I didn't go to business school. I learned how to build a multi-million dollar AI company from the trenches. After countless mistakes and a few lucky breaks, I've distilled my experience into these hard-won lessons. This is the stuff they don't teach you in books, the kind of knowledge you only get by being in the arena, getting your face kicked in, and getting back up again.
I remember a team that came to pitch me a few years ago. They were brilliant, truly. PhDs from Stanford, papers published in top journals, the whole nine yards. They had built an AI model that could predict, with startling accuracy, the box office performance of a movie based on its script. They showed me the demo, and it was technically magnificent. The data visualizations were slick, the model's architecture was elegant. They had spent two years and a million dollars in pre-seed funding building it.
"So," I asked, "who is going to pay for this?"
They looked at each other. "Well, movie studios, obviously."
"Have you talked to any?" I asked.
Silence.
They hadn't spoken to a single potential customer. Not one. They were so mesmerized by the technical challenge, so in love with their beautiful, complex model, that they forgot the single most important rule of business: you have to make something people actually want.
That company, despite its brilliant team and impressive tech, folded within six months. And their story isn't unique. It's the most common tragedy I see in the AI space.
After my last exit, I started angel investing, and I've now backed over 200 companies, including some names you might recognize like OpenAI, Anthropic, and Scale AI. This has given me a front-row seat to the AI revolution. And it's also given me a depressing amount of data on failure. We recently did an internal analysis of over 500 AI product launches from the past three years, from small startups to new features from FAANG companies. The results were stark.
The #1 reason AI products fail is that they are a solution in search of a problem.
It's not the tech. It's not the team's intelligence. It's not the funding. It's a fundamental misunderstanding of what a product is. A product is not a piece of technology; it's a tool that solves a problem for a specific person.
Too many founders, especially technical ones, get this backward. They get excited about a new model, a new technique, a new capability, and they think, "What can I build with this?" This is a fatal mistake. It's the hammer looking for a nail. You can have the most advanced hammer in the world, but if nobody needs a nail driven, you're not going to sell any hammers.
From our analysis, we saw this pattern manifest in a few key ways:
The "Magic Trick" Product
These are products that have a "wow" demo but no real-world utility. They perform a task that is technically impressive but doesn't solve a real, recurring problem. The movie prediction tool was a perfect example. It was a cool party trick, but it didn't fit into any existing workflow for a studio executive. The studios have their own complex, human-driven processes for greenlighting films. They weren't going to throw that away for a black-box AI they didn't understand, no matter how accurate it claimed to be.
We see this a lot with generative AI right now. Someone builds a tool that creates abstract art based on your mood, or writes poetry in the style of Shakespeare. It's fun to play with for five minutes, but it's not a business. It's a feature, maybe, but not a standalone product that people will pay for month after month.
The "Vitamin, Not a Painkiller" Problem
This is a classic startup trap, but it's amplified in AI. A "vitamin" is a product that's nice to have. It might make things a little better, a little more efficient. A "painkiller" solves a burning, urgent problem. People will crawl through broken glass to get a painkiller. They might forget to take their vitamins.
Many AI products are vitamins. They offer marginal improvements on an existing process. An AI that can summarize your emails a little bit better? Vitamin. An AI that helps a sales team write slightly more personalized outreach? Vitamin. These are not the billion-dollar opportunities.
The real opportunities are the painkillers. Find a process that is slow, expensive, and manual, and replace it with something 10x better. My first company, RemoteTeam, was a painkiller. Global payroll and compliance for remote employees was a nightmare. Companies were spending hundreds of hours and risking massive fines trying to figure it out. We automated it. That was a painkiller. People were desperate for a solution.
Ignoring the Last Mile
Another huge failure point is forgetting that the AI model is only one part of the product. You can have the most accurate model in the world, but if the user interface is confusing, the onboarding is a mess, and it doesn't integrate with the tools your customers already use, it will fail.
I call this the "last mile" problem. The last mile is the gap between the AI's output and the user's desired outcome. For example, an AI that identifies sales leads is great. But the last mile is getting that lead into the CRM, assigning it to a rep, and tracking the follow-up. If your product just gives the user a list of names in a CSV file, you haven't solved the whole problem. You've just created more work for them.
The best AI products are full-stack solutions. They own the entire workflow, from data input to final outcome. They don't just give you an answer; they help you take action on that answer.
So, How Do You Avoid These Traps?
It's not complicated, but it is hard. It requires discipline and a willingness to put your ego aside.
1. Fall in Love with the Problem.
Don't start with the tech. Start with the customer. Who are they? What keeps them up at night? What are they trying to do that is currently difficult, expensive, or frustrating? Go talk to them. Shadow them for a day. Become an expert in their problem. Don't even mention AI. Just listen. The goal is to find a problem so painful that people are already trying to solve it with duct tape and spreadsheets.
2. Build a Painkiller.
Once you have a deep understanding of the problem, ask yourself: is this a vitamin or a painkiller? A good test is to ask potential customers, "If I built this for you, would you be willing to prepay for a year upfront?" If the answer is a hesitant "maybe," it's a vitamin. If their eyes light up and they pull out their credit card, you've found a painkiller.
3. Solve the Whole Problem.
Don't just build a model. Build a workflow. Think about the entire user journey, from the moment they discover your product to the moment they achieve their desired outcome. What are all the steps involved? How can you make each step as seamless and automated as possible? Your job isn't done when the model spits out a prediction. Your job is done when the customer has successfully solved their problem.
4. Distribution, Distribution, Distribution.
This is the lesson that took me the longest to learn. The best product doesn't win. The best-distributed product wins. How are people going to find out about your product? How are you going to get it into their hands? This was key for MovieLaLa. We didn't just build an app; we built a community and a distribution engine through social media and partnerships. You need to think about your go-to-market strategy from day one.
Building a successful AI company is not about having the fanciest algorithm or the most PhDs on your team. It's about finding a real problem and solving it for real people. It's about empathy, discipline, and a relentless focus on the customer. Stop chasing the hype. Stop building magic tricks. Go find a painkiller. That's the secret they don't teach you in business school.
Frequently Asked Questions
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.
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.
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.
What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.