The AI landscape is shifting under our feet. Based on my conversations with hundreds of founders and investors, here are the three biggest trends in AI leadership that you need to prepare for now.
What I've Learned From 150 Companies
Across 234 angel investments, including Anthropic, a few patterns in AI leadership come up again and again.
The founders who handle AI leadership well understand that your team matters more than your technology.
The Reality Nobody Talks About
A lot of the advice on AI leadership rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that simplicity beats complexity every time. 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 the market doesn't care about your roadmap. Once they make the switch, everything changes.
The Framework That Actually Works
Here is a simple framework for evaluating AI leadership. It's not complicated, but it requires discipline.
Step 1: you need to move fast and break things
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: simplicity beats complexity every time
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 leadership right treat it as an ongoing process, not a one-time project.
Real Talk: What Actually Matters
Here is what actually matters in AI leadership.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on AI leadership. 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 leadership in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around AI leadership skills, AI change management, leading through uncertainty, AI transformation leadership 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 leadership 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 leadership 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:Can I implement all of these at once?
I'd strongly recommend against it. Pick the 2-3 items that resonate most with your current situation and focus there. Trying to do everything simultaneously is a recipe for doing nothing well.
Q:Are these recommendations still relevant in 2026?
Absolutely. While specific tools and tactics change, the underlying principles remain consistent. I update my thinking regularly based on what I'm seeing in the market and across my portfolio companies.
Q:How were these items selected?
Each item on this list comes from direct experience, either from building my own companies or from patterns I've observed across the 234 startups I've invested in. I prioritize practical, actionable items over theoretical concepts.