Our 2026 LP Meeting

1. Key Themes

Theme: Neutral-to-short AI beta, long the alpha

The firm's overarching posture is to avoid owning "AI exposure" broadly and instead pick specific, non-consensus positions where its small check size matters.

• "TL;DR: neutral to short AI beta. Long the alpha."
• They pass on "Infinite capital consumers. Anything that needs to access infinite money to work / where our money doesn't matter (we don't have infinite money!)"
• On models and compute: "Models and compute are hyper-legible and hyper-capitalized, creating a proxy war between the capital markets megaliths. Instead we want convex opportunities where our money actually matters."

Theme: Selling outcomes, not software

Software can now do work itself, so the larger and easier-to-sell market is labor/services spend rather than software spend.

• "Normal companies spend ≈3% of revenue on software vs ≈25% on labor and ≈10% on services, and no one has ever wanted to buy what SaaS companies sell. Software can do work now, and that's easier to sell than workflow, into much bigger markets."
• Structures include "vertically integrating around software, using FDE to own the outcomes around software, or verticalizing around hardware" or "buying the business, then transforming it via a GBO or AI rollup. If you sufficiently transform the earning power, keep the upside."
• Founders hold "a deep belief about the inherent worthlessness of application software."

Theme: Second-order effects of AI: systems built on effort break

When effort becomes free, markets and institutions that relied on effort being costly will need to be rebuilt or controlled.

• "Our markets and society are based on the presumption of effort being more expensive than attention. AI very clearly flips that."
• "AI won't make more attention or trust, but it creates infinite demand for both. This is happening/will happen across markets/transactions based on security, trust, attention, scarce physical assets, the law, etc."
• "So much of our economy and society relies on low agency to function but now agency is infinite. We back the companies that control or rebuild those systems."

Theme: Value accrues at the edges of the AI supply chain, including data

Pricing power sits at physical bottlenecks and in data, which is emerging as a new part of the capital structure.

• "Pricing power sits at the constrained/narrow points through which the (physical) AI supply chain has to run."
• "We've been focused on owning the business that models/robotics/hardware makes possible, not the hardware in line with our view on software (outcomes win)."
• "data and its value-added byproducts are emerging as a critical input to the supply chain and a new part of its capital structure."

Theme: "Post-AI" founders and coherence

The best new founders build from different assumptions about the future, and talent variance is rising.

• "The 'of the moment' founders are technical, commercial, and rangy. AI increases variance and this is especially true in talent markets."
• "These founders are super competitive, rangy, commercial, and technical."
• "Coherence is everything: one choice/one POV on tech, GTM, org chart, and talent. Any company you build has to be a reflection of that bet, expressed through every choice."

2. Contrarian Perspectives

Data value does not scale with company size (diseconomies of scale in data)

The prevailing assumption is that bigger companies' data is worth proportionally more (the "data is oil" view). The author presents evidence that price per revenue dollar falls sharply with scale.

• Evidence: "Small businesses ($1-2M sales) will routinely sell company data for 10-50% of revenue. Sometimes even >100%! Spirit airlines ($5B sales) sold its data for $10M (0.2% of revenue)."
• Possible explanations: few buyers for scaled data; a poorly run bankruptcy firesale; diminishing returns ("post training and RL require novelty not size"); high transaction costs at scale; and competition from synthetic data ("Real world data is necessary to establish a baseline but not to fully populate or scale an environment.").
• His read: "#1 and #2 are potentially bullish because they're transitory. #3 is bearish. #4 is neutral and bullish for infrastructure companies. #5 is probably right on a long enough time horizon."

Don't pay for legibility; it becomes a game of winning, not picking

Consensus says to back the most obviously great, well-understood companies. The firm explicitly passes on this and gets its edge from backing what the market doesn't grok.

• "Paying for legibility. Self explanatory. Becomes a winning game, not a picking game."
• "We get our price by backing founders who are not optimizing for fundraising (definitionally, low price/high CoC) and ideas that are hard to grok. That puts us further out on a limb than anyone else, and if the market is right about them, we eat it."

No one wants to buy SaaS

A blunt challenge to the software-as-product consensus: the problem was never that software was hard to sell well, but that buyers never wanted it.

