By the time a term sheet is on the table, most investors have already bought the market story. What they still have to price is the machine behind it. Before capital moves, the tech stack gets read the way a cap table does: what the company owns, what it rents and what breaks at ten times the load.
AI has sharpened that review. It sits in nearly every pitch deck now, so “we use AI” no longer separates anyone. Investors want to know how much of the business actually runs on it, how deeply it is built into daily workflows and how much of the development itself it handles.
Below is what gets checked, and how the answers feed into valuation and the size of the cheque.
Why the Stack Became a Diligence Item
For most businesses, the software is the asset. The stack tells an investor how fast the team ships, what the burn will look like, where the risk hides and whether anyone can copy the product over a long weekend.
It also tells them something more grounding: whether the founder understands their own business at a technical level, or is just narrating someone else’s work.
The Fundamentals Haven’t Gone Anywhere
AI hasn’t replaced the old checklist. It’s been added on top.
| Area | What investors look at | What makes them nervous |
| Architecture | How it’s structured, how it scales, cloud setup | A tangle only one engineer understands |
| Code quality | Repo history, tests, review habits | No tests, no reviews, hero commits at 2 a.m. |
| Security and data | Access control, data handling, relevant compliance | Customer data sitting where it shouldn’t |
| Infrastructure cost | Cloud bill against revenue as usage grows | Costs rising faster than income |
| Ownership | IP assignment, open-source licences, vendor lock-in | A core module written by a contractor who never signed anything |
| People | Who built it, who maintains it | The whole system lives in the founder’s head |
Not glamorous. But this is the floor, and a shaky floor makes every AI claim above it harder to believe.
The AI Question: What Investors Really Want to Know
“We use AI” used to be a differentiator. Now it’s table stakes, and investors know it. So they try to place a company on a spectrum, because the answer changes how they model margins, headcount and the amount of capital the business really needs.
They tend to come at it from three angles.
1. Where AI sits in the product
- Is AI the core of the product or a feature bolted on?
- Are the models proprietary, fine-tuned or simply API calls to someone else’s model?
- Is there proprietary data feeding it, or could a competitor wire up the same API in a weekend?
- What does inference cost per user, and how does that move when usage doubles?
A thin wrapper around a public model isn’t automatically a bad business. It just gets valued like one.
2. How deeply AI runs through daily workflows
This goes past the product. Does AI handle support triage, sales ops, QA, finance reconciliation, hiring screens? Investors read this as operating leverage. A team that automates well can carry more revenue per head, and that directly changes how much money the company needs to raise.
The baseline keeps moving, too. Stanford’s 2026 AI Index shows organisational AI use climbing from 55% in 2023 to 88% in 2025. When almost everyone says yes, the yes tells you nothing. Depth, measurement and results are what separate one company from the next.
3. How much AI is involved in development
Investors increasingly ask how the code gets written. Copilots? Agents? Who reviews what ships? There are two sides to it. If done well, it means a leaner team and faster releases. If done carelessly, it means unreviewed code, security gaps, and a codebase nobody fully understands.
Google’s 2025 DORA report, built on responses from nearly 5,000 technology professionals, found about 90% use AI at work. Its central finding is more useful than the headline: AI works as an amplifier. It magnifies strong engineering practices, and it magnifies messy ones.
And speed isn’t guaranteed. In a randomised controlled trial by METR, 16 experienced developers working in their own codebases took 19% longer when AI was allowed, even though they’d predicted a 24% speed-up. It was a small study on early-2025 tools, and the authors say as much. Still, the takeaway for founders is simple: don’t pitch AI-written code as a multiplier unless you have your own numbers to back it.
Here’s how the questions tend to land in the room.
| What the investor asks | What a solid answer sounds like |
| “How much of your code is AI-assisted?” | A real figure, how you track it, and which review gates it passes through |
| “What if your model provider changes pricing?” | An abstraction layer, a fallback model, a cost ceiling |
| “How do you know the output is any good?” | Evals, regression tests, human review loops, production monitoring |
| “Whose data trained or tuned this?” | Documented rights, consent and a retention policy |
Proof Beats Promises: The Handover Pack
Founders who can’t produce documents on request lose weeks. Founders who can tend to keep their valuation intact, because nothing is left to the imagination.
Outside engineering partners matter here as well. Firms like Appinventiv, which build AI-enabled products for businesses and larger enterprises, often nudge clients to keep architecture notes, model choices and evaluation logs current from the first sprint. Mostly because that paper trail is exactly what a diligence team asks for later. Boring work. Also the work that protects a valuation.
A pack that makes diligence quick usually has:
- An architecture diagram that matches what’s actually deployed
- A register of models and prompts in use, with version history
- Evaluation results and how often they’re re-run
- A breakdown of cloud and inference costs per customer or per user
- A written policy on how the team uses AI tools
- An IP and open-source licence audit
How AI Maturity Moves Valuation and Capital Needs
Private AI investment reached roughly $345 billion in 2025, per the Stanford AI Index. That’s a lot of pitches that sound alike, which is exactly why investors dig. No one prices a company off a table like the one below, but the reasoning tends to run this way.
| What investors see | What they conclude | Likely effect |
| ● AI-native product
● Proprietary data ● Measured workflow gains |
Defensible, and a lean team can carry more revenue | Stronger valuation case; capital can go to growth and data |
| AI as a feature on a conventional product | Useful, but copyable | Standard software logic; capital tied to sales and hiring |
| “AI-powered” label with no evidence | Narrative over substance | Valuation pressure; smaller or staged cheque, more conditions |
| ● Heavy AI-generated code
● No tests or review |
Hidden technical debt | Budget for hardening or a rewrite; funds released against milestones |
Notice that capital need moves both ways. A genuinely AI-leveraged company may need less money for the same output. A shaky one may need more than it admits, because someone has to fix the foundations.
A Founder’s Pre-Diligence Checklist
- Draw a one-page AI map covering product, operations and development, marking what’s yours and what’s third-party.
- Track the share of AI-assisted work, plus its effect on cycle time and bug rates.
- Put cost per inference and per user on a dashboard.
- Add evals before adding features.
- Clean up IP and licensing now, not mid-negotiation.
- Run a mock diligence with an outside engineer who’ll be blunt.
Conclusion
Investors aren’t hunting for the most AI. They’re hunting for the most honest picture of it: where it’s used, how it’s governed, what it costs and what it genuinely changes. A founder who can answer that cleanly makes the investor’s job easy, and a clean answer is often worth more than a flashy demo.
