings transcripts

Tech Incumbent AI Adoption = Top Priority 63 …Tech Incumbent AI Focus =

Talking-the-Talk…

Generative AI is going to reinvent virtually every customer experience we know and enable altogether new ones about which we’ve only fantasized.

The early AI workloads being deployed focus on productivity and cost avoidance…

…Increasingly, you’ll see AI change the norms in coding, search, shopping,

personal assistants, primary care, cancer and drug research, biology, robotics,

space, financial services, neighborhood networks – everything.

- Amazon CEO Andy Jassy in 2024 Amazon Shareholder Letter – 4/25

The chance to improve lives and reimagine things is why Google has

been investing in AI for more than a decade…

…We see it as the most important way we can advance our mission to organize the

world's information, make it universally accessible and useful...

…The opportunity with AI is as big as it gets.

- Google CEO Sundar Pichai @ Google Cloud Next 2025 – 4/25

Source: Amazon (4/10/25), Google (4/9/25), Techradar

Tech Incumbent AI Adoption = Top Priority 64 …Tech Incumbent AI Focus =

Talking-the-Talk…

There’s three places where [GenAI is]…helping us:

data creation…creating new features that were just not possible…

efficiencies everywhere in the company…

AI Going Full-Circle: …I should mention something amazing about [the new Duolingo curriculum in] chess is

DeepMind’s AlphaGo (2014)

started with humans training that it really started with a team of two people, neither of whom knew how to

machines…Duolingo Chess now program…and they basically made prototypes and did the whole curriculum

has machines training humans… of chess by just using AI. Also, neither of them knew how to play chess.

- Duolingo Co-Founder & CEO Luis von Ahn @ Q1:25 Earnings Call – 5/25

AI with Grok is getting very good…it’s important that AI be programmed with

good values, especially truth-seeking values. This is, I think, essential for AI safety…

…Remember these words: We must have a maximally truth-seeking AI.

- xAI Founder & CEO Elon Musk – 5/25

Note: On 3/28/25, Elon Musk announced that xAI had acquired X in an all-stock deal. The deal valued xAI at $80B and X at $33B ($45B less $12B debt). Source: Duolingo (5/1/25), DeepMind, Elon Musk (5/2/25), Fox News

Tech Incumbent AI Adoption = Top Priority 65 …Tech Incumbent AI Focus =

Talking-the-Talk

We view AI as a human acceleration tool that will allow individuals to do more...

I believe long term, we will see people coupled with…

the AI they use as the overall output of that person.

- Roblox Co-Founder, President, CEO & Chair of Board David Baszucki

@ Q1:25 Earnings Call – 5/25

I promise you, in ten years' time, you will look back and you will realize that AI has now

integrated into everything. And in fact, we need AI everywhere.

And every region, every industry, every country, every company, all needs AI.

AI [is] now part of infrastructure. And this infrastructure,

just like the internet, just like electricity, needs factories….

…And these AI data centers, if you will, are improperly described. They are, in fact,

AI factories. You apply energy to it, and it produces something incredibly valuable.

- NVIDIA Co-Founder & CEO Jensen Huang

@ COMPUTEX 2025 – 5/25

Source: Roblox (5/1/25), NVIDIA (5/18/25)

Tech Incumbent AI Adoption = Top Priority 66

‘Traditional’ Enterprise AI Adoption =

Rising Priority

67

Enterprise AI Focus – S&P 500 Companies =

50% & Rising Talking-the-Talk

Quarterly Earnings Call Mentions of ‘AI’ – S&P 500 Companies (2015-2025),

per Goldman Sachs Research

‘AI’

ing

n

tio

n

e

M

s

nie

a

p

Com

0

0

5

P

&

f S

% o

.

Source: Goldman Sachs Global Investment Research, ‘S&P Beige Book: 3 themes from 4Q 2024 conference calls: Tariffs, a stronger US dollar, and AI’ (2/25)

‘Traditional’ Enterprise AI Adoption = Rising Priority 68 Enterprise AI Focus – Global Enterprises =

Growth & Revenue…Not Cost Reduction

GenAI Improvements Targeted for Global Enterprises over Next 2 Years – 2024,

per Morgan Stanley

Production / Output

Customer Service

Sales Productivity

s

se Revenues

n

o

ROIC

Resp Marketing Spend Effectivity y e Margins rv u S Admin Costs f

% o Manufacturing Costs

SG&A / Marketing

Headcount

Hiring Costs

0% 25% 50% 75%

Revenue-Focused Cost-Focused

Note: Survey conducted 5/24, N=427. US-based companies = 43%, Japan 15%, UK 14%, France 14%, Germany 14%. Industry mix: 18% Technology, 18% Financial Services, 17% Healthcare, 17% Manufacturing, 15% Industrials, 15% Consumer,. Revenue mix: 13% $500MM-$750MM, 25% $751MM-$1B, 36% $1B-$5B, 10% $5B-$10B, 8% $10B-$15B, 3% $15B-$20B, 5% $20B+. ‘Revenue-Focused’ and ‘Cost-Focused’ categorizations per BOND, not Morgan Stanley. Source: AlphaWise, Morgan Stanley, ‘Quantifying the AI Opportunity’ (12/24)

‘Traditional’ Enterprise AI Adoption = Rising Priority 69 Enterprise AI Focus – Global CMOs =

75% Using / Testing AI Tools

Global Chief Marketing Officer (CMO) GenAI Adoption Survey – 2024,

per Morgan Stanley

Running Initial Tests /

Experiments

s

n Plan on Start Testing o se

Within 12 Months

Resp

y

e

u Fully Implemented rv

S

f

% o

Plan on Start Testing

Within 1-2 Years

0% 25% 50% 75%

Note: Survey question asked about the extent to which marketing executives worldwide are using generative AI for marketing activities. Survey conducted 7/24, N = 300 marketing executives at companies with 500+ employees worldwide. Survey geos: Australia, Belgium, Brazil, Canada, China, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Luxembourg, Mexico, Netherlands, Norway, Poland, Saudi Arabia, Spain, Sweden, UAE, UK, & USA. Source: eMarketer, Morgan Stanley, ‘Quantifying the AI Opportunity’ (12/24)

‘Traditional’ Enterprise AI Adoption = Rising Priority 70 Enterprise AI Adoption = Rising Priority…

Bank of America – Erica Virtual Assistant (6/18)

Bank of America Erica Virtual Assistant – 6/18-2/25,

per Bank of America

Cumulative Client Interactions with

Erica Virtual Assistant (MM)

2,500

2,000

M

M

s,

ion 1,500

Note: Erica is a conversational AI built into Bank of America’s mobile app that helps ract customers manage their finances by providing real-time insights, transaction search, te

bill reminders, and budgeting assistance. It has handled billions of interactions and In

serves as a 24/7 digital financial concierge for over 40 million clients. 1,000 e

iv

lat

Erica acts as both a personal concierge and u

mission control for our clients.

Cum 500

Our data science team has made more than 50,000 updates

to Erica’s performance since launch – adjusting, expanding

and fine-tuning natural language understanding capabilities,

0

ensuring answers and insights remain timely and relevant. 2 3 billion client interactions is a compelling milestone 18 19 19 20 20 21 22 22 23 24 24 25 /18 /20 /21 /2 /23 6

6/ 4/ 9/ 2/ 7/ 5/ 3/ 8/ 1/ 4/ 9/ 2/

11 12 10 11

though this is only the beginning for Erica.

- Head of Digital at Bank of America Nikki Katz, 4/24

Note: We assume a start at zero users from Erica’s launch in 6/18. Pilot users excluded. Source: Bank of America (2/21, 4/24, 2/25)

‘Traditional’ Enterprise AI Adoption = Rising Priority 71 Enterprise AI Adoption = Rising Priority…

JP Morgan – End-to-End AI Modernization (2020)

JP Morgan End-to-End AI Modernization – 2023-2025E,

per JP Morgan

JP Morgan Estimated Value from AI / ML

+65%

We have high hopes for the efficiency gains

we might get [from AI]… +35%

…Certain key subsets of the users tell us they are gaining

several hours a week of productivity, and almost by definition,

the time savings is coming from less valuable tasks…

…We were early movers in AI.

But we’re still in the early stages of the journey.

- JP Morgan CFO Jeremy Barnum, 5/25

Note: Superscript ‘2’, per JP Morgan, indicates ‘Value is described as benefit in revenue, lower expense, or avoidance of cost – majority is measured as the lift relative to prior analytical techniques with the remainder relative to a random baseline or holdout control.’ We indicate 2020 as the start year for JP Morgan’s AI Modernization (2020 Letter to Shareholders: ‘We already extensively use AI, quite successfully, in fraud and risk, marketing, prospecting, idea generation, operations, trading and in other areas—to great effect, but we are still at the beginning of this journey’). Source: JP Morgan Investor Day (5/25)

‘Traditional’ Enterprise AI Adoption = Rising Priority 72 Enterprise AI Adoption = Rising Priority…

Kaiser Permanente – Multimodal Ambient AI Scribe (10/23)

Kaiser Permanente Ambient AI Scribe – 10/23-12/24,

per New England Journal of Medicine

Unique Kaiser Permanente Physicians Ever Using

AI Scribe & Cumulative Number of Scribe Visits

Ambient artificial intelligence (AI) scribes, which use machine

learning applied to conversations to facilitate scribe-like

capabilities in real time, [have] great potential to reduce

documentation burden, enhance physician-patient

encounters, and augment clinicians’ capabilities.

The technology leverages a smartphone microphone to

transcribe encounters as they occur but does not retain audio

recordings. To address the urgent and growing burden of

data entry, in October 2023, The Permanente Medical Group

(TPMG) enabled ambient AI technology for 10,000 physicians

and staff to augment their clinical capabilities across

diverse settings and specialties.

- New England Journal of Medicine

Catalyst Research Report, 2/24

Source: Tierney, Aaron A. et al., ‘Ambient Artificial Intelligence Scribes to Alleviate the Burden of Clinical Documentation’ (3/24) & Tierney, Aaron A. et al., ‘Ambient Artificial Intelligence Scribes: Learnings after 1 Year and over 2.5 Million Uses’ (3/25) via Nestor Maslej et al., ‘The AI Index 2025 Annual Report,’ AI Index Steering Committee, Stanford HAI (4/25)

‘Traditional’ Enterprise AI Adoption = Rising Priority 73 Enterprise AI Adoption = Rising Priority…

Yum! Brands – Byte by Yum! (2/25)

Yum! Brands Byte by Yum! – 2/24-2/25, per Yum! Brands

Yum! Restaurants Using at Least One

Byte by Yum! Product

25,000

25,000

20,000

ts

Byte is Yum! Brands' AI-powered restaurant management platform designed to ran optimize store operations by automating repetitive tasks like inventory tracking, u

scheduling, and food preparation alerts. It leverages machine learning to improve a 15,000

decision-making at the restaurant level, enhancing efficiency, reducing waste, and Rest

supporting staff productivity. f

r 10,000 o

Backed by artificial intelligence, Byte by Yum! offers e

b

franchisees leading technology capabilities with advantaged

economics made possible by the scale of Yum!. Num

5,000

The Byte by Yum! platform includes online and mobile app

ordering, point of sale, kitchen and delivery optimization,

menu management, inventory and labor management, and 0

team member tools. 0

2/24 2/25

- Yum! Press Release, 2/25

Note: Yum! Brands names include KFC, Taco Bell, Pizza Hut, & The Habit. Byte by Yum! was officially launched in 2/25. While underlying technologies were previously in-use at restaurants in Yum!’s portfolio, the Byte by Yum! product suite had not yet officially been launched; hence, we illustratively show zero users in 2/24. Source: Yum!, ‘Introducing Byte by Yum! , an AI-driven restaurant technology platform powering customer and team member experiences worldwide’(2/25)

‘Traditional’ Enterprise AI Adoption = Rising Priority 74

Education / Government / Research AI Adoption =

Rising Priority

75

Education & Government =

Increasingly Announcing AI Integrations

Arizona State University’s

Oxford Partnership – 3/25 NextGenAI – 3/25

‘AI Acceleration’ – 8/23

New team of technologists creating 5-Year Partnership on Research & $50MM consortium with 15 research

artificial intelligence (AI) tools AI Literacy universities (MIT, Harvard, Caltech, etc.)

ChatGPT Gov – 1/25 USA National Laboratories – 1/25

ChatGPT tailored for USA federal agencies Partnering on Nuclear, Cybersecurity, & Scientific Breakthroughs

Source: Arizona State University (8/23), Oxford University (3/25), University of Michigan (3/25), Launch Consulting (1/25) via AI Advantage Daily News, NPR (1/25)

Education / Government / Research AI Adoption = Rising Priority 76 Government =

Increasingly Adopting Sovereign AI Policies

NVIDIA Sovereign AI Partners – 2/25, Per NVIDIA

Nations are investing in AI

infrastructure like they once

did for electricity and Internet.

- NVIDIA Co-Founder &

CEO Jensen Huang, 5/25

Source: NVIDIA (2/25 & 5/25)

Education / Government / Research AI Adoption = Rising Priority 77 Research =

Rapid Ramp in FDA-Approved AI Medical Devices, per Stanford HAI

New AI-Enabled Medical Devices Approved by USA Food & Drug Administration –

1995-2023, per Stanford HAI & USA FDA

AI-Enabled Medical Devices Approved New USA FDA AI Policy (5/25)

d 250 e In a historic first for the [USA

rov 223 FDA], FDA Commissioner Martin

p A. Makary, M.D., M.P.H., today budgets, especially in healthcare: p Government R&D funding has been a key part of AI development

A

announced an aggressive

ices -FY21-FY25 Federal USA AI Budget: $14.7B timeline to scale use of artificial

-FY25 Share Requested by National Institutes of Health: 34% 160

intelligence (AI) internally across

Dev all FDA centers by June 30, 129

ical 125 2025… 114

d

e …To reflect the urgency of this M 80 I effort, Dr. Makary has directed all A 64 f FDA centers to begin deployment o r immediately, with the goal of full e b 26 integration by the end of June. 18

1 5 6 6 3 3 0 1 1 2 0 0 1 0 0 1 1 2 0 0 0

Num - USA FDA Press Release, 5/25 0

1995 1999 2003 2007 2011 2015 2019 2023

Note: FY21, FY22 & FY23 USA government budget figures are actuals. FY24 data is enacted but not actual, FY25 data is requested. NIH share of total budget is requested. Source: Nestor Maslej et al., ‘The AI Index 2025 Annual Report,’ AI Index Steering Committee, Stanford HAI (4/25); USA Food & Drug Administration, ‘FDA Announces Completion of First AI-Assisted Scientific Review Pilot and Aggressive Agency-Wide AI Rollout Timeline’ (5/25); NITRD.gov (5/25)

Education / Government / Research AI Adoption = Rising Priority 78 Research =

30%-80% Reduction in Medical R&D Timelines, per Insilico Medicine & Cradle

AI-Driven Drug Discovery – 2021-2024, Per Insilico Medicine, Cradle & BioPharmaTrend

Months to Reach Pre-Clinical Candidate Status

s 50 tu

ta

S Traditional approaches te can take 2.5-4 years ida Pharma companies that use

d Cradle are seeing a 1.5x to 12x

Can speedup in pre-clinical research

l and development by using our

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th

o 0 n

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Solid Tumors Inflammatory Bowel Idiopathic Pulmonary Traditional

(QPCTL) Disease Fibrosis Approaches

(PHD1/2) (TNIK)

Note: Pre-Clinical Candidate Status marks the point at which a lead molecule (or biologic) has satisfied all discovery-stage gates and is officially handed off to the development organization for work related to beginning human clinical trials. Figures collected from 2021-2024. Source: Cradle, Insilico Medicine via BioPharmaTrend, ‘Insilico Medicine Reports Benchmarks for its AI-Designed Therapeutics’ (2/25)

Education / Government / Research AI Adoption = Rising Priority 79

AI User + Usage + CapEx Growth =

Unprecedented

80

AI Usage – ChatGPT =

Rising Rapidly Across Age Groups in USA, per Pew & Elon University

% of USA Adults Who Say They Have Ever Used ChatGPT –

7/23 per Pew & 1/25 per Elon University

100%

A gross oversimplification is: Older people use ChatGPT as, like, a

Google replacement. People in their 20s and 30s use it like a life advisor.

- OpenAI Co-Founder & CEO Sam Altman (5/25)

lts

d 55% u

A

USA 44% 50%

f

37%

% o 33% 30%

21% 20%

18%

13%

4%

0%

All USA Adults Ages 18-29 Ages 30-49 Ages 50-64 Ages 65+

7/23 – Per Pew 1/25 – Estimates Per Elon University

Note: 7/23 data per Pew Research study on ChatGPT use, n=10,133 USA adults. Those who did not give an answer are not shown. 1/25 data per Elon University study on use of any AI models, n=500 USA adults,. Figures estimated based on overall AI tool usage adjusted for an average 72% usage rate of ChatGPT amongst respondents who use any AI tools. Actual ChatGPT penetration may vary by cohort. Note that this chart aggregates data across survey providers and as such may not be directly comparable. Source: Pew Research Center (3/26/24), Elon University (released 3/12/25), Sam Altman (5/12/25) via Fortune

AI User + Usage + CapEx Growth = Unprecedented 81 AI Engagement (ChatGPT App as Proxy) =

+202% Rise in Daily Time Spent Over Twenty-One Months…

Minutes per Day that USA Active Users Spend on ChatGPT App – 7/23-4/25,

per Sensor Tower

20

USA

,

p

p +202%

A

n

o

t

e 10 n

p

S

s

te

u

n

Mi

Daily

0

7/23 8/23 9/23 10/23 11/23 12/23 1/24 2/24 3/24 4/24 5/24 6/24 7/24 8/24 9/24 10/24 11/24 12/24 1/25 2/25 3/25 4/25

Note: Data represents USA App Store & Google Play Store monthly active users. Data for ChatGPT standalone app only. ChatGPT app not available in China, Russia and select other countries as of 5/25. Source: Sensor Tower (5/25)

AI User + Usage + CapEx Growth = Unprecedented 82 …AI Engagement (ChatGPT App as Proxy) =

+106% Growth in Sessions & +47% Growth in Duration Over Twenty-One Months

Average USA Session Duration (Minutes) & Daily Sessions per User for ChatGPT App –

7/23-4/25, per Sensor Tower

3 9 )

e

rs) in

a L

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ssion s e/

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te e s ss

1 3 S

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7/23 8/23 9/23 10/23 11/23 12/23 1/24 2/24 3/24 4/24 5/24 6/24 7/24 8/24 9/24 10/24 11/24 12/24 1/25 2/25 3/25 4/25

Note: Data represents USA App Store & Google Play Store monthly active users. Data for ChatGPT standalone app only. ChatGPT app not available in China, Russia and select other countries as of 5/25. Source: Sensor Tower (5/25)

AI User + Usage + CapEx Growth = Unprecedented 83 AI Retention (ChatGPT as Proxy) =

80% vs. 58% Over Twenty-Seven Months, per YipitData

Consumer ChatGPT & Google Search Global Desktop User Retention Rates (1/23-4/25),

per YipitData

100%

%

,

n

tio

n

e

75%

Ret

kly

e

e

W

+2,259 bps

50%

ChatGPT Retention Google Search Retention

Note: Retention Rate = Percentage of users from the immediately preceding week that were users again in the current week. Data measures several million global active desktop users’ clickstream data. Data consists of users’ web requests & is collected from web services / applications, such as VPNs and browser extensions. Users must have been part of the panel for 2 consecutive months to be included. Panel is globally-representative, though China data may be subject to informational limitations due to government restrictions. Excludes anomalies in w/c 12/24/23, 12/31/23, 12/22/24, 12/29/24, 1/5/25, potentially due to holiday breaks causing less enterprise usage. Source: YipitData (5/25)

AI User + Usage + CapEx Growth = Unprecedented 84 AI Chatbots @ Work Tells =

>72% Doing Things Quicker / Better

% of Employed USA Adults Using AI Chatbots Who Say Tools Have Been ______

Helpful When It Comes to… – 10/24, per Pew

Allowing Them to Do

Things More Quickly

Improving the Quality

of Their Work

0% 25% 50% 75% 100%

Extremely / Very Somewhat Not Too / Not at All

% of Employed USA Adults

Note: N = 5,273 USA adults who are employed part time or full time and who have only one job or have more than one but consider one of them to be their primary job were surveyed. Source: Pew Research Center (10/24)

AI User + Usage + CapEx Growth = Unprecedented 85 AI Chatbots @ School Tells (ChatGPT as Proxy) =

Bias to Research / Problem Solving / Learning / Advice

OpenAI ChatGPT Usage Survey, USA Students Ages 18-24 – 12/24-1/25, per OpenAI

Note: Data per OpenAI survey (12/24), n = 1,299 USA college and graduate students across a mix of STEM and non-STEM disciplines; only answers from 18-24 year olds used. Sample includes both AI users and non-users but excludes “AI rejectors” – defined as non-users with little to no interest in adopting AI within the next 12 months. Source: OpenAI, ‘Building an AI-Ready Workforce: A Look at College Student ChatGPT Adoption in the US’ (2/25)

AI User + Usage + CapEx Growth = Unprecedented 86 AI Usage Expansion – Deep Research =

Automating Specialized Knowledge Work

Select AI Company Deep Research Capabilities – 12/24-2/25, per Google, OpenAI & xAI

Google Gemini OpenAI ChatGPT xAI Grok

Deep Research Deep Research DeepSearch

Get up to speed on just about anything with Today we’re launching deep research in To understand the universe, we must

Deep Research, an agentic feature in ChatGPT, a new agentic capability that interface Grok with the world…

Gemini that can automatically browse up to conducts multi-step research on the

hundreds of websites on your behalf, think internet for complex tasks. …As a first step towards this vision, we are through its findings, and create insightful It accomplishes in tens of minutes what rolling out DeepSearch – our first agent.

multi-page, reports that you can turn into would take a human many hours… It's a lightning-fast AI agent built to engaging podcast-style conversations … relentlessly seek the truth across the

…Deep research marks a significant step entire corpus of human knowledge.

…It’s a step towards more agentic AI that toward our broader goal of developing DeepSearch is designed to synthesize

can move beyond simple question- AGI, which we have long key information, reason about

answering to become a true collaborative envisioned as capable of producing conflicting facts and opinions, and

partner. novel scientific research. distill clarity from complexity.

- Google Deep Research Overview, - OpenAI Deep Research - xAI Grok 3 Beta Press Release, 2/25

launched 12/24 Press Release, 2/25

Source: Google (5/25), OpenAI (2/25), xAI (2/25), Digital Trends (1/25)

AI User + Usage + CapEx Growth = Unprecedented 87

AI Agent Evolution =

Chat Responses → Doing Work

88

A new class of AI is now emerging – less assistant, more service provider.

What began as basic conversational interfaces may now be evolving into something far more capable.

Traditional chatbots were designed to respond to user prompts, often within rigid scripts or narrow flows.

They could fetch answers, summarize text, or mimic conversation – but always in a reactive, limited frame.

AI agents represent a step-change forward. These are intelligent long-running processes

that can reason, act, and complete multi-step tasks on a user’s behalf. They don’t just answer questions –

they execute: booking meetings, submitting reports, logging into tools, or orchestrating workflows across platforms,

often using natural language as their command layer.

This shift mirrors a broader historical pattern in technology.

Just as the early 2000s saw static websites give way to dynamic web applications –

where tools like Gmail and Google Maps transformed the internet from a collection of pages into a set of utilities –

AI agents are turning conversational interfaces into functional infrastructure.

Whereas early assistants needed clear inputs and produced narrow outputs, agents promise to operate with goals,

autonomy and certain guardrails. They promise to interpret intent, manage memory, and coordinate across

apps to get real work done. It’s less about responding and more about accomplishing.

While we are early in the development of these agents, the implications are just starting to emerge.

AI agents could reshape how users interact with digital systems –

from customer support and onboarding to research, scheduling, and internal operations.

Enterprises are leading the charge; they’re not just experimenting with agents, but deploying them,

investing in frameworks and building ecosystems around autonomous execution.

What was once a messaging interface is becoming an action layer.

AI Agent Evolution = Chat Responses → Doing Work 89 AI Agent Interest (Google Searches) =

+1,088% Over Sixteen Months

Global Google Searches for ‘AI Agent’ (K) – 1/24-5/25, per Google Trends

3/11/25: OpenAI Introduces

Developer Tools for AI Agents

500

K

s,

rche

a

e

rd S

w +1,088% y 250 o

e

K

le

g

o

Go

kly

e

e

W

0

1/24 2/24 3/24 4/24 5/24 6/24 7/24 7/24 8/24 9/24 10/24 11/24 12/24 12/24 1/25 2/25 3/25 4/25 5/25

Source: Google Trends via Glimpse (5/15/24), OpenAI (3/25)

AI Agent Evolution = Chat Responses → Doing Work 90 AI Agent Deployments =

AI Incumbent Product Launches Accelerating

AI Incumbent Agent Launches

Agent Released Select Capabilities

Salesforce Agentforce • Automated customer support • Lead qualification

(10/24 = General Release) • Case resolution • Order tracking

Anthropic Claude 3.5 Computer Use • Control computer screen directly to perform tasks like (10/24 = Research Preview Release) pulling data from websites, making online purchases, etc.

OpenAI Operator • Control computer screen directly to perform tasks like (1/25 = Research Preview Release) pulling data from websites, making online purchases, etc.

Amazon Nova Act • Home automation • Purchasing (3/25 = Research Preview Release) • • Information collection Scheduling

Source: Salesforce (10/24), Salesforce Ben, Anthropic (10/24), OpenAI (1/25), Amazon (3/25)

AI Agent Evolution = Chat Responses → Doing Work 91

Next Frontier For AI =

Artificial General Intelligence

92

Artificial General Intelligence, or AGI, refers to systems capable of performing the full range of human intellectual tasks –

reasoning, planning, learning from small data samples, and generalizing knowledge across domains.

Unlike current AI models, which excel within specific (albeit broad) boundaries, AGI would be able to operate

fully flexibly across disciplines and solve unfamiliar problems without retraining.

It represents a major milestone in AI development – one that builds on recent exponential gains in model scale, training data, and computational efficiency.

Timelines for AGI remain uncertain, but expert expectations have shifted forward meaningfully in recent years.

Sam Altman, CEO of OpenAI, remarked in January 2025, We are now confident we know how to build AGI as we have

traditionally understood it. This is a forecast, not a dictum, but it reflects how advances in model architecture, inference* efficiency, and training scale are shortening the distance between research and frontier capability.

The broader thread is clear: AI development is trending at unprecedented speed, and

AGI is increasingly being viewed not as a hypothetical endpoint, but as a reachable threshold.

If / when achieved, AGI would redefine what software (and related hardware) can do. Rather than executing

pre-programmed tasks, AGI systems would understand goals, generate plans, and self-correct in real time.

They could drive research, engineering, education, and logistics workflows with little to no human oversight –

handling ambiguity and novelty with general-purpose reasoning. These systems wouldn’t require extensive

retraining to handle new problem domains – they would transfer learning and operate with context,

much like human experts. Additionally, humanoid robots powered by AGI would have the

power to reshape our physical environment and how we operate in it.

Still, the implications warrant a measured view. AGI is not a finish line, but a phase shift in capability – and how it

reshapes institutions, labor, and decision-making will depend on the safeguards and deployment

frameworks that accompany it. The productivity upside may be significant, but unevenly distributed.

The geopolitical, ethical, and economic implications may evolve gradually, not abruptly.

As with earlier transitions – from industrial to digital to algorithmic – the full consequences will be

shaped not just by what the technology can do, but by how society chooses to adopt and govern it.

*Inference = Fully-trained model generates predictions, answers, or content in response to user inputs. This phase is much faster and more efficient than training.

Next Frontier For AI = Artificial General Intelligence 93

AI User + Usage + CapEx Growth =

Unprecedented

94

To understand where technology CapEx is heading, it helps to look at where it’s been.

Over the past two decades, tech CapEx has flexed upward at points through data’s long arc –

first toward storage / access, then toward distribution / scale, and now toward computation / intelligence.

The earliest wave saw CapEx pouring into building internet infrastructure –

massive server farms, undersea cables, and early data centers that enabled Amazon, Microsoft, Google and

others to lay the foundation for cloud computing. That was the first phase: store it, organize it, serve it.

The second wave – still unfolding – has been about supercharging compute for data-heavy AI workloads,

a natural evolution of cloud computing. Hyperscaler* CapEx budgets now tilt increasingly toward

specialized chips (GPUs, TPUs, AI accelerators…), liquid cooling, and frontier data center design.

In 2019, AI was a research feature; by 2023, it was a capital expenditure line item.

Microsoft Vice Chair and President Brad Smith put it well in a 4/25 blog post:

Like electricity and other general-purpose technologies in the past, AI and

cloud datacenters represent the next stage of industrialization.

The world's biggest tech companies are spending tens of billions annually – not just to gather data,

but to learn from it, reason with it and monetize it in real time. It’s still about data – but now,

the advantage goes to those who can train on it fastest, personalize it deepest, and deploy it widest.

*Hyperscalers (large data center operators) are Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), Alibaba Cloud,

Oracle Cloud Infrastructure (OCI), IBM Cloud & Tencent Cloud.

