The first time I tried to implement the top 3 mistakes founders make when choosing an ai api at scale, everything broke. Not metaphorically. Actually broke.
I've been experimenting with SaaS pricing for over a decade. In this masterclass, I'll distill everything I've learned into 10 actionable lessons. This is a must-read for any founder who wants to master the art and science of pricing.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating the top 3 mistakes founders make when choosing an ai api. 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: your team matters more than your technology 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 in my portfolio that nail the top 3 mistakes founders make when choosing an ai api are the ones that treat it as an ongoing process, not a one-time project.
The Counterintuitive Truth
Here's what surprised me most about the top 3 mistakes founders make when choosing an ai api: the best practitioners do less, not more.
When I was building MovieLaLa, we tried to do everything at once. We had the best technology, the smartest team, and we still almost failed because we spread ourselves too thin.
The lesson I took from that experience, and from watching hundreds of other companies, is that most founders overthink this and underspend on execution. It sounds simple. It's incredibly hard to execute.
What I Tell Founders
When a founder in my portfolio asks me about the top 3 mistakes founders make when choosing an ai api, I usually start with three questions:
- What's your timeline? Because the right approach for a company with 6 months of runway is very different from one with 3 years.
- What have you already tried? Most founders have tried something. Understanding what didn't work is often more valuable than knowing what might.
- Who on your team owns this? If the answer is "everyone" or "no one," that's your first problem to solve.
These questions seem simple but they reveal a lot about where a company actually stands.
This connects to broader themes around cloud AI services, vertical SaaS, AI APIs, AI infrastructure costs that I've been thinking about a lot lately.
Final Thoughts
After two exits, 200+ investments, and more mistakes than I can count, here's what I know for sure about the top 3 mistakes founders make when choosing an ai api: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat the top 3 mistakes founders make when choosing an ai api as a competitive advantage, not a checkbox. They invest in it early, measure it obsessively, and never stop improving.
If you're just getting started with the top 3 mistakes founders make when choosing an ai api, don't be intimidated. Everyone starts somewhere. The key is to start with the right mindset and the right framework, and then execute like your company depends on it. Because it probably does.
Frequently Asked Questions
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.
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.
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.