Who owns what your AI learns. · VANDAM

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# Who owns what your AI learns. · VANDAM

> Most companies rent their AI. Why YC asks for startups that help companies own their intelligence, and the four things a mid-sized company can own this year.

URL: https://vandam.ai/notes/own-your-intelligence  
Contact: hendrik@vibelab.be

# Who owns what your AI learns.

Y Combinator now asks for startups that help companies own their intelligence. Before a mid-sized company owns a model, it can own the four things every model works with: context, evals, rules and connections.

Hendrik De Winne 29 October 2026 6 min read

In its request list for the Winter 2027 batch, [Y Combinator](https://www.ycombinator.com/rfs#own-your-intelligence) asks for startups that help companies own their intelligence. The essay, by partner Jon Xu, opens with a sentence I would put on the wall of every AI steering committee: “Today, nearly every company building with AI rents its intelligence. You send tokens to a frontier lab, get answers back, and pay the bill.”

Xu’s argument is that the trade made sense when the best models were years ahead of everything else. That gap has closed. “Open-weight models are now competitive with the top frontier models on meaningful benchmarks,” he writes, and companies “have become more aware of their dependency on model rental.” His prediction is that many will keep renting frontier models for their hardest problems and also “own specialized models that encode how their business works.”

I agree with the direction, and I think most of the companies I work with should start somewhere smaller. Before a mid-sized company owns a model, it can own everything the model works with. That part is available today, costs little, and decides how much the company keeps when the model changes.

## Renting has a cost that is easy to miss

The obvious cost of renting is the invoice. The less obvious one was spelled out in June by an engineer who posts as [dom](https://x.com/ryiacy/status/2066260212772679864). His reading of the market: the large labs sell forward-deployed engineers to big companies, build their workflows on proprietary models, and then use “the traces and context from this to build RL envs to improve the models.” In his words, “instead of buying this data they’re getting paid gigantic consulting fees to extract it.”

That is one person’s interpretation, and the labs would describe their services differently. The structural point holds either way. When the knowledge of how your business works is written into someone else’s system, the learning curve belongs to them. dom ends with the choice he sees: companies either keep paying for that arrangement or “find a way to build and own their own model ecosystem,” which he describes as “some combination of AI models, evals, RL envs, and workflows.”

The same theme runs through what investors and operators wrote this year. [Garry Tan](https://x.com/garrytan/status/2061076595474121073), who runs Y Combinator, put the bottleneck plainly in May: “The models are smart enough already. What is missing is the company-specific context locked in senior people heads.” [Nikita Bier](https://x.com/nikitabier/status/2109497987055640958) listed proprietary data first among the few moats left.

## Four things a company can own this year

In October [Greg Isenberg](https://x.com/gregisenberg/status/2110414696037716372) came back from two days of dinners with founders and executives in San Francisco. The best teams, he wrote, “treat the model as swappable.” He also gave the cleanest breakdown I have seen of what sits around the model in a working agent: “the connectors for that industry’s data, evals written by people who know the work, rules for what the agent can and can’t touch, and a clean screen for a human to review.”

Translated to a company of 50 or 300 people, that gives four assets, none of which needs a model of your own.

1. **Context.** The documents, decisions and exceptions an agent reads, kept as files in your own storage, with an owner for each part. This is what [compounds](https://vandam.ai/notes/context-compounds) while models come and go.
2. **Evals.** A set of real cases from your work, each with what a good answer contains, written with the people who do the job. [Garrett Lord](https://x.com/GarrettLord/status/2068754262440767500), CEO of [Handshake](https://joinhandshake.com/), which sells evals to labs and large companies, put it in one line: “In the world of agents, performance management is evals.”
3. **Rules.** Instructions, permissions and review steps, written in plain files that a person owns and dates, so they can be read, moved and corrected.
4. **Connections.** Access to your systems through an open standard such as MCP, so a new model plugs into the same doors as the old one.

With these four in your hands, a new model becomes a test you run, with your evals telling you whether it is better at your work. Without them, every switch is a rebuild, and every vendor contract is harder to leave than it looks.

If a new model drops next month, does your company get better or worse? (A question asked at one of those dinners, reported by Greg Isenberg, October 2026)

## Where owning the model comes in later

The strongest objection to all of this is that frontier models keep improving and most companies cannot match them. I agree, and so does Xu: the hardest problems will keep going to rented frontier models. Lord makes the same split for an insurer, which “should probably buy a coding agent off the shelf and pay for frontier tokens, while also building proprietary agents that encode its unique underwriting decisions as a sovereign IP asset.”

So the practical order is context, evals, rules and connections first, and a model of your own only when those exist. Xu describes the missing work in the same terms: someone has to “turn workflows into evals, expertise into training data, production failures into improvements.” A company that already has its evals and its curated context has done most of that work. If a regulator, a customer or its own data rules later require an open-weight model on its own servers, it can run that model against the same tests and see whether it is good enough.

For transparency: VibeLab, the team behind VANDAM, is a Claude implementation partner, and for new builds we usually suggest Claude first. That is a choice about today’s best fit. The context, evals, rules and connections we build stay in the client’s systems and work with whichever model the client picks next. Our [models page](https://vandam.ai/models) lists the ones we work with.

A quick test for your own company: pick one AI workflow that runs today and find its instructions, the cases it was tested on and the list of systems it can reach. If all three sit in places you control and can read, you own most of what matters. If they sit inside a vendor’s dashboard or one engineer’s laptop, that is the first thing to move.

## More notes

26 October 2026 9 min read

## Delete the step first and give the agent whatever survives.

Musk’s five-step method puts automation last, and most AI programs start there because an agent is the one step a vendor can sell. I walk accounts payable through the steps in his order and show why an agent that clears exceptions fast hides the steps that should go.

## Your agents need a critic and so does the plan they carry out.

Models grade their own work too kindly, and the labs now separate the agent that makes from the agent that checks, the way finance separates maker and checker. I set out a four-layer review design with a minutes budget, and why the brief itself needs a critic with no stake in it.

## Fix a mistake once by turning every correction into a rule the agents read.

Anthropic's data team took Claude from 21% to above 95% accuracy with written skills, and now turns every correction a colleague makes into a rule and a test for the person who owns that domain. I set out that loop for a mid-sized company, with where the rules live, who owns them, and the one number a COO should track: how often the same mistake comes back.