AI User + Usage + CapEx Growth = Unprecedented 95

CapEx Spend – Big Technology Companies =

On Rise for Years as

Data Use + Storage Exploded

96

CapEx Spend @ Big Six* Tech Companies (USA) =

+21% Annual Growth Over Ten Years

Big Six* USA Public Technology Company CapEx Spend ($B) vs. Global Data

Generation (Zettabytes) – 2014-2024, per Capital IQ & Hinrich Foundation

$250 As data volumes rise, CapEx required 150 )

to build more hyperscale data e

centers, faster network infrastructure, in

L

& more compute capacity d

rs) $200 120 (Re as

B teybatt (Blue $150 CapEx: +21% / Year 90 BZe $ Data: +28% / Year, , n d ntio era pe $100 60n S xe E G

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$50 l 30Da

a

b

lo

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$0 0

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

*Note: Big Six USA technology companies include Apple, Nvidia, Microsoft, Alphabet / Google, Amazon, & Meta Platforms / Facebook. Only AWS CapEx & revenue shown for Amazon (i.e. excludes Amazon retail CapEx). AWS CapEx estimated per Morgan Stanley – equals AWS net additions to property & equipment less finance leases and obligations. Global data generation figures for 2024 are estimates. Source: Capital IQ (3/25), Hinrich Foundation (3/25)

CapEx Spend – Big Technology Companies = On Rise for Years as Data Use + Storage Exploded 97 CapEx Spend for Tech Hyperscalers = Mirrored by…

+37% Annual Cloud Revenue Growth Over Ten Years

Global Hyperscaler Cloud Revenue ($B) – 2014-2024,

per Company Disclosures & Morgan Stanley Estimates

$400

$300

, $200 e u Year n $ +37% / B e

Rev

$100

$0

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Amazon AWS Microsoft Intelligent Cloud Google Cloud Oracle Cloud IBM Cloud Alibaba Cloud Tencent Cloud

Note: Companies do not report “hyperscaler cloud revenue” on like-for-like basis so data represents best estimates and may not align between companies. Oracle Cloud revenue includes Cloud Services & License Support, as well as Cloud License & On-Premise License. IBM Cloud includes all ‘Infrastructure’ line items due to reporting standards. Alibaba & Tencent Cloud revenues estimated per Morgan Stanley. Source: Company disclosures, Morgan Stanley (as of 4/25)

CapEx Spend – Big Technology Companies = On Rise for Years as Data Use + Storage Exploded 98

CapEx Spend – Big Technology Companies =

Inflected With AI’s Rise

99

AI Model Training Dataset Size =

250% Annual Growth Over Fifteen Years, per Epoch AI

AI Model Training Dataset Size (Tokens) by Model Release Year – 6/10-5/25, per Epoch AI

s

n

ke

o

T

,

e

iz

S

t

se

a

Dat

ing

rain Year +250% / T

Note: In AI language models, tokens represent basic units of text (e.g., words or sub-words) used during training. Training dataset sizes are often measured in total tokens processed. A larger token count typically reflects more diverse and extensive training data, which can lead to improved model performance – up to a point – before reaching diminishing returns. Source: Epoch AI (5/25)

CapEx Spend – Big Technology Companies = Inflected With AI’s Rise 100 CapEx Spend @ Big Six* Tech Companies =

+63% Y/Y & Accelerated…

Big Six* USA Public Technology Company CapEx Spend ($B) vs. Global ChatGPT

Weekly Active Users (MM) – 2014-2024, per Capital IQ & OpenAI

$250 ) 350

2023-2024 Change: ine

L

Big Six CapEx = +63% d

1 e

ChatGPT WAUs = +200%

$200 280 (R

M

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$0 b 0a

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Glo

1ChatGPT WAU data as of 11/23 & 12/24 due to data availability.

*Note: Big Six USA technology companies include Apple, Nvidia, Microsoft, Alphabet / Google, Amazon, & Meta Platforms / Facebook. Only AWS CapEx & revenue shown for Amazon (i.e. excludes Amazon retail CapEx). AWS CapEx estimated per Morgan Stanley – equals AWS net additions to property & equipment less finance leases and obligations. Source: Capital IQ (3/25), OpenAI disclosures (3/25)

CapEx Spend – Big Technology Companies = Inflected With AI’s Rise 101 …CapEx Spend @ Big Six* Tech Companies =

15% of Revenue & Accelerated vs. 8% Ten Years Ago

Big Six* USA Public Technology Company – CapEx Spend ($B) vs. % of Revenue –

2014-2024, per Capital IQ & Morgan Stanley

$250 15%

)

ine

$200 12% L

d

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B eun $150 9%e

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B Rev

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x

E

$50 3% Cap

$0 0%

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

*Note: Big Six USA technology companies include Apple, Nvidia, Microsoft, Alphabet / Google, Amazon, & Meta Platforms / Facebook. Only AWS CapEx & revenue shown for Amazon (i.e. excludes Amazon retail CapEx). AWS CapEx estimated per Morgan Stanley – equals AWS net additions to property & equipment less finance leases and obligations. Source: Capital IQ (3/25), Morgan Stanley (5/25)

CapEx Spend – Big Technology Companies = Inflected With AI’s Rise 102

CapEx Spend @ Amazon AWS =

Cloud vs. AI Patterns

103

CapEx as % of Revenue (AWS as Proxy) – AI vs. Cloud Buildouts =

49% (2024) vs. 4% (2018) vs. 27% (2013), per Morgan Stanley

Amazon AWS CapEx as % of Revenue – 2013-2024, Estimated per Morgan Stanley

60%

Initial Cloud Infrastructure AI / ML Infrastructure 49%

Build-Out Build-Out

40%

, AWS CapEx as % of revenue %

e

u decreased as upfront infrastructure n

e investments slowed & revenue grew…

27% will AI follow?

Rev

/

x

E

20%

Cap From 2020, AWS began rapidly

scaling CapEx (+30% Y/Y) to

build AI / ML infrastructure,

potentially restarting cycle

4%

0%

2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Note: Figures shown represent AWS only. AWS CapEx estimated per Morgan Stanley – equals AWS net additions to property & equipment less finance leases and obligations. Source: Amazon, Morgan Stanley (5/25)

CapEx Spend @ Amazon AWS = Cloud vs. AI Patterns 104

Tech CapEx Spend Partial Instigator =

Material Improvements in GPU Performance

105

NVIDIA GPU Performance =

+225x Over Eight Years

Performance of NVIDIA GPU Series Over Time – 2016-2024, per NVIDIA

$1B Data Center Comparison

GPT-MoE Inference Workload1

Pascal Volta Ampere Hopper Blackwell

2016 2018 2020 2022 2024 For a Theoretical $1B-Scale Data Center…

Number of GPUs 46K 43K 28K 16K 11K …Performance +225x over eight years

+225x while requiring 4x fewer GPUs…

Factory AI FLOPS 1EF 5EF 17EF 63EF 220EF

Annual Inference Tokens 50B 1T 5T 58T 1,375T …Inference token capacity +27,500x over

+30,000x eight years, implying +30,000x higher

Annual Token Revenue theoretical token revenue… $240K $3M $24M $300M $7B

DC Power 37MW 34MW 25MW 19MW 21MW …Data center power use down 43% over

+50,000x eight years, leading to +50,000x greater

Token Per MW-Year per-unit energy efficiency 1.3B 2.9B 200B 3T 65T

1 GPT-MoE Inference Workload = A type of workload where a GPT-style model with a Mixture-of-Experts (MoE) architecture is used for inference (i.e., making predictions). Note: Annual token revenue assumes a flat per-token cost. Source: NVIDIA (5/25)

Tech CapEx Spend Partial Instigator = Material Improvements in GPU Performance 106 NVIDIA Installed GPU Computing Power =

100x+ Growth Over ~Six Years

Global Stock of NVIDIA GPU Computing Power (FLOP/s) – Q1:19-Q4:24, per Epoch AI

Simultaneous expansion of GPU /

computing-related CapEx alongside

rising performance-per-GPU =

/s capacity Exponentially-greater computing OP

r, FL +130% / Year e

w

o

P

g

tin

u

p

Com

d

lle

sta

In

l

ta

o

T

Note: Analysis does not include TPUs or other specialized AI accelerators, for which less data is available. TPUs may provide comparable total computing power to NVIDIA chips. Source: Epoch AI (2/25)

Tech CapEx Spend Partial Instigator = Material Improvements in GPU Performance 107

Tech CapEx Spend Beneficiary =

NVIDIA

108

Key Tech CapEx Spend Beneficiary = NVIDIA…

25% & Rising of Global Data Center CapEx, per NVIDIA

Global Data Center CapEx ($B) vs. NVIDIA’s Data Center Revenue as

Percent of Data Center CapEx (Global) – 2022-2024, per NVIDIA @ GTC

$600 30%

l

a

r) b

a

B Glo

f )

(Blue % o ine L s Bd $ $400 20% ae , e x u(R E nx eE

Cap Rev r Cap r ter tete

Cen $200 10% Cen a Cen aa Dat l DatDat a b DIA Glo

NVI

$0 0%

2022 2023 2024

Note: NVIDIA data represents January FYE (e.g., 2024 = FY25 ending 1/25) vs calendar year for data center CapEx. Data presented by Jensen Huang at NVIDIA GTC 2025 (link). Source: Dell’Oro Research for CapEx (3/25); NVIDIA for data center revenue (3/25)

Tech CapEx Spend Beneficiary = NVIDIA 109

Technology Company Spend =

R&D Rising Along with CapEx

110

R&D Spend @ Big Six* USA Public Tech Companies =

13% of Revenue…vs. 9% Ten Years Ago

Big Six* USA Public Technology Company – R&D Spend ($B) vs. % of Revenue –

2014-2024, per Capital IQ

$300 15%

)

ine

L

a (R Beun $200 rs) ed

10% e

, f +20% / Year se% o n B Rev $ (Blue

p a x e s

se

$100 5% n

e

R&D E xp

R&D E

$0 0%

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

*Note: Big Six USA technology companies include Apple, Nvidia, Microsoft, Alphabet / Google, Amazon, & Meta Platforms / Facebook. R&D expense shown for Amazon, not AWS, as figures are not broken out in company financials; revenue therefore shown on like-for-like basis. Source: Capital IQ (3/25)

Technology Company Spend = R&D Rising Along with CapEx 111

Tech Big Six (USA) =

Loaded With Cash to Spend on AI & CapEx

112

Big Six* Generating Loads of Cash =

+263% Growth in Free Cash Flow Over Ten Years to $389B…

Big Six* Public Technology Companies – Free Cash Flow ($B) – 2014-2024, per Capital IQ

$100

B

$

,

w

o

Fl $50

Cash

e

e

Fr

$0

Apple NVIDIA Microsoft Google Amazon Meta

2014 2019 2024

*Note: Big Six USA technology companies include Apple, Nvidia, Microsoft, Alphabet / Google, Amazon, & Meta Platforms / Facebook. FCF calculated as cash flow from operations less capex to standardize definitions, as only some companies subtract finance leases and Amazon adjusts FCF for gains on sale of equipment. FCF shown for Amazon, not AWS, as figures are not broken out in company financials. Source: Capital IQ (3/25)

Tech Big Six (USA) = Loaded With Cash to Spend on AI & CapEx 113 …Big Six* Generating Loads of Cash =

+103% Growth in Cash Over Ten Years to $443B

Big Six* USA Public Technology Company Cash on Balance Sheet ($B) – 2014-2024,

per Capital IQ

$150

B

$

t, $100

e

e

h

S

ce

lan

a

B

n

o $50

Cash

$0

Apple NVIDIA Microsoft Google Amazon Meta

2014 2019 2024

*Note: Big Six USA technology companies include Apple, Nvidia, Microsoft, Alphabet / Google, Amazon, & Meta Platforms / Facebook. Figure measures cash and other equivalents (e.g., short-term investments and marketable securities) on companies’ balance sheets. Source: Capital IQ (3/25)

Tech Big Six (USA) = Loaded With Cash to Spend on AI & CapEx 114

Tech CapEx Spend Driver =

Compute Spend to Train & Run AI Models

115

To understand the evolution of AI computing economics, it’s constructive to look at where costs are concentrated –

And where they’re headed. The bulk of spending in AI large language model (LLM) development

is still dominated by compute – specifically, the compute needed to train and run models.

Training costs remain extraordinarily high and are rising fast,

often exceeding $100 million per model today. As Dario Amodei, CEO of Anthropic, noted in mid-2024,

Right now, [AI model training costs] $100 million. There are models in training today that are more like a billion…

I think that the training of…$10 billion models, yeah, could start sometime in 2025.

Around these core compute costs sit additional high-cost layers:

research, data acquisition and hosting, and a mix of salaries, general overhead, and go-to-market operations.

Even as the cost to train models climbs, a growing share of total AI spend is shifting toward inference –

the cost of running models at scale in real-time. Inference happens constantly,

across billions of prompts, queries, and decisions, whereas model training is episodic.

As Amazon CEO Andy Jassy noted in his April 2025 letter to shareholders,

While model training still accounts for a large amount of the total AI spend, inference…

will represent the overwhelming majority of future AI cost because customers

train their models periodically but produce inferences constantly.

NVIDIA Co-Founder & CEO Jensen Huang noted the same in NVIDIA’s FQ1:26 earnings call, saying

Inference is exploding. Reasoning AI agents require orders of magnitude more compute.

At scale, inference becomes a persistent cost center – one that grows in parallel with usage,

despite declines in unit inference costs.

The broader dynamic is clear: lower per-unit costs are fueling higher overall spend.

As inference becomes cheaper, AI gets used more.

And as AI gets used more, total infrastructure and compute demand rises – dragging costs up again.

The result is a flywheel of growth that puts pressure on cloud providers, chipmakers, and enterprise IT budgets alike.

The economics of AI are evolving quickly –

but for now, they remain driven by heavy capital intensity, large-scale infrastructure,

and a race to serve exponentially expanding usage.

Tech CapEx Spend Driver = Compute Spend to Train & Run Models 116

Data Centers =

Key Beneficiary of AI CapEx Spend

117

For one lens into the economics of AI infrastructure,

it’s useful to look at the pace and scale of data center construction.

The current wave of AI-driven demand has pushed data center spending to historic highs.

According to Dell’Oro Research, global IT company data center CapEx

reached $455 billion in 2024 and is accelerating.

Hyperscalers and AI-first companies alike are pouring billions into building out

compute-ready capacity – not just for storage, but for real-time inference and

model training workloads that require dense, high-power hardware.

As AI moves from experimental to essential, so too do data centers.

Per NVIDIA Co-Founder and CEO Jensen Huang, These AI data centers…are, in fact, AI factories.

That race is moving faster than many expected.

The most striking example may be xAI’s Colossus facility in Memphis, Tennessee which went

from a gutted factory to a fully operational AI data center in just 122 days.

As noted on page 122, at 750,000 square feet – roughly the size of 418 average USA homes –

it was built in half the time it typically takes to construct a single American house.

Per NVIDIA Co-Founder & CEO Jensen Huang,

What they achieved is singular, never been done before…That is, like, superhuman…

Data Centers = Key Beneficiary of AI CapEx Spend 118

…These kinds of timelines are no longer the exception. With prefabricated modules, streamlined permitting,

and vertical integration across electrical, mechanical, and software systems, new data centers are

going up at speeds that resemble consumer tech cycles more than real estate development.

But beneath that velocity lies a capital model that’s anything but simple.

CapEx is driven by land, power provisioning, chips, and cooling infrastructure –

especially as AI workloads push thermal and power limits far beyond traditional enterprise compute.

OpEx, by contrast, is dominated by energy costs and systems maintenance, particularly for high-density training clusters that operate near constant load.

Revenue is driven by compute sales – whether in the form of AI APIs, enterprise platform fees, or

internal productivity gains. But payback periods are often long, especially for vertically-integrated players

building ahead of demand. For newer entrants, monetization may lag build-out by quarters or even years.

And then there’s the supply chain. Power availability is becoming more of a gating factor.

Transformers, substations, turbines, GPUs, cables – these aren’t commodities

that can be spun up overnight. In this context, data centers aren’t just physical assets – they are strategic infrastructure nodes. They sit at the intersection of real estate, power,

logistics, compute, and software monetization.

The companies that get this right may do more than run servers –

they will shape the geography of AI economics for the next decade.

Data Centers = Key Beneficiary of AI CapEx Spend 119 Data Center Buildout Construction Value, USA =

+49% & Accelerated Annual Growth Over Two Years

USA Data Center Annualized Private Construction Value ($B) – 1/14-12/24,

per USA Census Bureau

$40

B

$

,

n

ctio $30

stru

Con +49% / te Year a

v

ri

P $20

f

o

lue

a

d +28% / e V

liz $10 Year a

u

n

n

A

$0

1/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 12/24

Note: All data are seasonally adjusted. Data obtained via USA Census Bureau’s Value of Construction Put in Place (VIP) Survey, which provides monthly estimates of the total dollar value of construction work done in USA. Data is annualized to avoid seasonal fluctuations. Source: USA Census Bureau

Data Centers = Key Beneficiary of AI CapEx Spend 120 Data Center New Construction vs. Existing Capacity, USA =

+16x in New vs. +5x in Existing Over Four Years

Data Center Capacity (Megawatts) by Real Estate Profile,

USA Primary Markets – 2020-2024, per CBRE

12,000 +16x

s

tt

a

w

a

g

e 8,000

M

,

city

a

Cap

r

te 4,000

n

a +5x ce

Dat

0

2020 2021 2022 2023 2024

Net Absorption Pre-Leased or Under Construction

Existing capacity New capacity

but newly-filled

Note: Primary USA markets only (Northern Virginia, Atlanta, Chicago, Phoenix, Dallas-Ft. Worth, Hillsboro, Silicon Valley, New York Tri-State.) Source: CBRE, ‘North America Data Center Trends H2 2024’ (2/25)

Data Centers = Key Beneficiary of AI CapEx Spend 121 Data Center Build Time (xAI Colossus as Proxy) =

122 Days vs. 234 for a Home

122 Days =

A Fully-Operational Data Center – 2024… 122 Days =

One Half-Built House – 2024

750,000 Sq. Ft = Size of 418 USA Homes (Average Build Time = 234 Days)

750,000 Square Feet 1,792 Square Feet

We were told it would take 24 months to

build. So we took the project into our own

hands, questioned everything, removed

whatever was unnecessary, and

accomplished our goal in four months.

- xAI Website

Note: Median USA home size shown as of January 2025, per FRED. Colossus was built in a former Electrolux factory in Memphis, TN, USA. Average build time shown for single-unit buildings. Measures time between start of onsite work & completion. Data reported in 2024 but measures build times for homes started in 2023. Source: xAI, USA Census Bureau, Federal Reserve Bank of St. Louis, Wikimedia Commons

Data Centers = Key Beneficiary of AI CapEx Spend 122 Data Center Compute (xAI Colossus as Proxy) =

0 to 200,000 GPUs in Seven Months

xAI Colossus GPUs – 4/24-11/24, per xAI

xAI Colossus GPUs (K)

200K

200

xAI ultimately plans on 1MM

GPUs, per Memphis Chamber

of Commerce

100K

We’re running the world’s biggest supercomputer, Colossus. 100

Built in 122 days – outpacing every estimate –

it was the most powerful AI training system yet. GPUs, K

Then we doubled it in 92 days to 200k GPUs.

This is just the beginning…

…We doubled our compute at an unprecedented rate,

with a roadmap to 1M GPUs. Progress in AI is driven by

compute and no one has come close to building at this

0K

magnitude and speed. 0

4/24 8/24 11/24

- xAI Website, 5/25

Note: We assume 200,000 GPUs as of 11/30/24 per xAI’s disclosure that ‘we doubled [GPU count] in 92 days to 200K GPUs.’ xAI Colossus ran its first job across 4 data halls on 8/30/24. We assume zero GPUs as of construction start date (122 days prior to assumed opening date of 8/30/24). Source: xAI (5/25), Memphis Chamber of Commerce (12/24)

Data Centers = Key Beneficiary of AI CapEx Spend 123

Data Centers =

Electricity Guzzlers

124

AI and energy observations / quotes (in italics) here and the two pages that follow are from

‘World Energy Outlook Special Report –

Energy and AI’ (link) from IEA (International Energy Agency)* – 4/10/25

To understand where energy infrastructure is heading, it helps to examine the rising tension between AI

capability and electrical supply. The growing scale and sophistication of artificial intelligence

is demanding an extraordinary amount of computational horsepower, primarily from AI-focused data centers.

These facilities – purpose-built to train and serve models –

are starting to rival traditional heavy industry in their electricity consumption.

There is no AI without energy – specifically electricity (p. 3).

Data centers accounted for around 1.5% of the world’s

electricity consumption in 2024 (p. 14). Energy demand growth has been rapid:

Globally, data centre electricity consumption has grown by around 12% per year since 2017,

more than four times faster than the rate of total electricity consumption (p. 14).

As power demand rises, so too does its concentration:

The United States accounted for…[45% of global data centre electricity consumption],

followed by China (25%) and Europe (15%)…

nearly half of data centre capacity in the United States is in five regional clusters (p. 14).

The flipside is true as well: Emerging and developing economies other than China account for 50% of the

world’s internet users but less than 10% of global data centre capacity (p. 18)…

Data Centers = Electricity Guzzlers 125

…AI’s power demands are increasing – and its progress is increasingly bottlenecked not by

data or algorithms, but by the grid and strains related to demand.

While AI presently places considerable demands on the energy sector,

it is also already unlocking major energy efficiency and operational gains…

AI is already being deployed by energy companies to transform and optimize energy and

mineral supply, electricity generation and transmission, and energy consumption (p. 16).

Current AI-driven demand is extremely high.

This is forecast to continue, especially as capital gushes into model providers that, in turn,

spend on more compute. At some point, these model builders

will need to turn a profit to be able to spend more.

While demand – for both compute and energy – will inevitably continue to rise as consumer and business usage does the same, data centers will ultimately only serve those who pay their bills.

*IEA member countries include Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, S. Korea, Latvia, Lithuania, Luxembourg, Mexico,

Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Spain, Sweden, Switzerland, Republic of Türkiye, United Kingdom, and United States. IEA Association countries include Argentina, Brazil, China,

Egypt, India, Indonesia, Kenya, Morocco, Senegal, Singapore, S. Africa, Thailand, and Ukraine.

All data shown, unless otherwise specified, is global. Italicized text is directly quoted from the report.

Data Centers = Electricity Guzzlers 126 Data Center Electricity Consumption, Global =

+3x Over Nineteen Years, per IEA

Data Center Energy Consumption by Data Center Type & Equipment, Global – 2005-2024,

per IEA

Source: International Energy Agency (IEA), ‘Energy and AI’ (4/25)

Data Centers = Electricity Guzzlers 127 Data Center Electricity Consumption by Region =

USA Leads, per IEA

Data Center Electricity Consumption by Region – 2005-2024, per IEA

Source: International Energy Agency (IEA), ‘Energy and AI’ (4/25)

Data Centers = Electricity Guzzlers 128

Outline

1 • Seem Like Change Happening Faster Than Ever?

Yes, It Is

2 • AI User + Usage + CapEx Growth =

Unprecedented

3 • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling =

Performance Converging + Developer Usage Rising

4 • AI Usage + Cost + Loss Growth =

Unprecedented

5 • AI Monetization Threats =

Rising Competition + Open-Source Momentum + China’s Rise

6 • AI & Physical World Ramps =

Fast + Data-Driven

7 • Global Internet User Ramps Powered by AI from Get-Go =

Growth We Have Not Seen Likes of Before

8 • AI & Work Evolution =

Real + Rapid

129

To understand where AI model economics may be heading, one can look at the mounting tension between capabilities and costs.

Training the most powerful large language models (LLMs) has become one of the most expensive / capital-intensive

efforts in human history. As the frontier of performance pushes toward ever-larger parameter counts and

more complex architectures, model training costs are rising into the billions of dollars.

Ironically, this race to build the most capable general-purpose models may be accelerating commoditization and driving

diminishing returns, as output quality converges across players and differentiation becomes harder to sustain.

At the same time, the cost of applying/using these models – known as inference – is falling quickly.

Hardware is improving – for example, NVIDIA’s 2024 Blackwell GPU consumes 105,000x less energy per token

than its 2014 Kepler GPU predecessor. Couple that with breakthroughs in models’ algorithmic efficiency,

and the cost of inference is plummeting.

Inference represents a new cost curve, and – unlike training costs – it’s arcing down, not up.

As inference becomes cheaper and more efficient, the competitive pressure amongst LLM providers increases –

not on accuracy alone, but also on latency, uptime, and cost-per-token*. What used to cost dollars can now cost pennies.

And what cost pennies may soon cost fractions of a cent.

The implications are still unfolding. For users (and developers), this shift is a gift:

dramatically lower unit costs to access powerful AI.

And as end-user costs decline, creation of new products and services is flourishing, and user and usage adoption is rising.

For model providers, however, this raises real questions about monetization and profits.

Training is expensive, serving is getting cheap, and pricing power is slipping. The business model is in flux. And there are new

questions about the one-size-fits-all LLM approach, with smaller, cheaper models trained for custom use cases** now emerging.

Will providers try to build horizontal platforms? Will they dive into specialized applications? Only time will tell.

In the short term, it’s hard to ignore that the economics of general-purpose LLMs

look like commodity businesses with venture-scale burn.

*Cost-per-token = The expense incurred for processing or generating a single token (a word, sub-word, or character) during the operation of a language model. It is a key metric used to

evaluate the computational efficiency and cost-effectiveness of deploying AI models, particularly in applications like natural language processing.

**E.g., OpenEvidence

AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = 130

Performance Converging + Developer Usage Rising

AI Model Compute Costs High / Rising

+

Inference Costs Per Token Falling

=

Performance Converging + Developer Usage Rising

131

AI Model Training Compute Costs =

~2,400x Growth Over Eight Years, per Epoch AI & Stanford

Estimated Training Cost of Frontier AI Models – 2016-2024,

per Epoch AI & Stanford

le) Right now, [AI model training costs]

Approx.

ca +2,400x $100 million. There are models in

S training today that are more like a g billion. Right. I think if we go to $10 or

(Lo $100 billion, and I think that will

happen in 2025, 2026, maybe 2027…

USD

, …I think that the training of…$10

billion models, yeah, could start

Cost sometime in 2025. ing

rain Dario Amodei (6/24)- Anthropic Co-Founder & CEO T

Note: Costs are estimates. Excludes most Chinese models due to lack of reliable cost data. Source: Epoch AI via Nestor Maslej et al., ‘The AI Index 2025 Annual Report,’ AI Index Steering Committee, Stanford HAI (4/25); In Good Company podcast (6/24)

AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = 132

Performance Converging + Developer Usage Rising

AI Model Compute Costs High / Rising

+

Inference Costs Per Token Falling

=

Performance Converging + Developer Usage Rising

133

To understand the trajectory of AI compute, it helps to revisit an idea from the early days of PC software. ‘Software is a gas…it expands to fill its container,’ said Nathan Myhrvold, then CTO of Microsoft in 1997.

AI is proving no different. As models get better, usage increases – and as usage increases, so does demand

for compute. We’re seeing it across every layer: more queries, more models, more tokens per task.

The appetite for AI isn't slowing down. It’s growing into every available resource –

just like software did in the age of desktop and cloud.

But infrastructure is not just standing still. In fact, it's advancing faster than almost any other layer in the stack,

and at unprecedented rates. As noted on page 136, NVIDIA’s 2024 Blackwell GPU

uses 105,000 times less energy to generate tokens than its 2014 Kepler predecessor.

It’s a staggering leap, and it tells a deeper story –

not just of cost reduction, but of architectural and materials innovation

that is reshaping what’s possible at the hardware level.

These improvements in hardware efficiency are critical to offset the strain of increasing

AI and internet usage on our grid. So far, though, they have not been enough.

This trend aligns with Jevons Paradox, first proposed back in 1865* –

that technological advancements that improve resource efficiency actually lead to increased overall usage

of those resources. This is driving new focus on expanding energy production capacity –

and new questions about the grid’s ability to manage.

Yet again, we see this as one of the perpetual ‘a-ha’s’ of technology:

costs fall, performance rises, and usage grows, all in tandem. This trend is repeating itself with AI.

*British economist William Stanley Jevons first observed this phenomenon in 19th-century Britain, where he noticed that improvements in the efficiency of coal-powered steam

engines were not reducing coal consumption but rather increasing it. In his book The Coal Question, he noted ‘It is wholly a confusion of ideas to suppose that the economical use

of fuel is equivalent to diminished consumption. The very contrary is the truth.’

AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = 134

Performance Converging + Developer Usage Rising

AI Inference ‘Currency’ =

Tokens

Additional context: 1MM tokens =

~750,000 words…roughly

• 3,500 pages of a standard book

(12-point font, double-spaced)

• 5,000 ChatGPT responses*

*Assumes that the average ChatGPT interaction consumes 200 total tokens (input + output), or 150 words. Thus, 1MM tokens equates to roughly 5,000 ChatGPT responses. Source: OpenAI (1/25)

AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = 135

Performance Converging + Developer Usage Rising

AI Inference Costs – NVIDIA GPUs =

-105,000x Decline in Energy Required to Generate Token Over Ten Years

Energy Required per LLM Token (Joules), NVIDIA GPUs – 2014-2024, per NVIDIA

50,000

les)

(Jou

n

ke

o

T

e 25,000 r

n -105,000x p

tio

p

m

su

n

Co

y

rg

e

n

E

0

Kepler Pascal Volta Ampere Hopper Blackwell (2014) (2016) (2018) (2020) (2022) (2024)

Note: Kepler released in 2012. NVIDIA materials mark performance threshold shown above for Kepler as of 2014. Source: NVIDIA Company Overview (2/25)

AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = 136

Performance Converging + Developer Usage Rising

AI Inference Costs – Serving Models =

99.7% Lower Over Two Years, per Stanford HAI

AI Inference Price for Customers (per 1 Million Tokens) – 11/22-12/24, per Stanford HAI

Note: Axis is

logarithmic;

every axis tick

represents a

10x price

change

Source: Nestor Maslej et al., ‘The AI Index 2025 Annual Report,’ AI Index Steering Committee, Stanford HAI (4/25)

AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = 137

Performance Converging + Developer Usage Rising

AI Cost Efficiency Gains =

Happening Faster vs. Prior Technologies

Relative Cost of Key Technologies by Year Since Launch,

per OpenAI, John McCallum, & Richard Hirsh

100%

)

0

r

a

e

Y

to 75%

d

e

x

e

d

In

r (

a

e

Y 50%

y

B

ce

Pl ri

ina 25% g

Ori

f

% o

0%

0 20 40 60 80

Electric Power Computer Memory ChatGPT: 75-Word Response

Note: Price change in consumer goods and services in the United States is measured as the percentage change since 1997. Data is based on the consumer price index (CPI) for national average urban consumer prices. Per OpenAI, 100 AI ‘tokens’ generates approximately 75 words in a large language model response; data shown indexes to this number of tokens. ‘Year 0’ is not necessarily the year that the technology was introduced, but rather the first year of available data. Source: Electricity Costs – Technology and Transformation in the American Electric Utility Industry, Richard Hirsh (1989); Computer Memory Storage Costs – John C. McCallum, with data aggregated from 72 primary sources and historical company sales documents; OpenAI, Wikimedia Commons

AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = 138

Performance Converging + Developer Usage Rising

Tech’s Perpetual A-Ha =

Declining Costs + Improving Performance → Rising Adoption…

USA Internet Users (MM) vs. Relative IT Cost – 1989-2023, per FRED & ITU

400 , 100y

log

o

n

ch

a T rs) e

B n 300 75 etio e) a

Users, 200 In (R : x0 50 e d 10 In M fo ed M (Blu rm Lin

rne ce 89 ri P t =

te 1/ r 1/ e In 100 25 m

su

USA

Con

USA

0 0

8 9

89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 0 09 10 11 12 13 14 15 16 17 18 1 20 21 22 23

0 0

19 19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20 20 20 2 20 20 20 20 20 20 20 20 20 20 2 20 20 20 20

Note: FRED data shows ‘Consumer Price Index for All Urban Consumers: Information Technology, Hardware and Services in U.S. City Average.’ Source: USA Federal Reserve Bank of St. Louis (FRED), International Telecommunications Union (via World Bank) (4/25)

AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = 139

Performance Converging + Developer Usage Rising

…Tech’s Perpetual A-Ha =

Prices Fall + Performance Rises

AI Model Training Compute (FLOP) vs. Relative IT Cost – 1989-2024, per Epoch AI & FRED

100

,

y

90 log

OP no

80 ch

, T FL e u ) 70 te n p tioa ine

Com ) rm L +360% d 60 fo lote / Year P In ing : r (R x 500 e rain tte0 d T 1 l In = e (Sca 409 d ce o ri/8 M P/1 I r 301 A e m le b su a 20

Not Con

10

USA

0

3 7 1 5 9

89 90 91 92 93 94 95 96 97 98 99 00 01 02 0 04 05 06 0 08 09 10 1 12 13 14 1 16 17 18 1 20 21 22 23 24

0 0 0 0 0

19 19 19 19 19 19 19 19 19 19 19 20 20 20 2 20 20 20 2 20 20 20 2 20 20 20 2 20 20 20 2 20 20 20 20 20

Note: A petaFLOP/s-day represents the total computational work performed by a system operating at 1 petaFLOP/s (10¹⁵ floating-point operations per second) for 24 hours, equivalent to approximately 8.64 × 10¹⁹ operations. This metric is commonly used to quantify the compute required for large-scale tasks like training machine learning models. FRED data shows ‘Consumer Price Index for All Urban Consumers: Information Technology, Hardware and Services in U.S. City Average.’ Note that, while training compute is not a direct measurement of model performance, it is typically closely correlated with performance. Source: USA Federal Reserve Bank of St. Louis (FRED); Epoch AI (5/25)

AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = 140

Performance Converging + Developer Usage Rising

AI Model Compute Costs High / Rising

+

Inference Costs Per Token Falling

=

Performance Converging + Developer Usage Rising

141

AI Model Performance =

Converging Rapidly, per Stanford HAI

Performance of Top AI Models on LMSYS Chatbot Arena – 1/24-2/25, per Stanford HAI

re

co

S

a

ren

A

S

Y

S

M

L

Note: The LMSYS Chatbot Arena is a public website where people compare two AI chatbots by asking them the same question and voting on which answer is better. The results help rank how well different language models perform based on human judgment. Only the highest-scoring model in any given month is shown in this comparison. Source: Nestor Maslej et al., ‘The AI Index 2025 Annual Report,’ AI Index Steering Committee, Stanford HAI (4/25)

AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = 142

Performance Converging + Developer Usage Rising

AI Model Compute Costs High / Rising

+

Inference Costs Per Token Falling

=

Performance Converging + Developer Usage Rising

143

To understand the surge in AI developer activity, it’s instructive to look at the extraordinary drop in

inference costs and the growing accessibility of capable models.

Between 2022 and 2024, the cost-per-token to run language models fell by an estimated 99.7% –

a decline driven by massive improvements in both hardware and algorithmic efficiency.

What was once prohibitively expensive for all but the largest companies

is now within reach of solo developers,

independent app builders, researchers on a laptop, and mom-and-pop shop employees.

The cost collapse has made experimentation cheap, iteration fast,

and productization feasible for virtually anyone with an idea.

At the same time, performance convergence is shifting the calculus on model selection.

The gap between the top-performing frontier models and smaller, more efficient alternatives is narrowing.

For many use cases – summarization, classification, extraction, or routing –

the difference in real-world performance is negligible.

Developers are discovering they no longer need to pay

a premium for a top-tier model to get reliable outputs. Instead, they can run cheaper models locally or

via lower-cost API providers and achieve functionally similar results,

especially when fine-tuned on task-specific data.

This shift is weakening the pricing leverage of model incumbents

and leveling the playing field for AI development…

AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = 144

Performance Converging + Developer Usage Rising

…At the platform level, a proliferation of foundation models has created a new kind of flexibility.

Developers can now choose between dozens of models – OpenAI’s ChatGPT, Meta’s Llama, Mistral’s

Mixtral, Anthropic’s Claude, Google’s Gemini, Microsoft’s Phi, and others –

each of which excels in different domains. Some are optimized for reasoning,

others for speed or code generation. The result is a move away from vendor lock-in.

Instead of consolidating under a single provider who can gate access or raise prices,

developers are distributing their efforts across multiple ecosystems. This plurality of options is

empowering a new wave of builders to choose the best-fit model for their technical or financial needs.

What’s emerging is a flywheel of developer-led infrastructure growth.

As more developers build AI-native apps,

they also create tools, wrappers and libraries that make it easier for others to follow.

New front-end frameworks, embedding pipelines, model routers, vector databases,

and serving layers are multiplying at an accelerating rate.

Each wave of developer activity reduces the friction for the next,

compressing the time from idea to prototype

and from prototype to product. In the process, the barrier to building with AI is collapsing –

not just in cost, but in complexity. This is no longer just a platform shift. It’s an explosion of creativity.

Technology history has shown – as memorialized by then-Microsoft President Steve Ballmer’s

repeat Developers! Developers! Developers… at a 2000 Microsoft Developers Conference (link)

– the platform that gets the most consistent developer user and usage momentum –

and can scale and steadily improve – wins.

AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = 145

Performance Converging + Developer Usage Rising

The AI Developer Next Door

146

AI Tool Adoption by Developers =

63% vs. 44% Y/Y

Share of Developers Currently Using AI in Development Processes – 2023-2024,

per Stack Overflow

75%

%

50%

rs,

e

lop

e

Dev

o 25% f

re

a

h

S

0%

Professional Developers Learning to Code

2023 2024

Note: 2023 N=89,184; 2024 N=65,437. Respondents are global. Source: Stack Overflow Developer Surveys (5/23 & 5/24-6/24)

The AI Developer Next Door 147 AI Developer Repositories – GitHub =

~175% Increase Over Sixteen Months

Number of AI Developer Repositories* on GitHub – 11/22-3/24, per Chip Hyuen

s

e

ri

sito

o

Rep

I

A

f

o

r

e

b

Num

e

iv

lat

u

Cum

*A repository is an online storage space where developers share and manage code, models, data, and documentation related to artificial intelligence projects. These enable collaboration, reuse, and distribution of AI tools and assets. Analysis shown includes GitHub repositories with 500+ stars. Infrastructure = tools for model serving, compute management, vector search & databases. Model development = frameworks for modeling & training, inference optimization, dataset engineering, & model evaluation. Application development = custom AI-powered applications (varied use cases). Source: Chip Hyuen via GitHub (3/24)

The AI Developer Next Door 148 AI Developer Ecosystem – Google =

+50x Monthly Tokens Processed Y/Y

Google Monthly Tokens Processed (T) – 5/24-5/25, per Google

500 480T

s

llion

ri

T

,

d

sse

roce 250 P s n

This time last year, we were processing 9.7 trillion tokens a ke

o

month across our products and APIs. Now, we’re processing T over 480 trillion ly – that’s 50 times more.th

n

o

- Google I/O 2025 Press Release, 5/25 M

10T

0

5/24 5/25

Note: Token usage shown across Google products & APIs. Per Google in 5/25, ‘This time last year, we were processing 9.7 trillion tokens a month across our products and APIs. Now, we’re processing over 480 trillion — that’s 50 times more…Over 7 million developers are building with Gemini, five times more than this time last year.’ Source: Google, ‘Google I/O 2025: From research to reality’ (5/25)

The AI Developer Next Door 149 AI Developer Ecosystem – Microsoft Azure AI Foundry =

+5x Quarterly Tokens Processed Y/Y

Microsoft Azure AI Foundry Quarterly Tokens Processed (T) – Q1:24-Q1:25, per Microsoft

100T

100

s

lion

ril

T

d,

[Microsoft Azure AI] Foundry is the agent and AI app factory. 50

It is now used by developers at over 70,000 enterprises and

digital natives – from Atomicwork, to Epic, Fujitsu, and Processe

Gainsight, to H&R Block and LG Electronics – to design, ns

customize, and manage their AI apps and agents. 20T

oke

T

We processed over 100 trillion tokens this quarter, up 5x

year-over-year – including a record 50 trillion tokens last

month alone. 0

Q1:24 Q1:25

- Microsoft FQ3:25 Earnings Call, 4/25

Note: Source: Microsoft FQ3:25 earnings call (4/25)

The AI Developer Next Door 150 AI Developer Use Cases =

Broad & Varied

AI Developer Use Cases – 2024, per IBM

Code Bug Detection & Testing Project / Workflow Documentation

Generation Fixing Automation Management

Refactoring & Security DevOps & CI / CD User Experience Architecture

Optimization Enhancement Pipelines Design Design

Note: CI / CD pipelines are continuous integration / continuous deployment pipelines.

Source: IBM, ‘AI in Software Development’ (2024); Anthropic; Katalon; AccelQ; Monday; Quill; Mintlify; Snyk; Ansible; UX Pilot; Ark Design AI

The AI Developer Next Door 151

AI Model Compute Costs High / Rising

+

Inference Costs Per Token Falling

=

Performance Converging + Developer Usage Rising

…(Likely) Long Way to Profitability

152

Outline

1 • Seem Like Change Happening Faster Than Ever?

Yes, It Is

2 • AI User + Usage + CapEx Growth =

Unprecedented

3 • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling =

Performance Converging + Developer Usage Rising

4 • AI Usage + Cost + Loss Growth =

Unprecedented

5 • AI Monetization Threats =

Rising Competition + Open-Source Momentum + China’s Rise

6 • AI & Physical World Ramps =

Fast + Data-Driven

7 • Global Internet User Ramps Powered by AI from Get-Go =

Growth We Have Not Seen Likes of Before

8 • AI & Work Evolution =

Real + Rapid

153

It’s different this time, we'll make it up on volume, and we’ll figure out how to monetize our

users in the future are typically three of the biggest danger statements in business.

That said, in technology investing every once in awhile they can be gold –

Amazon, Alphabet (Google), Meta (Facebook), Tesla, Tencent, Alibaba, Palantir…

In AI, it may indeed be different this time, and the leader(s) will make it up on volume and be able to

monetize users in the future. Though now, ‘different this time’ also means that competition is unprecedented…

We have never seen so many founder-driven / assisted (ex. Apple) companies*

with market capitalizations in excess of $1 trillion – most with gross margins of +50% plus free cash flow –

attacking the same opportunity at the same time in a relatively transparent world, adding in

high stakes competition between global powers – China and the United States.

Ernest Hemingway’s phrase gradually, then suddenly from ‘The Sun Also Rises’ applies to technology tipping points.

The tipping point for personal computers was the introduction of Apple’s Macintosh (1984) and Microsoft’s Windows 3.0 (1990).

With the Internet it was Netscape’s IPO (1995). With the Mobile Internet it was Apple’s iPhone App Store launch (2008).

With Cloud Computing it was the launch of AWS (Amazon Web Services) foundational products (2006-2009). With AI it was the launch of NVIDIA’s A100 GPU chip (2020) and OpenAI’s public version of ChatGPT (2022).

In effect, the global competition for AI kicked in with the launch of China’s DeepSeek (1/25) and

Jack Ma’s attendance at Chinese President Xi Jinping’s symposium of Chinese business leaders (2/25).

The money to fund AI’s growth (and losses) comes from big companies with big free cash flow and big balance sheets,

in addition to wealthy and ambitious capital providers from around the world.

No doubt, this dynamic combination of competition / capital / entrepreneurship will rapidly advance AI,

a riddle is determining which business models will be the last ones standing.

*Companies include NVIDIA, Microsoft, Amazon, Alphabet (Google), Meta (Facebook) & Tesla

AI Usage + Cost + Loss Growth = Unprecedented 154 Technology Disruption Pattern Recognition =

Hundreds of Years of Consistent Signals

Technology disruption has a long-repeating rhythm: early euphoria, break-neck capital formation,

bruising competition, and – eventually – clear-cut winners and losers.

Alasdair Nairn’s ‘Engines That Move Markets’ (link here) distills two centuries of such cycles,

and his observations are prescient for today’s AI boom.

Highlights of his observations follow…

There were several years of strong share-price growth when the railways were supplanting canals.

The bubble of the 1840s deflated under the weight of overheated expectations and changing economic conditions…

…Any technological advance which requires huge capital expenditure always runs a real risk of disappointing returns

in the early years, even if it is ultimately successful...

…Any technology that necessitates heavy capital expenditure and requires returns to be earned

over an extended period is always going to be a high-risk undertaking –

unless, that is, there is some form of protection against competition...

…The winners of these competitive struggles are not always those who have the best technology,

but those who can most clearly see the way that an industry or market is likely to develop…

…One of the clearest lessons of corporate and investment history is that without some barrier to entry,

first-mover advantage can be swiftly lost…

…A theme that recurs throughout this research is that while identifying the winners from any new technology

is often perilous and difficult, it is almost invariably simpler to identify who the ‘losers’ are going to be.

AI Usage + Cost + Loss Growth = Unprecedented 155

AI-Related Monetization =

Very Robust Ramps

156

To understand the evolution of AI hardware strategy, we’ll look at how control over chip design

is shifting from traditional vendors to the platforms that rely on them.

For years, NVIDIA has been at the center of the AI hardware stack.

Its GPUs (graphics processing units) became the default engine for training and inference,

prized for their ability to handle highly parallel computations at scale. Its proprietary technology – and

unparalleled scale – has led to industry leadership.

This reliance – combined with outsized sudden demand – has created constraints.

Despite NVIDIA’s rapid – and impressive – scale-up, demand for NVIDIA GPUs

has outpaced supply amid industry fervor for accelerated computing. Hyperscalers and

cloud providers are moving to improve their supply chains to manage long lead times.

That shift is accelerating the rise of custom silicon – especially ASICs, or application-specific integrated

circuits. Unlike GPUs, which are designed to support a wide range of workloads, ASICs are purpose-built

to handle specific computational tasks with maximum efficiency. In AI, that means optimized silicon

for matrix multiplication, token generation, and inference acceleration.

Google’s TPU (Tensor Processing Unit) and Amazon’s Trainium chips are now core components of their AI stacks. Amazon claims its Trainium2 chips offer 30-40% better price-performance than

standard GPU instances, unlocking more affordable inference at scale. These aren't side projects –

they’re foundational bets on performance, economics, and architectural control…

AI-Related Monetization = Very Robust Ramps 157

…Custom chips also reflect a broader effort to manage the economics of AI infrastructure.

As Amazon CEO Andy Jassy noted in early 2025, AI does not have to be as expensive as it is

today, and it won’t be in the future. Custom silicon is one lever to control these expenses.

At the same time, a new ecosystem of infrastructure specialists is emerging to meet this demand.

CoreWeave has become one of the fastest-scaling cloud GPU providers, repurposing gaming and

Crypto hardware supply chains to serve enterprise AI customers.

Oracle, long seen as a legacy IT vendor, has repositioned itself as a GPU-rich cloud platform

with AI-specific offerings. Astera Labs, a lesser-known but critical player,

builds high-speed interconnects that move data between GPUs and memory systems

with minimal latency – an increasingly important performance constraint.

These firms aren’t building foundation models, but they’re building what foundation models depend on.

As compute demand compounds, they’re becoming essential infrastructure in a

market where speed, availability, and efficiency are important differentiators.

AI-Related Monetization = Very Robust Ramps 158

AI Monetization =

Chips

159

AI Monetization…Chips =

NVIDIA Quarterly Revenue +78% to $39B Y/Y…

NVIDIA Quarterly Revenue by Business Line ($B) – 1/19-1/25, per NVIDIA

$40,000

M $30,000 $ Y/Y , M +78%

e

u

n

e

v

Re $20,000 y rl

rte

a

Qu

DIA $10,000

NVI

$0

Data Center Gaming Other

Fiscal Quarter Ending

Note: Gaming includes PC & console gaming. Other includes Enterprise / Pro Vis, Auto, & OEM / Other. NVIDIA’s fiscal year ends January 31. The figures in the title compare FQ4:25 to FQ4:24. Source: NVIDIA (1/25) via Morgan Stanley

AI Monetization = Chips 160 …AI Monetization…Chips =

NVIDIA Revenue +28x Over Ten Years…Big Six CapEx + R&D +6x

Big Six* USA Public Technology Company R&D + CapEx Spend ($B)

vs. NVIDIA Revenue ($B) – 2014-2024, per Capital IQ

$500 $150

r)

a

B

)

(Blue $400 ine $120 L Bd $e , d (R n eB p$ $300 $90, Se xu Ene

+ Rev $200 Cap

$60

DIA

R&D

ix NVI

S

ig $100 $30 B

$0 $0

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

*Note: Big Six USA technology companies include Apple, Nvidia, Microsoft, Alphabet / Google, Amazon, & Meta Platforms / Facebook. Includes CapEx for Amazon AWS + Retail as R&D expense is not regularly separated for those two business divisions. Source: Companies’ investor reports, Capital IQ (4/25)

AI Monetization = Chips 161 AI Monetization…Chips =

Google TPU Sales* +116% to $8.9B Y/Y, per Morgan Stanley

Google TPU (Tensor Processing Unit) Estimated Sales – 2021-2024, per Morgan Stanley

Google TPU Estimated Sales ($B)

$10

B

$

les,

Sl a

TPUs were purpose-built specifically for AI. TPUs are an a

u

application-specific integrated circuit (ASIC), a chip designed n $5

n

for a single, specific purpose: running the unique matrix and A vector- d based mathematics that’s needed for building and

te

running AI models. a

stim

Our first such chip, TPU v1, was deployed internally in 2015 E

and was instantly a hit across different parts of Google…

…‘We thought we'd maybe build under 10,000 of them,’ said

Andy Swing, principal engineer on our machine learning

hardware systems. ‘We ended up building over 100,000.’ $0

2021 2022 2023 2024

- Google Press Release, 7/24

*Figures are estimates per Morgan Stanley research. Note: Relative to GPUs, ASICs are custom-designed for specific tasks (e.g., AI model training,) whereas GPUs are general-purpose. Source: Google, Morgan Stanley, ‘GenAI Monetization – Assessing The ROI Equation’ (2/25)

AI Monetization = Chips 162 AI Monetization…Chips =

Amazon AWS Trainium* Sales +216% to $3.6B Y/Y, per Morgan Stanley

Amazon AWS Trainium Estimated Sales – 2024-2025, per Morgan Stanley

Amazon AWS Trainium Estimated Sales ($B)

$4

$3.6B

B

$

les,

a

S

AWS Trainium chips are a family of AI chips purpose built by l

a

AWS for AI training and inference to deliver high performance un $2

while reducing costs… n

A

d

AWS Trainium2 chip delivers up to 4x the performance of te

first-generation Trainium…[and offers] 30-40% better price a $1.1B

performance than the current generation of GPU-based EC2 stim

P5e and P5en instances. E

- Amazon AWS Trainium Overview, Accessed 5/25

$0

2024 2025

Note: Relative to GPUs, ASICs are custom-designed for specific tasks (e.g., AI model training,) whereas GPUs are general-purpose. Figures are estimates per Morgan Stanley research. Source: Amazon AWS, Morgan Stanley, ‘GenAI Monetization – Assessing The ROI Equation’ (2/25)

AI Monetization = Chips 163

AI Monetization =

Compute Services

164

AI Monetization…Cloud Computing =

CoreWeave Revenue +730% to $1.9B Y/Y

CoreWeave Revenue – 2022-2024, per CoreWeave

CoreWeave Revenue ($B)

$2

Q1:25 revenues

were $982MM:

+420% Y/Y growth

B

$

,

u $1 e

We've delivered an outstanding start to 2025 n

e

on multiple fronts. Our strong first quarter financial

performance caps a string of milestones including Rev

our IPO, our major strategic deal with OpenAI

as well as other customer wins, our acquisition of

Weights & Biases and many technical achievements…

…Demand for our platform is robust and accelerating

as AI leaders seek the highly performant AI cloud

infrastructure required for the most advanced applications.

We are scaling as fast as possible to capture $0

2022 2023 2024

that demand. The future runs on CoreWeave.

- CoreWeave CEO Michael Intrator, 5/25

Source: CoreWeave (as of 5/25)

AI Monetization = Compute Services 165 AI Monetization…AI Infrastructure =

Oracle Revenue +50x to $948MM Over Two Years

Oracle AI Infrastructure Revenue – F2022-F2024, per Oracle & Morgan Stanley Estimates

Oracle AI Infrastructure Revenue ($MM),

Estimated per Morgan Stanley

$1,000

M

M

$

e $500 , u

n

There are many, many [AI infrastructure] customers who have e

come on and that haven't gotten capacity yet…

Rev

…We've got at least 40 new AI bookings that are over a

billion (dollars) that haven't come online yet.

- Oracle CEO Safra Catz, 3/24

$0

F2022 F2023 F2024

Source: Oracle, Morgan Stanley estimates, ‘What’s Ahead for the AI Infrastructure Cycle’ (8/24)

AI Monetization = Compute Services 166 AI Monetization…Infrastructure Connectivity =

Astera Labs Revenue +242% to $396MM Y/Y

Astera Labs – 2022-2024, per Astera Labs

Astera Labs Revenue ($MM)

$400

M

M

$

e $200 , u

n

Astera Labs delivered strong Q4 results, with revenue e

growing 25% versus the previous quarter, and capped off a Rev

stellar 2024 with 242% revenue growth year-over-year…

…We expect 2025 to be a breakout year as we enter a new

phase of growth driven by revenue from all four of our product

families to support a diverse set of customers and platforms.

This includes our flagship Scorpio Fabric products for

head-node PCIe connectivity and backend

AI accelerator scale-up clustering. $0

2022 2023 2024

- Astera Labs CEO Jitendra Mohan, 2/25

Source: Astera Labs financial results (as of 4/25)

AI Monetization = Compute Services 167 AI Monetization…Data Collection + Supercomputing =

Tesla AI Training Capacity +8.5x

Tesla Dojo Custom Supercomputer – 6/21-9/24, per Tesla

Tesla AI Training Capacity (H100-Equivalent GPUs)

,

We’re continuing to simultaneously make city Us a

significant purchases of GPUs and also putting a t CapGP

which we believe has the potential for an order of lot of effort into Dojo [custom supercomputer], len ingaivu magnitude improvement in the cost of training… rainq T I E A0 …Dojo also has the potential to become a

companies, in the same way that Amazon Web sellable service that we would offer to other sla H10 e T

Services offers more web services, even though

it started out as a bookstore. So, I really think

that the Dojo potential is very significant.

- Tesla Co-Founder & CEO Elon Musk, 4/23

Note: Listing capacity in ‘H100-equivalent GPUs’ means Tesla converts the aggregate AI-training throughput of Dojo and its other accelerators into the number of NVIDIA Hopper H100 data-center GPUs that would deliver the same FP8/FP16 FLOPS, giving a single, industry-standard yard-stick for compute scale. Source: Tesla Q1:23 earnings call, Tesla Q3:24 investor presentation, Data Center Dynamics, Wikimedia Commons

AI Monetization = Compute Services 168

AI Monetization =

Data Layer

169

AI Monetization…Data Labeling & Evaluation =

Scale AI Revenue +160% to $870MM Y/Y

Scale AI Revenue – 2023-2024, per Scale AI

Scale AI Revenue ($MM)

$1,000

$870MM

M

M

$

Data abundance is not the default; it’s a choice. e,

It requires bringing together the best minds in engineering, n $500 e u

operations, and AI. Our vision is one of data abundance,

where we have the means of production to continue scaling Rev $335MM

frontier LLMs many more orders of magnitude.

We should not be data-constrained in getting to GPT-10.

- Scale AI Co-Founder & CEO Alexandr Wang, 5/24

We saw 160% revenue growth in 2024 from the previous

year, and we secured more than $1.5 billion in new business. $0

2023 2024

- Scale AI Head of Corporate and Product Comms Joe

Osborne, 4/25

Note: 2023 figures are estimates based on Joe Osborne (Head of Corporate and Product Comms at Scale AI,) who indicated, ‘We saw 160% revenue growth in 2024 from the previous

year, and we secured more than $1.5 billion in new business.’ Source: Scale AI, The Information (4/25) (link)

AI Monetization = Data Layer 170 AI Monetization…Data Storage / Management / Processing =

VAST Data Lifetime Sales From 0 to $2B in Just Over Six Years

VAST Data – 1/19-5/25, per VAST Data

Cumulative Lifetime Sales ($B)

$2B

$2

B

$

les,

a

S

Everything is accelerating. The rate of AI progress is e

each other’s discoveries and push the boundaries e constantly increasing as model builders build on tim

if $1

machines since early 2022, the advent of reasoning models ever farther. While we’ve been talking about thinking Leiv

in the last 12 months means that the era of lat

thinking machines is actually now upon us… u

Cum

…We at VAST believe that the path to the greatest potential

gain is to simplify and reduce the fundamental challenges

that need to be resolved. If we can build a simple approach $0B

to encompass nearly all of the infrastructure layers $0

needed for AI, without compromise… 1/19 5/25

customers supremely benefit.

- VAST Data CEO Renen Hallak, 5/25

Source: VAST Data, Silicon Angle

AI Monetization = Data Layer 171

AI-Related Cost Ramps Relative to Revenue =

Can Be Head-Turning

172

AI Monetization – OpenAI =

Revenue vs. Compute Expense, per The Information

OpenAI Revenue & Compute Expense ($B) by Year – 2022-2024, per The Information

$5

)

ine

) L

d

L (RB ine e

(Blue , $

B se

, e $0 $ n

e p

n E e u x

te

u

Rev p

I

n Com A

Op Ane e I

Op

-$5

2022 2023 2024

Note: No compute expense data available in 2022. Figures are estimates based off public reports & The Information reporting.