• "no one has ever wanted to buy what SaaS companies sell."
• "'Software can do a lot of cool stuff' is not a view on how you uniquely capture that value yourself."

3. Companies Identified

Slow Ventures

• Description: Venture firm where the author is a partner, leading pre-seed/seed from a ≈$325M fund.
• Why mentioned: The subject of the LP meeting and the source of the investing framework.
• Quotes:
  • "We had our ≈25 biggest institutional investors, representing ≈90% of our capital, for one day."
  • "I'm a partner at Slow Ventures, where I lead pre/seed rounds from a ≈$325M fund."

Spirit Airlines

• Description: Airline (≈$5B in sales) that went through bankruptcy.
• Why mentioned: Case study of data selling for a tiny fraction of revenue at scale.
• Quotes:
  • "Spirit airlines ($5B sales) sold its data for $10M (0.2% of revenue)"
  • "The bankruptcy process was not well run/not made for this type of asset (no samples). It was a firesale so don't extrapolate from firesale prices."

Google

• Description: Technology company; apparent buyer of Spirit's data.
• Why mentioned: Example of a buyer benefiting from an immature market.
• Quotes:
  • "So Google just got a great deal on Spirit because the market is so immature."

Abundance NY

• Description: NYC advocacy group; produced a voter guide on the city's ballot measures.
• Why mentioned: Source of the author's analysis of the five NYC charter amendments.
• Quotes:
  • "From my friends at Abundance NY (read their whole voter guide)"

4. People Identified

Yoni Rechtman

• Description: Partner at Slow Ventures; author of 99D.
• Why mentioned: Author; presented how the firm is investing in light of its AI view.
• Quotes:
  • "I presented how we're investing now as a reflection of our view on AI."
  • "I'm a generalist investor looking for weird takes on important stories: N-of-1 companies taking non-obvious approaches to markets that matter."

Will (Slow Ventures partner)

• Description: Slow Ventures partner (last name not given).
• Why mentioned: Presented the firm's macro view.
• Quotes:
  • "Will presented our view on the macro environment."

Sam (Slow Ventures partner)

• Description: Slow Ventures partner (last name not given).
• Why mentioned: Presented the AI-as-reflection-of-macro view.
• Quotes:
  • "Sam presented our view on AI as a reflection of the macro."

Kevin (Slow Ventures partner)

• Description: Slow Ventures partner (last name not given).
• Why mentioned: Presented the business of the firm.
• Quotes:
  • "Kevin presented the business of the firm."

5. Operating Insights

Be coherent in every choice

Don't cargo-cult; make one bet and express it everywhere.

• "Coherence is everything: one choice/one POV on tech, GTM, org chart, and talent. Any company you build has to be a reflection of that bet, expressed through every choice."

Sell the outcome, and capture upside by owning the work

Founders can verticalize around software or buy and transform existing businesses rather than sell a seat license.

• "Sometimes that's a product company delivering a service (vertically integrating around software, using FDE to own the outcomes around software, or verticalizing around hardware). Sometimes it's buying the business, then transforming it via a GBO or AI rollup."

Use your own point of view as a founder-screening filter

Foregrounding creative thinking about business models and terminal value attracts the right founders and repels the wrong fit.

• "By foregrounding this approach, we can select for the founders that we want to back, who are themselves maximally creative and entrepreneurial."
• "There's a certain type of person (some of whom are great!) who's just not interested in these questions and probably will not value partnering with us / will never take our money."

6. Overlooked Insights

The firm's own stated failure modes

The author lists specific ways the strategy could fail, which doubles as a checklist for market conditions to watch, including a tougher Series A/B environment.

• "Maybe the infinite money is truly infinite: capital asymptotically concentrates in infinite TAM, which ironically makes it harder, not easier, for almost everyone to raise, especially Series A/B rounds."
• "Maybe the Great Sorting is fully predetermined. There's no crossover, good businesses stay just good businesses forever, and getting in early buys us nothing."

The author is moving toward brokering data deals

He signals a hands-on step to learn the market for data as an asset class and is seeking large-deal experience.

• "I'm interested in helping someone sell data/acting as a broker or advisor on a couple deals. Seems like the natural and necessary step to learn more here."
• "I would love to talk to anyone who has worked on larger data deals: $5M+ transactions."