October 2023

GlobalInsights
B R AVE NE W WOR LD

AI and its Downstream Implications

EXECUTIVE SUMMARY

The advent of Artificial Intelligence may represent a watershed in human history, with thepotential to transform daily lives to an extent that may be difficult to appreciate fully at thismoment in time. But as unprecedented as the technological shock from Generative AI may prove
to be, the capital market response to it already follows familiar patterns.
Rather than simply separate reality from hype, successful investors must be able to map thatreality onto company fundamentals. This rewards second-and-third order thinking, as the mostsalient feature of the technological revolution – escalating revenue growth at companies at theepicenter of the technological quake – may ultimately prove to be a small fraction of the total
economic value it delivers.
As with the advent of electrification – a turning point to which the development of AI systems
has been compared – the main risk for investors today may be viewing the AI revolution too
narrowly. The productivity gains from investment in software development and life sciences,
content generation, and CRM systems already suggest that the assets best positioned to benefit
from AI may have not yet landed on the broader market’s radar.

2

It is difficult to overstate the transformation potential ofan unwillingness to deploy aggressively into the space

Artificial Intelligence (AI). We may soon live in a world where

reveals a lack of technical understanding. Detractors,

computer systems can generate new scientific knowledge

for their part, often dismiss novel valuation methods andand perform virtually any human task. As unprecedented as

optimistic “total addressable market” forecasts (Figure 1)

the technological shock may prove to be, the capital market

as tell-tale signs of a hype campaign designed to separate

response to it already follows familiar patterns.

credulous investors from their capital. Portfolios can bederided as uninformed or naïve, depending on perspective.

When a foundational technology enters the public’s
consciousness, investors naturally focus on the technology

Such discussions elide a crucial point. While dismissing

itself and companies thought to be operating at its frontier.

AI’s transformational potential could prove to be a very

Generative AI has been no exception. Asset prices quickly

expensive mistake, returns ultimately depend on how newreach levels difficult to rationalize using conventional

technology gets adopted and monetized. And this process

financial metrics; “value” comes to be associated withcan occur over long horizons and manifest on income

subjective impressions of the technology’s potential, barriers

statements some distance away from the initial shock. As

to entry, and ultimate scalability.

with the advent of electrification – a turning point to which
the development of AI systems has been compared – theDebates regarding the valuation of nascent technology

main risk for investors today may be viewing the AI revolution

often degrade on two axes. Enthusiasts, typically from the

too narrowly and failing to perceive all of the downstream

tech sector itself, recast investor skepticism as ignorance;

opportunities (and risks) it creates.

Figure 1: AI Market Size Expectations ($Trillions)Figure 1.
AI Market Size Expectations ($Billions)$1,600$1,500$1,400$1,300$1,200$1,100$1,000
$900
$800
$700
$600
$500
$400
$300
$200
$100
$0

2018

2019

2020

Globe Newswire (June 2022)

2021

2022

2023

2024

Grand View Research (June 2020)

2025

2026

2027

IDC (February 2021)

2028

2029

2030

Tractica (March 2020)

Figure 1. Source: IDC, Tractica, Grand View Research, Statista, GlobeNewswire, Jefferies Equity Research.

3

GROUNDBREAKING CAPABILITIES& ADOPTION RATES

software’s capacity to identify patterns in data and anticipate
sequences faster and more precisely than humans. Generative
AI represents the next step in this evolution, with software

Investor interest in “artificial intelligence” has spiked over

now able to synthesize data and curate responses beyond

the past year thanks to the release of Generative AI tools

those directly intended by the programmer (Figure 3, p. 5).

capable of producing content and analyses of unprecedented

And there is still ample opportunity to reinvent the language

sophistication and breadth in response to natural language

tools that help engineers develop new generations ofprompts. Most notable has been OpenAI’s release of ChatGPT,

software even more efficiently.1

which reached 100 million users in just two months, a small
fraction of the time it took Facebook and other social media