Source: The Information (4/25 and prior) (link, link, link, link, link & link)

AI-Related Cost Ramps Relative to Revenue = Can Be Head-Turning 173 AI Monetization – Microsoft / Amazon / Alphabet / Meta =

CapEx Up…Free Cash Flow Margins Down

Capital Expenditure, Free Cash Flow Margin, Revenue Growth – C2023-C2024,

per Capital IQ

CapEx vs. Free Cash Flow Margin Revenue

C2023 $35B 30% $228B

Microsoft C2024 $56B 27% $262B

Y/Y Change +58% -10% +15%

C2023 $53B 6% $575B

Amazon C2024 $83B 5% $638B

Y/Y Change +57% -8% +11%

Alphabet C2023 $32B 23% $307B (Google) C2024 $52B 21% $350B

Y/Y Change +63% -8% +14%

Meta Platforms C2023 $27B 33% $135B (Facebook) C2024 $37B 33% $165B

Y/Y Change +38% <1% +22%

Note: FCF calculated as cash flow from operations less capex to standardize, as only some companies subtract finance leases and Amazon adjusts FCF for gains on sale of equipment. Amazon statistics shown for both AWS & Retail; FCF not broken out across subsidiaries. Source: Capital IQ (5/25)

AI-Related Cost Ramps Relative to Revenue = Can Be Head-Turning 174

So…We Have…

High Revenue Growth +

High Cash Burn +

High Valuations +

High Investment Levels =

Good News for Consumers…

Others TBD…

175

Select Private AI Model Companies – 5/13/25 =

~$11B+ Annualized Revenue vs. ~$95B Raised…

Foundation Model Estimated Revenue & Capital Raised – 5/13/25,

per Pitchbook, The Information, Bloomberg, The Wall Street Journal & CNBC

Annualized Total Raised

Company Revenue ($MM) To-Date ($MM)

OpenAI 9,200 63,920

(4/25 estimated) (Last Raise: 3/25)

Anthropic 2,000 18,000

(3/25) (Last Raise: 3/25)

Materially

xAI 12,130

North of 100* (Last Raise: 11/24)

(4/25)

Perplexity 120 1,410

(5/25) (Last Raise: 5/25)

*Select media reports have xAI revenue being as high as $1B as of 4/25. Note: OpenAI annualized revenue estimated based upon full-year 2024 & 2025 revenue estimates as

published by The Information & Bloomberg, assuming linear revenue growth. Figures are rounded. Source: Source: Pitchbook (5/25), The Information (link), Bloomberg (link & link) &

CNBC (link & link)

So…We Have…High Revenue Growth + High Cash Burn + High Valuations + High Investment Levels =

Good News for Consumers…Others TBD… 176 …Select Private AI Model Companies – 5/13/25 =

High Valuation-to-Revenue Multiples

Foundation Model Estimated Revenue Multiple – 5/13/25,

per Pitchbook, The Information, Bloomberg, The Wall Street Journal & CNBC

Annualized Latest Valuation Revenue

Company Revenue ($MM) ($MM) Multiple

OpenAI 9,200 300,000 33x (4/25 estimated) (3/25)

Anthropic 2,000 61,500 31x (3/25) (3/25)

xAI 80,000 N/A Materially

North of 100* (3/25)

(4/25)

Perplexity 120 9,000 75x (5/25) (12/24)

*Select media reports have xAI revenue being as high as $1B as of 4/25. Note: OpenAI annualized revenue estimated based upon full-year 2024 & 2025 revenue estimates as published by The Information & Bloomberg, assuming linear revenue growth. xAI valuation per Elon Musk. Figures are rounded. Perplexity was reported to be in advanced talks to raise capital at a $14B post-money valuation as of 5/14/25; however, as this is not finalized at time of publication, we quote their last finalized funding round here. Source: Pitchbook (5/25),

The Information (link), Bloomberg (link & link) & CNBC (link & link)

So…We Have…High Revenue Growth + High Cash Burn + High Valuations + High Investment Levels =

Good News for Consumers…Others TBD… 177 Valuation-to-Revenue Multiple – OpenAI =

Looks Expensive…

Estimated Enterprise Value / Next 12 Months Revenue Multiple – 5/25,

per Capital IQ & Bloomberg

25x

e

u

n

e

20x

Rev

s

th

n

o

M 15x

2

1

t

Nex

/ 10x

lue

a Median = 6.9x

V

se

rpri 5x

te

n

E

0x

OpenAI Duolingo Meta Spotify Alphabet Pinterest

Note: OpenAI figures are estimates. Next 12 months revenue multiples for companies other than OpenAI are consensus estimates per Capital IQ. OpenAI NTM revenue estimates are

as of 12/24 due to data availability. Source: Capital IQ (5/15/25), Bloomberg (link)

So…We Have…High Revenue Growth + High Cash Burn + High Valuations + High Investment Levels =

Good News for Consumers…Others TBD… 178 …Revenue-per-User Multiple – OpenAI =

In-the-Range

Estimated Annual Revenue Per User ($) – 2024,

per Capital IQ, Morgan Stanley, Semrush, The Information & Company Disclosures

$80

$ $60

User,

r

e

p

u $40 e

n

e

OpenAI sees high

Rev valuation despite mid-pack l a annual revenue per user Median = $23 u n n A $20

$0

OpenAI Alphabet Meta Spotify Pinterest Duolingo

Note: OpenAI figures are estimates as of 4/25. All other public-company figures are as of 12/31/24, using CY2024 data. OpenAI data uses WAUs due to data availability (conservatively assumed as MAUs); other figures use MAUs. Here we assume average weekly active ChatGPT users of 300MM based off OpenAI’s 12/24 disclosure. We estimate 2024 ChatGPT revenue of $3.7B, per company estimates. Monthly active user figures are estimates for Alphabet based off website traffic measurements & global internet user data. Meta last reported MAPs for app family in Q4:23, we conservatively assume no growth since.

Source: Capital IQ (12/24), The Information (4/25 and prior) (link, link, link, link & link), Semrush (11/24), Morgan Stanley, ITU, company disclosures, BOND estimates

So…We Have…High Revenue Growth + High Cash Burn + High Valuations + High Investment Levels =

Good News for Consumers…Others TBD… 179

As global digital user bases have grown and potential rapidity of usage traction has risen in tandem,

areas of corporate investment (for companies new and old) have become

increasingly competitive and capital-intensive.

The AI tech cycle of creative disruption has historical analogs.

Head turners of the semi-recent past include Apple’s near bankruptcy in 1997

when its market capitalization was $1.7B*, now $3.2T.

Amazon.com’s near death moment happened in Q4:00 when it reported a net loss

of -$545MM on revenue of $972MM.

Founder and then-CEO Jeff Bezos noted in the 2000 Shareholder Report that

It’s been a brutal year for many in the capital markets and certainly for Amazon.com shareholders.

As of this writing, our shares are down more than 80% from when I wrote you last year.

At post-loss trough in Q3:01 its market cap was $2.2B while it supported 23MM active customer accounts.

The market cap is now $2.2T.

All in, Amazon lost -$3B in the twenty-seven quarters between its launch in Q2:97

and the end of its first net income-positive year (2003).

For its most recent twenty-seven most recent quarters (Q3:18-Q1:25),

Amazon’s cumulative net income was $176B.

Google’s IPO filing (April 2004) noted that in Q1:04, after having only raised a Series A funding round,

it spent 22% of revenue ($86MM of $390MM) on capital expenditures –

at the time it was an incomprehensibly high number.

It went public at a $23B market cap, now $2.0T…

*Market capitalization taken as of 7/1/97. Microsoft finalized its investment in Apple just over one month later, on 8/6/97.

Note: Present market capitalization figures are shown as of 5/14/25.

So…We Have…High Revenue Growth + High Cash Burn + High Valuations + High Investment Levels =

Good News for Consumers…Others TBD… 180

…Uber burned -$17B* between 2016 and 2022 (and materially more before that)

before its first free cash flow-positive year in 2023.

In 2022, it had 131MM monthly active platform consumers.

Uber’s last equity financing was a Series G.

Its fully-diluted IPO market cap was $82B, now $189B.

Tesla burned -$9.2B between 2009 and 2018 before becoming free cash flow positive in 2019. In the ten years between 2009 and 2018, it lost a cumulative -$5.6B delivering ~540K vehicles.

It went public in 2010 at a market cap of $1.6B.

From 2019-2024, it then earned $40B delivering 6.7MM vehicles.

Its market cap is now $1.1T.

It is important to remember – most of the time, when all is said and done –

a business’s valuation should represent the present value of its future free cash flows.

The aforementioned companies – with aggressive cash burn –

tested this premise hard, built large-scale data-driven network effects

based on product excellence / constant improvement,

developed technology-driven competitive advantage and ultimately proved the naysayers wrong.

Only time will tell which side of the money-making equation the current AI aspirants will land.

*Measured as unlevered free cash flow.

Note: Present market capitalization figures are shown as of 5/14/25.

So…We Have…High Revenue Growth + High Cash Burn + High Valuations + High Investment Levels =

Good News for Consumers…Others TBD… 181

Usage + Cost + Loss Growth =

Unprecedented…

What About Future Monetization + Profits?

182

AI Monetization Possibilities =

New Entrants & / Or Tech Incumbents?

183

To understand where AI model economics may be heading, one can look at

the mounting tension between capabilities and costs.

Training the most powerful large language models (LLMs) has become one

of the most expensive / capital-intensive efforts in human history. As the frontier of performance

pushes toward ever-larger parameter counts and more complex architectures, model training costs

are rising into the billions of dollars.

Ironically, this race to build the most capable general-purpose models

may be accelerating commoditization and driving diminishing returns, as output quality converges

across players and differentiation becomes harder to sustain.

At the same time, the cost of applying/using these models – known as inference – is falling quickly.

Hardware is improving – for example, NVIDIA’s 2024 Blackwell GPU consumes 105,000x less energy

per token than its 2014 Kepler GPU predecessor. Couple that with breakthroughs in

models’ algorithmic efficiency, and the cost of inference is plummeting.

Inference represents a new cost curve, and – unlike training costs – it’s arcing down, not up.

As inference becomes cheaper and more efficient, the competitive pressure amongst LLM providers

increases – not on accuracy alone, but also on latency, uptime, and cost-per-token*.

What used to cost dollars can now cost pennies.

And what cost pennies may soon cost fractions of a cent…

*Cost-per-token = The expense incurred for processing or generating a single token (a word, sub-word, or character) during the operation of a language model. It is a key metric used to

evaluate the computational efficiency and cost-effectiveness of deploying AI models, particularly in applications like natural language processing.

Consumer AI Monetization Possibilities = New Entrants & / Or Tech Incumbents? 184

…The implications are still unfolding. For users (and developers), this shift is a gift:

dramatically lower unit costs to access powerful AI.

And as end-user costs decline, creation of new products and services is flourishing, and user and usage adoption is rising.

For model providers, however, this raises real questions about monetization and profits.

Training is expensive, serving is getting cheap, and pricing power is slipping.

The business model is in flux. And there are new questions about the one-size-fits-all LLM approach,

with smaller, cheaper models trained for custom use cases* now emerging.

Additionally, traditional business moats are being disrupted. Look no further than Google.

The company launched AI Overviews in May of last year – they sit above many Google

search results. The company highlighted it had 1.5B AI Overviews MAUs as of 4/25…it’s

notable that in the last few weeks, Google began adding advertisements to select AI Overviews.

Will providers try to build horizontal platforms? Will they dive into specialized applications?

Will one or two leaders drive dominant user and usage share and related monetization,

be it subscriptions (easily enabled by digital payment providers), digital services, ads, etc.?

Only time will tell. In the short term, it’s hard to ignore that the economics of

general-purpose LLMs look like commodity businesses with venture-scale burn.

*E.g., OpenEvidence

Consumer AI Monetization Possibilities = New Entrants & / Or Tech Incumbents? 185 AI Company Landscape =

Varying Degrees of Vertical Integration

Specializations of Ten Leading AI Companies – 4/25, per The Wall Street Journal

Developing Models & Chatbots

All 10 of these companies are building generative-

AI tools that can create content including text,

images and video.

Building AI Infrastructure

These seven companies – both tech giants and AI upstarts – are also building the hardware and data

centers that provide the power and infrastructure

needed to run AI systems.

Providing AI Cloud Services

The top cloud providers offer platforms that help

businesses leverage AI tech in their own products

and workflows.

*Has a partnership with Oracle, SoftBank and MGX to build out the proposed Stargate data-center network. Source: Wall Street Journal, ‘Here’s How Big the AI Revolution Really Is, in Four Charts’ (4/25)

Consumer AI Monetization Possibilities = New Entrants & / Or Tech Incumbents? 186

AI Monetization Possibilities =

New Entrants & / Or Tech Incumbents?

187

AI – New Entrants =

Rapidly Laying Groundwork

188

AI Monetization…Foundation Models =

Consumer Subscription Models Driving Monetization…

OpenAI ChatGPT, xAI Grok, Google Gemini, Anthropic Claude & Perplexity

Consumer Pricing – 5/25, per Companies

OpenAI ChatGPT xAI Grok Google Gemini

$0 (Free) / $20 (Plus) / $200 (Pro) $0 (Free) / $3 (Basic) / $8 (Premium) / $0 (Free) / $19.99 (AI Pro) /

per Month 1 $40 (Premium+) per Month $250 (AI Ultra) per Month

Anthropic Claude Perplexity

$0 (Free) / $172 (Plus) / $100 (Max) $0 (Free) / $20 (Pro)

per Month per Month

Note: Excludes enterprise plans. 1. Grok pricing is bundled with X premium subscriptions. X premium subscriptions include additional benefits beyond improvements to Grok usage limits. 2. With annual discount. Source: OpenAI, X, Google, Anthropic, Perplexity websites (5/25)

AI – New Entrants = Rapidly Laying Groundwork 189 …AI Monetization…Foundation Models =

Developer API Fees Driving Monetization

OpenAI ChatGPT, xAI Grok, Google Gemini, Anthropic Claude & Perplexity

Developer API Pricing – 5/25, per Companies

OpenAI ChatGPT xAI Grok Google Gemini

From $0.40 (GPT-4.1 nano) to $40 (o3) $0.50 (grok-3-mini-beta) to $25 (grok-3-fast) $0.15 (1.5 Flash-8B) to $15 (2.5 Pro Preview)

per 1MM Output Tokens 1 per 1MM Output Tokens per 1MM Output Tokens

Anthropic Claude Perplexity

From $1.25 (Claude 3 Haiku) to $75 (Claude 3 Opus) $1 (Sonar) to $15 (Sonar Pro)

per 1MM Output Tokens per 1MM Output Tokens

1. Gemini prices by prompt size. Gemini 1.5 Flash-8B = $0.15 per 1MM tokens for prompts ≤128K tokens; Gemini 2.5 Pro Preview = $15 per 1MM tokens for prompts >200K tokens. Source: OpenAI, X, Google, Anthropic, Perplexity websites (5/25)

AI – New Entrants = Rapidly Laying Groundwork 190

AI – New Entrants =

Rapid Revenue Growth

191

AI Monetization – Foundation Models =

OpenAI Revenue +1,050% Annually to $3.7B

ChatGPT Paid Subscribers (MM) & Revenue ($B) – 10/22-4/25,

per OpenAI & The Information

Paid Subscribers Revenue

20 $4

M

M

,

rs B$ e +153% / , be Yearu scrin be u S 10 $2 1,050% / Rev id I Year a A n P e T P Op tG

Cha

0 $0

10/22 8/23 6/24 4/25 2022 2023 2024

Source: OpenAI disclosures (as of 4/25), The Information (4/25) (link, link, link & link)

AI – New Entrants = Rapid Revenue Growth 192 AI Monetization – API & Generative Search =

Anthropic Annualized Revenue +20x to $2B in Eighteen Months

Anthropic: API & Generative Search – 9/23-3/25, per Reuters, Bloomberg & CNBC

Annualized Revenue ($B)

$2

B

$

,

e

u

We’ve developed Claude 3.7 Sonnet with a different e 6.4x / n

deep reflection, we believe reasoning should be an integrated philosophy from other reasoning models on the market. Just $1 Year Rev as humans use a single brain for both quick responses and d e liz capability of frontier models rather than a separate model a u entirely. This unified approach also creates a more n n seamless experience for users… A

…we’ve optimized somewhat less for math and computer

science competition problems, and instead shifted

focus towards real-world tasks that better reflect

how businesses actually use LLMs.

$0

9/23 12/23 3/24 6/24 9/24 12/24 3/25

- Anthropic Press Release, 2/25

Source: Anthropic; Reuters, ‘Anthropic forecasts more than $850 mln in annualized revenue rate by 2024-end – report’ (12/23) (link); Bloomberg, ‘Anthropic Finalizes Megaround at

$61.5 Billion Valuation’ (3/25) (link); CNBC, ‘Anthropic closes $2.5 billion credit facility as Wall Street continues plunging money into AI boom’ (5/25) (link)

AI – New Entrants = Rapid Revenue Growth 193 AI Monetization – Generative Search =

Perplexity Annualized Revenue +7.6x to $120MM in Fourteen Months

Perplexity: Generative Search – 3/24-5/25, per Perplexity & Bloomberg

Annualized Revenue ($MM)

$120

M

Perplexity is best described as an answer engine. M $80 $ , e u n e

You ask it a question, you get an answer. Except the

difference is, all the answers are backed by sources. Rev

This is like how an academic writes a paper…What makes ed

humans special is that we are creatures of curiosity. We a $40 liz

u

need to expand on that and discover more knowledge using n

the power of AI. n

A

- Perplexity Co-Founder & CEO Aravind Srinivas, 6/24

What if accessing information felt like talking to a personal

research assistant?

$0

3/24 5/24 7/24 9/24 11/24 1/25 3/25 5/25

- Perplexity Co-Founder & CEO Aravind Srinivas, 5/25

Note: 3/24 annualized revenue figure is an estimate per Perplexity Co-Founder & CEO Aravind Srinivas’s 3/25 LinkedIn post saying ‘Perplexity has crossed $100m in annualized

revenue…6.3x growth Y/Y and remains highly under monetized.’

Source: Lex Fridman Podcast (6/24), UC Berkeley (5/25), LinkedIn (3/25), Bloomberg, ‘AI Startup Perplexity Nears Funding at $14 Billion Value’ (5/25) (link)

AI – New Entrants = Rapid Revenue Growth 194 AI Monetization – Enterprise Search + Agents =

Glean Annualized Revenue +10x to $100MM in Twenty-Four Months

Glean – FQ4:23-FQ4:25, per Glean

Annual Recurring Revenue (ARR) ($MM)

$100

M

M

$

,

e

u

n

e

We’re honored to help some of the world’s largest Rev $50 companies adopt AI to transform their businesses. ing

To truly unlock new levels of creativity, productivity, rr

and operational efficiency, AI needs to draw on

You shouldn’t have to be a prompt engineering expert the full picture of an organization’s knowledge – Recul and it needs to be accessible by everyone. aunnA

to find answers, generate content,

and automate work with AI.

- Glean Co-Founder & CEO Arvind Jain (9/24) $0

FQ4:23 FQ4:24 FQ4:25

Note: Glean’s fiscal year ends in January. Source: Glean (2/25, 11/24)

AI – New Entrants = Rapid Revenue Growth 195 AI Monetization – 2024 vs. 2018 =

35% Faster Ramp to $5MM ARR vs. SaaS Comparables, per Stripe

Top 100 AI Companies vs. Top 100 Saas Companies

Median Time to Annualized Revenue Milestone ($MM) – 2018 vs. 2024, per Stripe

24 Months 37 Months

$5

$4

M

M

$

,

e $3

u

n

e

Rev

d $2

e

liz

a

u

n

n

A $1

$0

0 10 20 30 40

Months

Source: Stripe Annual Letter (2/25)

AI – New Entrants = Rapid Revenue Growth 196

AI Monetization Possibilities =

New Entrants & / Or Tech Incumbents?

197

AI – Tech Incumbents =

Broad & Steady Product / Feature Rollouts

198

Tech Incumbents =

Optimizing Product Distribution to Roll Out AI

…Tech Incumbents Have Billions of Global

While ChatGPT Has 800MM+ Users Users on Devices & Platforms With

Via Its Website & App… Ongoing AI Product Rollouts

Google

4.9B Search Users, 3B+ Android Users, 1.5B

AI Overviews Users & 1B+ Assistant Devices

Meta Users*

3.4B+

Apple Devices**

2.35B

TikTok Users***

1B+

Microsoft

1B LinkedIn Members &

400MM+ Office 365 Paid Seats

Spotify Users

678MM

Amazon

600MM+ Alexa Devices &

200MM+ Prime Subscribers

X Users

500MM+

Canva Users

230MM+

*Meta includes Facebook, Instagram, WhatsApp, & Messenger. **Apple includes iPhones, iPads, Macs, & other Apple devices worldwide. ***As of 2021; no more recent company data available. Note: Some figures are estimates based off past company disclosures & web traffic / purchase history analytics. Different companies may define ‘users’ differently based on frequency. Source: Statcounter (2/25), Google (5/25), Meta 10Q (4/25), Apple (1/25), TikTok (7/21), LinkedIn (5/25), Microsoft (1/24), Spotify (5/25), Amazon (2/25 & 10/24), Elon Musk via X (7/23), Canva (4/25), OpenAI disclosures (4/25), Wikimedia Commons

AI – Tech Incumbents = Broad & Steady Product / Feature Rollouts 199 Tech Incumbent AI Rollouts =

Canva – Background Remover & Magic Media (12/19)

Canva Background Remover & Magic Media – 2023-2024, per Canva

Number of Tool Uses (B): Background

Remover & Magic Media

4

B

One of our community’s favorite Canva features has been the e Uses,

iv 2

one-click image Background Remover, launched in lat

December 2019...[to] wild success and community love. u

- Canva Press Release, 9/22

Cum

Magic Media lets you turn your imagination into reality by

watching your words transform into stunning, one-of-a-kind

images – and now videos and graphics, too…In less than a

year since launching Magic Media’s text to image, we’ve been

overwhelmed by our community’s enthusiastic response, with

almost 290 million images being created and applied to a 0

range of practical use cases from social media posts to 2023 2024

presentations, business flyers, and even logos. Background Remover Magic Media

- Canva Press Release, 10/24

Source: Canva announcements & press releases (2022-2024)

AI – Tech Incumbents = Broad & Steady Product / Feature Rollouts 200 Tech Incumbent AI Rollouts =

Spotify – AI DJ (2/23)

Spotify AI DJ – 2/23-5/25, per Spotify

Global Markets with AI DJ Available

60

le

b

ila

v 40 a

AI DJ and music videos…are truly moving averages… DJ AI AI DJ, we’re seeing amazing results, A not just on quantitative metrics, but also onith quality metrics, how people feel about Spotify,ws what they say they love about Spotify.

rket 20

- Spotify Co-Founder & CEO Daniel Ek, 11/24 a

M

l

Back in 2018, we said something internally that still a

b

holds true today: machine learning – what most people

called AI back then Glo – was the product…

AI is really the next step in evolution,

where machine learning allows personalization,

0

AI also allows for real time interactivity and

reasoning on top of your data.

- Spotify Co-President, Chief Product &

Technology Officer Gustav Söderström, 4/25

Source: Company announcements (2/23, 5/23, 8/23, 11/24, 4,25, 5/25)

AI – Tech Incumbents = Broad & Steady Product / Feature Rollouts 201 Tech Incumbent AI Rollouts =

Microsoft – Copilot (2/23)

Microsoft: Copilot – 8/23-12/24, per Microsoft

Microsoft Copilot Cumulative Chats Held (B)

15

B

ts, 10

To empower people to unlock the joy of discovery, feel the Cha

e

wonder of creation and better harness the world’s knowledge, iv

today we’re improving how the world benefits from the web by latu reinventing the tools billions of people use every day, the

search engine and the browser. Cum

5

Today, we’re launching an all new, AI-powered Bing search

engine and Edge browser, available in preview now at

Bing.com, to deliver better search, more complete answers, a

new chat experience and the ability to generate content. We

think of these tools as an AI copilot for the web.

- Official Microsoft Blog, 2/23 0

8/23 12/23 4/24 8/24 12/24

Note: We assume zero users in the launch month. We assume 15B cumulative chats as of 12/24 due to Microsoft’s 1/24 announcement of 5B cumulative chats, and 12/24 announcement of 10B more chats being held in 2024. We assume the Verge’s announcement of ‘There have also been over 1 billion chats on Bing Chat’ as of 8/23 is wholly inclusive of Copilot chat volumes as of that date. Source: Microsoft announcements & earnings reports, The Verge citing Microsoft disclosures (8/23)

AI – Tech Incumbents = Broad & Steady Product / Feature Rollouts 202 Tech Incumbent AI Rollouts =

Meta Platforms – Meta AI (9/23)

Meta Platforms: Meta AI – 11/23-4/25, per Meta Platforms

Meta AI Monthly Active Users (MM)

1,000 Q1:25 Earnings Call

(4/30/25):

Across our apps, there

are now almost a billion

monthly actives using

rs, MM Meta AI

- Meta Platforms Cofounder

Use & CEO Mark Zuckerberg e

I expect that this is going to be the year when a highly intelligent ctiv

A

and personalized AI assistant reaches more than ly 500

1 billion people, and I expect Meta AI to be that leading AI th

n

assistant. Meta AI is already used by more o

people than any other assistant… MI

A

…I also expect that 2025 will be the year when it becomes tae possible to build an AI engineering agent that hasM

coding and problem-solving abilities of around a

good mid-level engineer…

…Whichever company builds [a high-skill AI engineering agent]

first, I think it's going to have a meaningful advantage in 0

deploying it to advance their AI research and shape the field. 11/23 3/24 7/24 11/24 3/25

- Meta Platforms CEO Mark Zuckerberg, 1/25

Note: We assume zero users in 11/23 per Meta’s 12/23 blog post noting, ‘To chat with our AIs, start a new message and select “Create an AI chat” on Instagram, Messenger or WhatsApp. They’re now available to anyone in the US.’ Source: Meta Platforms announcements & earnings reports

AI – Tech Incumbents = Broad & Steady Product / Feature Rollouts 203 Tech Incumbent AI Rollouts =

X – Grok (11/23)

X: Grok – 12/24-4/25, per xAI & Similarweb

Grok Global Desktop Visits* (MM)

150

M 100 M

ts,

isi

V

l

The mission of xAI and Grok is to understand the universe. a

b

We want to answer the biggest questions.

Glo

50

- xAI Founder & CEO Elon Musk, 2/25

2/17/25: Grok 3 is

AI with Grok is getting very good…it’s important that AI be released & desktop

visits jump 42x M/M

programmed with good values, especially truth-seeking

values. This is, I think, essential for AI safety…

…Remember these words: We must have a maximally truth- 0

seeking AI. 12/24 1/25 2/25 3/25 4/25

- xAI Founder & CEO Elon Musk, 5/25

*Excludes X visits. China data may be subject to informational limitations due to government restrictions. Source: xAI announcements & investor filings; Elon Musk; Fox News; Similarweb (5/25)

AI – Tech Incumbents = Broad & Steady Product / Feature Rollouts 204 Tech Incumbent AI Rollouts =

Google – Gemini & AI Overviews (12/23)

Alphabet: Gemini & AI Overviews – 3/25-5/25, per Alphabet & Business Insider

Gemini Chatbot Global MAUs (MM)

AI Overviews

embedded in 400MM

Google Search; 400

@ 1.5B MAUs

(4/25) 350MM

Us, MM

A

Ml ab

Glo

Our differentiated, full stack approach to AI continues to be m 200

central to our growth. This quarter was super exciting as we ste

rolled out Gemini 2.5, our most intelligent AI model, sy

which is achieving breakthroughs in performance, and it’s co E widely recognized as the best model in the industry. ppA

- Alphabet CEO Sundar Pichai, 4/25 ini

m

Google Gemini is a family of multimodal AI models, capable of Ge

understanding and generating various types of data including text,

0

code, audio, images, and video.

3/25 5/25

Source: Google Gemini

Note: Gemini launched 12/23…App launched 2/24. Data shown for apps in Gemini ecosystem. User counts may differ from those as measured by third-party data providers / panels like Similarweb & Sensor Tower as they measure only visits to desktop sites and standalone mobile apps, respectively. Source: Google announcements (4/25 & 5/25) & Business Insider, ‘Google's Gemini usage is skyrocketing, but rivals like ChatGPT and Meta AI are still blowing it out of the water’ (4/25)

AI – Tech Incumbents = Broad & Steady Product / Feature Rollouts 205 Tech Incumbent AI Rollouts =

Amazon – Rufus (2/24)

Amazon: Rufus – 12/22-3/25, per Amazon & Morgan Stanley Estimates

Amazon North America Retail Estimated Gross

Merchandise Value ($B), Last 12 Months

$600

B

$

2/24: Rufus announced

GMV, $400 il a

Ret

s

th

n

We have so many customers now who just use Rufus to help o

M

them find a quick fact about a product. They also use Rufus to e

figure out how to summarize customer reviews, so they don't lv $200

e

have to read 100 customer reviews to get a sense of what w

people think about that product…the personalization st T

keeps getting much better… aL

…And so, we expect throughout 2025, that the number of

occasions where you're not sure what you want to buy and

$0

you want help from Rufus are going to continue to increase

and be more and more helpful to customers.

