I'm probably going to get a lot of hate for this, but it needs to be said: your approach to user research is fundamentally flawed. We're all chasing shiny AI objects and forgetting the first principles of building great products. Here's the unpopular opinion that might just save your startup.
I see it every day. Founders and product managers get so excited about the latest AI tool that they completely lose sight of the human beings they're supposed to be building for. They throw a bunch of data at a model and expect it to spit out profound insights. That's not how it works. You can't just automate empathy.
Let me tell you a story. Back when we were building RemoteTeam, we were trying to solve the problem of making remote workers feel more connected. We had all this data on how people were using our platform, what features they were clicking on, and how much time they were spending on different tasks. We could have easily fed all of that into an AI model and gotten some interesting correlations. But we didn't.
Instead, we spent hours every week talking to our users. Real, messy, human conversations. We learned about their anxieties, their frustrations, and their hopes for the future of work. We learned that what they really wanted wasn't another feature, but a sense of belonging. That insight didn't come from a spreadsheet. It came from a real conversation with a software engineer in Poland who was feeling isolated from his team in San Francisco.
That's the kind of empathy you can't get from an algorithm. And that's what's missing from so many of the AI-powered user research tools I see today.
The Allure of the Shiny Object
It's easy to see why people are so drawn to these tools. They promise a shortcut. A way to get all the insights without any of the hard work. Just upload your user data, and our magical AI will tell you exactly what to build. It's a seductive pitch. But it's a dangerous one.
When you rely too heavily on AI to do your thinking for you, you start to lose your own product sense. You become a glorified data entry clerk, plugging numbers into a machine and hoping for the best. You forget how to ask the right questions. You forget how to listen.
I've seen this happen at some of my portfolio companies. A team will come to me with a new feature idea that they're really excited about. I'll ask them why they think it's a good idea. And they'll show me a chart from their fancy AI user research platform that says "users are 37% more likely to engage with this feature."
But when I ask them to tell me a story about a real user who would benefit from this feature, they can't. They don't have one. Because they haven't talked to any.
The Unscalable Secret to Great Products
Here's the secret that most people don't want to admit: building great products is unscalable. It requires doing things that don't scale. Like having one-on-one conversations with your users. Like manually reviewing every piece of feedback. Like taking the time to really understand the problem you're trying to solve.
AI can be a powerful tool to help you with this process. It can help you identify patterns in your data that you might have missed. It can help you automate some of the more tedious parts of user research. But it can't replace the human element. It can't replace empathy.
At MovieLaLa, we had a similar experience. We were trying to build a better way for people to discover new movies. We could have used an algorithm to recommend movies based on what people had watched in the past. And we did, to some extent. But the real breakthroughs came from talking to our users and understanding their emotional connection to movies. We learned that people don't just want to find a movie to watch. They want to feel something. They want to be transported to another world. They want to connect with characters and stories that resonate with their own lives.
That's the kind of insight that you can only get from real human interaction. And that's the kind of insight that will help you build a product that people love.
A Better Way Forward
So what's the solution? Should we all just abandon AI and go back to doing everything manually? Of course not. That would be like throwing the baby out with the bathwater.
The key is to find the right balance. To use AI as a tool to augment your own intelligence, not to replace it. Here are a few practical ways to do that:
- Use AI to generate hypotheses, not conclusions. Instead of asking your AI to tell you what to build, ask it to give you a list of interesting questions to explore. Then go out and talk to your users to find the answers.
- Automate the boring stuff. Use AI to transcribe your user interviews, to tag your feedback, and to identify high-level themes. But don't let it do the thinking for you. The real insights are in the details.
- Never lose sight of the "why." Behind every data point is a real human being with a real problem. Your job is to understand that problem and to find a way to solve it. Don't let the data obscure the story.
I've been fortunate to invest in some incredible AI companies like Anthropic, OpenAI, Scale AI, and Hugging Face. I'm a huge believer in the power of this technology to change the world for the better. But I also know that it's not a silver bullet. It's a tool. And like any tool, it can be used for good or for ill.
It's up to us, the builders, the creators, the entrepreneurs, to make sure that we're using it for good. To make sure that we're building products that are not just smart, but also wise. Not just efficient, but also empathetic. Not just profitable, but also human.
The next frontier of user research isn't about bigger models or more data. It's about deeper empathy. It's about getting back to the first principles of building great products. And it's about remembering that at the end of the day, we're not building for machines. We're building for people.
That’s the unpopular opinion that might just save your startup. Don’t be afraid to do the unscalable work. It’s the only way to build something that truly matters.
Frequently Asked Questions
How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
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'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.