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.
What I've Learned From 79 Companies
Across 234 angel investments, including Anthropic, a few patterns in AI automation come up again and again.
The founders who handle AI automation well understand that customer feedback is the only metric that matters.
The Framework That Actually Works
Here is a simple framework for evaluating AI automation. It's not complicated, but it requires discipline.
Step 1: most founders overthink this and underspend on execution
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: you should focus on one thing and do it exceptionally well
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 automation right treat it as an ongoing process, not a one-time project.
The Reality Nobody Talks About
A lot of the advice on AI automation rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that the best solutions are often the simplest ones. I've seen this play out across dozens of companies. The pattern is unmistakable.
Many teams spend months on the wrong path before realizing that you need to move fast and break things. Once they make the switch, everything changes.
Real Talk: What Actually Matters
Here is what actually matters in AI automation.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on AI automation. Many fail because they moved too slowly.
Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.
Third, talk to your users. It sounds obvious, but many founders make decisions about AI automation in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around AI automation, AI tools for founders, AI email, AI writing tools that I've been thinking about a lot lately.
What's Next
Best practice in AI automation is changing 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.
If you're building in this space, the best time to get AI automation 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
Q:What tools do I need to get started?
Start with the basics. You don't need expensive software or fancy tools. A spreadsheet, a note-taking app, and direct access to your customers will get you further than any enterprise platform. Add tools only when you hit a specific bottleneck.
Q:How long does it take to get AI automation right?
The timeline varies depending on your starting point and resources. For most founders, expect 2-4 weeks for initial setup and 2-3 months to see meaningful results. I've seen teams move faster when they focus on one thing at a time rather than trying to do everything at once.
Q:How do I measure success with this approach?
Pick one or two metrics that directly tie to your goal and track them weekly. Vanity metrics like page views or follower counts rarely matter. Focus on metrics that reflect real engagement or revenue impact.