- Amazon CEO Andy Jassy, 2/25 Quarter Ending

Source: Amazon; Morgan Stanley estimates

AI – Tech Incumbents = Broad & Steady Product / Feature Rollouts 206 Tech Incumbent AI Rollouts =

TikTok – Symphony AI Assistant (6/24)

TikTok: Symphony Assistant – 1/24-4/25, per TikTok & Similarweb

Global Website Visits to TikTok.com (B)

(Where Symphony Assistant is Hosted)

3

B

,

m

k.co

o 2 ikT

Creativity thrives on TikTok. When brands truly lean into T

creative bravery and experimentation, they are able to speak to

ts

directly to their community and invite them to join in the isi

conversation. At TikTok World 2024 we launched Symphony, V

our suite of ad solutions powered by generative AI… site 1 b e

…With Symphony, businesses of all sizes, creators and

agencies can blend human imagination with AI-powered W

efficiency to help scale content development, creativity, and

productivity on TikTok. Research has proven that not only does

creating TikTok-first ads boost purchase intent by +37% and

brand favorability by +38%, but also 79% of TikTok users show

a preference for brands that demonstrate a clear understanding 0

of how to create content specifically for the platform. 1/24 4/24 7/24 10/24 1/25 4/25

- TikTok Press Release, 6/24

Note: Includes both mobile & desktop website visits. China data may be subject to informational limitations due to government restrictions. Source: TikTok; Similarweb (5/25)

AI – Tech Incumbents = Broad & Steady Product / Feature Rollouts 207 Tech Incumbent AI Rollouts =

Apple – Apple Intelligence (10/24)

Apple: Apple Intelligence – 9/23-3/25, per Apple & IDC Estimates

Estimated Global Sales of iPhone 15 Pro /

Pro Max & iPhone 16 (MM) – 9/23-3/25

Apple Intelligence-Capable Devices

$75

M $50 M

Apple Intelligence builds on years of innovations we've made les,

a

across hardware and software to transform how users S

experience our products. Apple Intelligence also empowers e

n

users by delivering personal context that's relevant to them. o

And importantly, Apple Intelligence is a breakthrough for $25 iPh

privacy and AI with innovations like private cloud compute…

…[in] the markets where we had rolled out Apple

Intelligence…year over year performance on the iPhone 16

family was stronger than those where Apple Intelligence

was not available.

$0

9/23 12/23 3/24 6/24 9/24 12/24 3/25

- Apple CEO Tim Cook, 1/25

Quarter Ending

Note: Counts sales of iPhone 15 Pro, iPhone 15 Pro Max, & iPhone 16 devices. Figures are estimates. Source: Company announcements & investor filings; IDC via Morgan Stanley (4/25)

AI – Tech Incumbents = Broad & Steady Product / Feature Rollouts 208

AI – Tech Incumbents =

Rapid Revenue + Customer Growth

209

AI Monetization – ‘AI Product’ =

Microsoft AI Revenue +175% to $13B Y/Y

Microsoft AI Product Revenue – 2023-2024, per Microsoft

Estimated Microsoft AI Product

Annual Run-Rate Revenue ($B)

$15

Q1:25 Earnings Call

(4/30/25): $13B

B

$

, Revenue from our AI e

u business was above n

e expectations.

$10 Commercial bookings

Rev increased 18%. e

- Microsoft CFO Amy Hood

We are innovating across our tech stack and -Rat

helping customers unlock the full ROI of AI to Runl capture the massive opportunity ahead…

a ~$5B u $5 n

…Already, our AI business has surpassed an annual revenue n

run rate of $13 billion, up 175% year-over-year. A

- Microsoft CEO Satya Nadella, 1/25

$0

2023 2024

Note: Microsoft AI revenue likely includes Azure AI services, Microsoft 365 Copilot, GitHub Copilot, Dynamics 365 Copilot, Azure OpenAI Services, and others. Detailed breakdowns not provided on earnings calls. Source: Microsoft Press Release, ‘Microsoft Cloud and AI strength drives second quarter results’ (1/25); & other Microsoft announcements

AI – Tech Incumbents = Rapid Revenue + Customer Growth 210 AI Monetization – Generative Search =

xAI Annualized Revenue Up Materially in 2025

xAI: Generative Search, per xAI & The Wall Street Journal

Annualized Revenue ($B)

$1.0

We are pleased to introduce Grok 3, our most advanced B Revenue up $ materially in 2025* , e u n e

model yet: blending strong reasoning with extensive pretraining knowledge. Trained on our Rev $0.5 d

Colossus supercluster with 10x the compute of previous e

state-of-the-art models, Grok 3 displays significant liz

a

improvements in reasoning, mathematics, coding, u

n

world knowledge, and instruction-following tasks. n

A

- xAI Grok 3 Press Release, 2/25

$0.1B

[Grok is a] maximally truth-seeking AI, even if that truth is $0.0

sometimes at odds with what is politically correct. 11/24 4/25

- xAI Founder & CEO Elon Musk, 2/25

*Select media reports have xAI revenue being as high as $1B as of 4/25. Source: xAI (2/25); The Wall Street Journal, ‘Elon Musk’s xAI Startup Is Valued at $50 Billion in New Funding

Round’ (11/24) (link); CNBC, ‘Musk says he’s looking to put ‘proper value’ on xAI during investor call, sources say’ (4/25) (link)

AI – Tech Incumbents = Rapid Revenue + Customer Growth 211 AI Monetization – AI Services =

Palantir USA Commercial Customers +65% to 432 Y/Y

Palantir USA Commercial Customers – Q1:23-Q1:25, per Palantir

Palantir USA Commercial Customers

500

rs

e

m

o

Custl arci

e

We achieved a $1 billion annual run rate in our US m 250

commercial business for the first time as AIP [Artificial

Intelligence Platform] continues to drive both new customer Com

conversions and existing customer expansions in the US..

USA

- Palantir CFO David Glazer , 5/25 tir

lan

a

As AI models progress and improve, we continue enabling our P

customers to maximally leverage these models in production,

capitalizing upon the rich context within the enterprise through

the Ontology. We remain differentiated in our elite execution to 0

deliver quantified exceptionalism for our customers, ever Q1:23 Q1:24 Q1:25

widening their advantage over the AI have-nots.

- Palantir CRO & Chief Legal Officer Ryan Taylor, 5/25

Source: Palantir

AI – Tech Incumbents = Rapid Revenue + Customer Growth 212

AI Monetization Possibilities – Enterprise =

Horizontal Platform

& / Or

Specialized Software?

213

To understand where enterprise AI monetization is headed, it helps to ask

where software itself is consolidating.

For decades, business software followed a familiar pattern:

build a specialized tool, sell it to a narrow user base, and

scale up within a vertical. This was the age of vertical SaaS – Toast for restaurants,

Guidewire for insurance, Veeva for life sciences...Each tool solved a deep, narrow problem.

But with the rise of foundation models and generative AI, others are gunning for these prizes.

Enter the horizontal enterprise platforms – horizontal layers that combines

AI-native productivity, search, communication, and knowledge management into one unified interface.

Think of it as Slack meets Notion meets ChatGPT, all in one platform.

Horizontal enterprise platforms could usher in a new form of monetization:

not by selling siloed software licenses, but by charging for intelligence, embedded throughout the stack.

The value shifts from tools to outcomes – from CRMs to automated deal summaries,

from service desks to AI-powered resolution flows.

These horizontal capabilities are still early,

but they're already being harnessed by incumbents and upstarts alike.

Microsoft is integrating Copilot across the stack.

Zoom and Canva are layering GenAI into user-facing workflows,

while Databricks is infusing GenAI into its data and developer stack.

Meanwhile, startups like Glean are betting on AI-first workflows to challenge the suite model…

AI Monetization Possibilities – Enterprise = Horizontal Platform & / Or Specialized Software? 214

…But specialist vendors aren’t standing still. If anything, they’re absorbing AI faster –

embedding copilots, automating workflows, and

fine-tuning models on proprietary industry data. These platforms already have the workflows, the trust,

and the structured data that AI thrives on. That gives them a head start

in deploying domain-specific intelligence –

AI that doesn’t just summarize a meeting, but flags regulatory risks, optimizes pricing in real time,

or drafts FDA-compliant documentation. In many cases, their incumbency becomes their advantage:

they can roll out AI as a feature, not a product, and monetize it without changing the buying motion.

The next chapter of AI monetization may not be a winner-take-all battle, but a convergence.

Horizontal platforms will push breadth, stitching together knowledge across functions;

specialists will push depth, delivering AI that speaks the language of compliance,

contracts, and customer intent.

The question isn’t whether platforms or specialists win –

it’s who can abstract the right layer, own the interface, and capture the logic of work itself.

In the AI era, monetization won’t just follow usage – it will follow attention, context, and control.

AI Monetization Possibilities – Enterprise = Horizontal Platform & / Or Specialized Software? 215

AI Monetization Possibilities – Enterprise =

Horizontal Platform

& / Or

Specialized Software?

216

Horizontal Enterprise Platform =

SaaS Incumbents

Or

Large Language Model Challengers?

217

Enterprise SaaS Incumbent AI Rollouts =

Broad & Steady Cadence

Number of Mentions of ‘AI’ on Corporate Earnings Calls – Q1:20-Q1:24, per Uptrends.ai

Source: Uptrends.ai (6/24), company announcements & investor filings

Horizontal Enterprise Platform = SaaS Incumbents Or Large Language Model Challengers? 218 Enterprise SaaS Incumbent AI Rollouts =

Microsoft GitHub Copilot – 6/22

Microsoft GitHub Copilot – 6/17-6/24, per GitHub, Microsoft & Wells Fargo

GitHub Revenue ($MM)

$500

GitHub

Copilot

Public

M Launch M

$

,

e

GitHub Copilot is by far the most widely adopted AI-powered un $250

developer tool. Just over two years since its general e

availability, more than 77,000 organizations – from BBVA, Rev

FedEx, and H&M, to Infosys and Paytm –

have adopted Copilot, up 180% year-over-year.

- Microsoft CEO Satya Nadella, 7/24

We have been delighted by the early response to GitHub

Copilot and vs. Code with more than 1 million sign-ups in just

the first week post launch. All up, GitHub now is home to 150

million developers, up 50% over the past two years. $0

6/17 6/18 6/19 6/20 6/21 6/22 6/23 6/24

- Microsoft CEO Satya Nadella, 1/25

Quarter Ending

Note: GitHub revenue is disclosed irregularly; 3 datapoints are from company leadership’s disclosures. Public developer launch date shown. GitHub reports annualized revenue; here, we translate this to quarterly revenue. Source: Company announcements & investor filings

Horizontal Enterprise Platform = SaaS Incumbents Or Large Language Model Challengers? 219 Enterprise SaaS Incumbent AI Rollouts =

Microsoft 365 Copilot – 3/23

Microsoft 365 Copilot – Q2:23-Q4:24, per Microsoft & Morgan Stanley

% of CIOs Expecting to Use Microsoft

365 Copilot over Next 12 Months,

per Morgan Stanley Survey

80%

We are seeing accelerated customer adoption across all deal

sizes as we win new Microsoft 365 Copilot customers and

see the majority of existing enterprise customers come back

to purchase more seats. When you look at customers who CIOs 40% f purchased Copilot during the first quarter of availability,

they have expanded their seat collectively by more than % o 10x over the past 18 months. And overall, the number of

people who use Copilot daily, again, more than

doubled quarter over quarter.

Employees are also engaging with Copilot more than ever.

Usage intensity increased more than 60% quarter over

quarter, and we are expanding our TAM with Copilot Chat,

which was announced earlier this month.

0%

Q2:23 Q4:23 Q2:24 Q4:24

- Microsoft CEO Satya Nadella, 1/25

Note: N=61 CIOs in the USA & EU. Microsoft 365 Copilot was announced in 3/23 but was not made generally available for enterprise customers until 11/23. Source: Company announcements & investor filings, Morgan Stanley, ‘4Q24 Preview – Can Microsoft Add Clarity to the AI Monetization Question?’ (7/24)

Horizontal Enterprise Platform = SaaS Incumbents Or Large Language Model Challengers? 220 Enterprise SaaS Incumbent AI Rollouts =

Adobe Firefly – 3/23

Adobe Firefly – 5/23-4/25, per Adobe

Cumulative Number of Digital Assets

Generated Using Adobe Firefly (B)

25

B

,

d

te 20 a

e

Cr

ts

The release of the Adobe FireFly video model in February, a sse 15 commercially safe generative AI video model, has been very A

positively received by brands and creative professionals… itagl

…User engagement has been strong with over Die 10 90% of paid users generating videos… iv

lat

u

…We're delighted with the early interest in these new

include, continued strong adoption of GenAI in our products offerings. Other creative professional and creator highlights Cum 5

with Photoshop GenAI monthly active users at approximately

35% and Lightroom GenAI monthly active users at 30%.

Users have generated over 20 billion assets with Firefly.

0

- Adobe President of Digital Media David Wadhwani, 3/25 5/23 8/23 11/23 2/24 5/24 8/24 11/24 2/25

Note: We assume zero users in the launch month. Adobe Firefly was released as a public beta in March 2023. Source: Adobe announcements (9/23, 10/23, 3/24, 4/24, 10/24, 12/24, 2/25)

Horizontal Enterprise Platform = SaaS Incumbents Or Large Language Model Challengers? 221 Enterprise SaaS Incumbent AI Rollouts =

Atlassian Intelligence – 4/23

Atlassian Intelligence – 12/23-12/24, per Atlassian

Atlassian Intelligence Users (K)

1,000K

1,000

K

,

ce

n

e

llig

te

Today, more than 1 million monthly active users are utilizing In

our Atlassian intelligence features to unlock enterprise

knowledge, supercharge workflows, and accelerate their ssian 500 team collaboration. These features are clearly delivering tla

value as we've seen a number of AI interactions A

increase more than 25x year over year… ng

…Atlassian Intelligence [saw a] 25x improvement in the rs Usi

number of features used over the last year. e

m

o

- Atlassian Co-Founder & Co-CEO Michael Cannon, 2/25

Cust ~20K

0

12/23 12/24

Note: 12/23 users includes beta users. We assume 20,000 users based on Atlassian’s disclosure that ‘Nearly 10% of Atlassian’s 265,000+ customers have already leveraged Atlassian Intelligence through our beta program.’ Source: Atlassian announcements (4/23, 12/23, 12/24)

Horizontal Enterprise Platform = SaaS Incumbents Or Large Language Model Challengers? 222 Enterprise SaaS Incumbent AI Rollouts =

Zoom AI Companion – 9/23

Zoom AI Companion – 9/23-12/24, per Zoom

Estimated Zoom Accounts with

AI Companion Activated (MM)

4

M

M

ts,

n

u

Growth in monthly active users of Zoom AI Companion

accelerated to 68% quarter over quarter, demonstrating the cco

A 2

real value AI is providing customers. m

Zoom AI Companion has emerged as a driving force o behind our transformation into an AI-first company… Zo

e

…As part of AI Companion 2.0, we added advanced agentic ctiv

A

capabilities, including memory, reasoning, orchestration, and

seamless integration with Microsoft and Google services.

- Zoom Founder & CEO Eric Yuan, 2/25

0

9/23 12/23 3/24 6/24 9/24 12/24

Note: AI Companion MAUs are estimates based on company disclosures. As of 7/30/24, Zoom disclosed they had 1.2MM accounts with AI Companion activated. In Q3 2024, they disclosed 59% Q/Q growth in active accounts; in Q4 2024, they disclosed further 68% Q/Q growth. We assume zero users in the launch month. Source: Zoom announcements (9/23. 10/23, 2/24, 5/24, 7/24, 9/24, 12/24)

Horizontal Enterprise Platform = SaaS Incumbents Or Large Language Model Challengers? 223 Enterprise SaaS Incumbent AI Rollouts =

Canva Magic Studio – 10/23

Canva Magic Studio – 10/23-5/25, per Canva

Cumulative Canva Magic

Studio AI Tool Uses (B)

20

16B

B

With Magic Studio there’s no need to toggle between multiple Uses,eiv AI tools or learn lots of different software – all the best of AI is

at your fingertips. Created for the 99% of the world without lat 10 u

complex design skills, Magic Studio is jam-packed with easy-

to-use AI-powered features across every part of Cum

Canva to help you work smarter.

- Canva Press Release, 10/23

Magic Studio is designed to supercharge creativity across our

entire community – from enterprise teams to educators and

nonprofits. Its easy-to-use AI features are woven into every 0B

part of Canva, enabling anyone to spark inspiration, 0

streamline workflows, and scale their content. In fact, our AI 10/23 5/25

tools have been used more than 10 billion times to date.

- Canva Press Release, 10/24

Note: We assume zero users in the launch month. Source: Canva announcements (10/23, 10/24, 5/25)

Horizontal Enterprise Platform = SaaS Incumbents Or Large Language Model Challengers? 224 Enterprise SaaS Incumbent AI Rollouts =

Salesforce Agentforce – 9/24

Salesforce Agentforce – 12/24-2/25, per Salesforce

Number of Paid Agentforce Deals Signed

3,000

3,000

ls

We ended this year with $900MM in Data Cloud and AI ARR. Dea

It grew 120% year over year. We've never seen products grow rce

at these levels, especially Agentforce… o

tf 1,500 n

…Just 90 days after it went live, we've already had 3,000 paying e

g

Agentforce customers who are experiencing unprecedented A 1,000

levels of productivity, efficiency, and cost savings… id

a

P

…Data Cloud is the fuel that powers Agentforce and our

customers are investing in it. And Data Cloud surpassed 50

trillion, that's trillion with a T, records, doubling year over year as

customers increase their consumption and

investment in our data platform. 0

12/24 2/25

- Salesforce Co-Founder & CEO Mark Benioff, 2/25

Note: Agentforce was announced on 9/12/24 but became generally available on 10/29/24. We assume zero users in the launch month. Source: Salesforce announcements (10/24, 12/24, 2/25)

Horizontal Enterprise Platform = SaaS Incumbents Or Large Language Model Challengers? 225

Horizontal Enterprise Platform =

SaaS Incumbents

Or

Large Language Model Challengers?

226

OpenAI ChatGPT =

Potential Horizontal Enterprise Platform?...

OpenAI = Next-Gen All-in-One Enterprise Platform?

Microsoft Office Suite OpenAI ChatGPT

9 Applications 1 Application

400MM Paid Users Over 34 Years 20MM Paid Users Over 2.5 Years

1990-2024 11/22-4/25

Source: Microsoft (1/24), Office365 Pros, OpenAI, The Information (4/25) (link)

Horizontal Enterprise Platform = SaaS Incumbents Or Large Language Model Challengers? 227 …OpenAI ChatGPT =

Potential Horizontal Enterprise Platform?

ChatGPT Enterprise – 8/23-3/25, per OpenAI, Bloomberg, Reuters, & The Wall Street Journal

Number of ChatGPT Business Users (MM)

(Includes Enterprise / Team / Education)

2

M

M

Users,

Since ChatGPT’s launch just nine months ago, we’ve seen ss

heard from business leaders that they’d like a simple and safe sine 1 u teams adopt it in over 80% of Fortune 500 companies. We’ve

way of deploying it in their organization. Early users of B

ChatGPT Enterprise…are redefining how they operate and are of

r

using ChatGPT to craft clearer communications, accelerate e

b

coding tasks, rapidly explore answers to complex business

questions, assist with creative work, and much more. Num

ChatGPT Enterprise removes all usage caps and performs

up to two times faster [vs. ChatGPT Free]…

…ChatGPT Enterprise also provides unlimited access to

advanced data analysis, previously known as Code Interpreter. 0

8/23 2/24 8/24 2/25

- ChatGPT Enterprise Release Statement, 8/23

Note: We assume zero users in the launch month. Source: OpenAI announcements (12/23, 4/24, 9/24, 3/25), Bloomberg (4/24), Reuters (9/24), The Wall Street Journal (3/25)

Horizontal Enterprise Platform = SaaS Incumbents Or Large Language Model Challengers? 228

AI Monetization Possibilities – Enterprise =

Horizontal Platform

& / Or

Specialized Software?

229

AI Monetization – Enterprise =

Specialized Software Opportunities in Fragmented Markets, per Prosus

USA Industries by Number of Companies & Market Share – 2024, per Prosus

Source: Prosus, ‘The Timeless Appeal of Vertical SaaS’ (3/24)

AI Monetization Possibilities – Enterprise = Horizontal Platform & / Or Specialized Software? 230

AI-Enabled Specialized Software @

Large Service Industries =

Growing Very Quickly…

Software Engineering

Product Development

Healthcare

Legal

Customer Service

Financial Services

231

AI-Enabled Specialized Software Companies @

Large Service Industries =

Growing Very Quickly…

Software Engineering

232

Specialized AI – Software Engineering (Code Editor) =

Anysphere Cursor AI ARR @ $1MM to $300MM in Twenty-Five Months

Anysphere Cursor AI – 3/23-4/25, per Anysphere

Annual Recurring Revenue (ARR) ($MM)

$300

Something beautiful is happening to code…our aim with Cursor M

is to continue to lead this shift, by building a magical tool that M

will one day write all the world's software… , $

e

u

n

…Already, in Cursor, hours of hunting for the right primitives are e

being replaced by instant answers. Mechanical refactors are being reduced to single ‘tabs.’ Terse directives are getting Rev $150

expanded into working source. And thousand-line changes are ing

rr

rippling to life in seconds.

- Anysphere Press Release (8/24) Recu

l

a

u

…We're delighted to report that Cursor is now used by millions n

n

of programmers as their editor of choice. Our proprietary A

models now generate more code than almost any LLMs in the

world and edit over a billion characters per day.

Our business is large and fast growing, having exceeded

$100MM in recurring revenue. $0

3/23 8/23 1/24 6/24 11/24 4/25

- Anysphere Team (8/24 & 1/25)

Note: Cursor launched in 4/23. We show 3/23 as the first datapoint with an assumed $0 in ARR. Source: Cursor / Anysphere (8/24, 11/24 & 1/25), Anysphere Co-Founder & CEO Michael Truell via Lenny’s Newsletter, ‘The rise of Cursor: The $300M ARR AI tool that engineers can’t stop using’ (5/1/25)

AI-Enabled Specialized Software Companies @ Large Service Industries = 233

Growing Very Quickly…Software Engineering

AI-Enabled Specialized Software Companies @

Large Service Industries =

Growing Very Quickly…

Product Development

234

Specialized AI – Product Development (No-Code Product-Building) =

Lovable ARR +13x to $50MM in Five Months

Lovable – 12/24-5/25

Annual Recurring Revenue (ARR)** ($MM)

$50

M

M

$

,

e

u

n

e

Note: Lovable is an AI-powered application development platform that enables users

to create full-stack web applications by describing their ideas in natural language. Rev

The platform translates these descriptions into functional applications, handling $25 ing frontend and backend code generation, database integration, and deployment. rr

verge of a paradigm shift where the barriers to building The opportunity here is immense. We are on the Recul au software-based products disappear.nn Now, anyone can become an entrepreneur,A

launch a product and build a business in minutes.

- Frederik Cassel, Creandum,

‘Backing Lovable: Move Fast and Make Things,’ 2/25* $0

12/24 1/25 2/25 3/25 4/25 5/25

*Per Creandum website. **From Lovable Co-Founder & CEO Anton Osika’s LinkedIn posts & podcast appearances. Source: Lovable (5/25), Creandum (2/25)

AI-Enabled Specialized Software Companies @ Large Service Industries = 235

Growing Very Quickly…Product Development

AI-Enabled Specialized Software Companies @

Large Service Industries =

Growing Very Quickly…

Healthcare

236

Specialized AI – Healthcare (Clinical Conversations) =

Abridge @ $50MM to $117MM CARR in ~Five Months

Abridge – 10/24-3/25, per Abridge & The Information

Contracted Annual Recurring

Revenue (CARR) ($MM)

$120 $117MM

M

M

$

Yazdi Bagli, Kaiser’s EVP of IT and enterprise business services, e,

said he believes [Kaiser Permanente’s] Abridge partnership is nu

e

one of the largest generative AI deployments in health care…

Rev $80

…The national rollout includes more than 25,000 doctors and

clinicians, 40 hospitals, and north of 600 medical offices… ingrr

‘You’d have to take it away from my cold, dying hands.’ ‘It saved my marriage.’ And: Recul au …The feedback from doctors has been effusive: $50MM

n $40 n

- Fortune Magazine (2/25) A

d

We are incredibly proud of our partnership with Kaiser – cte

where a majority of Kaiser doctors are using Abridge to tra

summarize patient visits, with over 10 million completed to date.

As one of our earliest deployments, it is a great example Con $0

of how we are building alongside our many hospital partners 10/24 3/25

and helping them grow with Abridge.

- Abridge CFO Sagar Sanghvi (5/25)

Note: 3/25 figure is quoted as being as of Q1:25. We conservatively assume this maps to 3/25. Abridge’s CARR goes live within weeks of contracting. Source: Abridge (12/24 & 5/25),

Fortune (2/25), The Information (10/24 & 5/25) (link & link)

AI-Enabled Specialized Software Companies @ Large Service Industries = 237

Growing Very Quickly…Healthcare

AI-Enabled Specialized Software Companies @

Large Service Industries =

Growing Very Quickly…

Legal

238

Specialized AI – Legal (Workflows) =

Harvey @ $10MM to $70MM ARR in Fifteen Months, per The Information & Business Insider

Harvey – 12/23-4/25, per The Information & Business Insider

Annual Recurring Revenue (ARR) ($MM)

$75

M

M

$

,

e

u $50

In 2024, we saw 4x annual recurring revenue (ARR) growth and n

e

expanded from 40 customers to 235 customers in 42 countries,

including the majority Rev

of the top 10 USA law firms. ing

rr

We’ve also seen the legal and professional services industry

models, and enterprises are driving significant savings with AI-shift faster than ever before. Lawyers are adopting technology Recul $25 at an unprecedented rate, a u centuries-old firms are experimenting with new business n n A

enabled workflows. The pace of change will

only accelerate in 2025.

- Harvey Co-Founder & CEO Winston Weinberg

$0

& Co-Founder & President Gabe Pereyra (2/25)

12/23 3/24 6/24 9/24 12/24 3/25

Source: Harvey (2/25), The Information estimates (1/25) (link, link), & Business Insider (5/25) (link)

AI-Enabled Specialized Software Companies @ Large Service Industries = 239

Growing Very Quickly…Legal

AI-Enabled Specialized Software Companies @

Large Service Industries =

Growing Very Quickly…

Customer Service

240

Specialized AI – Customer Service (AI Support Agents) =

Decagon @ ~$1MM to $10MM ARR in One Year

Decagon – 2023-2024, per Decagon

Annual Recurring Revenue (ARR) ($MM)

$10MM

$10

ARR growth

M accelerating in 2025 M

$

,

e

u

n

e

AI is often seen as destroying jobs, but at Decagon,

we believe the opposite. Our AI agents are enhancing jobs, Rev $5

not replacing them… ing

rr

…In a few years, every company will have AI agents running

their customer experiences. Customer support staff are no Recu

l

longer fielding routine tasks; they are now becoming AI a

managers n – configuring, training and overseeing the nu

AI agents that handle repetitive work. A $1MM

- Decagon Co-Founder & CEO Jesse Zhang (10/24) $0

2023 2024

Note: Source: Decagon (12/23, 10/24, 12/24)

AI-Enabled Specialized Software Companies @ Large Service Industries = 241

Growing Very Quickly…Customer Service

AI-Enabled Specialized Software Companies @

Large Service Industries =

Growing Very Quickly…

Financial Services

242

Specialized AI – Financial Services (Research & Analysis) =

AlphaSense @ ~$150MM to ~$420MM in Two Years

AlphaSense – 2022-2024, per AlphaSense

Annual Recurring Revenue (ARR) ($MM)

500

M

M

$

,

e

We are at a tipping point where AI-driven insights are no longer u

n

a luxury but a necessity – every company’s e

market value is the sum of the decisions it makes.

Rev 250

Surpassing $400 million in ARR and our rapid growth are clear ing

signals that businesses are recognizing the transformative rr

market intelligence platform. Recul power of our end-to-end

a

u

As we scale, our focus remains on product and n

n

technology innovation, ensuring we deliver A

high-value solutions and cutting-edge AI and

smart workflow capabilities to our customers.

0

- AlphaSense Co-Founder & CEO Jack Kokko (3/25)

2022 2023 2024

Source: AlphaSense (3/25)

AI-Enabled Specialized Software Companies @ Large Service Industries = 243

Growing Very Quickly…Financial Services

Next AI Use Case Frontiers =

Broad & Varied

244

Next AI Use Case Frontiers =

Broad & Varied

Next AI Use Case Frontiers – 5/25

Highlights =

Pages 246-247

Medical Discovery Precision Multi-Purpose Autonomous Supply Chain

& Development Manufacturing Robotics Scientific Research Optimization

Cybersecurity & Personalized Autonomous Environmental & Energy Grid

Threat Detection Education Finance Climate Monitoring Management

Note: List is not comprehensive. Source: Drug Development & Discovery = Insilico; Precision Manufacturing = Landing AI; Multi-Purpose Robotics = Figure AI; Autonomous Scientific Research = IBM’s RoboRXN; Supply Chain Optimization = o9 Solutions; Cybersecurity & Threat Detection = Vectra AI; Personalized Education = Khanmigo; Autonomous Finance = Kasisto; Environmental & Climate Monitoring = ClimateAI; Energy Grid Management = Uplight; BOND analysis

Next AI Use Case Frontiers = Broad & Varied 245 Next AI Use Case Frontier – Protein Sequencing =

Model Size +290% Annually to 98 Billion Parameters Over Four Years

Size of Major Protein Sequencing Models (B Parameters) – 2020-2024,

per Stanford RAISE Health

Per Stanford HAI (4/25): The past year has witnessed remarkable progress in AI models applied to protein sequences.

Large-scale machine learning models have improved our ability to predict protein properties, accelerating research in structural biology and

molecular engineering…These AI-driven approaches have transformed protein science by minimizing reliance on costly,

time-intensive experimental methods, enabling rapid exploration of protein function and design.

