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Good morning 👋
Welcome back to part three of a four-part series on how I think about designing a thesis-driven investment firm.
Philosophies → Themes → Theses → Investments
We have broken down guiding philosophies and broader investment themes.
To recap:
Guiding philosophies are overarching frameworks that are fundamental for asset class selection, investment theme selection portfolio construction, positioning, and how a person conducts business.
Investment themes are based on macro patterns on where the world is heading.
Investment theses are more granular and are investable sleeves of the broader investment themes considered within scope.
Outlaw maintains active theses in four areas where we believe the next legacy-defining businesses will be built.
I have broken down a deeper look at my thinking on each of those below.
1. Artificial labor markets
2. The token revolution
3. Software 3.0
4. Modernization of the private investment firm
Investment theses
Artificial labor markets
"When a $500/month agent outperforms a $100k/year employee, hiring humans stops being an operational necessity and becomes a margin decision.”
For public companies, the trend of growing revenue-per-employee has been clear for several decades. At the private level, the trend is even more extreme.
Artificial labor markets are here
The TAM is labor markets.
Dropping inference costs make it economically irrational to hire a $100K/year employee when a $500/month agent can handle the same workload.
The job posting of 2026: "Seeking SDR. Requirements: 99.9% uptime, <200ms response time, fluent in 47 languages, costs $0.003 per conversation."
Human labor markets took decades to build trust infrastructure. We believe artificial labor markets will build better trust infrastructure far faster. Every function that used to require a person now has an agent alternative, and businesses need systems to find, vet, and deploy them.
The pursuit of the 10-person billion-dollar company
"There'll soon be a 1-person billion-dollar company."
Whether or not you agree with that statement is up for debate, but the trend is clear of companies needing less human labor to create more revenue and equity value. Maybe a 1-person-billion-dollar company is not realistic, but let’s play out a scenario for a 10-person target for the same goal.
In our view, the only way to reach the level of scale required for this mark (~$100m ARR) without a proportional amount of FTEs is by consolidating everything that doesn't differentiate. Finance, accounting, support, HR, legal - these become AI + workflow + 1 person overseeing the output.
Massive distribution (1m customers at $100 ARR or 10k customers at $10K ARR) is required, exquisite taste (to build what people want before competitors) is required, and the ability to outsource everything else to AI is required. Every dollar and hour not spent on revenue generation is waste.
ACV x customer count numbers in order to create a $1b business assuming a 10x revenue multiple.
Revenue-per-employee arms race
As the North star for all companies shifts to revenue-per-employee, a new quality benchmark can become established.
This topic has been covered in length my people like Tomasz Tunguz in 2013 (linked), the SaaS Capital guys (linked), the Meritech guys (linked), and the latest lean AI-native company leaderboard (linked).
We think as more companies develop the playbook of scale as an AI-native company, there will become an explosion of other companies who follow the same path.
Companies to study
Company | Estimated revenue | FTEs | Revenue-per-employee |
|---|---|---|---|
$500m | 40 | $12.5m | |
$1b | 110 | $9.09m | |
$3.5m | 1 | $3.5m | |
$75m | 30 | $2.5m | |
$100m | 50 | $2m | |
$100m | 52 | $1.92m | |
$75m | 40 | $1.86m | |
$50m | 30 | $1.67m | |
$50m | 40 | $1.25m | |
$220m | 220 | $1m | |
$15m | 35 | $428.57k | |
$10m | 25 | $400k |
The token revolution
"Over the next decade, how you create, transform, source, store, and distribute tokens will define nearly all companies on the planet."
As the “Everything is computer” theme plays out and more of the world economy transforms around intra-computer coordination, every company will need to answer the question of which part of the token factory they are servicing.
We owe a lot of our thinking here back to the guys at Ribbit. They coined the phrase, pioneered the thinking, and we’re just investing within the category they defined with language.
Here are the high-level thoughts on the growing need to create, orchestrate, organize, and invest in the token revolution taking place:
Every business is becoming a token factory - supplying, building, or orchestrating tokens (data, identity, money, expertise) that feed AI agents
Tokens are making the world accessible and legible to machines
They are both the DNA and feedstock for AI - encoding who we are, what we own, what we do, and what we want
"Once you see tokens as DNA and fuel for AI, you stop thinking 'what features does this product have?' and start thinking 'what streams of tokens does this product control?'”
Understanding tokenization
"Tokenization is the process of rendering the world for computers - making what people know and do across human operating systems safely accessible to software, including autonomous software.”
Three categories of tokens:
Tokens for value: How machines store/transfer financial resources
Asset tokens: Bitcoin, stablecoins, tokenized real estate
Tokens for expertise: How machines learn to do what humans do
Expert tokens: Specialized domain knowledge
Knowledge tokens: Company data, research
Web corpus tokens: General internet knowledge
Tokens for personalization: How machines learn about individuals
Identity tokens: Who you are (government IDs, biometrics)
Context tokens: What you do (shopping history, browsing patterns)
Memory tokens: What AI learns about you directly
Access tokens: Keys to your confidential information
The token factory framework
“Every software business can be modeled as supplying, building, or managing token factories.”