One notable subset of Generative AI is large language

platforms to achieve similar scale (Figure 2). These models

models (LLMs). Impressive as this class of deep-learning

can reason probabilistically, have been trained on virtuallyalgorithm is, it represents but one step on a longer roadthe entire internet corpus, and can be directed to process

to “Artificial General Intelligence” – autonomous computerthat information through conventional text that one mightsystems that can learn to perform virtually any task ofotherwise put into an email (not arcane code).

scientific or economic value. While many AI researchers would
argue that we’re on the cusp of this world-historical turning

Generative AI already represents an historic technologicalpoint, others contend that AGI may be decades away if it’sleap, at least as meaningful as internet-based search engines’

ever achieved at all. Much of the disagreement centers on

displacement of reference libraries. But whereas that

arcane Cartesian questions of self-awareness and mysteries

revolution liberated information from the physical constraints

surrounding the biochemistry of human consciousness andof the analog world, AI liberates information flows from humancognition.2 The more practical and economically relevant the

Figure 2: Time to 100 Million definition,Users
the closer to AGI we may be.

intermediation. Machine Learning algorithms demonstrated

Figure 2.
Time to 100 Million Users
THE TIME IT TOOK FOR SELECTED ONLINE SERVICES TO REACH 100 MILLION USERS

11
Years

2008

10
Years

1999

8
Years

2008

5
Years

2006

4.5
Years

2004

4
Years

2008

3.5
Years

2009

2.5
Years

2010

9
Months2016

2
Months2022

Figure 2. Source: Visual Capitalist, February 2023.
1.
"Developer Tools 2.0,” Sequoia Capital, March 2023.
2.
C.f. Landgrebe, J. and B. Smith. Why Machines Will Never Rule the World. Routledge, 2022.

4

Figure 3.
Figure3: Next Step in Evolution of Software
Next Step in Evolution of Software

SOFTWARE 1.0

SOFTWARE 2.0

TRADE SECRET AND STRICTLY CONFIDENTIAL

Programmer-readable code

Interpreter / Compiler

Machine Program

Programmer-readable code

Interpreter / Compiler

Machine Program

Optimizer / Compiler

Statistically-based
Machine Program

Data + NN architecture

Programmer-readable code

Natural-language
like instructions
SOFTWARE 3.0

Machine Program
AI agent

Data
Programmer-readable code

Statistically-based
Machine Program

NN architecture

3

IMMEDIATE APPLICATIONS

precisely tailored, both in terms of the content of advertising
campaigns and the targeting of audiences most likely

All major technology and software vendors are currently

to act on them. AI will revolutionize customer relations

embedding Generative AI into their stack. Desktop

management (CRM) across industries, generating upselling

applications (email, word-processing, etc.), e-commerce,

proposals in real time based on text from the conversationinternet search, social media, and content consumption

cross-referenced with internal customer data, external

will all integrate AI functionality. Such efforts remain in amarket trends, and other relevant information. Chatbots may

beta stage with limited visibility into monetization. But thesoon account for the bulk of consumer-facing interactions

user experience is likely to improve immeasurably acrossin travel, finance, and e-commerce and eventually guideeach of these dimensions, with significant scope for labor

customers’ entire shopping experience.

productivity gains from accelerated information gatheringand idea and text generation (Figure 4, p. 6).

The applications for media and education are obvious.
Generative AI applications can produce new music, fictional

More consequential may be the evolution of businessnarratives, poetry, visual artwork, and digital imagery. The

models and corporate strategy. Management teams could

recent Screen Actors Guild (SAG) and Writers Guild of America

increasingly rely on AI to formulate marketing strategies(WGA) strikes have been fomented, in part, by concerns about

and pricing decisions and diligence potential acquisition

AI’s displacement potential. AI-generated content raises novel

targets. Digital marketing is likely to become even more

copyright issues since existing works are accessed to produce

Figure 3. Source: Itamar Friedman, Software 3.0 — the era of intelligent software development, May 2022.

