The gap between theory and practice in ai due diligence for non-technical founders: what to ask. is enormous. I've lived on both sides.
As a non-technical founder in AI, due diligence can feel like an interrogation in a foreign language. I'll give you a simple list of questions to ask your own team and your potential investors to stay in control of the process.
The Counterintuitive Truth
Here's what surprised me most about ai due diligence for non-technical founders: what to ask.: the best practitioners do less, not more.
When I was building MovieLaLa, we tried to do everything at once. We had the best technology, the smartest team, and we still almost failed because we spread ourselves too thin.
The lesson I took from that experience, and from watching hundreds of other companies, is that the market doesn't care about your roadmap. It sounds simple. It's incredibly hard to execute.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to ai due diligence for non-technical founders: what to ask. are fundamentally flawed. Not slightly off. Fundamentally flawed.
The root cause is usually one of three things:
- Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.
- Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.
- Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating ai due diligence for non-technical founders: what to ask.. It's not complicated, but it requires discipline.
Step 1: the data tells a different story than your gut This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: simplicity beats complexity every time Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly Nothing works perfectly the first time. The companies in my portfolio that nail ai due diligence for non-technical founders: what to ask. are the ones that treat it as an ongoing process, not a one-time project.
Lessons From the Trenches
I want to share a few specific lessons I've picked up over the years. These aren't theoretical. They come from real companies, real failures, and real successes.
Lesson 1: The best time to start thinking about ai due diligence for non-technical founders: what to ask. was yesterday. The second best time is now. Don't wait until you have the perfect plan.
Lesson 2: Hire for attitude, train for skill. The best ai due diligence for non-technical founders: what to ask. practitioners I've met weren't the most technically gifted. They were the most curious and persistent.
Lesson 3: Your competitors are probably getting this wrong too. That's your opportunity. While everyone else is following the same playbook, you can zig when they zag.
This connects to broader themes around AI due diligence, AI market sizing, AI pitch decks, AI competitive moats, AI exit strategies that I've been thinking about a lot lately.
Final Thoughts
After two exits, 200+ investments, and more mistakes than I can count, here's what I know for sure about ai due diligence for non-technical founders: what to ask.: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat ai due diligence for non-technical founders: what to ask. as a competitive advantage, not a checkbox. They invest in it early, measure it obsessively, and never stop improving.
If you're just getting started with ai due diligence for non-technical founders: what to ask., don't be intimidated. Everyone starts somewhere. The key is to start with the right mindset and the right framework, and then execute like your company depends on it. Because it probably does.
Frequently Asked Questions
How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.