100

B

rs,

te

e

P / Year f o r a +290% 50 ram e

b

Num

0

ProGen ProtBert ProGen 2 ProT5 ESM2 ESM3

2020 2022 2023 2024

Note: List of models may not be comprehensive.

Source: Stanford RAISE Health via Nestor Maslej et al., ‘The AI Index 2025 Annual Report,’ AI Index Steering Committee, Stanford HAI (4/25)

Next AI Use Case Frontiers = Broad & Varied 246 Next AI Use Case Frontier – Protein Sequencing =

Synthetically Generated Protein Data Yields 1,000x Expansion via AlphaFold

Experimentally Expanded Coverage with

Determined Structure Prediction

214,121 214MM

Protein Structures Predicted Protein Structures

in PDB (2024) in AFDB (2024)

Note: AFDB predicted protein structure counts may be higher as of year-end 2024. Source: Google DeepMind, RCSB Protein Data Bank (2024)

Next AI Use Case Frontiers = Broad & Varied 247

Outline

1 • Seem Like Change Happening Faster Than Ever?

Yes, It Is

2 • AI User + Usage + CapEx Growth =

Unprecedented

3 • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling =

Performance Converging + Developer Usage Rising

4 • AI Usage + Cost + Loss Growth =

Unprecedented

5 • AI Monetization Threats =

Rising Competition + Open-Source Momentum + China’s Rise

6 • AI & Physical World Ramps =

Fast + Data-Driven

7 • Global Internet User Ramps Powered by AI from Get-Go =

Growth We Have Not Seen Likes of Before

8 • AI & Work Evolution =

Real + Rapid

248

AI Monetization Threats

=

Rising Competition

+

Open-Source Model Momentum

(& China’s Rise)

249

Rising Competition =

AI Model Releases

250

On the back of Google’s ‘Attention is All You Need’ Transformers research paper in 2017,

the first wave of ‘modern AI’ (read: LLMs) focused on text: models such as OpenAI’s GPT-3 and Meta’s Llama-1

showed that teaching computers to finish sentences at scale could unlock broad reasoning abilities.

Yet human communication is rarely text-only, and often not even text-first.

Images, audio, video, and sensor readings carry context that words alone miss,

so researchers at the same companies –

and peers like Google, Anthropic, and xAI, among others –

began extending language models to handle additional signals.

Multimodal AI models are the result. They embed text, pictures, sound, and video

into a shared representation and generate outputs in any of those formats.

A single query can reference a paragraph and a diagram, and the model can respond

with a spoken summary or an annotated image – without switching systems.

Each new modality forces models to align meaning across formats rather than optimize for one.

The path to this capability unfolded stepwise: OpenAI’s CLIP paired vision and language in 2021;

Meta followed with ImageBind in 2023 and Chameleon in 2024;

and by 2024-2025, frontier systems such as GPT-4o, Claude 3, and Chameleon had become fully multimodal.

Each new modality forced the models to align meaning across formats rather than optimize for one.

The payoff is practical.

A field engineer can aim a phone camera at machinery and receive a plain-language fault diagnosis;

a clinician can attach an X-ray to a note and get a structured report draft;

and an analyst can combine charts, transcripts, and audio clips in a single query.

Compared with text-only models, multimodal systems cut context switching,

capture richer detail, and enable applications –

quality control, assistive tech, content creation – where visual or auditory information matters as much as words.

Rising Competition = AI Model Releases 251 Large-Scale AI Multimodal* Model Competition =

+1,150% Rise in Models Released Over Two Years, per Epoch AI

Multimodal Models – Large-Scale** Multimodal Models –

Examples Releases

25 +1,150%

r

a

e

Y 20 r

e

p

d

a 15 se

Rele

s

m

ste 10 y

S

f

o

r

e

b 5

Num

0

2017 2018 2019 2020 2021 2022 2023 2024 2025

(as of

5/25)

*A multimodal AI model is one that can process and integrate multiple types of data, e.g., text, images, audio, or video, to understand and generate outputs across different modalities. **Epoch AI defines large-scale as models where their training compute is confirmed to exceed 1023 floating-point operations. An AI system can operate in more than one domain and may be double-counted across pages. Source: Epoch AI via Our World in Data (4/25), OpenAI, DeepSeek, Google

Rising Competition = AI Model Releases 252 Large-Scale AI Language Model Competition =

+420% Increase in Models Released Over Two Years, per Epoch AI

Language Models – Large-Scale* Language Models –

Examples Releases

125 +420%

r

a

e

Y 100 r

e

p

d

a 75 se

Rele

s

m

ste 50 y

S

f

o

r

e

b 25

Num

0

2017 2018 2019 2020 2021 2022 2023 2024 2025

(as of

5/25)

*Epoch AI defines large-scale as models where their training compute is confirmed to exceed 1023 floating-point operations. An AI system can operate in more than one domain and may be double-counted across pages. Many models shown are multimodal. Source: Epoch AI via Our World in Data (4/25), OpenAI, DeepSeek, Google

Rising Competition = AI Model Releases 253 Large-Scale AI Vision Model Competition =

+109% Increase in Models Released Y/Y, per Epoch AI

Large-Scale* Image Models –

Vision Models* – Examples Releases

30

r

a

e

Y

r

e

d 20 p se

a

Meta Llama 3.2 Rele +109% – 9/24 s

m

y 10 ste S

f

o

r

e

b

Num

0

2017 2018 2019 2020 2021 2022 2023 2024 2025

Qwen2-VL (as of – 12/24

5/25)

*Epoch AI defines large-scale as models where their training compute is confirmed to exceed 1023 floating-point operations. An AI system can operate in more than one domain and may be double-counted across pages. Many models shown are multimodal. Source: Epoch AI via Our World in Data (4/25), Meta, Alibaba

Rising Competition = AI Model Releases 254 Large-Scale AI Speech / Audio Model Competition =

+367% Increase in Models Released Y/Y, per Epoch AI

Speech / Audio Models –

Examples Speech / Audio Models – Releases

15

r

a

e

Y

r

e

p

d

se 10 a

OpenAI GPT 4o Speech Rele – 5/24s

m +367% ste

y

S

f

o 5 r

e

b

Num

Microsoft VALL-E 0 – 1/23

2017 2018 2019 2020 2021 2022 2023 2024

Note: An AI system can operate in more than one domain and may be double-counted across pages. Includes models without verified training compute. Many models shown are multimodal. Source: Epoch AI (5/25), Microsoft (1/23), OpenAI (5/24), Amazon, Pinterest

Rising Competition = AI Model Releases 255 Large-Scale AI Video Model Competition =

+120% Increase in Models Released Y/Y, per Epoch AI

Large-Scale* Video Models –

Video Models – Examples Releases

15

r

a

e

Y

r

e

d 10 p

OpenAI Sora – 12/24 sea

Rele +120% s

m

y 5 ste S

f

o

r

Amazon Nova Reel – e 12/24 b

According to academic studies, 50% of the human brain is Num wired for visual processing. The ability for users to explore

their interest visually and take action on them… 0

is particularly relevant for Gen Z… 2017 2018 2019 2020 2021 2022 2023 2024 2025

who have been raised on an internet of visual content (as of

across images and video. 5/25)

- Pinterest CEO Bill Ready (5/25)

*Epoch AI defines large-scale as models where their training compute is confirmed to exceed 1023 floating-point operations. An AI system can operate in more than one domain and may be double-counted across pages. Many models shown are multimodal. Source: Epoch AI via Our World in Data (4/25), OpenAI, Amazon, Pinterest, Pinterest

Rising Competition = AI Model Releases 256 LLM Competition – Website Visits =

OpenAI ChatGPT Biggest @ 5.1B Site Visits…

OpenAI ChatGPT Global Website Visits (MM) – 5/24-4/25, per Similarweb

6,000

4,000

MM

isits,

site V

b

e 2,000

W

0

5/24 6/24 7/24 8/24 9/24 10/24 11/24 12/24 1/25 2/25 3/25 4/25

chatgpt.com (OpenAI)

Note: Includes desktop & mobile (non-app) website visits. China data may be subject to informational limitations due to government restrictions. Source: Similarweb (5/25)

Rising Competition = AI Model Releases 257 …LLM Competition – Website Visits =

DeepSeek & xAI Grok Also Rising @ 196-480MM Visits Each

DeepSeek, xAI Grok, Perplexity & Anthropic Claude Global Website Visits (MM) –

5/24-4/25, per Similarweb

750

500

MM

isits,

site V xAI Grok rose b rapidly as of 3/25 e 250

W

0

5/24 6/24 7/24 8/24 9/24 10/24 11/24 12/24 1/25 2/25 3/25 4/25

deepseek.com (DeepSeek) grok.com (xAI) perplexity.ai (Perplexity) claude.ai (Anthropic)

Note: Includes desktop & mobile (non-app) website visits. China data may be subject to informational limitations due to government restrictions. Source: Similarweb (5/25)

Rising Competition = AI Model Releases 258 LLM Competition – Product Releases During Week of May 19, 2025 =

It Wasn't Just Google's Annual I/O Conference

Select AI Product Announcements – 5/19/25-5/23/25,

per Google, Microsoft, Anthropic & OpenAI

• Gemini Live camera & screen sharing • ‘Try on’ experiment • Magentic-UI • Project Mariner computer use • Agentic checkout • Copilot Studio multi-agent orchestration • Updated Gemini 2.5 Flash • Gemini interactive quizzes • GitHub Copilot asynchronous functioning • Gemini 2.5 Pro • Canvas Create menu • Azure AI Foundry expansion • Native audio output for 2.5 Flash & Pro • LearnLM integration into Gemini 2.5 • NLWeb

Previews • SDK support for Model Context Protocol • Model Context Protocol (MCP) integration

• Thinking Budgets for Gemini 2.5 Pro (MCP) definitions in Gemini API • Entra Agent ID • Deep Think • Gemini Diffusion • SQL Server 2025 • Project Astra capabilities • SynthID Detector • Windows Subsystem for Linux Open-• Gemini in Chrome • Conversational tutor prototype Source • Deep Research improvements • Google Live API audiovisual input & • GitHub Copilot Chat Extension • Gemini Agent Mode native audio out dialogue • Aurora AI-Powered Weather Forecasting • Google AI Pro Subscription • Gemma 3n

• Google AI Ultra Subscription • AI studio enhancements • Google Beam • Android Studio Journeys • Google Meet speech translation • Android Studio Version Upgrade Agent • Personalized Smart Replies • Wear OS 6 Developer Preview • Claude Opus 4 • Jules • Gemini Code Assist • Claude Sonnet 4 • Imagen 4 • New Firebase features • Veo 3 • Google AI Edge Portal • Lyria 2 • Google Vids • Flow TV • Enhanced Audio Overviews • Project Moohan • Sparkify experiment • Glasses with Android XR • Acquisition of io

Note: Announcements include products that were made immediately-available and forthcoming products. List is non-exhaustive. Source: Google Microsoft, Anthropic, OpenAI (5/25)

Rising Competition = AI Model Releases 259

AI Monetization Threats

=

Rising Competition

+

Open-Source Model Momentum

(& China’s Rise)

260

To understand where AI model development is headed, it helps to examine how two distinct approaches –

closed-source and open-source – have evolved and diverged.

In the early days of modern machine learning (2012-2018), most models were open-source,

rooted in academic and collaborative traditions.

But as AI systems became more powerful and commercially valuable, and as development shifted from academia to industry,

a parallel movement emerged – around 2019 (when GPT-2 launched with restricted weights), the development of proprietary

(closed-source) models, motivated by proprietary interests, competitive advantage, and safety concerns.

Closed models follow a centralized, capital-intensive arc. These models – like OpenAI’s GPT-4 or Anthropic’s Claude –

are trained within proprietary systems on massive proprietary datasets, requiring months of compute time and millions in spending.

They often deliver more capable performance and easier usability, and thus are preferred by enterprises and consumers,

and – increasingly – governments. However, the tradeoff is opacity: no access to weights, training data, or fine-tuning methods.

What began as a research frontier became a gated product experience, served via APIs, licensed to enterprises,

and defended by legal and commercial firewalls. Now, the AI race is coming full circle.

As LLMs mature – and competition intensifies – we are seeing resurgence of open-source models owing to their lower costs,

growing capabilities, and broader accessibility for developers and enterprises alike.

These are freely available for anyone to use, modify, and build upon, and thus are

generally preferred by early-stage startups, researchers / academics, and independent developers.

Platforms like Hugging Face have made it frictionless to download models like Meta’s Llama or Mistral’s Mixtral,

giving startups, academics, and governments access to frontier-level AI without billion-dollar budgets.

Open-source AI has become the garage lab of the modern tech era: fast, messy, global, and fiercely collaborative.

And China (as of Q2:25) – based on the number of large-scale AI models* released – is leading the open-source race,

with three large-scale models released in 2025 – DeepSeek-R1, Alibaba Qwen-32B and Baidu Ernie 4.5**.

The split has consequences. Open-source is fueling sovereign AI initiatives, local language models, and community-led innovation.

Closed models, meanwhile, are dominating consumer market share and large enterprise adoption.

We’re watching two philosophies unfold in parallel – freedom vs. control, speed vs. safety, openness vs. optimization –

each shaping not just how AI works, but who gets to wield it.

*Large-scale AI models = Models with training compute confirmed to exceed 10 23 floating point operations.

**To be made open-source as of 6/30/25, per Baidu.

AI Monetization Threats = Rising Competition + Open-Source Momentum + China’s Rise 261 Closed vs. Open-Source Models – Monthly Active Users (MAUs) =

Closed Models Dominating With Consumers, per YipitData

Estimated Share of Global Monthly Active Users (MAUs) Across Six Leading LLMs – 4/25,

per YipitData

75%

%

r 50% e Users,

m

su

Con

l

a

b

Glo 25%

f

o

re

a

h

S

0%

OpenAI: Google: DeepSeek xAI: Perplexity Anthropic:

ChatGPT Gemini Grok* Claude

Closed Open

*xAI open-sourced the Grok-1 base model in March 2024, but newer versions and full chatbot features remain proprietary. Note: Data is a subset of global internet users and absolute user data will be understated; however, given that the panel is globally-representative (with limitations on China-specific data), relative comparisons / trends are informative. Desktop users only. Figures calculate the number of users on a given platform, divided by the number of users on all platforms combined. Figures are non-deduped (i.e., users using multiple platforms may be counted twice). Data measures several million global active desktop users’ clickstream data. Data consists of users’ web requests & is collected from web services / applications, such as VPNs and browser extensions. Panel is globally-representative (with limitations on China-specific data). Users must have been part of the panel for 2 consecutive months to be included. Source: YipitData (5/25)

AI Monetization Threats = Rising Competition + Open-Source Momentum + China’s Rise 262 Closed vs. Open-Source Models – Compute Investment =

Closed Models Higher, per Epoch AI

Training Compute Resources for Open vs. Closed LLMs – 2/18-9/24, per Epoch AI

Source: Epoch AI (11/24)

AI Monetization Threats = Rising Competition + Open-Source Momentum + China’s Rise 263 Closed vs. Open-Source Models – Performance =

Gap Closing…China Rising, per Epoch AI…

Performance on MATH Level 5 Test, Open vs. Closed LLMs by Year Released – 6/23-4/25,

per Epoch AI

Non-Downloadable Downloadable (Closed) (Open)

DeepSeek R1 (1/25)

scored 93% vs. o3-

mini’s (1/25) score of

95%

Note: MATH Level 5 pass@1 refers to the accuracy of an AI model on the MATH benchmark, a dataset of high school competition-level mathematics problems. Level 5 indicates the most challenging problems in the benchmark. ‘pass@1’ measures whether the model correctly solves the problem on its first attempt. Source: Epoch AI (5/25)

AI Monetization Threats = Rising Competition + Open-Source Momentum + China’s Rise 264 …Closed vs. Open-Source Models – Performance =

Gap Closing…China Rising, per Artificial Analysis

AI Model Performance by Provider – 1/25, per Artificial Analysis

100

re

co

S

x

e

d

I 50 n

lity

a

Qu

sis

ly

a

Al n

a

fici

rti

A

0

Coding Quantitative Reasoning Reasing & Knowledge Scientific Reasoning &

Knowledge

DeepSeek OpenAI Anthropic Meta Alibaba

Open Closed Closed Open Open

Note: Scores are out of 100. The models for each company that are measured: for OpenAI, o1; for Alibaba, Qwen 2.5 72B; for Meta, Llama 3.1 405B; for Anthropic, Claude 3.5 Sonnet. The tests used are HumanEval, MATH-500, MMLU and GPQA Diamond. Source: Artificial Analysis via NBC News, ‘Why DeepSeek is different, in three charts’ (1/25)

AI Monetization Threats = Rising Competition + Open-Source Momentum + China’s Rise 265

Rising Performance of Open-Source Models

+

Falling Token Costs

=

Explosion of Usage by Developers Using AI

266

Closed-source models – like GPT-4, Claude, or Gemini –

have dominated usage among consumers and large enterprises,

largely because of their early performance advantage, ease of use, and broader awareness.

These models came bundled in clean, productized interfaces and offered reliable outputs with minimal setup.

For enterprises, they promise security and ease-of-use for non-technical employees. For consumers, they came with name recognition, fast onboarding, and polished UX.

That combination has kept closed models at the center of the AI mainstream.

But performance leadership is no longer a given. Open-source models are closing the gap – faster than many expected –

and doing so at a fraction of the cost to users. Models like Llama 3 and DeepSeek have demonstrated competitive reasoning,

coding, and multilingual abilities, while being fully downloadable, fine-tunable, and deployable on commodity infrastructure.

For developers, that matters. Unlike enterprise buyers or end-users,

developers care less about polish and more about raw capability, customization, and cost efficiency.

And it is developers – more than any other group –

who have historically been the leading edge of AI usage.

The recent trend appears increasingly clear: more developers are gravitating toward low-cost,

high-performance open models, using them to build

apps, agents, and pipelines that once required closed APIs.

Time will tell if that advantage scales beyond the developer ecosystem.

Many open-source tools still lack the brand power, plug-and-play user experience (UX),

and managed services that drive adoption among consumers and large organizations.

But as the cost-performance ratio of open models continues to improve –

and if the infrastructure to support them becomes more turnkey –

those advantages could start to spread beyond the developer community.

Rising Performance of Open-Source Models + Falling Token Costs = Explosion of Usage by Developers Using AI 267 Developer AI Model Activity =

+3.4x Increase in Downloads of Meta Llama in Eight Months

Meta Llama – 8/24-4/25, per Meta Platforms

Meta Llama Downloads (MM) – 8/24-4/25

1,200

M

M

s,

d

800

loa

n

Dow

I predicted that 2025 was going to be the year that open source a

became the largest type of model that people are developing lam with, and I think that’s probably going to be the case. That’s L

kind of how we’re thinking about this overall. ta 400 e

M

- Meta Platforms CEO Mark Zuckerberg, 5/25

The groundswell of support for Llama has been awesome.

We announced ten weeks ago a billion downloads after the

release of Llama 4. In just ten weeks, that number is now 1.2.

And if you look at Hugging Face (where the downloads are

happening), what’s cool is that most of these are derivatives. 0

8/24 10/24 12/24 2/25 4/25

We have thousands of developers contributing.

- Meta Platforms Chief Product Officer Chris Cox, 5/25

Note: 12/24 disclosure counted downloads of Llama and its derivatives. Source: Meta Platforms (8/24, 12/24, 3/25, 4/25), Stratchery podcast (5/25)

Rising Performance of Open-Source Models + Falling Token Costs = Explosion of Usage by Developers Using AI 268 Developer AI Model Activity =

+33x Increase in AI Models on Hugging Face – 11/24 vs. 3/22

AI Models Available from Hugging Face – 3/22-11/24, per Hugging Face

3/25: 100k

1.16MM derivative models

built off Meta

Llama alone

ls

e

d

o

M +33x I

A

f

o

r

e

b

Num

~35K

Note: Hugging Face is an online platform that hosts and shares machine learning models, datasets, and tools – commonly used to access, test, and deploy AI models, including large language models. It has become a central hub for the open-source AI community. May include open-source and closed models. Source: Hugging Face (5/25), Meta (3/25)

Rising Performance of Open-Source Models + Falling Token Costs = Explosion of Usage by Developers Using AI 269

AI Monetization Threats

=

Rising Competition

+

Open-Source Model Momentum

(& China’s Rise)

270

As noted on page 8, Meta CTO Andrew Bosworth referred to the current state of AI as

our space race and the people we’re discussing, especially China, are highly capable…

In this context, it is important to remember what the stakes of the Space Race were: proving which political system

could innovate faster and win the world’s trust in the process. Coming out on top in the Space Race

played a role in enhancing USA’s strategic deterrence and cementing the primacy of western democratic values.

The AI ‘space race,’ also has the potential to reshape the world order.

China certainly knows these stakes. Back in 2015, ‘Made in China 2025,’ a new Chinese government initiative

to shift the country from low-cost to high-value manufacturing in critical industries, seemed decades away.

Fast forward to today, and China has dramatically accelerated its capabilities in these strategic sectors

like robotics, electrification, and ‘information technology’ – best expressed by world-class artificial intelligence.

Chinese AI capabilities now underpin nationally strategic areas such as battlefield logistics, target recognition,

cyber operations, and autonomous decision-making platforms. In 2025, Chinese state media highlighted the

integration of AI into non-combat support functions (e.g., military hospitals), while the

Ministry of Science and Technology reinforced its commitment to ‘indigenous innovation’ in strategic technologies.

The implications of Chinese AI supremacy would be profound.

As OpenAI’s Sam Altman noted in a July 2024 Washington Post Op-Ed, If [authoritarian regimes] manage to

take the lead on AI, they will force U.S. companies and those of other nations to share user data, leveraging the technology to

develop new ways of spying on their own citizens or creating next-generation cyberweapons to use against other countries.

AI Monetization Threats = Rising Competition + Open-Source Momentum + China’s Rise 271

…Meanwhile, alongside AI, broader economic trade tensions between the USA and China continue to escalate,

driven by competition for control over strategic technology inputs. China, for now, remains the dominant global supplier

of ‘rare earth elements’ – materials essential to advanced electronics, defense systems, and clean energy infrastructure –

an imbalance that the USA is working hard to counter. Simultaneously, the USA has prioritized the reshoring of semiconductor

manufacturing, supported by the CHIPS and Science Act, and bolstered its partnerships with allied nations

(including Japan, South Korea and the Netherlands) to reduce reliance on Chinese supply chains.

Taiwan continues to play a pivotal role in this dynamic. Despite American invention of core semiconductor technology

like transistors and EUV lithography, it is Taiwan’s TSMC – the world’s most advanced semiconductor foundry –

that drives global semiconductor production and is therefore central to both countries’ strategic calculations.

It has taken a long time for the USA to wake up, but after two decades of inaction,

both political parties are calling loudly for change. While each has taken a different approach (export controls in the

Biden administration, economic nationalism and reshoring in the Trump administration), the move towards

treating cutting-edge technology development as a core part of the national interest is a welcome adjustment.

As Senators John Cornyn and Mark Warner noted in 2020 regarding semiconductors,

America’s innovation in semiconductors undergirds our entire innovation economy…unfortunately,

our complacency has allowed our competitors – including adversaries – to catch up.

However, despite these measures, American intellectual property remains at risk; per OpenAI,

We know PRC (China) based companies – and others – are constantly trying to distill the models of leading

US AI companies…it is critically important that we are working closely with the US government to best

protect the most capable models from efforts by adversaries and competitors to take US technology.

What is clear, however, is that the American tone about Chinese technology has morphed since

the early 2000s enthusiasm around China’s entry into the World Trade Organization (WTO). AI, semiconductors, and

critical minerals, and technology developments in general, are no longer viewed solely as economic or technology assets –

they represent strategic levers of national resilience and geopolitical power, core to both the USA and China.

AI Monetization Threats = Rising Competition + Open-Source Momentum + China’s Rise 272

Public Market Capitalization Leader

Tells of Last Thirty Years =

Extraordinary USA Momentum…

China Rising

273

Global Public Market Capitalization Leaders – May, 2025 =

83% (25 of 30) USA-Based…

Global Public Companies Ranked By Market Capitalization – 5/15/25, per Capital IQ

Rank Market Cap 2025 Company HQ Country Sector ($B) 1 Microsoft USA Software / AI $3,368B 2 NVIDIA USA Semis / AI 3,288 3 Apple USA Hardware / AI 3,158 4 Amazon USA Internet / AI 2,178 5 Alphabet (Google) USA Internet / AI 1,997 6 Saudi Aramco Saudi Arabia Energy 1,686 7 Meta Platforms (Facebook) USA Internet / AI 1,619 8 Tesla USA Auto / AI 1,104 9 Broadcom USA Semis / AI 1,094 10 Berkshire Hathaway USA Finance 1,093 11 TSMC Taiwan Semis / AI 856 12 Walmart USA Consumer Products 771 13 JP Morgan Chase USA Finance 743 14 Visa USA Finance 678 15 Eli Lilly USA Healthcare 658 16 Tencent China Software / AI 591 17 Mastercard USA Finance 529 18 Netflix USA Internet / AI 501 19 Exxon Mobil USA Energy 468 20 Costco Wholesale USA Consumer Products 448 21 Oracle USA Hardware / AI 447 22 Procter & Gamble USA Consumer Products 381 23 Home Depot USA Consumer Products 376 24 Johnson & Johnson USA Consumer Products 360 25 SAP Germany Software / AI 343 26 Bank of America USA Finance 334 27 ICBC China Finance 330 28 AbbVie USA Healthcare 321 29 Coca-Cola USA Consumer Products 308 30 Palantir USA Software / AI 302

Source: Capital IQ (as of 5/15/25)

Public Market Capitalization Leader Tells of Last Thirty Years = Extraordinary USA Momentum…China Rising 274 …Global Public Market Capitalization Leaders – December, 1995 =

53% (16 of 30) USA-Based

Global Public Companies Ranked By Market Capitalization – 12/31/95, per Bloomberg

Rank Market Cap 1995 Company HQ Country Sector ($B) 1 Nippon Telegraph Japan Telco $128B 2 General Electric USA Industrials 120 3 AT&T USA Telco 103 4 Exxon USA Energy 100 5 Coca-Cola USA Consumer Products 94 6 Merck USA Healthcare 81 7 Toyota Japan Automotive 79 8 Roche Switzerland Healthcare 78 9 Altria USA Consumer Products 75 10 Industrial Bank of Japan Japan Finance 71 11 MUFG Bank Japan Finance 68 12 Sumimoto Mitsui Japan Finance 66 13 Fuji Bank Japan Finance 64 14 Dai-Ichi Kangyo Bank Japan Finance 61 15 UFJ Bank Japan Finance 59 16 Novartis Switzerland Healthcare 57 17 Procter & Gamble USA Consumer Products 57 18 Johnson & Johnson USA Consumer Products 55 19 Microsoft USA Software 52 20 Walmart USA Consumer Products 51 21 IBM USA Hardware / Software 51 22 DirecTV USA Media 49 23 Intel USA Hardware 47 24 BP United Kingdom Energy 46 25 Nestle Switzerland Consumer Products 45 26 Mobil USA Energy 44 27 PepsiCo USA Consumer Products 44 28 AIG USA Finance 44 29 Shell United Kingdom Energy 44 30 Sakura Bank Japan Finance 43

Source: Bloomberg (as of 5/15/25)

Public Market Capitalization Leader Tells of Last Thirty Years = Extraordinary USA Momentum…China Rising 275

Over the past thirty years (1995 to 2025), just six companies remained on the

top 30 most highly valued publicly traded global companies –

Microsoft / Walmart / Exxon Mobil / Procter & Gamble /

Johnson & Johnson / Coca-Cola.

New entrants are NVIDIA / Apple / Amazon / Alphabet (Google) / Saudi Aramco /

Meta Platforms (Facebook) / Tesla / Broadcom / Berkshire Hathaway / TSMC / JP Morgan Chase /

Visa / Eli Lilly / Tencent / Mastercard / Netflix / Costco Wholesale / Oracle / Home Depot / SAP /

Bank of America / ICBC / AbbVie / Palantir.

In 1995, USA had 53% (16 of 30) of the most valuable companies and 83% (25 of 30) in 2025.

Japan came next with 9, now 0.

Switzerland followed with 3, now 0. UK had 2, now 0.

In 2025, new geographic entrants include

China with 2 and Saudi Arabia / Taiwan / Germany with 1 each.