5

TRADE SECRET AND STRICTLY CONFIDENTIAL

Figure 4: AI Use Cases

Figure 4.
AI Use Cases
CONTENTGENERATIONSOFTWARE
DEVELOPMENT

IMAGE
GENERATIONNEW PRODUCTDEVELOPMENT• Content• SEO• Primary research• Synthesis• Alert generation• Support ticketingsystems• Language translation• Create website
drafts• Automatic code
generation• CoPilots• Regenerative code
• Test script
generation• Bug fixes• Custom generated
photos• Image touch up• Banner creation• Medical imaging• Product detail page
image generation• “Try it on” AR• Interactive data
products• Conversational
interface & querying• Whitelabeled 1st
party trained models• UX design• Translation fromdesign to code

SALES &
MARKETING

Q&A
INTERFACES• Content creation• Lead generation• Sales forecasting• Personalized ads• R&D idea generation• Identity verification• Order taking• Advanced Chatbots• Org specific sales
collateral• Customer support• Conversion rate
optimization• A/B testing• Disaster planning &recovery• Strategy
development• Competitor research“substantially similar” outputs.3 Technologically, the horse is

While guidance from experienced engineers is fundamental

out of the barn; the question is whether owners of existing

to enable LLMs to write code, LLMs create significant

copyrights will be the only ones legally sanctioned to employ

efficiencies by filling in coding gaps in simplified prompts.

AI to assist in the formulation, production, and marketing of

Eventual gains from such automation may be especiallycinematic, televisual, and audio works.

pronounced among video game makers operating at the4intersection of AI-generated content and software.
ChatGPT easily passed the Uniform Bar Examination taken
by U.S. law school graduates and would earn a respectable

Companies will increasingly rely on Generative AI to clean existing

3.4 grade point average (on a 4-point scale) if enrolled asdata and produce prototype designs and accelerate producta freshman at Harvard College. Generative AI’s prowess

development. Life sciences companies, for instance, already use

writing essays and taking tests raise thorny issues about the

AI to generate sequences of amino acids and DNA nucleotides

future of educational integrity, but also open the door toto shorten the drug design phase from months to weeks. Existinga new generation of digital tutors, autodidacts, and moredevelopment programs require researchers to sort throughflexible educational arrangements.

millions of potential chemical reactions to synthesize a target

4

molecule. AI models trained on existing chemical reactions
Huge productivity gains are already evident in software

data have already yielded a 15% reduction in development

development, where Generative AI has halved the time

costs.6 We should expect to see comparable productivity gains

necessary to write and test new code (Figure 5, p. 7). LLMs

wherever R&D depends on time-consuming, iterative processes

can predict the next lines of code based on the code

based on complex interactions between variables or inputs.

already written and generate new code in response to
tailored prompts from software engineers who are skilled in

Manufacturers can not only use Generative AI to design new

natural language describing software structures. As LLMs

products, but also optimize supply chains and automate

become familiar with the functionality and structure ofshipping and production processes. The automotive industry

programming languages, prompts can become less precise,has been especially aggressive in its adoption of AI andallowing neophytes to code like seasoned professionals.5

antecedent algorithmic technologies to these ends.

Figure 4. Source: Carlyle Analysis, 2023.
3.
ABA Journal, March 2023. “ChatGPT goes to Harvard,” Substack, July 2023.
4.
“Beyond The Hype: How Generative AI Is Transforming Software Development,” Towards Data Science, May 2023.
5.
G2Retro as a two-step graph generative models for retrosynthesis prediction, Communications Chemistry, May 2023.
6.
G2Retro as a two-step graph generative models for retrosynthesis prediction, Communications Chemistry, May 2023.

