I almost gave up on data moats in the age of llms: a product manager's guide entirely. Then something clicked that changed my whole approach.
I didn't go to business school. I learned how to build a multi-million dollar AI company from the trenches. After countless mistakes and a few lucky breaks, I've distilled my experience into these 5 hard-won lessons. This is the stuff they don't teach you in books.
What I've Learned From 53 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with data moats in the age of llms: a product manager's guide.
The biggest misconception is that you need to the data tells a different story than your gut. That's backwards. The companies that win are the ones that the market doesn't care about your roadmap.
I remember sitting with the Anthropic team early on and discussing how they thought about data moats in the age of llms: a product manager's guide. Their approach was counterintuitive but brilliant.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to data moats in the age of llms: a product manager's guide 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.
Real Talk: What Actually Matters
I'm going to cut through the noise and tell you what actually matters when it comes to data moats in the age of llms: a product manager's guide.
First, execution speed beats perfection. Every time. I've never seen a company fail because they moved too fast on data moats in the age of llms: a product manager's guide. I've seen plenty fail because they moved too slow.
Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.
Third, talk to your users. This sounds obvious but you'd be amazed how many founders build their data moats in the age of llms: a product manager's guide strategy in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around product analytics ai, data strategy, competitive advantage 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 data moats in the age of llms: a product manager's guide: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat data moats in the age of llms: a product manager's guide 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 data moats in the age of llms: a product manager's guide, 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 should I work through this guide?
Don't try to absorb everything in one sitting. Read through once to get the big picture, then go back and work through each section as it becomes relevant to your current challenges. Bookmark it and return to it regularly.
Who is this guide designed for?
This guide is written for founders and operators who want practical, actionable advice rather than theoretical frameworks. Whether you're just starting out or scaling an existing business, the principles here apply across stages.
Is this guide based on real experience?
Every recommendation in this guide comes from direct experience, either from building and selling my own companies, or from patterns I've observed across 200+ angel investments. I don't write about things I haven't personally tested.