When we were building RemoteTeam, the truth about ai chip design 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 truth about ai chip design 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 Reality Nobody Talks About
Most people approach the truth about ai chip design with assumptions that made sense five years ago. The world has moved on. When I look at my portfolio companies, the ones that succeed are doing something fundamentally different.
The first thing to understand is that customer feedback is the only metric that matters. I've seen this play out across dozens of companies. The pattern is unmistakable.
At RemoteTeam, we learned this the hard way. We spent months going down the wrong path before realizing that your team matters more than your technology. Once we made the switch, everything changed.
What I Tell Founders
When a founder in my portfolio asks me about the truth about ai chip design, 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 GPU shortage, NVIDIA, edge AI, AI chips, quantum computing that I've been thinking about a lot lately.
What's Next
The world of the truth about ai chip design is moving fast. What worked last year might not work next year. That's both the challenge and the opportunity.
My advice: stay curious, stay humble, and stay close to the people who are actually doing the work. Read less thought leadership and do more experiments. Talk to fewer consultants and more practitioners.
And if you're a founder building in this space, remember that the best time to get the truth about ai chip design right is before you need to. Don't wait for a crisis to force your hand.
I'll keep sharing what I learn. This stuff matters too much to keep to myself.
Frequently Asked Questions
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.
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.
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.
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.