6

Figure 5: Accelerated Software Development

Figure 5.
Accelerated Software Development

TASK COMPLETION TIME USING GENERATIVE AI, %

100
45–50

80

20–30

35–45

<10

60
40
20
0

Code documentation

Code generation

Code refactoring

Without generative AI

RISKS & JOB LOSS

High-complexity tasks

With generative AI

on the speed with which companies adopt AI capabilities to
cut costs and increase scalability. Competitive pressure this

The Janus face of new technology is obsolescence. It is estimatedgreat naturally opens the door to charlatanism. Companies

that Generative AI applications could eventually automate

will market themselves opportunistically and, occasionally,

60% to 70% of employee workloads,7 and this naturally arouses

deceptively. Mentions of “AI” on corporate earnings calls

fear of job loss. It is important to note that this estimate refers

has risen exponentially (Figure 8, p. 9), and the more “AI”

to employee tasks not the employees themselves. For mostis invoked by competitors, the more susceptible laggard

occupations, we subscribe to the view that AI won’t take yourmanagement teams become to imprudent budgeting andjob; someone using AI will. This will result in dynamic adjustments

fairy-tale solutions.

in labor demand across occupations and activities rather thanjob loss (Figure 6, p. 8). Workload automation should increase

We must also be mindful of the “hallucination problem” withthroughput volumes, naturally increasing productivity levels

LLMs, or their tendency to generate factually incorrect text

(output per hour of work); and by freeing managers’ finite timethat may seem semantically or syntactically plausible based

and attention and speeding more junior employees’ progression

on the corpus of data on which it has been trained. These

up the learning curve, AI also could facilitate a sustained increase

statistical models predict the next word based on massive

in productivity growth rates as human capital gets deployed

volumes of data and past context. They are built for fluency

more creatively (Figure 7, p. 9).

rather than reason, which means human verification of their
outputs will still be required in many cases, and their use

Obsolescence may be of greater concern for businesses

in mission critical applications like aeronautics or defense

and business models, as competition increasingly depends

could lay very far in the future.

Figure 5. Source: McKinsey, 2023.
7.
“Economic Potential of Generative AI,” McKinsey & Co. June 2023.

7

Figure 6: Dynamic Adjustment in Labor Demand
Figure 6.
Dynamic Adjustment in Labor Demand

Midpoint automation
adoption by 2030, %Estimated labor demand change and generative AI
automation acceleration by occupation, US, 2022–30

15–
25

35

25–
35

Employment,
absolute
35–
40

5M

10M

Healthprofessionals
30
Health aides,
technicians,
and wellness

25

STEM
professionals
20

Increasing labor demand
and modest change ofwork activities

15
Changein labor
demand, %10

Builders
Transportation
services

Increasing labor demandand high change ofwork activities
Managers

Creatives andarts management

Property
maintenance5

Mechanicalinstallation
and repair

Agriculture

Business andlegal professionals

Community
services

Education andworkforcetraining

05

Production
work
–5

–10

–15

–20

10

15

20

Food
services

Decreasing labor demand
and modest change ofwork activities
Customer
serviceand sales
OfficesupportIncrease in automation adoption driven by generative AI acceleration, percentage points

Figure 6. Source: https://www.mckinsey.com/mgi/our-research/generative-ai-and-the-future-of-work-in-america.

8

TRADE SECRET AND STRICTLY CONFIDENTIAL

Figure7: Economy-Wide Positive Productivity Shock
Figure 7.
215%
195%
175%
155%
135%
115%

Baseline

One-Time Generative AI Shock

2042

2041

2040

2039

2038

2037

2036

2035

2034

2033

2032

2031

2030

2029

2028

2027

2026

2025

75%

2024

95%
2023

Productivity Relative to 2022 Baseline

Economy-Wide Positive Productivity Shock

Persistent Acceleration
TRADE SECRET AND STRICTLY CONFIDENTIAL

Figure8: Mentions of AI on Company Earnings Calls
Figure 8.

