Getting AI infrastructure wrong can sink a young company.
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
Many common approaches to AI infrastructure are flawed at the root.
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
In AI infrastructure, the best practitioners often do less, not more.
The lesson: your team matters more than your technology. It sounds simple. It's incredibly hard to execute.
What I've Learned From 150 Companies
A few patterns in AI infrastructure come up again and again.
The founders who handle AI infrastructure well understand that you should focus on one thing and do it exceptionally well.
The Numbers Don't Lie
Investing early in AI infrastructure can be 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
There are no shortcuts in AI infrastructure, but there are smarter paths.
The strongest teams treat AI infrastructure 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 infrastructure, 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
Q: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.
Q: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 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
Q: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.
Q: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.