Five Risks of Using AI Agents in Venture Capital Decision-Making

Signup Write New Story Five Risks of Using AI Agents in Venture Capital Decision-Making by Alexander Kopylkov by Alexander Kopylkov | @ alexanderkopylkov Venture Capital Investor at Kopylkov Alexander Kopylkov is a venture capital investor, entrepreneur, strategist, and founder with decades of experience.

Alexander Kopylkov is a venture capital investor, entrepreneur, strategist, and founder with decades of experience.

Speed looks like an edge until the model is confidently wrong about something it was never built to understand.

AI agents now sit inside nearly every stage of venture investing. They scan the market, draft the first summary of a company, and sometimes shape what a partner reads before a meeting even starts. Adoption happened fast. Lots of venture firms already use AI tools to find deals, and in the first quarter of 2026 alone, AI startups pulled in roughly 80 percent of all global venture funding. The tools arrived before anyone finished building the guardrails around them.

AI models are built to produce a confident answer, not to flag the parts they are unsure of. A Stanford University study on legal AI tools found error rates as high as 69 to 88 percent on complex legal questions, the exact kind of language that fills contracts and deal documents. The model does not pause and say it is guessing. It states the wrong answer in the same calm tone as the right one, and that is what makes it dangerous in a diligence process. Those numbers are large enough to end a deal.

When research took a week, the slowness forced more eyes onto the material. People had time to ask a second question. Agents now compress that same research into minutes, which is genuinely useful, but it also means fewer people look at the work before a decision gets made. Regulators have noticed. FINRA's 2026 oversight report expanded its warnings on generative AI tools in finance, flagging accuracy and the risk of agents acting beyond the scope anyone actually intended for them.

Most AI agents draw from similar public data and similar training. When many funds point similar tools at the same sector, their conclusions start to converge, even when the underlying businesses are quite different. That is a version of the groupthink that researchers studying real-world multi-agent systems have named as a risk, alongside error cascades and weak auditability, in investment settings. It is one quiet reason capital keeps piling into the same handful of companies instead of spreading out to find the next one. It looks like independent analysis. It reads more like everyone opening the same page.

If a partner is asked to justify turning down or funding a company, "the model recommended it" is not a real answer. Regulators are already moving in this direction for other financial decisions: in Europe, AI systems used to assess a person's creditworthiness will be required to give an explanation a human can actually follow. That is a reasonable standard, and it is likely to spread to other corners of finance. It is also a standard that most AI agents used in investing today cannot clearly meet, because even the people who built them cannot always say exactly why the model landed on a given answer.

Models trained on normal conditions tend to struggle most when conditions stop being normal, which is precisely when a wrong call costs the most. Financial regulators have already flagged AI-driven misstatements in company numbers as a live compliance risk in their latest oversight reports. Markets under stress produce noisier, less reliable information, and that is exactly when human judgment is hardest to replace and easiest to skip.

They save real time on research, and that is value I would not give back. But every one of the five problems above comes from the same root cause: treating a fast answer as a finished one. The agent can summarize a company, flag a risk, even draft the memo. Whether to wire the money is still my call, every time, and I check the work myself before I make it.

That part of the job was true before any of these tools existed, and it is still true now. Capital keeps moving toward AI. The responsibility for where it lands has not moved anywhere at all.