Most of what you've read about the anatomy of a failed ai pivot: a post-mortem. is wrong. I know because I believed it too, and it cost me.
Our pivot looked great on paper, but it was a disaster in reality. This is a post-mortem on a failed AI pivot, analyzing the flawed assumptions, the execution errors, and the warning signs we missed along the way.
What I've Learned From 39 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with the anatomy of a failed ai pivot: a post-mortem..
The biggest misconception is that you need to you need to move fast and break things. That's backwards. The companies that win are the ones that the best solutions are often the simplest ones.
I remember sitting with the Anthropic team early on and discussing how they thought about the anatomy of a failed ai pivot: a post-mortem.. Their approach was counterintuitive but brilliant.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to the anatomy of a failed ai pivot: a post-mortem. 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 AI Angle
I can't talk about the anatomy of a failed ai pivot: a post-mortem. in 2026 without mentioning AI. As someone who's invested in Anthropic, OpenAI, Scale AI, and Hugging Face, I have a front-row seat to how AI is transforming this space.
The short version: AI makes good practitioners better and bad practitioners worse. It's an amplifier, not a replacement.
I've seen companies use AI to 10x their the anatomy of a failed ai pivot: a post-mortem. capabilities. I've also seen companies waste millions on AI solutions that solved the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around AI competitive moats, AI market sizing, AI exit strategies, AI startup pivots, AI talent wars 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 the anatomy of a failed ai pivot: a post-mortem.: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat the anatomy of a failed ai pivot: a post-mortem. 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 the anatomy of a failed ai pivot: a post-mortem., 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
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.
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.
How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.