The first time I tried to implement my failed attempts at ai automation and the at scale, everything broke. Not metaphorically. Actually broke.
For years, I struggled with writer's block. I tried everything, but nothing worked until I discovered how to leverage AI for automated scheduling. In this post, I'm sharing the exact framework that helped me reclaim 20 hours per week and scale my business.
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 data tells a different story than your gut. It sounds simple. It's incredibly hard to execute.
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: your team matters more than your technology This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: most founders overthink this and underspend on execution 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.
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 automation, AI email, AI writing tools, AI scheduling, AI personal assistant that I've been thinking about a lot lately.
The Bottom Line
Look, my failed attempts at ai automation and the isn't rocket science. But it does require intentionality, consistency, and a willingness to learn from mistakes.
If you take one thing from this article, let it be this: start now, start small, and iterate. The founders who win at my failed attempts at ai automation and the aren't the ones with the best strategy on paper. They're the ones who execute, learn, and adapt faster than everyone else.
I've been doing this for over a decade. The patterns are clear. The companies that take my failed attempts at ai automation and the seriously outperform the ones that don't. Every single time.
If you're working on something interesting in this space, I'd love to hear about it. Drop me a line.
Frequently Asked Questions
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.
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.
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.