7

Mentions of AI on Company Earnings Calls
90

83

80
70

65
56

60

53

52

50
40

42

38

30

21

20

0

12

8

7

10

0
NvidiaAlphabet

Meta

Microsoft
Q1 2022

Salesforce

12
0

AMD

AmazonQ1 20238

Figure 7. Source: Carlyle Analysis, Brookings Institution, 2023.
Figure 8. Note: Includes mentions of “AI” in analyst/journalist questions. Source: Company data, Statista, Goldman Sachs Global Investment Research.

9

BARRIERS TO ENTRY

Nvidia’s market capitalization since ChatGPT’s release – andcreates significant headwinds for new entrants and small

At this stage, most of the market discourse has focused oncompanies across much of the value chain.

those companies directly responsible for the development of
LLMs. And, given the enormous costs involved, this has beenThis has not stopped capital from flowing to newer andand is likely to continue to be dominated by massive, cash-

younger companies, however. Over the past year, virtuallyrich incumbents. Developing a state-of-the-art Generative

any asset with known “AI upside” has become very richly

AI model requires massive computational resources,

valued, especially on a relative basis (Figure 10, p. 11). Whilespecialized hardware like Graphics Processing Units (GPUs)

all industries have been affected by the decline in venture

and Tensor Processing Units (TPUs), and vast datasets thatand growth capital over the past year, AI companies havemust be collected, stored, and curated. A single training

captured a larger share of that funding, especially those

run for a model comparable to ChatGPT requires millions offocused on novel approaches to AGI. In the U.S., AI’s share ofdollars.8 Rather than compete with better funded and more

funding rounds reached 23% in Q2-2023, more than tripling

sophisticated incumbents, enterprises seeking to integrate

over the past 10 years and now the highest among all

AI into their products and services are more likely to partner

industry verticals (Figure 11, p 11). In terms of invested capital,

with them. This has led to a boom in the market values ofAI’s share has increased even more over the past year

industry-leading hardware, software, and data cloudthanks, in large part, to Microsoft’s $10 billion investment in

platforms (Figure 9) – including a $700 billion increase in

OpenAI and Stripe’s $6.3 billion Series I round.9

TRADE SECRET AND STRICTLY CONFIDENTIAL

Figure 9.
Figure9: MegaCap AI Companies’ Share of Total Returns
MegaCap AI Companies’ Share of Total Returns
SHARE OF RETURN BY COMPANY

BREAKDOWN OF RETURN BY COMPANY

100%

12.7%

30%
20%

6.3%

Top 7 Stocks’
Total Share

69.2%

15.7%

S&P 500

NVIDIA

Meta

Tesla

AmazonAlphabet

Apple

0%

Microsoft

10%

1.3%

10%

1.4%

8%
2.3%

6%
4%

1.3%

1.1%

Top 7 Stocks’
PP Contribution

12.6%

S&P 500

40%

7.2%

NVIDIA

7.9%

2.9%

2%
0%

Meta

50%

2.2%

12%

Tesla

60%

14%

Amazon7.2%

5.6%

Alphabet

12.1%

70%

18%
16%

Microsoft

30.8%

80%

Apple

Share of S&P 500 Return90%

Contribution to S&P 500 Return in
Percentage Points

20%

Source: Carlyle Analysis of Bloomberg Data, July 21, 2023.

9

Figure 9. Source: Carlyle Analysis of Bloomberg Data, July 21, 2023.
8.
“Can You Build Large Language Models Like ChatGPT At Half Cost?” UniteAI, May 2023.
9.
Global Private Markets Quarterly Q2-2023, Carlyle Global Investment Solutions, July 2023.

