I almost gave up on email until a mentor showed me this counterintuitive approach. It felt wrong at first, but after implementing this AI-driven system, my productivity skyrocketed by 475%. Here's the step-by-step guide so you can do it too.
The Framework That Actually Works
Here is a simple framework for evaluating AI productivity. It's not complicated, but it requires discipline.
Step 1: customer feedback is the only metric that matters
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.
Why Most Approaches Fail
Many common approaches to AI productivity are flawed at the root.
The root cause is usually one of three things:
Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.
Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.
Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.
What I've Learned From 95 Companies
Across 234 angel investments, including Anthropic, a few patterns in AI productivity come up again and again.
The founders who handle AI productivity well understand that you should focus on one thing and do it exceptionally well.
The Numbers Don't Lie
Investing early in AI productivity can be 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 writing tools, AI automation, AI scheduling, AI email 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: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: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.
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.