AI Technical Diligence for Investment Decisions
Bounded AI technical diligence for investors and venture builders: assess the technical thesis, the evidence behind it, and the gaps, with conflicts disclosed.
Direct answer
Direct answer: Technical diligence
Technical diligence on an AI company should answer a narrow question well: does the technical thesis hold, what evidence supports it, and what remains unproven. I review architecture, evaluation, data rights, dependence on model providers, and the team’s ability to operate the system, then write up the evidence gaps that matter to your decision. It is technical decision support, not a legal opinion, a valuation, or an investment recommendation.
The question
“We are evaluating an AI company. Who can assess the technical thesis and evidence gaps without pretending to provide legal or valuation assurance?”
Who it is for
Venture and growth investors, private-equity deal teams, corporate development teams, and venture builders assessing an AI company or product.
Technical diligence from claims to decision
Choose the specific need
Where to start
Technical Venture-Building Advisory
We need periodic technical judgement on a venture’s assumptions, not an undisclosed operating cofounder or engineering team. What advisory scope fits?
Output: An assumption review, decision memo, and technical milestone map.
Decision resourceAI Technical Diligence Checklist
What evidence should we request to test a company’s technical claims before deciding whether deeper work is needed?
Output: A technical evidence request list, red-flag questions, and conflict-disclosure prompts.
What you leave with
Bounded technical decision support: a written assessment of the technical thesis, evidence, and gaps.
- 01 A plain statement of the technical thesis and the assumptions it depends on
- 02 An evidence review: what has been demonstrated, measured, or only claimed
- 03 The material gaps, red flags, and questions to put to the company
- 04 A view on which claims would need an independent experiment to resolve
How it runs
Format, method, and preparation
- Duration
- Scoped to the decision timeline, typically a document review, management sessions, and a written assessment
- Delivery
- Remote, with data-room or repository access as agreed
- Participants
- The deal or investment team and the company’s technical leadership
Scope and conflicts
Agree the decision, the claims in scope, the access available, and disclose any relationship I have with the company or its competitors.
Review the evidence
Read the architecture, evaluation results, data sources and rights, provider dependencies, and operating practices.
Interview the team
Test the thesis with the people who built and run the system, including how they handle failure and change.
Write the assessment
Report the thesis, the evidence, the gaps, and the questions that should shape your decision.
Useful to have ready
- →A clear description of the investment or partnership decision and its timeline
- →Access to technical documentation, evaluation results, and the technical team
- →Disclosure of any existing relationship that could create a conflict
Evidence
What this draws on
The Correctness Illusion in LLM-Generated GPU Kernels
Operator-aware testing caught 10 of 10 seeded defects while 16 of 16 correct controls stayed clean across five GPU classes. A controlled corpus result, not a deployed-model defect rate.
Inspect the source ↗Before the Pull Request
In a controlled experiment, duplicate or conflicting multi-agent rework fell from 78% to 0% and useful throughput more than tripled. A controlled setup, not a universal productivity forecast.
Inspect the source ↗Boom investor commitments
Boom recorded more than $2.5M in investor commitments and commercial letters of intent. That is not money raised, recognised revenue, or production adoption.
Read more →Provisional patent filings
90+ provisional filings across Hike and Orangewood Labs in AI, computer vision, robotics, messaging, and consumer systems. Provisional filings, not granted patents.
Scope and limits
What this is not
- —This is not legal, regulatory, tax, or valuation advice, and it is not a recommendation to invest or not to invest.
- —If I have built, advised, or hold an interest in the company or a direct competitor, I disclose it before starting. Where that compromises the work, I decline or narrow the scope. Working under a different domain does not create independence.
- —The assessment relies on the material and access provided. Running new experiments to test a specific claim is a separately scoped validation study.
If the need is different
Common questions
Answers before you commission
What does AI technical due diligence cover?+
The technical thesis, architecture, evaluation evidence, data sources and rights, dependence on model providers, operating maturity, and the team’s ability to run and change the system. It ends with the gaps that matter to the decision.
Do you provide a valuation or investment recommendation?+
No. The work reports on the technology and its evidence. The investment decision, valuation, and legal review remain with you and your advisers.
How do you handle conflicts of interest?+
Any relationship with the company, its competitors, or its technology is disclosed at the start. If the conflict would compromise the review, I decline or narrow it.
When is a separate validation study needed?+
When the decision depends on a specific claim, such as benchmark performance or correctness, that the company’s own material cannot establish. That test is scoped separately as an experimental study.
Related