Yes -- a growing share of investors now run the first pass of your technical diligence through an AI agent. It reads your deck and your data room, then generates a question list built around your specific claims, and it asks sharper, stranger, more thorough questions than a junior associate ever did. The practical change for you is that the gaps you assumed nobody would bother to check now get checked, so the way to prepare is to make every technical claim in your materials traceable to something a machine can follow without a phone call.
I have watched this shift happen in the room over the last few quarters. A founder forwards me the diligence request list an investor sent, and instead of the usual dozen generic lines, it is thirty questions that clearly read the actual deck. One of them names a specific number from slide 14 and asks how it was measured. That list was not written by a person at 11pm. It was drafted by an agent and lightly edited by the associate, and it changes what a founder needs to have ready.
What the AI actually does before a partner reads anything
The pattern is consistent across the firms I see. A founder sends over a deck, a data room link, and maybe a technical appendix. Before a human spends real time, an associate feeds those materials into an internal agent. The agent extracts the claims, cross-checks them against public sources, and produces a diligence checklist tailored to that one company. Then a person reviews the list, cuts the noise, and sends you the questions that survived.
The important part is what the machine is good at. It does not get bored, it does not skim, and it does not assume a claim is fine because the founder seems credible. It reads every slide with the same flat attention it gave the first one. If your deck says the product handles a certain load, the agent notices there is nothing in the data room that shows it. If your architecture diagram names a database and your cost model implies a different one, the agent flags the mismatch. A tired associate at the end of a long week misses that. The agent does not miss it, because catching that kind of thing is the one job it was pointed at.
This does not replace the partner's judgment. It front-runs it. By the time a human decides whether you are worth a deep session, the easy holes have already been found and written down.
The questions are more specific, not more generic
The old fear was that AI would make diligence shallower -- a checklist run by a bot. What I actually see is the opposite. The questions got more specific, because the agent anchors every one to something you actually wrote.
Here is the difference. The generic version of a diligence question is "tell us about your scalability." The agent version is "your deck claims sub-200ms response times at the volume on slide 9; the data room has no load test showing that; how was the number produced, and on what hardware?" You cannot hand-wave the second one. It names the claim, names the missing evidence, and asks for the method. A founder who was ready to talk vaguely about scale is suddenly being asked to produce a specific artifact they may not have.
I worked with a founder whose deck said they had "enterprise-grade security." The AI-drafted question that came back did not ask about their security posture in general. It asked whether they had completed a specific type of penetration test, when, and by whom, and it asked why their public privacy page described a data practice that their technical appendix contradicted. The agent had read both documents and noticed they disagreed. That is a level of cross-referencing a human reviewer usually only reaches on the companies they have already decided to take seriously. Now every company gets it on the first pass.
The strategic effect is that vagueness stopped being a safe place to hide. A claim you cannot back is no longer likely to slip through because the reviewer ran out of time. The reviewer's assistant does not run out of time.
What this rewards: a data room a machine can read
If the first reader of your materials is an agent, the winning move is to build your materials so a machine can follow every claim to its evidence without asking you.
That means your deck's technical numbers each point somewhere. When you claim a performance figure, the data room has the test that produced it, dated, with the conditions written down. When you name an architecture, the diagram, the cost model, and the appendix all describe the same system. When you say you are compliant with something, the document is in the room, not "available on request." The technical half of your data room is exactly the part most early teams leave empty, and it is now the part the machine reads first.
It also means consistency across documents matters more than it used to. A human reviewer reads three or four of your documents and forms an impression. The agent reads all of them and compares them line by line. Two files that disagree -- a pricing page and a cost model, a privacy policy and a codebase description, a deck number and an appendix number -- used to be a small risk that a busy reviewer might never connect. Now the contradiction is close to certain to surface, because finding contradictions across a pile of documents is precisely the task these agents are strongest at. Before you send anything, read your own materials the way the agent will: as one corpus that has to agree with itself.
What it punishes: claims with nothing behind them
The flip side is blunt. A claim with no artifact behind it is now a liability, not a rounding error.
Every number in your deck is a promise you may be asked to prove on demand, and the agent is very good at spotting the ones you cannot. The ones that get founders in trouble are the confident-sounding lines written to impress that were never backed by anything: a benchmark nobody ran, a compliance status that is aspirational, a scale figure that is really a hope. Against a human reviewer, some of those survived because the conversation moved on. Against the agent, they get pulled out and turned into a pointed question, and now you are explaining a gap during diligence, which is the worst possible time to be explaining anything.
The data-provenance version of this is the sharpest right now. Investors have started leaning hard on where your training data came from, and it is a natural question for an agent to generate, because it can read your product description, infer that you must have trained or fine-tuned on something, and ask what and under what license. Founders who cannot answer that cleanly are watching it become the question that stalls the round. An unbacked claim used to cost you a little credibility. Now it costs you a specific, documented question you have to answer in writing, and every one of those slows the deal.
What has not changed
It is worth being clear about the limits, because the temptation is to over-rotate. The agent runs the first pass, not the decision. The partner still decides. AI-generated diligence is best at high-volume, repetitive checking -- reading everything, flagging mismatches, generating the list -- and it is weak at judgment, context, and the parts of the story that do not fit in a document. A good investor uses the agent to clear the underbrush so their own time goes to the questions that actually decide the investment.
So the goal has not changed either. You still want the human conversation to be about the real risks and the real plan, the material that belongs in a three-page technical memo written for a person. What changed is the cost of getting there. The path to that conversation now runs through a reader that checks everything and forgets nothing, and the founders who clear it fastest are the ones whose materials were built to be checked.
If you are heading into a raise and you want a second set of eyes on your technical materials before an agent gives them a cold read, book a call or start with a teardown and we will find the claims that have nothing behind them while you still have time to fix it.
FAQ
Are investors really using AI to run technical diligence now?
A large and growing share of firms do, especially for the first pass. The common pattern is that an associate feeds your deck and data room into an internal agent that generates a tailored question list, and a human edits it down before it reaches you. It is not the whole process, but it is increasingly the first reader of your materials.
Does this make diligence easier or harder for founders?
Harder if your materials rely on vagueness, easier if they are well-organized and honest. The agent rewards consistency and traceable claims and punishes anything unbacked. A founder with a clean, machine-readable data room clears the first pass faster than before. A founder whose numbers cannot be sourced gets caught earlier and more precisely.
What is the single best thing to do to prepare?
Make every technical claim in your deck point to evidence in your data room, and make your documents agree with each other. Read your own materials as one corpus and hunt for contradictions before the agent does, because the two things these agents are best at are checking claims against evidence and finding places where your own files disagree.
Should I worry that an AI will misjudge my company?
The agent generates questions; it does not make the decision. If it flags something wrongly, you get a chance to answer, and a good investor knows the tool is for flagging, not judging. The real risk is not being misjudged by the machine -- it is being asked a precise question about a real gap you were hoping nobody would reach.