10Figure 10: Rise in AI Attention & Valuations

Figure 10.
Rise in AI Attention & Valuations

AI-RELATED STOCKS DROVE VIRTUALLY ALL THE
RETURNS OF THE S&P 500 THIS YEAR

4000

3500

3500

3000

3000

4100

4000

4000

3900

3900

3800

3800

3700

3700

3600

3600
Jan/234100

Mar/23

Aug/22

Jun/21

Jan/22

Apr/20Nov/200Sept/190
Jul/18500
Feb/191000

500
Dec/171000

Oct/16

1500

May/171500

Mar/162000

Jan/152000

Aug/15

2500

2500

4200

May/234000

4200

Apr/234500

Mar/234500

Feb/23

SG AI NEWSFLOW INDICATOR CONTINUE TO SURGE

S&P ex-Al Boom stocks

S&P 500

Figure 11: AI’s Increasing Share of VC Funding

Non-AI

AI

2013 H1

2022 H2

2022 H1

2021 H2

2021 H1

AI Funding Rounds in % of Total Rounds

0%2020 H2

02020 H1

5%

2019 H2

1,000

2019 H1

10%

2018 H2

2,000

2018 H1

15%

2017 H2

3,000

2017 H1

20%

2016 H2

4,000

2016 H1

25%

2015 H2

5,000

2015 HI30%

2014 H2

6,000

2014 H1

Number of VC & Growth Capital
Funding Rounds

Figure 11.
AI’s Increasing Share of VC Funding

AI ShareFigure 10. "SG AI Newsflow Indicator Continue to Surge" Source: Factiva, SG Cross Asset Research/Equity Strategy. Data as of 08/05/2023.
"AI-Related Stocks Drove Virtually All the Returns of the S&P 500 This Year" Source: Datastream, SG Cross Asset/Research/Equity Strategy. Data as of 11/05/2023.
Figure 11. Source: Carlyle Global Investment Solutions, Global Private Markets Quarterly, Q3-2023.

11

LESSONS FROM ELECTRIFICATION

downstream users of the new technology rather than thecompanies responsible for its introduction.

One wonders if by focusing narrowly on the assets closest
to the epicenter of this technological quake, investors

The same dynamics are likely at play today withmay be repeating the mistakes of the past. Generative

Generative AI. Specialized semiconductor sales may

AI has been analogized to the advent of electricity, andindeed go through the roof, just as demand for the mostthis comparison may be apt for reasons that extend

advanced boilers rose exponentially during the period

well beyond its technological significance. Thoughof mass electrification. A step-function increase in the

discovered in the 1880s, electric current only began tovolume of data generated, stored, and analyzed bytransform society in the 1920s when mass electrificationcompanies will almost surely benefit cloud platforms just

was made possible by high-pressure steam power plants

as a comparable jump in the regional transmission ofand centralized generation, distribution, and systemelectric current benefited electric utilities. Future growth

management. In just a few years, electric companies’

in the utility sector will require significant investment inrevenues grew by more than 3.4x (~35% CAGR) during

Generative AI to support power grid development. And

a period of consumer price deflation. The valuations

companies at the forefront of the design of advanced

assigned to those fundamentals doubled during this time

AI systems today will likely be as influential to economic(Figure 12, p. 13), as investors aggressively bid up the market

development as those responsible for developing thevalues of companies operating at the frontier of thislatest iteration of high-pressure steam turbines then.

technological revolution.

But the bulk of the economic value may, once again, becreated by the companies most adept at capitalizing on

As it turned out, far more economic value was being createdthese trends by slashing production costs and developing

by the companies buying that power. Electrification allowed

the new products and services made possible by thesemanufacturers to use a large number of complex machines

new technologies. This is likely to be especially true insimultaneously, which made mass production processes

software, pharmaceuticals, and other sectors where

possible and sharply reduced the cost of producing

Generative AI can reduce the enormous sums spentconsumer durables like refrigerators, washing machines,

developing intangible assets that can be infinitely

and radios (Figure 13, p. 13). And since these products had

reproduced at nearly zero marginal cost.