Public Market Capitalization Leader Tells of Last Thirty Years = Extraordinary USA Momentum…China Rising 276 Global Public Technology Market Cap Leaders – May, 2025 =

70% (21 of 30) USA-Based…

Global Technology Companies Ranked By Market Capitalization – 5/15/25, per Capital IQ

Rank Market Cap 2025 Company HQ Country Sector ($B) 1 Microsoft USA Software / AI $3,368B 2 NVIDIA USA Semis / AI 3,288 3 Apple USA Hardware / AI 3,158 4 Amazon USA Internet / AI 2,178 5 Alphabet (Google) USA Internet / AI 1,997 6 Meta Platforms (Facebook) USA Internet / AI 1,619 7 Tesla USA Auto / AI 1,104 8 Broadcom USA Semis / AI 1,094 9 TSMC Taiwan Semis / AI 856 10 Tencent China Software / AI 591 11 Netflix USA Internet / AI 501 12 Oracle USA Hardware / AI 447 13 SAP Germany Software / AI 343 14 Palantir USA Software / AI 302 15 ASML Netherlands Semis / AI 300 16 Alibaba China Internet / AI 281 17 Salesforce USA Software / AI 279 18 T-Mobile USA Telco 273 19 Samsung S. Korea Hardware / AI 268 20 Cisco USA Semis / AI 256 21 IBM USA Hardware / AI 243 22 China Mobile China Telco 241 23 Reliance India Telco 216 24 ServiceNow USA Software / AI 214 25 Intuitive Surgical USA Health Tech 201 26 AT&T USA Telco 197 27 Siemens Germany Hardware / AI 194 28 Uber USA Internet / AI 189 29 AMD USA Semis / AI 186 30 Intuit USA Software / AI 185

Source: Capital IQ (as of 5/15/25)

Public Market Capitalization Leader Tells of Last Thirty Years = Extraordinary USA Momentum…China Rising 277 …Global Public Technology Market Cap Leaders – December, 1995 =

53% (16 of 30) USA-Based

Global Technology Companies Ranked By Market Capitalization – 12/31/95, per Bloomberg

Rank Market Cap 1995 Company HQ Country Sector ($B) 1 Nippon Telegraph Japan Telco $128B 2 AT&T USA Telco 103 3 Microsoft USA Software 52 4 IBM USA Hardware / Software 51 5 Intel USA Hardware 47 6 BellSouth USA Telco 43 7 HP USA Hardware 43 8 GTE USA Telco 42 9 BT United Kingdom Telco 34 10 Panasonic Japan Hardware 34 11 SingTel Singapore Telco 34 12 Motorola USA Hardware 34 13 Hitachi Japan Hardware 33 14 Verizon USA Telco 29 15 Toshiba Japan Hardware 26 16 Peraton USA Software / Hardware 25 17 Nynex USA Telco 24 18 Sony Japan Hardware 22 19 Cisco USA Hardware 21 20 Fujitsu Japan Hardware 20 21 PCCW Hong Kong Telco 20 22 NEC Japan Software 19 23 Oracle USA Hardware 18 24 MCI USA Telco 18 25 Sharp Japan Hardware 18 26 TelMex Mexico Telco 17 27 KDDI Japan Telco 17 28 US West USA Telco 17 29 Cable & Wireless USA Telco 16 30 Telekom Malaysia Malaysia Telco 16

Source: Bloomberg (as of 5/15/25)

Public Market Capitalization Leader Tells of Last Thirty Years = Extraordinary USA Momentum…China Rising 278

Over the past thirty years (1995 to 2025), just five companies remained on the

top 30 most highly valued publicly traded global technology companies –

Microsoft / Oracle / Cisco / IBM / AT&T.

New entrants are NVIDIA / Apple / Amazon / Alphabet (Google) /

Meta Platforms (Facebook) / Tesla / Broadcom / TSMC / Tencent / Netflix / SAP / Palantir / ASML /

Alibaba / Salesforce / T-Mobile / Samsung / China Mobile / Reliance / ServiceNow /

Intuitive Surgical / Siemens / Uber / AMD / Intuit.

In 1995, USA had 53% (16 of 30) of the most valuable tech companies

and 70% (21 of 30) in 2025.

In 1995, Japan had 30% (9 of 30) of the top tech companies and 0 in 2025.

UK / Singapore / Hong Kong / Mexico / Malaysia had 1, now 0.

In 2025, new geographic entrants include China with 3, Germany with 2, Taiwan with 1,

Netherlands with 1, South Korea with 1 & India with 1.

Note that while Taiwan has only one company on the list – TSMC – the company

produces 80%-90% of the world’s most advanced semiconductors and

62%+ of global semiconductors as of Q2:24, per The Center for Strategic & International Studies &

Counterpoint Research.

It’s stunning how much can change in a generation…

the emergence of internet connectivity was foundational to most of the new adds.

The emergence of AI will have the same type of effect over the next three decades,

but likely faster.

Source: Center for Strategic & International Studies, ‘A Strategy for The United States to Regain its Position in Semiconductor Manufacturing’ (2/24); Counterpoint Research, ‘Global Semiconductor Foundry Market Share: Quarterly’ (3/25)

Public Market Capitalization Leader Tells of Last Thirty Years = Extraordinary USA Momentum…China Rising 279

USA vs. China in Technology =

China’s AI Response Time

Significantly Faster vs. Internet 1995

280

AI Large Language Model (LLM) Leadership =

USA & China Outpacing Rest of World (RoW), per Epoch AI

Cumulative Large-Scale AI Systems by Country* – 2017-2024,

per Epoch AI

150

s United States m

ste

y

S

I

A 100 China le

ca

-S

e

rg

a

L

e

iv

u Multinational lat 50

Cum United Kingdom

France

Canada

Hong Kong

Germany

0

2017 2018 2019 2020 2021 2022 2023 2024

*Hong Kong is a Special Administrative Region (SAR) of China, not an independent country. Note: Epoch AI defines AI models as ‘large-scale’ when their training compute is confirmed to exceed 1023 floating-point operations. Source: Epoch AI via Our World In Data (5/25)

USA vs. China in Technology = China’s AI Response Time Significantly Faster vs. Internet 1995 281 China AI = Rapid Relevance…

DeepSeek R1 – 1/20/25…

We believe that as the economy develops,

China should gradually become a contributor

instead of freeriding. In the past 30+ years of

the IT wave, we basically didn’t participate in

real technological innovation. We’re used to

Moore’s Law falling out of the sky, lying at

home waiting 18 months for better hardware

and software to emerge. That’s how the

Scaling Law is being treated…

What we see is that Chinese AI can’t be in the

position of following forever. We often say that

there is a gap of one or two years between

Chinese AI and the United States, but the real

gap is the difference between originality and

imitation. If this doesn’t change,

China will always be only a follower – so some

exploration is inescapable.

- DeepSeek CEO Liang Wenfang, 11/24

Source: Reuters, ‘DeepSeek narrows China-US AI gap to three months, 01.AI founder Lee Kai-Fu says’ (3/25); China Talk Media (11/24)

USA vs. China in Technology = China’s AI Response Time Significantly Faster vs. Internet 1995 282 …China AI = Rapid Relevance…

Alibaba Qwen 2.5-Max – 1/29/25…

Qwen2.5-Max outperforms DeepSeek V3 in

benchmarks such as Arena-Hard, LiveBench,

LiveCodeBench, and GPQA-Diamond, while

also demonstrating competitive results in other

assessments, including MMLU-Pro.

Our base models have demonstrated

significant advantages across most

benchmarks, and we are optimistic that

advancements in post-training techniques will

elevate the next version of

Qwen2.5-Max to new heights.

The scaling of data and model size not only

showcases advancements in model

intelligence but also reflects our unwavering

commitment to pioneering research. We are

dedicated to enhancing the thinking and

reasoning capabilities of large language

models through the innovative application of

scaled reinforcement learning.

- Alibaba Qwen 2.5 Press Release, 1/25

Source: Mashable, ‘Meet Alibaba’s Qwen 2.5, an AI model claiming to beat both DeepSeek and OpenAI’s ChatGPT’ (1/25); Alibaba (1/25)

USA vs. China in Technology = China’s AI Response Time Significantly Faster vs. Internet 1995 283 …China AI = Rapid Relevance…

Baidu Ernie 4.5 Turbo – 4/25/25

ERNIE 4.5 Turbo is the newest member of the

flagship ERNIE foundation model family.

Imagine an AI that's not just smart, but also

affordable and versatile. Here's why it's turning

heads:

- Multimodal Prowess: It excels in handling

text, images, and even videos, making it a

Swiss Army knife for developers.

- Cost-Effectiveness: Priced at just RMB 0.8

per million tokens for input and RMB 3.2 for

output, it's 80% cheaper than its predecessor –

and a fraction of the cost of leading

competitors. It costs only 40% of DeepSeek V3

and just 0.2% of GPT-4.5.

- High Performance: Benchmark tests show it

matches GPT-4.1 and outperforms GPT-4o in

most multimodal tasks – delivering high-impact

results with every run.

- Baidu Post on X, 4/24/25

Source: Reuters, ‘Baidu launches new AI model amid mounting competition’ (4/24/25); Baidu via X, ‘Supercharging AI Innovation with More Powerful and More Affordable New Models’ (4/24/25)

USA vs. China in Technology = China’s AI Response Time Significantly Faster vs. Internet 1995 284 China AI =

LLM Performance Catching Up to USA Models, per Stanford HAI…

Performance of Top-Scoring USA vs. Chinese AI Model

on LMSYS Chatbot Arena – 1/24-2/25, per Stanford HAI & LMSYS

Note: The LMSYS Chatbot Arena is a public website where people compare two AI chatbots by asking them the same question and voting on which answer is better. The results help rank how well different language models perform based on human judgment. Only the highest-scoring model in any given month is shown in this comparison. Source: LMSYS via Nestor Maslej et al., ‘The AI Index 2025 Annual Report,’ AI Index Steering Committee, Stanford HAI (4/25)

USA vs. China in Technology = China’s AI Response Time Significantly Faster vs. Internet 1995 285 …China AI =

LLMs Achieving Performance with Lower Training Costs, per Epoch AI…

LLM Training Cost by Year Released – 2022-2024, per Epoch AI & NBC News

Source: Epoch AI via NBC News, ‘Why DeepSeek is Different, in Three Charts’ (1/25)

USA vs. China in Technology = China’s AI Response Time Significantly Faster vs. Internet 1995 286 …China AI =

LLMs Increasingly Powered by Local Semiconductors…

Huawei has started the delivery of its

advanced artificial intelligence chip ‘cluster’ to

Chinese clients who are increasing orders after

being cut off from Nvidia’s semiconductors

because of Washington’s export restrictions…

- Financial Times, 4/29/25

Source: Financial Times, ‘Huawei delivers advanced AI chip ‘cluster’ to Chinese clients cut off from Nvidia’ (4/29/25)

USA vs. China in Technology = China’s AI Response Time Significantly Faster vs. Internet 1995 287 …China AI =

Industrial Robot Installed Base Higher vs. Rest of World…

Number of Industrial Robots Installed (China vs. Rest of World) (K) – 2023, per IFR

China

Rest of World

Source: International Federation of Robotics (IFR) (2024) via Nestor Maslej et al., ‘The AI Index 2025 Annual Report,’ AI Index Steering Committee, Stanford HAI (4/25)

USA vs. China in Technology = China’s AI Response Time Significantly Faster vs. Internet 1995 288 …China AI =

Industrial Robot Installed Base Higher vs. Rest of World

Number of Industrial Robots Installed (China vs. Rest of World) (K) – 2014-2023,

per IFR

300

China

K

,

d

lle Rest of World

sta (excl. USA & China)

In 200

ts

o

Robl a

stri

u

d

In 100

f

o

r

e

b

Num USA

0

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023

Source: International Federation of Robotics (IFR) (2024)

USA vs. China in Technology = China’s AI Response Time Significantly Faster vs. Internet 1995 289 Robots – Industrial & Humanoid =

Creating New Data @ New Scale

Images of Industrial & Humanoid Robots, per The Wall Street Journal

Source: The Wall Street Journal (2/18, 5/22, 9/22, 5/25)

USA vs. China in Technology = China’s AI Response Time Significantly Faster vs. Internet 1995 290

China Consumer AI Usage =

DeepSeek Rose Quickly

291

To understand how the generative AI market is evolving, it helps to examine the divergence in provider usage across

regions, channels, and user preferences. At a global level, OpenAI’s ChatGPT remains the clear leader in both

desktop and mobile user share. But underneath the surface, the market is shifting.

Platforms like Anthropic’s Claude are gaining momentum, and Google’s Gemini continues to grow.

xAI’s Grok posted a staggering +294% increase in global website visits month-over-month

according to Similarweb – making it the fastest-growing AI assistant during the 2/25-3/25 window.

Geography is also playing an increasingly central role in shaping which models win. ChatGPT dominates in

most countries – excluding Russia and China, where ChatGPT cannot operate and DeepSeek is strong.

China users are turning to local models at scale. According to Roland Berger Consulting, the top 10 AI apps by monthly

active users in China are domestically developed…DeepSeek, Kimi, Nami AI, and ERNIE Bot are each racking up tens of

millions of users. The story is different outside China, where ChatGPT leads by a wide margin.

The bifurcation is clear: domestic champions are emerging in China, while global platforms dominate elsewhere.

This reflects differences in regulation, language, cultural alignment, and platform reach.

It’s foundational to remember how China has restricted platform access in its country.

Facebook, Twitter, Google and YouTube have been unavailable to Chinese citizens since 2010 or earlier.

Other restricted platforms include the likes of Instagram, WhatsApp, Wikipedia, Telegram and Spotify,

and more recently, the likes of ChatGPT, Google Gemini, Anthropic Claude, Meta AI and Microsoft Copilot.

Sentiment is varied too. According to Stanford HAI and Ipsos, China citizens are materially more optimistic

about AI’s net benefits than their USA counterparts. 83% of Chinese respondents in 2024 said AI products and

services have more benefits than drawbacks – up from 78% in 2022.

In contrast, only 39% of USA respondents shared that view, with little change over the two-year period.

It also reflects a deeper philosophical divide in how societies are adapting to AI: not just who builds it,

but how it’s perceived and embraced. In this environment, platform choice isn’t just about price or performance.

It may be increasingly shaped by national identity.

China Consumer AI Usage = DeepSeek Rose Quickly 292 LLM User Share – Desktop Users =

OpenAI ChatGPT Leads…DeepSeek Rose Quickly, per YipitData…

Estimated Global Monthly Active Desktop User Share – 2/24-4/25, per YipitData

100%

% -1,504 bps

Users,

p

o

Deskt 50% l a b

Glo -619 bps f o

re +1,007 bps a +845 bps h S +73 bps +198 bps

0%

OpenAI: Google: DeepSeek xAI: Perplexity Anthropic:

ChatGPT Gemini* Grok Claude

2/24 2/25 4/25

*Chatbot only. Does not include other places Gemini is integrated. Note: User share shown across these five providers; other LLMs’ user share not shown. Desktop users only. Figures calculate the number of users on a given platform, divided by the number of users on all platforms combined. Figures are non-deduped (i.e., users using multiple platforms may be counted twice). Data is a subset of global internet users and absolute user data will be understated; however, given that the panel is globally-representative (with limitations on China-specific data), relative comparisons / trends are informative. Data measures several million global active desktop users’ clickstream data. Data consists of users’ web requests & is collected from web services / applications, such as VPNs and browser extensions. Panel is globally-representative (with limitations on China-specific data). Users must have been part of the panel for 2 consecutive months to be included. Data is non-deduped; i.e., some users may use multiple platforms. Source: YipitData (accessed 5/25)

China Consumer AI Usage = DeepSeek Rose Quickly 293 …LLM User Share – Mobile App Users =

OpenAI ChatGPT Leads…DeepSeek Rose Quickly, per Sensor Tower…

LLMs – Global Monthly Active Mobile App User Share – 2/24-4/25, per Sensor Tower

-1,617 bps

100%

%

Users,

p

p

A

l

a 50%

b

Glo

f

o

re

h +837 bps S a

+272 bps +469 bps -9 bps +49 bps

0%

OpenAI: DeepSeek Google: xAI: Perplexity Anthropic:

ChatGPT Gemini* Grok Claude

2/24 2/25 4/25

*Chatbot only. Does not include other places Gemini is integrated. Note: User share shown across these five providers; other LLMs’ user share not shown. China data may be incomplete due to reporting gaps. ChatGPT app not available in China, Russia and select other countries as of 5/25. Data is non-deduped; i.e., some users may use multiple platforms. Data for standalone apps only. Source: Sensor Tower (accessed 5/25)

China Consumer AI Usage = DeepSeek Rose Quickly 294 …LLM User Share – Mobile App Downloads + Users =

ChatGPT Supporting Strong Momentum…

Global Statistics on Apple App Store + Google Play Store – 2/25-4/25, per Sensor Tower

Downloads (MM) MAUs (MM)

2/25 3/25 4/25 2/25 3/25 4/25

ChatGPT 56MM 80MM 124MM 378MM 432MM 530MM

DeepSeek 34 20 18 43 48 55

Grok 4 14 16 3 16 31

LLM

Apps Gemini* 16 17 15 20 21 21

Perplexity 3 4 4 10 12 14

Claude 1 1 1 3 4 3

YouTube 13 10 9 2,799 2,805 2,809

‘Traditional’

Google Chrome 9 9 7 2,369 2,380 2,387

Apps

Facebook 46 47 45 2,104 2,110 2,103

*Chatbot only. Does not include other places Gemini is integrated. Note: China data may be incomplete due to reporting gaps. ChatGPT app not available in China, Russia and select other countries as of 5/25. Data is non-deduped; i.e., some users may use multiple platforms. Data for standalone apps only. Source: Sensor Tower (accessed 5/25)

China Consumer AI Usage = DeepSeek Rose Quickly 295 …LLM User Share – Query Volume =

OpenAI ChatGPT Leads, per Google

LLMs – Global Daily Query Volume (MM) – 3/28/25, per Google

1,200

M

M 800

s,

e

ri

e

u

Q

Daily 400 l a b

Glo

0

OpenAI: Meta: Google: xAI: DeepSeek Perplexity

ChatGPT Meta AI Gemini* Grok

*Chatbot only. Does not include other places Gemini is integrated. Note: DeepSeek data excludes China usage. Figures are rounded. Meta AI data quoted as ‘>200M.’ Source: Google disclosed during testimony given in the remedies phase of ‘United States v. Google LLC’ (1/24/23-4/17/25). Data derived from company disclosures, Sensor Tower, AppAnnie, Similarweb, & market intelligence estimates, as reported by Business Insider, ‘Google's Gemini usage is skyrocketing, but rivals like ChatGPT and Meta AI are still blowing it out of the

water’ (4/25) (link)

China Consumer AI Usage = DeepSeek Rose Quickly 296 China AI Users =

Using Local AI Platforms, per Roland Berger Consulting

AI Platforms – Monthly Active Users (MM), China vs. Global – 3/25,

per Roland Berger Consulting

Top Chinese AI Platforms Top Global AI Platforms

HQ HQ

Doubao ChatGPT

DeepSeek Doubao

Kimi Nova

Nami AI DeepSeek

Tencent Yuanbao Remini

ERNIE Bot Talkie AI

ChatGLM Character AI

xinye ChatOn

Dreamina Gemnius

Mooxiang Gemini

0 50 100 0 200 400

Monthly Active Users, MM Monthly Active Users, MM

Note: HQ = Headquarters. Axes for two charts are to different scales.

Source: Roland Berger via AICPB, ‘Five key trends in China's generative AI market in 2025’ (3/25); China National Bureau of Statistics (1/25); USA Census Bureau (4/25)

China Consumer AI Usage = DeepSeek Rose Quickly 297 AI Benefits vs. Drawbacks – China vs. USA Citizens =

China Materially More Optimistic Regarding Benefits

‘Products & Services Using AI Have More Benefits than Drawbacks’ – 2022-2024,

per Stanford HAI & Ipsos

100%

’

ree

t ‘Ag

a

th

nts 50%

de

n

o

f Resp

% o

0%

China USA

2022 2024

Note: N = 19,504 online adults aged 16-74 across 28 countries.

Source: Ipsos, 'AI Monitor 2024' (6/24) as quoted in Nestor Maslej et al., ‘The AI Index 2025 Annual Report,’ AI Index Steering Committee, Stanford HAI (4/25)

China Consumer AI Usage = DeepSeek Rose Quickly 298

Outline

1 • Seem Like Change Happening Faster Than Ever?

Yes, It Is

2 • AI User + Usage + CapEx Growth =

Unprecedented

3 • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling =

Performance Converging + Developer Usage Rising

4 • AI Usage + Cost + Loss Growth =

Unprecedented

5 • AI Monetization Threats =

Rising Competition + Open-Source Momentum + China’s Rise

6 • AI & Physical World Ramps =

Fast + Data-Driven

7 • Global Internet User Ramps Powered by AI from Get-Go =

Growth We Have Not Seen Likes of Before

8 • AI & Work Evolution =

Real + Rapid

299

For the most part, we have focused on AI momentum and monetization of desktop / mobile software…

AI momentum and monetization in our physical world is, in some respects, even more head-turning.

We are entering an era where intelligence is not just embedded in digital applications,

but also in vehicles, machines, and defense systems.

Beyond the rise of digital agents, the world is increasingly experiencing the rise of physical agents.

Self-driving fleets like Waymo’s and Tesla’s Full Self-Driving (FSD) beta are no longer science projects

confined to test tracks – they’re revenue-generating deployments, logging millions of driverless miles

with increasingly autonomous software loops. The stack beneath them is getting smarter,

and the data is more vast and richer. Applied Intuition, for example, is building simulation platforms

and software-defined vehicle systems that abstract autonomy away from hardware –

so manufacturers can ship intelligence as easily as parts. Per Uber CEO Dara Khosrowshahi,

Fast forward 15, 20 years, I think that the autonomous driver is going to be a better driver

than the human driver. They will have trained on lifetimes of driving that no person can,

they’re not going to be distracted.

We are seeing the early architecture of AI-native infrastructure for the physical world.

In defense, companies like Anduril are redefining what defense looks like –

shipping autonomous drones and counter-intrusion systems with AI in every edge node, not just the

command center. In agriculture, companies like Carbon Robotics are

putting AI into the dirt – using computer vision to eliminate weeds without herbicides.

We believe that these are examples of a broader shift: a world where AI turns capital assets into

software endpoints. Intelligence, once confined to screens and dashboards, becomes kinetic.

AI & Physical World Ramps = Fast + Data-Driven 300 Physical World AI – Vertically-Integrated Electric Vehicles (Tesla) =

~100x Increase in Fully Self-Driven Miles Over Thirty-Three Months

Tesla Vertically-Integrated Electric Vehicles

Tesla Cumulative Fully Self-Driven Miles (MM) –

6/22-3/25, per Tesla

M 4,000 M

,

n

e

v

s Dri

ile

M

For full self-driving, we’ve released version 12, which is a ing

complete architectural rewrite compared to prior versions. 2,000-Dr This is end-to-iv end artificial intelligence… lf

e

S

…And it really is…quite a profound difference… ll

Fu

…So, this is the first time AI is being used, not just for object ive

perception, but for path planning and vehicle controls. We lat

replaced 330,000 lines of C++ code with neural nets. It's u

really quite remarkable. So, as a side note, I think Tesla is

Cum

probably the most probably the most efficient 0

company in the world for AI inference. Out of necessity. 3 sla 22 22 23 23 24 24 24 25 e /22 /2 /23 /24 T 6/ 9/ 3/ 6 9/ 3/ 6/ 9/3/ 12 12 12

- Tesla CEO Elon Musk, 1/24

Source: Tesla Disclosures & Q1:25 Investor Deck

AI & Physical World Ramps = Fast + Data-Driven 301 Physical World AI – Fully-Autonomous Vehicles (Waymo) =

0% to 27% Share of San Francisco Rideshares Over Twenty Months, per YipitData

Waymo Fully-Autonomous Vehicles

Estimated Market Share (Gross Bookings) – 8/23-4/25,

San Francisco Operating Zone, per YipitData

75%

s

king

o

o

ss B 50%

Gro

[We are creating] an end-to-end, very, very robust, and

large end-to-end system that’s multi-modal in its foundation cisco so that perception planning and prediction…na can become even more robust than it is today. Fr 25% n a- Waymo Co-CEO Tekedra Mawakana, 1/25 S f

% o

What we’ve done in San Francisco is prove to ourselves –

and to the world – that not only does autonomy work, 0% but it works at scale in a market and can 8/23 12/23 4/24 8/24 12/24 4/25

be a viable commercial product.

Waymo Uber Lyft

- Waymo Co-CEO Dmitri Dolgov, 3/25

Note: Data derived from USA-user email receipt panel composed of >1mm monthly transacting USA email accounts from all available domains. Paid rides only. Numbers are estimates due to sample size. Source: Waymo, Tech Brew (1/25), Fast Company (3/25), YipitData (4/4/25)

AI & Physical World Ramps = Fast + Data-Driven 302 Physical World AI –Vehicle Intelligence (Applied Intuition) =

Serving Automotive, Trucking, Construction & Defense

Applied Intuition Vehicle Intelligence

Applied Intuition Top Global Auto OEMs Served –

2016-2024, per Applied Intuition*

d 20 e 18 rv

e

s S

M

Within the last few years, we’ve seen massive advances in OE

artificial intelligence that will have groundbreaking impacts on to

u

the industries that Applied Intuition serves. Our role as a 10 A

p

leader in the ecosystem is to bring the best of what Silicon o

Valley has to offer to our global customer base. T

f

o

r

- Applied Intuition Co-Founder & CEO Qasar Younis, 3/24 e

b

We've seen accelerating adoption of our AI-powered tools, Num 0 autonomy software, and vehicle operating system as 0

traditional OEMs are seeing strong ROI. The Defense sector 2016 2024

is also looking for vehicle intelligence solutions. We've

provided our off-road autonomy stack for defense for several

* Applied Intuition serves a broad base of customers in

years, and have expanded our defense tech product portfolio

different verticals, such as Porsche / Toyota (auto),

significantly over the past year.

Traton / Isuzu (trucking), Caterpillar (construction) and

several US military branches (defense).

- Applied Intuition Co-Founder & CTO Peter Ludwig, 5/25

Note: OEM = Original Equipment Manufacturer.

Source: Applied Intuition

AI & Physical World Ramps = Fast + Data-Driven 303 Physical World AI – USA Defense (Anduril) =

+2x Y/Y Revenue Growth for Last Two Years

Anduril AI-Enabled Autonomous USA Defense Systems

Anduril Estimated Revenue ($MM) –

F2020-F2024, per News Reports

$1,000

M

M

$

At Anduril, we firmly believe that today’s most pressing ,

e

national security challenges cannot be solved without AI- un $500

enabled systems and autonomy at scale. These systems will e

help to keep our service members safe and empower them to make better decisions at the speed of modern warfare… Rev

l

a

…When developed and deployed properly, [AI and nu

n

autonomous systems] can make warfare more proportional, A

more precise, and less indiscriminate than it

has ever been before.

- Anduril Co-Founder & CEO Brian Schimpf, 12/23

$0

F2020 F2021 F2022 F2023 F2024

Source: Anduril, Forbes, TechCrunch, CNBC

AI & Physical World Ramps = Fast + Furious 304 Physical World AI = AI-Driven Mining Exploration (KoBold Metals) =

Reversing Trend in Exploration Inefficiency

KoBold Metals AI-Driven Mining Exploration

Mineral Deposit Discoveries per $B of Exploration

Spend – 1975-2023, per KoBold Metals

16

KoBold Metals

d

n

e

p

S 12 n

tio

We're looking to expand and diversify the supply of these lorap Industry Average x

metals all over the world, but we're taking a totally different E

approach [from conventional mining companies]. Two-thirds f 8

of our team are software engineers or data scientists. o

B

$

r

- KoBold Metals Co-Founder & CEO Kurt House, 12/22 e

p

KoBold’s Machine Prospector technology combines never before riev used datasets with conventional geochemical, geophysical, & e 4 s

geological data in statistical association models to identify sco

prospects. KoBold’s technology accelerates exploration by Di

efficiently screening large regions & makes our search more

0

effective by identifying the most promising locations. 1975 1983 1991 1999 2007 2015 2023

- KoBold Metals Website

Source: KoBold Metals, Wired (12/22)

AI & Physical World Ramps = Fast + Furious 305 Physical World AI – Agricultural Modernization (Carbon Robotics) =

230K+ Acres Weeded / 100K+ Gallons of Glyphosate Prevented

Carbon Robotics AI-Driven Agricultural Modernization

Carbon Robotics Cumulative Fleet Acres

Weeded (K) – 1/23-5/25, per Carbon Robotics

250

, 200 K d

e

d

e

e

W

150

The LaserWeeder leverages our sophisticated laserweeding

technology, driven by AI deep learning models and computer cres

A

vision software, to efficiently identify, target, and eliminate t

weeds by zapping them at the meristem. The implement can e 100 e

cover up to 2 acres per hour and shoot up to 200,000 weeds. Fl

e

iv

- Carbon Robotics Founder & CEO Paul Mikesell, 12/22 lat

u

50

We learned from farmers that their biggest challenges Cum

continue to be around labor and labor availability. If they

could, they would run everything 24/7. They would run 0

everything every minute of farming season to get as much

done as possible.

- Carbon Robotics Founder & CEO Paul Mikesell, 3/25

Source: Carbon Robotics, Organic Produce Network (12/22), GeekWire (3/25)

AI & Physical World Ramps = Fast + Furious 306 Physical World AI – Intelligent Grazing (Halter) =

+150% Net-New Livestock Collars Contracted Y/Y

Halter AI-Driven Intelligent Grazing

Halter Net New Collars Contracted (K) –

2023-2025*, per Halter

400

K

,

d

cte

We’ve seen firsthand the care and dedication ranchers have

tra

for their land and animals. We’ve also seen how agriculture,

one of the oldest and most vital industries, has yet to receive Con the full benefits of modern technology. This leaves enormous rs 200

opportunity for ranchers to unlock greater productivity and lla

sustainability across their operations. Co

We believe grazing management holds the key. Effective

New

rotational grazing enables more efficient use of natural

resources and increased productivity, while also enhancing Net soil health and improving root structures to sequester more

carbon. We don’t believe more productivity needs to come at

the cost of sustainability. We can do good for ranchers, and

the planet. 0

2023 2024 2025*

- Halter (as of 5/25)

*2025 figures annualized as of Q1:25. Source: Halter (5/25)

AI & Physical World Ramps = Fast + Data-Driven 307

Outline

1 • Seem Like Change Happening Faster Than Ever?

