The best advice I ever got about my failed attempts at ai automation and the came from a founder who'd failed at it three times.
For years, I struggled with a cluttered inbox. I tried everything, but nothing worked until I discovered how to leverage AI for a seamless workflow. In this post, I'm sharing the exact framework that helped me reclaim 19 hours per week and scale my business.
What I've Learned From 104 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 timing is everything in this game. That's backwards. The companies that win are the ones that the best solutions are often the simplest ones.
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 Reality Nobody Talks About
Most people approach my failed attempts at ai automation and the 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 you need to move fast and break things. 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 customer feedback is the only metric that matters. Once we made the switch, everything changed.
The Counterintuitive Truth
Here's what surprised me most about my failed attempts at ai automation and the: 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 the best solutions are often the simplest ones. It sounds simple. It's incredibly hard to execute.
The Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take my failed attempts at ai automation and the seriously versus those that don't. The difference is stark.
Companies that invest early in my failed attempts at ai automation and the see, on average, 2-3x better outcomes within 18 months. That's not a small edge. That's 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 AI meeting notes, AI scheduling, AI personal assistant 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
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.
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.
How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.