I’m going to tell you something that might get me kicked out of the Silicon Valley entrepreneur’s club. For the first five years of my journey into building AI companies, I was doing it almost completely wrong. I didn’t have a grand vision that magically materialized. I didn’t have a secret playbook. I had a technical background, a lot of coffee, and a stubborn refusal to quit. And I made a ton of mistakes.
We managed to build RemoteTeam into something Gusto wanted to acquire, and I saw MovieLaLa get bought by Gfycat. I’ve been lucky enough to write checks for companies I admire, like Anthropic and OpenAI. But none of that came from following the script. It came from messing up, learning, and trying again. This isn't the stuff you read in business books. This is the stuff you learn in the trenches.
So, here are the 10 hard-won lessons I learned while building AI products from zero to a multi-million dollar exit.
1. Stop Fetishizing the Model
My first instinct, and the first instinct of almost every technical founder, is to build the coolest, most complex model possible. We’d spend months debating architectures, tweaking hyperparameters, and chasing a few extra points of accuracy. Here’s the brutal truth: your customers don’t care about your model.
They care about their problem. Does your product solve it? Is it fast? Is it reliable? In the early days of RemoteTeam, we were obsessed with creating a sophisticated NLP model to understand employee sentiment. It was clever, but it was slow and expensive to run. We eventually replaced it with a much simpler system based on keyword matching and rules. It was 80% as accurate but 100x faster and cheaper. No one noticed the difference, except our CFO, who was thrilled.
2. Your Real Moat is the Data Flywheel, Not the Algorithm
Algorithms are becoming a commodity. The latest and greatest from Google or OpenAI is available through an API. You can’t build a long-term, defensible business on a model that your competitor can rent for a few cents per call. Your real competitive advantage is your data.
Specifically, it’s the unique data you generate through user interaction. At MovieLaLa, we didn’t just recommend movies. We created a feedback loop. Every time a user rated a movie, they were making our recommendation engine smarter. More users meant more data, which meant better recommendations, which attracted more users. That’s a flywheel. That’s a moat. Focus on how your product can create that loop. How does using your product make your product better?
3. The Last 10% is 90% of the Work
Getting a prototype to work in a Jupyter notebook is easy. Fun, even. But turning that prototype into a production-ready AI product that can handle real-world, messy data at scale? That’s a different beast entirely. This is the stuff that kills most AI startups.
It’s the data cleaning, the edge case handling, the monitoring, the CI/CD for models, the latency optimizations. It’s the unglamorous, back-breaking work that makes the magic actually work. I’ve seen teams spend six months on a model and a year on the infrastructure to support it. Budget your time and resources accordingly. Don’t think you’re almost there just because the model works on a clean dataset.
4. “Human-in-the-Loop” Isn’t a Crutch, It’s a Feature
There’s this obsession in AI with full automation. The idea of a system that runs itself with no human intervention is seductive. It’s also mostly a fantasy. For any problem worth solving, the corner cases are infinite. Your model will eventually get it wrong.
Instead of fighting this, embrace it. Build systems that expect to be wrong and make it easy for a human to correct them. At RemoteTeam, our first payroll automation tool was basically a fancy checklist for our own operations team. It would flag anything it was unsure about, and a human would make the final call. Over time, we used those corrections to train the model, and the automation level crept up from 50% to 80% to 95%. It’s a partnership between the human and the machine, not a replacement.
5. Sell the Sizzle, Not the Steak
I used to get into meetings and immediately start talking about our recurrent neural networks and attention mechanisms. I could see the investors’ eyes glaze over. They didn’t care how it worked. They cared what it did for them.
Your pitch should be about the outcome. It’s not “we use a transformer model to analyze legal documents.” It’s “we save law firms 20 hours a week by automatically summarizing contracts.” Frame your product in terms of value, not technology. The tech is the engine, but you’re selling the car. Nobody asks about the piston design when they’re buying a Ferrari.
6. The UI is Part of the AI
An AI product isn’t just an API endpoint. The user interface is where the user experiences the AI. It’s how you build trust, handle uncertainty, and gather feedback. A confusing or misleading UI can make even the most brilliant AI model feel useless.
When your AI is uncertain, how do you communicate that? Do you just show a low confidence score, or do you explain why it’s uncertain? When it makes a mistake, how easy is it for the user to correct it? These are not just design questions; they are fundamental product questions. We spent as much time designing the UI for our AI features as we did on the models themselves.
7. Distribution is Your Toughest Problem
A brilliant AI product that no one uses is a tree falling in an empty forest. I’ve seen countless startups with amazing technology fail because they couldn’t figure out how to get it into the hands of users. They assumed that if they built it, customers would come.
They don’t. You need to solve the distribution problem from day one. Is it a sales-led motion? Product-led growth? A marketplace? An API? This will have a bigger impact on your success than your model architecture. We made this mistake early on. We built a tool, and only then did we ask, “Okay, who’s going to buy this and how will we reach them?” It should have been the first question.
8. Don’t Boil the Ocean
The potential of AI is so vast, it’s tempting to try and build a product that does everything. A general-purpose AI that can solve any problem for any user. This is a recipe for disaster. You’ll end up building a mediocre product that does nothing well.
Be ruthlessly specific. Find a small, painful, and well-defined problem for a specific user group. Solve that problem better than anyone else. At RemoteTeam, we didn’t try to solve all of HR. We started with one thing: payroll for remote employees in different countries. It was a niche, but it was a painful one. Once we nailed that, we could expand. But you have to earn the right to go broad by first going narrow.
9. Your First Model is a Hypothesis
Don’t spend a year in a cave building the perfect model. Your goal for the first version is to build the fastest, cheapest thing you can to test your core hypothesis. Is this problem solvable with AI? Do users actually want a solution? Can we get the data we need?
Your first model is not meant for production. It’s a tool for learning. It will probably be a mess of scripts and heuristics. That’s fine. Get it in front of a few friendly users. See how they use it. See where it breaks. The feedback you get will be more valuable than any amount of offline model tuning.
10. The Real Magic is Boringly Consistent Execution
From the outside, building an AI company looks like a series of brilliant breakthroughs. The reality is far more mundane. It’s about showing up every day. It’s about the discipline of running weekly sprints, of prioritizing ruthlessly, of having tough conversations, of fixing bugs, of talking to customers when you’d rather be coding.
Success in this game is not about a single moment of genius. It’s about thousands of small, unglamorous decisions made consistently over a long period. It’s about grinding it out when you’re not sure it’s going to work. The most successful founders I know aren’t necessarily the most brilliant. They are the most resilient.
Building an AI company has been the hardest and most rewarding thing I’ve ever done. I’m still learning, still making mistakes. But these are the lessons that have stuck with me. If you’re on a similar journey, I hope they can help you avoid some of the potholes I fell into. Now, go build something. And don’t be afraid to do it the wrong way first.
Frequently Asked Questions
What would you do differently looking back?
I'd move faster on the things that were working and cut the things that weren't sooner. Most founders, myself included, hold onto failing strategies too long because of sunk cost. Speed of learning is everything.
What was the biggest challenge in this case?
Almost always, the biggest challenge is people and alignment, not technology or strategy. Getting the right team focused on the right problem is harder than any technical challenge I've encountered.
How long did it take to see results?
Most meaningful business results take 3-6 months to materialize. Anyone promising overnight success is selling something. The companies in my portfolio that grew fastest were the ones that stayed patient and consistent.
Can these results be replicated?
The specific numbers will vary, but the underlying patterns and principles are transferable. The key is understanding the context behind the results, not just copying the tactics. Every company has unique constraints that shape what works.