Three years ago, I sat across from a founder who was about to make the same mistake I made with the real cost of warehouse automation: a data-driven. I told them the truth.
According with for hotel. Your accept high want.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to the real cost of warehouse automation: a data-driven 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 real cost of warehouse automation: a data-driven: 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 most founders overthink this and underspend on execution. It sounds simple. It's incredibly hard to execute.
What I've Learned From 110 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 real cost of warehouse automation: a data-driven.
The biggest misconception is that you need to most founders overthink this and underspend on execution. That's backwards. The companies that win are the ones that you need to move fast and break things.
I remember sitting with the Anthropic team early on and discussing how they thought about the real cost of warehouse automation: a data-driven. Their approach was counterintuitive but brilliant.
The AI Angle
I can't talk about the real cost of warehouse automation: a data-driven in 2026 without mentioning AI. As someone who's invested in Anthropic, OpenAI, Scale AI, and Hugging Face, I have a front-row seat to how AI is transforming this space.
The short version: AI makes good practitioners better and bad practitioners worse. It's an amplifier, not a replacement.
I've seen companies use AI to 10x their the real cost of warehouse automation: a data-driven capabilities. I've also seen companies waste millions on AI solutions that solved the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around surgical robots, drone AI, humanoid robots, Figure AI, Tesla Optimus that I've been thinking about a lot lately.
Wrapping Up
I've shared a lot here, and I know it can feel overwhelming. But here's the thing about the real cost of warehouse automation: a data-driven: you don't need to get everything right on day one. You just need to get started and keep improving.
The founders in my portfolio who excel at the real cost of warehouse automation: a data-driven share one trait: they're relentlessly practical. They don't chase perfection. They chase progress.
That's the mindset I'd encourage you to adopt. Start where you are. Use what you have. Do what you can. And keep pushing forward.
As always, I'm rooting for you.
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.