The next time a portfolio company asks for a bridge round or a follow-on, ask them one question first: can your team build with AI, or do they just use it?
The answer should inform the investment decision. Here is why.
AI-dependent vs AI-literate is a valuation difference
A company that uses AI tools is building on rented ground. Their workflows depend on specific vendors, specific pricing, and specific model behaviors. When any of those variables change, the company is exposed.
A company that builds with AI owns its capability. Their workflows are internal, modifiable, and vendor-independent. When the market shifts, they adapt. When a tool changes, they rebuild.
This is not a theoretical distinction. Companies that depend on a single AI vendor for core workflows carry concentration risk. That risk does not appear on the balance sheet, but it affects the defensibility of the business.
AI-literate teams are structurally more valuable because their capabilities are internal assets, not external dependencies.
The team capability gap is invisible in a pitch deck
A founder can demo an impressive AI-generated product without understanding how it was built. A deck can show AI-powered features without revealing whether the team can modify, maintain, or rebuild them.
The due diligence question is not "are you using AI?" Every company is using AI. The question is "can your team rebuild this if the tool breaks?"
If the answer is no, the company has a single point of failure in its product development pipeline. The AI tool is not a capability. It is a dependency that looks like a capability.
Test this in diligence: ask the CTO to modify a feature in the AI-powered product during the meeting. Not a rehearsed demo. A live change. If the team can do it, the capability is real. If they cannot, the capability is the vendor's, not theirs.
The training cost is trivial relative to the risk
An enterprise AI training program for a 20-person team costs less than one week of runway for most venture-backed companies. Depending on the scope, it ranges from $10,000 to $30,000.
The downside of skipping it: an AI deployment failure that requires external consultants to diagnose, potential re-architecture of affected systems, and the time and trust cost of explaining to your investors why the AI strategy did not work as presented.
A single failed AI deployment can cost more in remediation than three years of training. The training is not an investment in education. It is insurance against a category of failures that are becoming more common as AI adoption accelerates without corresponding capability development.
The requirement
Make it a condition. Before the next funding round, the team completes a structured AI training program. Not a webinar. Not a video series. A program that results in: every leader can direct an AI build, the team has a written governance document, and at least one internally-built tool exists that the team owns and maintains.
The companies that meet this bar are the ones you want in your portfolio. The ones that cannot are telling you something about their organizational capability.
Talk to us about portfolio AI readiness — we assess and train portfolio teams directly. Reach out to discuss how this fits into your diligence process.
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