Mapping the AI Revolution and State of Adoption in 2023

1. Mapping the AI Revolution and State of Adoption

2. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
We spent Q3 2023 mapping out the AI revolution to target our theses
and investments alongside engaging with a multitude of startups and
companies to inform our strategy and investment decisions.
Our thesis: to invest in startups solving well-defined problems with the
power of data and AI in ways that traditional software has not been
able to accomplish yet.
Defined is led by a proven VC and former operator and engineer with
15+ years of company building and investing in data, automation and AI.
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Overview

3. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
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Humans + Machines Relationship Inversion
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It used to be to that humans had to communicate with machines in programming languages
designed for machines to comprehend – now they take input in our language and meet use
where we are at.
GenAI and NLP represent not just an AI revolution, but a profound inversion in the relationship
between humans and machines
It used to be that humans would create and computers would validate results – now the
machines create and humans QA.
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Specialized and technically advantaged pick-and-shovel building blocks benefiting from
arms race
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With new LLM capabilities, we see applications that go beyond what humans have been
capable of alone – look no further than machine-driven breakthrough in the natural sciences
like AlphaFold and GNoMe – and the emergence of systems of intelligence.
This is setting the stage for a profound shift in how products provide value and AI is adopted
into workflows

4. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
To Achieve Adoption, Need to Embrace “Four A’s” of Human Enablement with
AI to complete “Jobs to be Done”
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I no longer need to do this
Job To be Done (JBTD)
because of AI.
AI helps automate low-
level, repetitive tasks
like debugging, lead
gen and outbound
emailing.
AI can help me do this
JTBD better.
More human-centric,
higher ROI work like
customer service,
strategy and
campaigns.
Automation
(tasks)
Augmentation
(capabilities)
Alignment
AI helps the whole company
do their jobs better
High potential to coordinate
with teams, department,
business units and wide
company towards shared
JBTDs, outcomes and goals
(KPIs, OKRs).
Adoption
Value of AI can only be
unlocked if can overcome
barriers to adoption of
humans and companies.

5. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
Key Criteria to Evaluate Attractiveness of JTBDs for AI
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• A top pain point in many industries is workforce retention, especially among
workers who require significant upfront and ongoing training. These are areas
where hiring “AI staff members” (versus buying software and mandating
employees to change their workflow to use it) has high potential for uptake.
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2
3
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• Areas in which humans are prone to error or are generally slow and inefficient
(even when supported by software products) are most likely to benefit from AI
approaches.
• Areas of higher, more specialized labor spend, or areas in which an AI product
could simply ride existing revenue or transaction rails–either for substitute
human services or software tools.
Area of high
spend on highly
trained labor
Potential 10x
performance
with AI
Areas with low
adoption of
software
Established revenue
rails and financial
incentives
• Enterprises are more likely to adopt AI if its cost benefit is at least an order of
magnitude (and ideally more!) better than the status quo. Therefore, we’re likely to
see a stronger opportunity in areas that have a low penetration of existing
software tools, where AI cost benefit is being compared to human labor, versus
software.

6. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
Adoption Rate of Disruptive New Technologies
After its first decade, the cloud reach 30% of enterprise software spend; the internet 45% penetration;
and mobile nearly 85%, the pace of AI adoption will be dramatically faster.
Source: Menlo Ventures
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
1990 1995 2000 2005 2010 2015 2020 2025
Internet Smartphones Cloud AI
US Technology Adoption %
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7. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL 7
Comparison: Autonomous Vehicles vs. Generative AI
Gen AI advancing much faster than previous technological waves.
Levels of Autonomy Autonomous Vehicles Generative AI
L5
L4
L3
L2
L1
Fully autonomous
Highly autonomous
Self-driving with light
intervention
Tesla autopilot
Cruise control
Superhuman reasoning & perception
AI autopilots for complex tasks
AI co-pilot for skilled labor
Supporting humans with basic tasks
Generating basic content
15
Years
5
Years
Source: Coatue

8. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL 8
Magnitude
Wave 1: AI Natives – Bard, Character, Midjourney, OpenAI
Wave 2.0: Early startup wave - Harvey, Perplexity, Langchain
Wave 2.1: Fast mid-market companies - Notion, Zapier
Wave 3 (Pending): Next startup wave - Showing sustainable value
Wave 4 (Pending): REAL enterprise adoption - BIG WAVE
Time
TODAY
AI Adoption Curves
True enterprise adoption is
still many quarters/years
away.
Given that large enterprise
planning cycles often take
3-6 months, and then
prototyping and building will
take a year for a large
company, we are still very
far away from peak AI usage
or peak AI hype.
Source: Elad Gill

9. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
Supervised Learning
(Labelling Things)
Generative AI
Reinforcement
Learning
Value from AI Technology Today → 3 Years
Supervised learning is massive majority of AI deployment, and Andrew Ng predicts it should double in the next 3
years. Generative AI should more than double, but it won't catch up in terms of scale.
Don't let online hype lead you astray. Learning the fundamentals is as important as it's always been.
Rather than view LLMs, Transformers, and diffusion models as part of a continuum with past "AI", it is worth
thinking of this as an entirely new era and discontinuity from the past
Unsupervised
Learning
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Source: Andrew Ng, Stanford

10. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
AI Spend
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Enterprise investment in GenAI - which is estimated to be $2.5B in 2023 is surprisingly small compared to
the enterprise budgets for traditional AI ($70B) and cloud software ($400B).
$400B
$70B
Cloud software spend Total AI Spend Gen. AI Spend
AI spend has potential to grow by
up to 6x in the next 7 years to
match current Cloud spend
$3B
Source: Menlo Ventures

11. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
Potential Annual Value of AI and Analytics Across Industries
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CLICK HERE FOR INTERACTIVE CHART
$9.5T - $15.4T
Focusing investments where the most significant unlocks in value and market adoption will materialize
Source: MckInsey

12. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
State of AI Adoption – Where are we today?
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• A majority (77.1%) of survey
respondents said that their
companies have made
some sort of effort to adopt
AI.
• But around half (48.9%)
said those efforts were
fledgling—just getting
started or ad-hoc use
cases.
• A non-trivial 15.7% haven’t
really started yet, and might
not anytime soon.
`
15.7% 29.6% 19.3% 14.8% 13.4%
We haven’t
started
adopting yet
We’re getting
the basics in
place
We have
some ad-hoc
use cases in
production
We have
several use
cases in
production
We’re leading
the industry in
AI adoption
It is early days for AI in most organizations with experimentation before production.
Company’s Level of AI Adoption
Source: Retool

13. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
The State of AI Adoption - Where are we today?
Data Is Key, But It’s Not Ready
• CDOs believe data is key to preparing for generative AI, but they haven’t done much with it yet.
• 93% agree that “data strategy is crucial to getting business value from generative AI.”
• However, 57% said that they had made no changes to data yet to prepare for generative AI.
• Only 38% agreed that “My team and I have the right data foundation to pivot to generative AI,” and only
11% agreed strongly with the statement.
• 71% agreed that “generative AI is interesting, but we are more focused on other data initiatives to
achieve more tangible value.” Tangible value is great, but perhaps this low priority is why many CDOs
haven’t been given responsibility for generative AI.
• At least they are planning to spend more on the technology: 62% said that their teams are planning on
investing more in generative AI.
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Source: Menlo Ventures

14. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
Slow adoption at first and widespread adoption in second half of decade.
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0%
10%
20%
30%
40%
50%
60%
70%
80%
F500 CEO Survey: % expectation of AI impact on headcount
Lower Labour Need Unchanged Labour Need
Lower Labour Need. Unchanged Labour Need.
Next Year Next 5 Years
Executive
Leadership
Senior
Management
Engineering
Sales
& Support
Growth
& Marketing
Operations
Finance
& Legal
Leadership
Product & Engineering
Sales, Support
& Marketing
Finance
& Legal
AI as a co-pilot or autopilot could transform how organizations scale for growth
→ Previously meant scaling headcount, with AI means scaling compute
Today Near Future
Product
The State of AI Adoption - Where are we going?
Source: Goldman Sachs, KPMG, Gartner, Coatue

