Sahin Boydas
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What 10,000 Hours of User Research Taught Me About Building Lovable AI

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.

2025-01-285 min read582 words

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 Counterintuitive Truth

In user research, the best practitioners often do less, not more.

The lesson: the data tells a different story than your gut. It sounds simple. It's incredibly hard to execute.

Why Most Approaches Fail

Many common approaches to user research are flawed at the root.

The root cause is usually one of three things:

Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.

Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.

Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.

Real Talk: What Actually Matters

Here is what actually matters in user research.

First, execution speed beats perfection. Every time. Companies rarely fail because they moved too fast on user research. 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 user research in a vacuum. Get out of the building. Talk to real people.

This connects to broader themes around user research ai, customer feedback, ai ethics that I've been thinking about a lot lately.

What's Next

Best practice in user research is changing fast. What worked last year might not work next year. That's both the challenge and the opportunity.

My advice: stay curious, stay humble, and stay close to the people who are actually doing the work. Read less thought leadership and do more experiments. Talk to fewer consultants and more practitioners.

If you're building in this space, the best time to get user research right is before you need to. Don't wait for a crisis to force your hand.

I'll keep sharing what I learn. This stuff matters too much to keep to myself.

FAQ

Frequently Asked Questions

Q: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.

Q: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.

Q: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 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.

Q: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.

Written by

Sahin Boydas

Serial entrepreneur, #1 angel investor, author of Becoming Top 1%

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