to be plugged in to operate, mass electrification not only
drove down manufacturers’ production costs, but also
stimulated demand for their products.
In the ten years from the start of the sustained boom inelectricity generation, durable goods manufacturers
generated a 200% total return, on average, in the depthsof the Great Depression (!), which was more than 2x theaverage total return to electric companies over the same
period (Figure 14, p. 14). No sane person could contend
that mass electrification was mere “hype,” as eventual
market demand for electricity met or exceeded the most
optimistic forecasts. But the displacement of kerosenefired illumination was but the tip of the iceberg, as thevast majority of the economic value accrued to the"But the bulk of the economic
value may, once again, be createdby the companies most adept at
capitalizing on these trends byslashing production costs anddeveloping the new products andservices made possible by these
new technologies."
12Figure 12: Rise in Valuation Ratios, 1925-29
Figure 12.
Rise in Valuation Ratios, 1925-29
150.0%

95%

100.0%

50.0%

30%

Utilities

Health Care

Tech

Industrials

Telecom

Consumer Prod

Railroad/Other

Durable Goods

Retail-50.0%

Oil & Gas

0.0%

Figure 13: Two-Year Decline in Production Costs by Item

Figure 13.
Two-Year Decline in Production Costs by Item
CoffeeMaker

Electric
BlanketPeak Two Year Price Decline, 1926-1936

0%

Radio

Fan

CookerWasherToaster

Refrigerator

Flatiron

Range

-10%
-20%

-17%

-30%

-28%
-34%

-40%
-44%

-50%
-60%
-70%

-58%

-54%

-52%

-41%

-50%

-69%

-80%

Figure 12. Source: Carlyle Analysis; CRSP Database, December 2021.
Figure 13. Source: Ronald C. Tobey, 1997, “Technology as Freedom: The New Deal and the Electrical Modernization of the American Home.”

13

Figure 14.
Total Stock Market Returns by Sector

Figure 14: Total Stock Market Returns by Sector
4.5x
4.0x

Cumulative MOIC

3.5x
3.02x

3.0x
2.5x
2.0x

1.96x

1.5x

1.37x

1.0x
0.5x

Jul-26Oct-26
Jan-27Apr-27
Jul-27Oct-27Jan-28Apr-28
Jul-28Oct-28Jan-29Apr-29Jul-29Oct-29Jan-30Apr-30Jul-30Oct-30Jan-31
Apr-31
Jul-31
Oct-31
Jan-32
Apr-32
Jul-32Oct-32Jan-33Apr-33
Jul-33Oct-33Jan-34Apr-34Jul-34Oct-34Jan-35
Apr-35Jul-35Oct-35Jan-36
Apr-360.0x

Market Average

PLAYING THE LONG(ER) GAME

Utilities

Durable Goods

the technological quake – may ultimately prove to be a small
fraction of the total economic value it delivers. One company’s

Rather than simply separating reality from hype, successful

revenue is another’s investment. And the productivity gains

investors must be able to map that reality onto company

from investment in software development and life sciences,

fundamentals. This rewards second-and-third order thinking,content generation, and CRM systems already suggest thatas the most salient feature of the technological revolution –the assets best positioned to benefit from AI may have not yet

escalating revenue growth at companies at the epicenter oflanded on the broader market’s radar.

Figure 14. Source: Carlyle Analysis; CRSP Database, December 2021.

14

Jason Thomas
HEAD OF GLOBAL RE SEARCH & INVE STME NT STRATEGY

jason.thomas@carlyle.com / (202) 729-5420

Jason Thomas is the Head of Global Research & Investment

Mr. Thomas received a BA from Claremont McKenna College

Strategy at Carlyle, focusing on economic and statisticaland an MS and PhD in finance from George Washington

analysis of Carlyle portfolio data, asset prices, and broader

University, where he studied as a Bank of America

trends in the global economy.

Foundation, Leo and Lillian Goodwin Foundation, andSchool of Business Fellow.

