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
Here is a simple framework for evaluating AI productivity. It's not complicated, but it requires discipline.
Step 1: the market doesn't care about your roadmap
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: timing is everything in this game
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 that get AI productivity right treat it as an ongoing process, not a one-time project.
The Counterintuitive Truth
In AI productivity, the best practitioners often do less, not more.
The lesson: customer feedback is the only metric that matters. It sounds simple. It's incredibly hard to execute.
The AI Angle
Any discussion of AI productivity in 2026 has to include 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.
Some teams use AI to get far more out of AI productivity. Others spend heavily on AI that solves the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around AI scheduling, AI automation, AI email, AI writing tools, AI meeting notes that I've been thinking about a lot lately.
The Bottom Line
None of this is 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 AI productivity aren't the ones with the best strategy on paper. They're the ones who execute, learn, and adapt faster than everyone else.
Companies that take AI productivity seriously tend to pull ahead of the ones that don't.
If you're working on something interesting in this space, I'd love to hear about it. Drop me a line.
Frequently Asked Questions
Q: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.
Q: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.
Q: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.