Yes, It Is

2 • AI User + Usage + CapEx Growth =

Unprecedented

3 • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling =

Performance Converging + Developer Usage Rising

4 • AI Usage + Cost + Loss Growth =

Unprecedented

5 • AI Monetization Threats =

Rising Competition + Open-Source Momentum + China’s Rise

6 • AI & Physical World Ramps =

Fast + Data-Driven

7 • Global Internet User Ramps Powered by AI from Get-Go =

Growth We Have Not Seen Likes of Before

8 • AI & Work Evolution =

Real + Rapid

308

Thanks to the rise in low-cost satellite-driven Internet connectivity / access,

the potential for the 2.6B (or 32% of the world’s population) that is not online to come online is increasing.

These new users will start from scratch with AI functionality. Wow!

When these new users come online, they likely won’t be met by browsers and search bars.

They’ll start with AI – and in their native language.

Imagine a ‘first experience’ of the internet that doesn’t involve typing

a query into a search engine but instead talking to a machine that talks back.

Imagine skipping the traditional application layer entirely, with an agent-driven interface

managing disparate tech platforms from one place while understanding

users’ local language, context, and intent. An agent-first internet experience could upend

existing tech hierarchies, disintermediating dominant platforms and redistributing value.

In this model, the winners wouldn’t be those who own the app, but those who own the interface.

Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before 309 Global Internet Users =

Epic Growth Over Past Thirty-Three Years, per ITU

Internet Users by World Region (B) – 1990-2022, per ITU

6

Sub-Saharan Africa

South Asia

4

B

North America

Middle East & North Africa

t Latin America & Caribbean Users,

rne

te

In Europe & Central Asia 2

East Asia & Pacific

0

1990 1994 1998 2002 2006 2010 2014 2018 2022

Note: 2021 data interpolated due to data gaps for select nations. Regions are per United Nations definitions. Data is occasionally unavailable for select nations in select years, which

may lead to trendline choppiness or minor discrepancies vs. global user figures. Source: United Nations / International Telecommunications Union (3/25)

Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before 310 Global Internet Penetration =

68% vs. 16% Nineteen Years Ago, per ITU

Global Internet Penetration – 2005-2024, per ITU

100%

% 75% l, 68% a b

Glo

,

n

tio

tra 50%

e

n

e

P

t

rne

te

In 25%

16%

0%

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Source: United Nations / International Telecommunications Union (3/25)

Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before 311 Global Internet Penetration by Region @ +70% =

All Regions Except South Asia + Sub-Saharan Africa, per ITU

Regional Internet Penetration – 2005-2023, per ITU

100%

North America

Europe & Central Asia

% East Asia & Pacific , 75% Latin America & Caribbean ion Middle East & North Africa

Reg

y

b

n

tio 50% South Asia tra e n e P t

rne Sub-Saharan Africa te 25%

In

0%

2005 2007 2009 2011 2013 2015 2017 2019 2021 2023

Note: Data unavailable for South Asia region for 2023. 2021 data interpolated due to data gaps for select nations. Regions are per United Nations definitions. Data is occasionally unavailable for select nations in select years, which may lead to trendline choppiness. Source: United Nations / International Telecommunications Union (3/25)

Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before 312 Global Internet Penetration by Population Density =

83% of Urban Dwellers Online vs. 48% Rural

Internet Penetration By Urban Status – 2019-2024, per ITU

100%

% 83% s,

tu 72% 75%

ta

S

n

a

b

Ur

b 50% 48% y

n

tio

tra

n 31% e

P 25% t e

rne

te

In

0%

2019 2020 2021 2022 2023 2024

Urban Rural

Source: United Nations / International Telecommunications Union (3/25)

Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before 313 Global Internet Users @ 5.5B =

+6% Y/Y & Accelerating, per ITU

Global Internet Users (B) vs. Y/Y Growth – 2005-2024, per ITU

6 24%

rs)

a

B ) e

ine

(Blu 4 16% Ld Be l, (R a b% , Glothw

Users, Gro

t 2 8% /Y

Y

rne

te

In

0 0%

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Source: United Nations / International Telecommunications Union (3/25)

Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before 314 ChatGPT Mobile App @ 530MM MAUs in Twenty-Three Months =

Global Growth We Have Not Seen Likes Of Before

ChatGPT App Monthly Active Users (MAUs) (MM) – 5/23-4/25, per Sensor Tower

600

Sub-Saharan Africa

rs, MM South Asia

Use 400 North America e

ctiv Middle East & North Africa

A

ly

th Latin America & Caribbean n o

M 200 p p

A Europe & Central Asia T P

tG

Cha East Asia & Pacific

0

Note: Regions are per United Nations definitions. ChatGPT app not available in China, Russia and select other countries as of 5/25. Includes only Android, iPhone & iPad users. Figures may understate true ChatGPT user base (e.g., desktop or mobile webpage users). Data for standalone app only. Source: Sensor Tower (5/25)

Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before 315 ChatGPT Mobile App – Top User Countries =

India @ 14%...USA @ 9%...Indonesia @ 6%, per Sensor Tower

ChatGPT Mobile App Monthly Active Users (MM), Top 10 Countries – 5/23-4/25,

per Sensor Tower

% of Global Users

Country (4/25)

300

Vietnam 2.6%

France 2.9%

Germany 3.0%

rs, MM Pakistan 3.0%

Mexico 3.5%

Use 200

e Egypt 3.9% ctiv

A Brazil 5.4%

ly

th Indonesia n 5.7% o

M

p 100

p USA 8.9% A

T

P

tG

India 13.5%

Cha

0

23 23 23 23 24 24 24 24 24 24 24 24 25 25 25 25 /2 3 4 /23 /23 /23 /2 /24 /24 /24

5/ 6 7/ 8/ 9/ 1/ 2/ 3/ 4/ 5/ 6/ 7/ 8 9/ 1/ 2/ 3/ 4/

10 11 12 10 11 12

Note: Regions are per United Nations definitions. ChatGPT app not available in China, Russia and select other countries as of 5/25. Includes only Android, iPhone & iPad users. Figures

may understate true ChatGPT user base (e.g., desktop or mobile webpage users). Data for standalone app only. Source: Sensor Tower (5/6/25)

Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before 316 DeepSeek Mobile App @ 54MM MAUs in Four Months =

Growth Concentrated in China (34% Users) & Russia (9%)

DeepSeek Mobile App Monthly Active Users (MAUs) (MM) – 1/25-4/25,

per Sensor Tower

% of Global Users

Country (4/25)

60

50

rs, MM

Use Others 36.2%

e 40

A Egypt 2.7% Brazil ctiv

ly 3.1%

n USA 4.4% o th 30 Indonesia 3.5%

M India 6.9%

p Russia 9.2% 20 p

A

k

e

e

S

p

10 China 33.9%

Dee

0

1/25 2/25 3/25 4/25

Note: Regions are per United Nations definitions. Includes only Android, iPhone & iPad users. Figures may understate true DeepSeek user base (e.g., desktop or mobile webpage users). Data for standalone app only. Data may be incomplete for China, Russia, and select other countries due to informational restrictions. Source: Sensor Tower (5/6/25)

Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before 317

New Internet User Growth =

Enabled by AI + Satellites

318

Orbital / Satellite Launch Market Share, Global =

SpaceX Rising

Orbital Launches by Year & Country – 1957-2025,

per SpaceX, Space Stats & USA FAA

300

Cold War / Space Race Post-Cold War Commercial + National

(1957-1991) (1992-2007) Renaissance

(2008-2024)

r 200

a

e

Y

r

e

p

s

che

u 100 a n

L

0

SpaceX United States (Excluding SpaceX) China Russia Others

Note: Orbital launches from other celestial bodies than Earth are not included (e.g., Apollo LM ascents from the Moon’s surface). Source: SpaceX public announcements (1/25), Space Stats (3/25), USA Federal Aviation Administration (3/25)

New Internet User Growth = Enabled by AI + Satellites 319 SpaceX Starlink @ 5MM+ Subscribers =

+202% Annual Growth Over 3.2 Years

Starlink Global Number of Subscribers (MM) – 2021-2024,

per SpaceX Announcements

SpaceX: Announced 5MM+

subscribers on X (2/25)

5

4

M

M

rs,

b 3 e

scri

b

Sl u

a

b 2

Glo

ink

rl

ta 1 S

0

2021 2022 2023 2024

Source: SpaceX public announcements

New Internet User Growth = Enabled by AI + Satellites 320 SpaceX Starlink Ecosystem =

Coverage Expanding Globally

Starlink Global Coverage – 5/25, per SpaceX

Source: SpaceX website (5/25)

New Internet User Growth = Enabled by AI + Satellites 321 Starlink =

Unlocking Previously-Inaccessible Internet Access in AI Era

Select Global Starlink Use Cases – 4/25, per SpaceX

Coco, Monterrey, Mexico

Starlink's technology has enabled Chile School District

Coco's operations, delivering high-

speed, reliable internet that bridges [Our] school went from slow,

the digital divide in rural Mexico. ineffective connectivity for even 2-3

Through our streamlined community computer stations, to having high-

WiFi services, we're not just offering speed internet where all 36 of our

connectivity, we're opening a window children can have effective internet

to the world for hundreds in remote connectivity simultaneously...a class-

areas. With Starlink, we've boosted changing event for our teachers and

connection speeds and efficiency, students. transforming disconnected regions

into digitally engaged communities.

Seaspan Corporation,

Brightline Trains, USA

Global

Starlink gave us the new beginning

we were looking for. It gave us Deploying SpaceX Starlink's low Earth

connectivity we can be proud to orbit, low-latency, high bandwidth

share with our guests. It gave us the service across our fleet is a major

knowledge we needed to continue to milestone in addressing connectivity

build better train connectivity beyond challenges in an industry with a global

the satellite [internet] itself…and, and mobile workforce. It allows us to

most of all, it gave us a new treat our vessels no differently than

beginning for train enthusiasts to get remote offices, supporting crew safety

excited about because it is doable, it and wellness – and it enables us to

is maintainable, [and] it is as exciting develop new solutions that were

as it seems. technically and financially unviable

just a few years ago.

Source: SpaceX website (4/25)

New Internet User Growth = Enabled by AI + Satellites 322

Outline

1 • Seem Like Change Happening Faster Than Ever?

Yes, It Is

2 • AI User + Usage + CapEx Growth =

Unprecedented

3 • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling =

Performance Converging + Developer Usage Rising

4 • AI Usage + Cost + Loss Growth =

Unprecedented

5 • AI Monetization Threats =

Rising Competition + Open-Source Momentum + China’s Rise

6 • AI & Physical World Ramps =

Fast + Data-Driven

7 • Global Internet User Ramps Powered by AI from Get-Go =

Growth We Have Not Seen Likes of Before

8 • AI & Work Evolution =

Real + Rapid

323

AI is foundationally changing the way we work. Alongside growth in physical automation (think adoption of robots and drones),

we are now also seeing the rise of cognitive automation, where AI systems can reason, create, and solve problems.

The ramifications are widespread.

The pace of improvement in AI's cognitive ability is astounding.

In the three years since ChatGPT’s 11/22 public launch, we've gone from the reasoning capabilities of a high school student

to those of a PhD candidate. Professions centered on intaking large bodies of structured, historical data and

outputting rules-based decisions and judgement, fall squarely in the core competency of generative AI.

In this emerging landscape, a unit of labor could shift from human hours to computational power.

Data centers and foundation models – in many instances – could dictate the availability and quality of certain types of labor.

As a result, some tout an 'agentic future' where AI agents replace humans in many white-collar jobs.

Although possible, history and pattern recognition suggest the role of humans is enduring and compelling. Technology-forward

leaps have typically driven productivity and efficiency gains and more – but new – jobs. That said, this time it’s happening faster.

In an extreme, entirely agentic future, humans maintain a role in the system, pivoting towards oversight, guidance, and training.

Imagine facilities filled with humans teaching robots intricate movements or offices full of workers providing

reinforcement learning* human feedback (RLHF) to optimize algorithms. This is not conjecture.

Companies like Physical Intelligence and Scale AI, respectively, are

building powerful businesses based on this view of the world.

The idea of the human workforce re-configured to teach and refine machines as a primary function might sound dystopic.

But it’s worth remembering historical parallels. Fifty years ago, this prospect of rows of cubicles and uniformed office workers

sitting quietly in front of LED computers ten hours a day likely sounded equally dystopic. Yet here we are.

Technology has constantly redefined and evolved the nature of work and productivity…AI is no different.

*Reinforcement Learning = An ML approach where agents learn by receiving rewards or penalties for actions.

AI & Work Evolution = Real + Rapid 324 AI Impact on Business =

Diverse & Broad

Industries That Could Be Affected by AI, per NVIDIA

Note: Global data shown. Source: NVIDIA

AI & Work Evolution = Real + Rapid 325 AI In Workforce – Shopify =

Reflexive AI Usage Is Now a Baseline Expectation…

We are entering a time where more merchants and entrepreneurs could be created than any other in history.

We often talk about bringing down the complexity curve to allow more people to choose this as a career. Each step along the entrepreneurial path is rife with decisions requiring skill, judgement and knowledge.

Having AI alongside the journey and increasingly doing not just the consultation,

but also doing the work for our merchants is a mind-blowing step function change here.

Our task here at Shopify is to make our software unquestionably the best canvas on which to develop

the best businesses of the future. We do this by keeping everyone cutting edge and bringing all the best tools

to bear so our merchants can be more successful than they themselves used to imagine.

For that we need to be absolutely ahead.

Reflexive AI usage is now a baseline expectation at Shopify.

Maybe you are already there and find this memo puzzling. In that case you already use AI as a thought partner,

deep researcher, critic, tutor, or pair programmer. I use it all the time, but even I feel I'm only scratching the surface.

It’s the most rapid shift to how work is done that I’ve seen in my career…

…Using AI effectively is now a fundamental expectation of everyone at Shopify.

It's a tool of all trades today, and will only grow in importance. Frankly, I don't think it's feasible to opt out of learning the skill of

applying AI in your craft; you are welcome to try, but I want to be honest I cannot see this working out today,

and definitely not tomorrow.

Stagnation is almost certain, and stagnation is slow-motion failure. If you're not climbing, you're sliding…

Shopify Co-Founder & CEO Tobias Lütke in Internal Memo on AI – 3/25

Source: Tobi Lutke via X (4/25), Shopify

AI & Work Evolution = Real + Rapid 326 …AI In Workforce – Duolingo =

Duolingo Is Going to be AI-First

I’ve said this in Q&As and many meetings, but I want to make it official: Duolingo is going to be AI-first.

AI is already changing how work gets done. It’s not a question of if or when. It’s happening now. When there’s a shift this big, the worst thing you can do is wait. In 2012, we bet on mobile. While others were focused on mobile companion apps

for websites, we decided to build mobile-first because we saw it was the future. That decision helped us win the 2013

iPhone App of the Year and unlocked the organic word-of-mouth growth that followed…

…AI isn’t just a productivity boost. It helps us get closer to our mission. To teach well, we need to create a massive

amount of content, and doing that manually doesn’t scale. One of the best decisions we made recently was replacing a

slow, manual content creation process with one powered by AI. Without AI, it would take us decades to scale our content

to more learners. We owe it to our learners to get them this content ASAP…

…Being AI-first means we will need to rethink much of how we work. Making minor tweaks to systems designed for

humans won’t get us there…We can’t wait until the technology is 100% perfect. We’d rather move with urgency and take

occasional small hits on quality than move slowly and miss the moment.

We’ll be rolling out a few constructive constraints to help guide this shift…:

• …AI use will be part of what we look for in hiring

• AI use will be part of what we evaluate in performance reviews • Headcount will only be given if a team cannot automate more of their work • Most functions will have specific initiatives to fundamentally change how they work…

Duolingo Co-Founder & CEO Luis von Ahn in All-Hands Memo on AI – 4/25

Source: Duolingo via LinkedIn (4/25)

AI & Work Evolution = Real + Rapid 327 AI Adoption @ USA Firms =

Rising…

% of USA Firms Using AI – 3/25, per USA Census Bureau & Goldman Sachs Research

Note: Question asked was ‘In the last six months, did this business use Artificial Intelligence (AI) in producing goods or services?’ BTOS data are representative of all employer businesses in the USA economy, excluding farms. The BTOS sample consists of approximately 1.2MM businesses with biweekly data collection. Source: Census Bureau’s BTOS (Business Trends & Outlook Survey) via Goldman Sachs Global Investment Research, ‘2025 Q1: Adoption Makes Modest Progress, Labor Impacts Still Negligible’ (3/25)

AI & Work Evolution = Real + Rapid 328 …AI Adoption @ USA Firms =

+21% Q/Q @ ~7% of Companies (Q1:25)

Change in % of USA Firms Using AI – Q4:24-Q1:25,

per USA Census Bureau & Goldman Sachs Research

Note: Question asked was ‘In the last six months, did this business use Artificial Intelligence (AI) in producing goods or services?’ BTOS data are representative of all employer businesses in the USA economy, excluding farms. The BTOS sample consists of approximately 1.2MM businesses with biweekly data collection. Source: Census Bureau’s BTOS (Business Trends & Outlook Survey) via Goldman Sachs Global Investment Research, ‘2025Q1: Adoption Makes Modest Progress, Labor Impacts Still Negligible’ (3/25)

AI & Work Evolution = Real + Rapid 329 AI Impact on Workforce =

Employers Adopting AI to Drive Productivity Improvements

Objectives of Corporate AI / LLM Initiatives – Q3:23-Q3:24,

per Morgan Stanley & AlphaWise

Broader Internal Employee Productivity (e.g., CoPilot)

Specialized Worker Labor Savings / Productivity Improvement

(e.g., Contact Center, Financial Processes Simplification)

s

n Customer-Facing Applications to Drive Additional Revenues se

o

Resp Customer-Facing Applications to Drive Better Customer y Satisfaction e

rv

u

S Lower Risk Within the Organization f

% o Faster Product Development (e.g., Drug Discovery, Model

Development, Software Development)

N/A, Not Evaluating Recent Innovations in Artificial Intelligence At

This Time

0% 10% 20% 30%

Q3:23 Q2:24 Q3:24

Source: Morgan Stanley, ‘GenAI: Where are We Seeing Adoption and What Matters for ‘25?’ (11/24)

AI & Work Evolution = Real + Rapid 330 AI Impact on Workforce =

Seeing Productivity Gains, per Stanford HAI

Impacts of AI on Worker Productivity – 4/23, per Stanford HAI

2.97

3

t +14% n 2.6 e g A

rt

o

p

u 2 S p

r

e

m

o

Cust

r

e

p

ts 1

a

Ch

y

rl

Hou

0

Did Not Use AI Used AI

Note: Left chart: N = 5,179 customer support agents. Right chart: N = 1,018 scientists.

Source: Erik Brynjolfsson et al., ‘Generative AI at Work’ (2/25) via Nestor Maslej et al., ‘The AI Index 2025 Annual Report,’ AI Index Steering Committee, Stanford HAI (4/25)

AI & Work Evolution = Real + Rapid 331 Employment Evolution – 1/18-4/25 =

AI Job Postings +448% Over 7 Years While Non-AI IT Jobs -9%

Change in USA AI & Non-AI IT Job Postings – 1/18-4/25,

per University of Maryland & LinkUp

550%

s,

g

stin % o, P8 350% b /1

Jo 1

to

USA exd

in ed e gIn n 150%

Cha

-50%

2018 2019 2020 2021 2022 2023 2024 2025

USA AI Job Postings (All) USA Non-AI IT Job Postings

Note: 'AI Job' refers to a job posting that requires AI skills. AI skills requirement in job postings determined using University of Maryland’s language processing model. USA-based jobs only. Figures are rounded. Source: University of Maryland’s UMD-LinkUp AIMaps (in collaboration with Outrigger Group) (2/25)

AI & Work Evolution = Real + Rapid 332 Employment Evolution – Q2:22-Q2:24 =

AI-Related Job Titles +200% Over Two Years

Cumulative # of New Global Job Titles With AI Terms Newly-Added – Q2:22-Q2:24,

per ZoomInfo

120,000

itles

T

b

Jo 60,000 e iv

lat

u

Cum

0

Q2:22 Q3:22 Q4:22 Q1:23 Q2:23 Q3:23 Q4:23 Q1:24 Q2:24

Note: The data in this report is sourced from ZoomInfo’s proprietary professional contacts database – a leading platform that detects more than 1.5MM personnel changes per day. To compile the trends in job titles, ZoomInfo’s data scientists analyzed announcements from hundreds of companies detailing their AI titles from 1/1/22 through 6/30/24. ZoomInfo’s database includes 100MM companies, 340MM professionals, & 11MM C-Suite leaders. Source: ZoomInfo (8/24)

‘Traditional’ Enterprise AI Adoption = Rising Priority 333 Employment Evolution – Apple =

600+ Openings for Generative AI Jobs

Apple Job Postings Related to ‘Generative AI’ – 5/25

Example job description:

As a member of the team you will be responsible

for bringing innovative ideas and applying

modern machine learning methods to solve

problems that matter. From ideation to

productization, you will participate in the full

development cycle of core technologies,

including handwriting and text recognition,

handwriting synthesis, document understanding,

freeform drawing recognition and generation.

The ideal candidate should have experience in

computer vision, speech recognition, deep

learning, and/or other applications of machine

learning systems.

Source: Apple (4/25)

Tech Incumbent AI Adoption = Top Priority 334 USA Labor Productivity =

Has Happened Alongside Job Growth Over Seventy-Seven Years

Relative Change in USA Non-Farm Employment & Labor Productivity – 1947-2024,

per Federal Reserve Bank of St. Louis

5

&

n % +89% since 2000 e , 7 t

m 4 y9 lo 1 p

rm exed E d m to

-Fa n 2.5 I n ity

No

ctiv

u

USA

in P +31% since 2000 r rod

e

g o

n b

a

L

Cha 0

1947 1951 1955 1959 1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 2023

Technology Minicomputer / PC Desktop Internet Mobile Internet AI

Cycles (1995-2006) Era Era Era Era

(1981-1994) (2007-2021) (2022+)

USA Nonfarm Labor Productivity USA Nonfarm Employment

Note: Here we define the start of the PC Era as 1981 (launch of IBM PC). We define the start of the desktop internet era as 1995 (Netscape’s IPO). We define the start of the mobile internet era as 2007 (the launch of Apple’s iPhone). We define the start of the AI Era as 2022 (the public launch of ChatGPT). Source: Federal Reserve Bank of St. Louis (2024)

AI & Work Evolution = Real + Rapid 335 AI In Workforce – NVIDIA =

You’re Not Going to Lose…Your Job to an AI…[But] to Somebody Who Uses AI

All of you have heard a lot about [AI] job displacement. Every job will be affected. Some jobs will be lost,

some jobs will be created, but every job will be affected. And immediately it is unquestionable,

you're not going to lose a job – your job to an AI, but you're going to lose your job to somebody who uses AI…

…But let me give you the two extremes that you might want to consider as well. Computer technology, computer science has benefited about 30 million people.

There are about 30 million people in the world who know how to program and use this technology to its extreme…

…The other eight, seven and a half billion people don't. I'll put on the table that, in fact,

artificial intelligence is the greatest opportunity for us to close the technology divide.

And let me prove it to you. You know, if we just look in this room, it's very unlikely that more than a handful of people know how to program with C++, and an equal number know how to program in C. And yet, 100 percent of you know

how to program in AI. And the reason for that is because the AI will speak whatever language you wanted to speak…

…The number of people who are using ChatGPT and Gemini Pro and these AIs kind of

demonstrate that, in fact, this is one of the easiest to use technologies in history…

…The other extreme that I will say is that, remember, we’re – we have a shortage of labor.

We have a shortage of workers.

We don't have an abundance of workers. We have a shortage of and for the very first time in history,

we actually have – we can imagine the opportunity to close that gap to put 30-40 million workers back into the workforce

that otherwise the world doesn't have. And so you could argue that artificial intelligence is probably our best

way to increase the GDP, the global GDP, and so those are two other ways to look at it.

In the meantime, I would recommend 100% of everybody you know take advantage of AI and

don't be that person who ignores this technology.

NVIDIA Co-Founder & CEO Jensen Huang @ Milken Institute Global Conference – 5/25

Source: Milken Institute (5/25)

AI & Work Evolution = Real + Rapid 336

Summary…

Imagine, for a moment, how different your next week would look if there were no internet. Every facet of modern life –

how we work, how we communicate, how we govern, and more – would likely be turned on its head. The internet

has been woven into so many facets of life, big and small, that – for many – it is difficult to imagine a world without it.

In the next decade or two, imagining a world without AI will likely feel the same.

Artificial intelligence is reshaping the modern landscape at breakneck speed. What began as research has scaled into emerging core infrastructure across industries – powering everything from customer support to software development, scientific discovery,

education, and manufacturing. This document has aimed to map the pace and breadth of AI’s expansion, with particular focus

on usage trends, cost dynamics, infrastructure buildout, and early monetization models.

The through-line is clear: AI is accelerating, touching more domains, and becoming more embedded in how work gets done.

Catalyzing this growth is the global availability of easy-to-use multimodal AI tools (like ChatGPT) on pervasive mobile devices, augmented by a steep decline in inference costs and an explosion in model availability. Both closed and open-source tools are

now widely accessible and increasingly capable, enabling solo developers, startups, and enterprises alike to experiment and

deploy with minimal friction. Meanwhile, large tech incumbents are weaving AI deeper into their products – rolling out copilots,

assistants, and even agents that reframe how users engage with technology. Whether through embedded intelligence in SaaS

or agentic workflows in consumer apps, the interface layer is being rewritten in real time.

On the compute side, investment continues to scale dramatically. Capital expenditures across major cloud providers,

chipmakers, and hyperscalers have hit new highs, driven by the race to enable real-time, high-volume inference at scale. The investment is not just in chips, but also in new data centers, networking infrastructure, and energy systems to support growing

demand. Whether this level of capital expenditure persists remains to be seen, but as AI moves closer to the edge – in vehicles,

farms, labs, and homes – the distinction between digital and physical infrastructure continues to blur.

The global race to build and deploy frontier AI systems is increasingly defined by the strategic rivalry between the United States and China. While USA companies have led the charge in model innovation, custom silicon, and cloud-scale deployment to-date,

China is advancing quickly in open-source development, national infrastructure, and state-backed coordination.

Both nations view AI not only as an economic tailwind but also as a lever of geopolitical influence.

These competing AI ecosystems are amplifying the urgency for sovereignty, security, and speed…

337

…Summary

…In this environment, innovation is not just a business advantage; it is national posture.

As Microsoft Vice Chair and President Brad Smith recently noted,

Given the nature of technology markets and their potential network effects, this race between the U.S. and China for

international influence likely will be won by the fastest first mover. Hence, the United States needs a smart international

strategy to rapidly support American AI around the world…

…The Chinese wisely recognize that if a country standardizes on China’s AI platform, it likely will continue to rely on that

platform in the future. The best response for the United States is not to complain about the competition but to ensure we win

the race ahead. This will require that we move quickly and effectively to promote American AI as a superior alternative.

And it will need the involvement and support of American allies and friends.

Lastly, AI is changing how we interact with the world around us. With affordable satellite connectivity expanding access to

remote and underserved regions, the next wave of internet users will likely come online through AI-native experiences –

skipping traditional app ecosystems and jumping straight into conversational, multimodal agents.

Similarly, AI uptake is accelerating in the workplace and has the potential to shape how people spend the one third of their lives at work. As usage patterns evolve and unit costs decline, we may be witnessing the

early stages of an internet where intelligence is the default interface – accessible, contextual, and increasingly personal.

This is all amplified by the growing flow and transparency of information and capital –

and the increasing examples of weaponization.

It comes at a time when global powers are more openly asserting autocracy-versus-democracy agendas.

As technology and geopolitics increasingly intertwine, uncertainty is rising.

One thing is certain – it’s gametime for AI, and it’s only getting more intense…

and the genie is not going back in the bottle.

338

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made pursuant to offering documents related to such security, if any.

The document relies on data + insights from a wide range of sources, including public + private companies, market research

firms + government agencies. We cite specific sources where data is public; the document is also informed by non-public

information + insights. We disclaim any + all warranties, express or implied, with respect to the document.

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Data Provided Under License to BOND

339

Table of Contents

1 • Seem Like Change Happening Faster Than Ever? 9-51 Yes, It I

2 • AI User + Usage + CapEx Growth = 52-128 Unprec

3 • AI Model Compute Costs High / Rising + Inference Costs Per Token Falling = 129-152

4 • AI Usage + Cost + Loss Growth = 153-247 Unpr

5 • AI Monetization Threats =

6 • AI & Physical World Ramps = 299-307 Fast

7 • Global Internet User Ramps Powered by AI from Get-Go =

8 • AI & Work Evolution =