Prior to joining Carlyle, Mr. Thomas served on the WhiteHouse staff as Special Assistant to the President and Director

Mr. Thomas has earned the chartered financial analyst

for Policy Development at the National Economic Council.

designation and is a Financial Risk Manager certified by

In this capacity, Mr. Thomas acted as the primary adviser tothe Global Association of Risk Professionals.

the President for public finance.

Economic and market views and forecasts reflect our judgment as of the date of this presentation and are subject to change without notice. In particular, forecasts are estimated, based onassumptions, and may change materially as economic and market conditions change. The Carlyle Group has no obligation to provide updates or changes to these forecasts. Certain information
contained herein has been obtained from sources prepared by other parties, which in certain cases have not been updated through the date hereof. While such information is believed to be reliable
for the purpose used herein, The Carlyle Group and its affiliates assume no responsibility for the accuracy, completeness or fairness of such information. References to particular portfolio companies
are not intended as, and should not be construed as, recommendations for any particular company, investment, or security. The investments described herein were not made by a single investment
fund or other product and do not represent all of the investments purchased or sold by any fund or product. This material should not be construed as an offer to sell or the solicitation of an offer tobuy any security in any jurisdiction where such an offer or solicitation would be illegal. We are not soliciting any action based on this material. It is for the general information of clients of The Carlyle
Group. It does not constitute a personal recommendation or take into account the particular investment objectives, financial situations, or needs of individual investors.

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Michael Wand
MANAG ING DIRECTOR AND CO - HEAD OF CARLYLE EU ROPETECHNOLOGY PARTNE RS INVE STME NT ADVISORY TEAM
Michael Wand is a Managing Director and Co-Head of theCommittee which was formed in 2022 to drive deeper

Carlyle Europe Technology Partners investment advisory

collaboration across all the firm’s Corporate Private Equity

team. His main investment focus lies in enterprise andactivities in the region.

infrastructure software, media technology, IT security, data
analytics and digital services. He is based in London.

Prior to joining Carlyle in 2001, Mr. Wand worked forapproximately 10 years in investment banking, the last seven

Mr. Wand has led numerous technology investments, notably

years of which were in the technology sector. As a Managing

P&I AG, FRS Global, UC4/Automic, Foundry, eggplant and Dept,

Director, he was responsible for the European software andand advised on a dozen completed realizations. Mr. Wand isInternet research team at Deutsche Bank in London. Prior tocurrently a member of the Boards of Directors of Shopware,

that, he worked as a European Software Analyst for Paribas

SER Group, HSO, Dept, Jagex, Disguise, LiveU and Incubeta.

in London and BHF-Bank in Frankfurt, Germany. During hisbanking career, Mr. Wand was the underwriting analyst forIn addition, Mr. Wand is Co-Head of Europe Private Equity,

European technology innovators such as SAP, Autonomy,

sitting on Carlyle’s European Private Equity Executive

SurfControl, Utimaco and nCipher.

Economic and market views and forecasts reflect our judgment as of the date of this presentation and are subject to change without notice. In particular, forecasts are estimated, based onassumptions, and may change materially as economic and market conditions change. The Carlyle Group has no obligation to provide updates or changes to these forecasts. Certain information
contained herein has been obtained from sources prepared by other parties, which in certain cases have not been updated through the date hereof. While such information is believed to be reliable
for the purpose used herein, The Carlyle Group and its affiliates assume no responsibility for the accuracy, completeness or fairness of such information. References to particular portfolio companies
are not intended as, and should not be construed as, recommendations for any particular company, investment, or security. The investments described herein were not made by a single investment
fund or other product and do not represent all of the investments purchased or sold by any fund or product. This material should not be construed as an offer to sell or the solicitation of an offer tobuy any security in any jurisdiction where such an offer or solicitation would be illegal. We are not soliciting any action based on this material. It is for the general information of clients of The Carlyle
Group. It does not constitute a personal recommendation or take into account the particular investment objectives, financial situations, or needs of individual investors.

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