AI & Startup Law
What do investors check in AI startup due diligence?
Investors diligencing AI startups increasingly focus on production realities — training-data provenance, model-license chain of title, IP assignments that explicitly cover model weights and fine-tuning artifacts, and open-source compliance across the stack — rather than policy documents alone. A startup that cannot show clean chain of title for its data and weights risks repricing or a collapsed round. Lysinski & Associates P.C. runs pre-diligence reviews that surface these gaps before investors do.
What do investors actually diligence in an AI startup?
Increasingly the production realities — training-data provenance, model-license chain of title, IP assignments that explicitly cover model weights and fine-tuning artifacts, and open-source compliance across the stack — not policy documents alone.
A startup that cannot show clean chain of title for its data and weights risks repricing or a collapsed round.
What is 'chain of title' for a model and its data?
A documented trail showing the company has the rights to its training data, its model weights, and any fine-tuning artifacts — including assignments that name those assets specifically.
Generic IP assignments that do not mention weights or training data can leave gaps. See the AI-generated-code and open-source pages.
What goes in an AI data room?
Training-data provenance records, model and dependency licenses, IP assignments covering weights and fine-tuning, open-source compliance documentation, and versioning and checkpoint provenance.
Investors look for written proof of each item, not assurances — so document them before diligence begins.
What if I trained on copyrighted or scraped data?
It is a real diligence risk, and the law is still developing — recent training-data copyright rulings are fact-specific and not settled nationally — so document what data you used and how you acquired it, and get it reviewed before investors ask.
In recent litigation the distinction between lawfully acquired and pirated copies has mattered; treat these rulings as fact-specific, not as a green light.
How early should I prepare?
Before the term-sheet clock starts — a pre-diligence review surfaces gaps while you can still fix them.
An acquirer's ML engineer will trace your model's IP trail; tracing it first is cheaper and calmer.
Talk to an attorney who builds AI
For the firm’s related legal service, see AI startup & product counsel.
Frequently asked questions
What do VCs ask AI startups in IP due diligence?
Where the training data came from and whether you had the right to use it, whether your IP assignments explicitly cover model weights and fine-tuning artifacts, and whether your open-source dependencies are compliant. Policy documents alone do not satisfy this; the underlying chain of title does.
How do I prove chain of title for my training data?
Document how each dataset was acquired and under what license or terms, keep provenance and versioning records, and use assignments that name the data and the resulting weights specifically. The goal is a trail an investor's engineer can follow without gaps.
What is in an AI data-room checklist?
Training-data provenance, model and dependency licenses, IP assignments covering weights and fine-tuning, open-source compliance records, and checkpoint and version provenance. Presenting these as an organized, labeled set signals a clean chain of title.
Do generic IP assignments cover model weights?
Often not explicitly. A standard assignment written for software may not clearly capture model weights, fine-tuning artifacts, or embeddings. Name those assets specifically so there is no ambiguity about what the company owns.
When should I run a pre-diligence review?
Before you start raising. Gaps found internally are far cheaper and less disruptive to fix than gaps surfaced by an investor's diligence team after a term sheet is on the table.
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- Is AI-Generated Code Safe to Use Commercially? (Vibe-Coding Legal Review)
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- Internal AI-Use Policy and Shadow AI
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