Through September 2026, the PitchBook-NVCA Venture Monitor reports $515.8 billion across an estimated 14,199 U.S. venture deals. AI companies received 82.7% of that dollar value, with a handful of very large rounds contributing heavily. These figures show the scale and concentration of capital at stake in venture decisions. Deals are funding transactions, not counts of new ideas or unique startups. The data do not establish how often investors misunderstand claims; that question requires evidence about their actual evaluation process.

The Financial Stakes of Evaluating AI Ventures

Scientists and investors can both encounter technical claims outside their expertise. Each may need background knowledge to examine the reasoning, evidence and assumptions. A scientist asks what a research result establishes. An investor also asks whether a team can turn an innovation into a viable business and whether the investment terms are attractive. Scientific support alone does not answer those business questions. The learning need can overlap, while the evidence and judgment required remain specific to each decision.

Scientists and Investors Examine Claims for Different Decisions

A startup pitch combines technical claims, claims about customers and markets, and a plan for turning them into a business. Some decisive claims may depend on knowledge unfamiliar to the investor. A convincing explanation can leave unclear which parts are established, which are assumptions, and what still needs checking. How can an investor acquire enough background to investigate those claims and their combined conclusion, without first becoming a specialist in the whole field?

A proposal for an innovation can mix established facts, new claims and assumptions in a persuasive account. AI can help produce that account, but an appealing presentation is not evidence for every claim. In one business-plan experiment, evaluators rated AI-generated versions slightly higher than the entrepreneurs’ own; in a separate preprint, prompted rhetorical styles shifted people’s choices in selected AI debates. Neither tested 1Cademy or established an inevitable AI-to-AI persuasion loop. How can people and AI make each claim explicit, learn what is needed to examine it, and identify what remains unsupported?

A Convincing Innovation Still Needs Evidence

Paul A. Gompers, Will Gornall, Steven N. Kaplan and Ilya A. Strebulaev. Journal of Financial Economics 135(1), 169–190 (2020).

https://doi.org/10.1016/j.jfineco.2019.06.011

The authors surveyed 885 institutional venture capitalists at 681 firms about screening, investment structure, monitoring and advice. Respondents evaluated management teams alongside business models, products, markets and other factors; priorities varied by stage and industry. These historical self-reports show the breadth of investment evaluation. They do not measure current AI-driven deal volume, isolate a lack of understanding, or test 1Cademy or investment returns from a learning tool.

Source inspected: publisher abstract and introduction; related working-paper record https://www.nber.org/papers/w22587. Inspected 10 November 2026.

How do venture capitalists make decisions?

We propose one learning and review process for scientists, investors and AI agents: make an innovation’s claims explicit, including combined conclusions; learn the necessary background; and examine each topic’s reasoning, evidence and uncertainties. The decisions differ. A scientist asks whether a result is supported. A venture’s product and customer claims can also be tested, but an investment further depends on the team, market, valuation, terms and future execution. A founder gains if the pitch is believed. Reviewed public background could be reused across research and venture evaluation; confidential deal material should stay out until safeguards are verified. Whether this improves either group’s judgment or efficiency needs comparison with their current methods, including capable AI.

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