Three years ago, I sat across from a founder who was about to make the same mistake I made with 10 ai infrastructure mistakes that are secretly killing. I told them the truth.
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.
The Counterintuitive Truth
Here's what surprised me most about 10 ai infrastructure mistakes that are secretly killing: 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 simplicity beats complexity every time. It sounds simple. It's incredibly hard to execute.
What I've Learned From 117 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with 10 ai infrastructure mistakes that are secretly killing.
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 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 10 ai infrastructure mistakes that are secretly killing. Their approach was counterintuitive but brilliant.
What I Tell Founders
When a founder in my portfolio asks me about 10 ai infrastructure mistakes that are secretly killing, I usually start with three questions:
- What's your timeline? Because the right approach for a company with 6 months of runway is very different from one with 3 years.
- What have you already tried? Most founders have tried something. Understanding what didn't work is often more valuable than knowing what might.
- Who on your team owns this? If the answer is "everyone" or "no one," that's your first problem to solve.
These questions seem simple but they reveal a lot about where a company actually stands.
This connects to broader themes around TPU, AI cloud, AI data centers 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 10 ai infrastructure mistakes that are secretly killing: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat 10 ai infrastructure mistakes that are secretly killing 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 10 ai infrastructure mistakes that are secretly killing, 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
Which item on this list has the highest impact?
It depends on your stage and context, but in my experience, the items near the top of the list tend to have the broadest applicability. That said, sometimes the less obvious items create the biggest breakthroughs for specific situations.
How do I know which items apply to my situation?
Start by honestly assessing where your biggest bottleneck is right now. The items that address that specific constraint will give you the highest return on your time and energy.
Are these recommendations still relevant in 2026?
Absolutely. While specific tools and tactics change, the underlying principles remain consistent. I update my thinking regularly based on what I'm seeing in the market and across my portfolio companies.
Can I implement all of these at once?
I'd strongly recommend against it. Pick the 2-3 items that resonate most with your current situation and focus there. Trying to do everything simultaneously is a recipe for doing nothing well.