15. Market Assessment and 2024 Predictions

16. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
Market Assessment
In long-term value will accrue in unexpected ways as realized during significant technological shifts historically.
In medium-term, rapid adoption of AI is a baseline, not differentiator for healthy growth, and it’s easy to forget that network effects (human, data,
brand, trust, distribution, etc.) and effective GTM serve as key differentiators between lasting winners and losers.
→ Likely majority of value created in 2-3 years after a platform disruption – Uber, Airbnb, and Instagram all created <3 years of the iPhone launch
AI advances (i.e. OpenAI Dev Day and Github Universe) are causing weekly disruption across the entire knowledge stack, from content creation
and code generation to intelligent decision-making systems, unlocking massive opportunities for growth and innovation on a scale that surpasses
previous AI milestones.
→ The result: reduced barriers to entry across the board for businesses and uncertainty on where value will truly accrue long term
Form factor is evolving. GenAI apps are now going beyond "first draft + human review" to increased autonomy to solve end to end (0 to level 5
autonomy). Midjourney’s introduction of camera panning and infilling is a nice illustration of how the generative AI-first user experience is evolving
with a new set of knobs and switches that are very different from traditional editing workflows – advancing from zero-shot to ask-and-adjust.
→ Form factors are evolving from individual to system-level productivity and from human-in-the-loop to execution-oriented agentic systems.
There is still an expectation vs. reality gap. Generative AI’s biggest problem is not finding use cases or demand or distribution, it is proving value.
User engagement is lackluster. Some of the best consumer companies have 60-65% DAU/MAU; WhatsApp’s is 85%. By contrast, generative AI apps
have a median of 14%. This means that users are not finding enough value in Generative AI products to use them every day yet.
→ To build enduring business, need to fix retention and generate deep enough value for customers that they stick and become daily active users
Despite challenges GenAI has already had a more successful start than SaaS, with >$1 billion in revenue from startups alone (it took the SaaS
market years, not months, to reach the same scale). Hype and flash are giving way to real value and whole product experiences. A shared playbook is
developing as companies figure out the path to enduring value. We now have shared techniques to make models useful, as well as emerging UI
paradigms that will shape generative AI’s second act.
Business models for AI are emerging to sell work, not software. We are seeing startups differentiating their business model compared to incumbents
by instead of selling software on a per seat basis to selling units of work our outcomes based on a user consumption basis. Selling work opens new
vertical opportunities that wouldn’t have otherwise supported a software company.
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17. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
Is the GenAI bubble about to burst?
• In GenAI, we are experiencing Amara’s Law -- the phenomenon that we tend to overestimate the effect of a technology in the
short run and underestimate the effect in the long run or as futurist Paul Saffo says:
• “Never mistake a clear view for a short distance”
• While the technology holds profound promise, we see early signs that GenAI may get a “cold shower” in 2024 as the costs,
risks and complexity associated with the technology reach a tipping point.
• The hype of 2023 has ignored several obstacles that will slow progress in the short term. The cost of deployment is a
prohibitive factor for many organizations and developers. Additionally, future regulation and the social and commercial risks
of deploying generative AI in certain scenarios result in a period of evaluation prior to roll-out.
• We are therefore applying patience and judgment in our investment decisions, with careful attention to how founders are
solving the value problem.
• This prediction hold even more weight in light of several other recent developments:
• AI relies on chips to run, and there are serious concerns about a growing global chip shortage.
• The computing power necessary to keep large language models running is tremendous — not to mention the
environmental impact.
• AI startups seem to be facing increasing pressures too, with AI speech recognition startup Deepgram recently cutting
staff and AI marketing startup Jasper slashing revenue projections.
• Generative AI deals are also down per Pitchbook, with both deals and deal value slowing in the third quarter of 2023.
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18. Defined Capital | LP Update Q3 2023 | CONFIDENTIAL
2024 Predictions
The next big phase of AI will be multi-agent models. Soon, we’ll enter a world where you might just be interacting with one model on the surface, yet that model can
search for and leverage many unique models “under the hood”.
→ Will see emergence of Large Behavior, Action, Vision Models (LBMs, LAMs and LVMs) and potentially other modalities.
Despite GenAI having a cold shower in 2024, almost all enterprise software companies will embed generative AI in at least some of their products in 2024.
Powerful pre-trained open-source models will dominate in the enterprise, with only a few (or maybe one) giant foundation model companies serving consumers
→ Models will go to the data lakes, not the other way around
EU AI Act is delayed and redrawn multiple times owing to the speed of AI advancement which makes the construction of a robust and workable regulatory
framework extremely difficult. There are differences of opinion between the US, EU and market participants, with Europe taking a far more structured and robust
approach to regulation. Legislation is not finalized until late 2024, leaving the industry to take the initial steps at self-regulation.
AI oversight committees become commonplace in large organizations by 2024. Companies establish diverse oversight committees composed of AI ethics experts,
legal advisors, data scientists and representatives to review applications of AI in the business, set guidelines, conduct audits and address ethical and legal concerns.
SLMs (Small Language Models) are likely to become a force to be reckoned with as LLMs keep pushing the scaling laws and become bigger and bigger, whereas the
SLM thesis centers around the viability of smaller, highly specialized, more affordable models for specific use cases (movement has partly been catalyzed by the rise
of open-source GenAI models)
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19. Thank you
Mark Trevitt
Managing Partner
mark@definedvc.com