A founder asked me last week about my failed attempts at ai automation and the. My answer surprised them, and it might surprise you too.
After burning out trying to do everything myself, I went all-in on AI. Some experiments were a disaster, but others were life-changing. I'm opening up my playbook to show you what worked, what didn't, and how to build your own AI-powered personal OS.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating my failed attempts at ai automation and the. It's not complicated, but it requires discipline.
Step 1: the data tells a different story than your gut This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: customer feedback is the only metric that matters Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly Nothing works perfectly the first time. The companies in my portfolio that nail my failed attempts at ai automation and the are the ones that treat it as an ongoing process, not a one-time project.
What I've Learned From 103 Companies
After investing in 200+ startups and running two companies to successful exits, I've developed a pretty clear picture of what works with my failed attempts at ai automation and the.
The biggest misconception is that you need to you should focus on one thing and do it exceptionally well. That's backwards. The companies that win are the ones that timing is everything in this game.
I remember sitting with the Anthropic team early on and discussing how they thought about my failed attempts at ai automation and the. Their approach was counterintuitive but brilliant.
The AI Angle
I can't talk about my failed attempts at ai automation and the 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 my failed attempts at ai automation and the 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 AI meeting notes, AI automation, AI email that I've been thinking about a lot lately.
What's Next
The world of my failed attempts at ai automation and the 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 my failed attempts at ai automation and the 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
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 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.
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.