Building an AI startup is anything but glamorous. I want to take you behind the curtain and share the unfiltered reality of our journey. From the heated debates over our roadmap to the bug that almost derailed our launch, this is the real story of what it takes to build and ship an AI product.
The Reality Nobody Talks About
A lot of the advice on AI product metrics rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that you need to move fast and break things. 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 timing is everything in this game. Once they make the switch, everything changes.
What I've Learned From 35 Companies
Across 234 angel investments, including Anthropic, a few patterns in AI product metrics come up again and again.
The founders who handle AI product metrics well understand that the best solutions are often the simplest ones.
Real Talk: What Actually Matters
Here is what actually matters in AI product metrics.
First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on AI product metrics. 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 product metrics in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around product analytics ai, founder story, mental health 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 product metrics 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 product metrics 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:What are the most common mistakes with AI product metrics?
The biggest mistake I see is overcomplicating things early on. Start with the simplest version that works, get real feedback, and iterate from there. Another common trap is copying what worked for someone else without understanding the context behind their decisions.
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.
Q:Do I need technical skills for AI product metrics?
Not necessarily. While technical understanding helps, the most important skills are clear thinking and the ability to break problems into smaller pieces. Many successful founders I've invested in started with zero technical background and either learned enough to be dangerous or found the right technical partner.
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.