Input tokens | Token factories | Output tokens |
|---|---|---|
Identity (credentials, KYC/KYB) | Savings/Investment | WIP (Work in Progress) |
Asset (accounts, holdings) | Debt/Payments | Expert tokens with specialized knowledge |
Access (permissions, API keys) | Healthcare | Finished expert tokens for different use cases |
Memory (preferences, history) | Risk/Compliance | |
W2s/1099s |
Software 3.0 + the dev tool Golden Era
Software is eating the software that eats software.
Software 1.0 | Handcrafted logic (explicit human-written code, predictable but limited by human effort) |
|---|---|
Software 2.0 | Earned systems (neural networks trained on data, emergent logic, scales with compute not humans. This is software eating software). |
Software 3.0 | Generative and self-improving (LLMs produce code, design models, orchestrate systems with minimal human intervention). |
A recent market map from Bessemer shows the different application layers and opportunity zones within dev tools today.
The entire dev tools stack is being re-platformed, and new categories are being created.
Complete AI-native dev opportunity: Like Auth0 eliminated months of authentication work, Stripe abstracted payment complexity, Twilio turned SMS into a few lines of code, Software 3.0 needs equivalent foundational layers for AI-native dev.
Feedback loops accelerate innovation: AI tools create better AI tools - compounds at unprecedented speed.
Billions in VC flowing into AI dev tools: Already seeing blockbuster acquisitions (Cognition/Devin acquired by Windsurf).
Definition of "developer" expanding dramatically: 17M JavaScript developers today → 100M in next 10 years (Netlify CEO). Vibe coders creating custom software without writing code. Domain expertise and customer communication now matter more than coding ability.
New network effects emerging: Agent-to-agent network effects (AI agents communicate/compose with other agents via MCP). Data network effects amplified (more context = more useful agents, Linear's Product Intelligence example). Integration lock-in weakening (AI makes switching APIs easier). First-mover advantages compound (speed matters more than ever, quality products benefit regardless).
Budget sources shifting: AI dev tooling breaking out of traditional budgets, dedicated AI budgets emerging, trade-offs between AI tools vs. hiring engineers.
Platform control creates defensibility: Entry point control (GitHub owning repos, VS Code dominating editors), proprietary data advantages, continuous evolution paramount.
The modernization of the private investment firm
"Private markets are having their Bloomberg Terminal moment. What automated bond trading in the 1980s is to what AI will do to venture capital, private equity, and all private investing in the 2020s.”
Evolution of the asset class
VC 1.0 (1960s-1994) | Cottage industry, <150 GPs, low profile, founders bootstrapped to millions before raising |
|---|---|
VC 2.0 (1994-2022) | Software era, Internet browser unlocked massive value creation, knowledge diffusion (blogs 2004, accelerators 2004, TechCrunch 2005, AngelList 2010, Signal.nfx 2018), VCs exploded in number but model stayed same |
VC 3.0 (2022+) | AI transforms operations, VC expands beyond software to defense/biotech/energy/climate/space, mega funds emerge, PE/VC models blur, declining late-stage returns as entry valuations compress |
The Bloomberg Terminal moment
The finance industry has a history of participating late in the adoption curve.
Prior to 1984, the work done within traditional finance was mostly analogue. Pen and paper were the standard, workflows were slow, and most of the work was a snooze fest.
Then Bloomberg entered the picture.
1984: Bloomberg arrives to provide real-time data, instant calculations, all bond info in one place
1986: 10x more terminals sold than 1984
1996: 10x more than 1986
Result: Early-adopter alpha vanished, but market structure fundamentally changed
Secondary effects: Electronic trading, proprietary data became only edge, relationships shifted
That shift happened suddenly, then all at once.
We think the private markets in 2026 are at a similar inflection point as the traditional finance industry was in 1984.
If you look under the hood at different funds today, you will see a massive gap in the sophistication of their workflows. Based on our experience, the best funds today operate more like software firms than traditional financial services businesses, and there is a high correlation between firms who have rebuilt modern workflows funds who have distributed capital + been able to raise fresh funds.
"Eventually, we're going to do everything. We're going to do the modeling. We're going to do investment committee memos. We're going to write that outbound email for you. We're going to update your CRM. Really, we're going to replace and augment everything you do."
If you are using / building other tools not on this list, I’d love to talk.
Fund updates
Thanks for following along as I document how I think about early-stage investing and the frameworks that have lead me through firm building. I will share more on the full thesis and founder selection next week.
For those of you curious about Outlaw, I have included more below.
Becoming chief capitalist to future world-defining individuals.
Thesis
Outlaw is a thesis-driven investment product designed for out-of-distribution individuals.
We are seeking 1.5% of a $5b business. Every decision centers around the pursuit of finding the founders capable of this type of scale, owning as much of their company as we can, and building a scalable investment product that acts as an extension of their business.
The fund invests in individuals, not companies. We do not fish in the same ponds as multi-stage investors, we generally avoid live rounds, and we tune out everything except the founders in our underwriting.
We believe in the art of finding under-discovered talent. The most talented people in the world were once unknown names. In a world of consensus investors, we believe the most upside belongs to those willing to do the work finding talent before it becomes obvious in hindsight.
For those of you looking to get more involved, I’m happy to chat.
My calendar is linked.
Thanks for reading this far and giving us a little bit of your attention this week. Feel free to unsubscribe whenever this stops becoming valuable to you.
