When we were building RemoteTeam, the 7 ai infrastructure mistakes that are secretly nearly killed us before we figured it out.
After years in the trenches of Silicon Valley, I've seen firsthand how the right AI hardware can make or break a company. I'm sharing the hard-won lessons and contrarian insights I wish I had when I started, from navigating the GPU shortage to building our own custom silicon.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to the 7 ai infrastructure mistakes that are secretly 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 Counterintuitive Truth
Here's what surprised me most about the 7 ai infrastructure mistakes that are secretly: 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 your team matters more than your technology. It sounds simple. It's incredibly hard to execute.
What I've Learned From 150 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 7 ai infrastructure mistakes that are secretly.
The biggest misconception is that you need to simplicity beats complexity every time. That's backwards. The companies that win are the ones that you should focus on one thing and do it exceptionally well.
I remember sitting with the Anthropic team early on and discussing how they thought about the 7 ai infrastructure mistakes that are secretly. Their approach was counterintuitive but brilliant.
The Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take the 7 ai infrastructure mistakes that are secretly seriously versus those that don't. The difference is stark.
Companies that invest early in the 7 ai infrastructure mistakes that are secretly see, on average, 2-3x better outcomes within 18 months. That's not a small edge. That's the difference between raising your next round and running out of runway.
One of my portfolio companies went from struggling to profitable in under a year after they finally got serious about this. The founder told me later that they wished they'd started sooner.
This connects to broader themes around GPU shortage, quantum computing, AI cloud, AI chips 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 7 ai infrastructure mistakes that are secretly: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat the 7 ai infrastructure mistakes that are secretly 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 7 ai infrastructure mistakes that are secretly, 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 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.
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.
Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.