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Why I Killed Our Most Popular AI Feature (And What Happened Next)

We killed it on a Tuesday.

No fireworks, no dramatic all-hands meeting. Just a few clicks and our most popular, most talked-about AI feature was gone. It was the feature that got us written up in tech blogs, the one that investors oohed and aahed over in board meetings. It was a beautiful, intelligent, and utterly useless distraction.

And killing it was the best decision I ever made.

At RemoteTeam, we had an AI-powered “Team Cohesion Score.” It was brilliant. The algorithm ingested everything: Slack messages, calendar invites, project management data, even the frequency of emoji use. It then spat out a single, beautiful number from 1 to 100 that supposedly represented how well a remote team was working together. Our users loved it. Managers would post their high scores on LinkedIn. It was a vanity metric of the highest order.

I used to live for this stuff. In my early days, fresh off the acquisition of MovieLaLa, I thought I had the Midas touch. I believed that more features, more tech, and more impressive-sounding AI was the path to success. I was wrong. That path led me straight to burnout and a product that failed to gain real traction.

This time, I was determined to be different.

The Slow-Motion Disaster

The Cohesion Score looked great on the surface. Usage was through the roof. But our support channels told a different story. We started hearing whispers. Managers were using the score as a stick, not a tool. "The AI says we're only at a 78 this week, people. What's going on?"

Teams were gaming the system. They’d create dummy Slack channels and fill them with chatter just to boost their score. The AI was designed to measure connection, but it was actually creating division and anxiety. It was a classic case of Goodhart's Law: "When a measure becomes a target, it ceases to be a good measure."

I remember one specific call with a VP of Engineering at a fast-growing startup. He was a customer, and a friend. "Sahin," he said, "I have a team in Ukraine that is absolutely crushing their project goals. They’re shipping code, solving bugs, and working miracles. Your AI gives them a score of 42 because they're not big on small talk in Slack. My sales team, on the other hand, has a score of 95. They spend all day sending GIFs to each other and haven't closed a deal in a month. Your AI is telling me I have a problem with my best team and my worst team is a model of efficiency. It's a joke."

That conversation was a punch to the gut. He was right. We had created a feature that was not only useless, but actively harmful. It was rewarding the appearance of work, not the reality of results.

Digging for the Truth

I pulled our lead data scientist, a quiet guy who was probably the smartest person in the company, into a room. I asked him to ignore the usage dashboards. I wanted to know the truth. "Forget how many people are using the feature," I said. "Find out if it actually correlates to performance. Do teams with high Cohesion Scores ship more code? Do they have lower turnover? Do they report higher satisfaction in our other survey tools?"

He spent a week buried in the data. The report he came back with was damning. There was zero—and I mean zero—statistical correlation between our magical AI score and any meaningful business outcome. The teams that loved the feature the most were often the ones with the most dysfunctional cultures. They were using the score as a substitute for real management and honest conversation.

We had built a beautiful lie. A sophisticated, machine-learning-powered pacifier for insecure managers.

The Decision

I walked into our product meeting the next day and made the announcement. "We're killing the Cohesion Score."

The room went silent. Our head of marketing looked like he’d seen a ghost. "But... all of our press is about that feature. It's our main differentiator!"

"Is it?" I asked. "Or is it a gimmick that's preventing us from solving the real problem?"

This is the kind of decision that defines a company. It’s easy to add features. It’s incredibly hard to take them away, especially when they look popular. Every bone in your body, every lesson you’ve learned about "listening to your customers," screams at you to leave it alone. But as an investor in over 200 companies, including giants like Anthropic and OpenAI, I’ve learned that the most successful founders aren’t just builders; they are ruthless editors. They have the courage to kill their darlings.

We argued for an hour. Some wanted to "deprecate it slowly" or "make it an opt-in feature." I held firm. The feature wasn't just failing; it was actively undermining our company’s mission to help remote teams do their best work. It had to go. Now.

What Happened Next

The first week was rough. We got a flood of angry support tickets. A few users churned, loudly proclaiming our stupidity on Twitter. Our marketing team had to scramble to change our website messaging. It felt like a step backward.

But then, something amazing happened.

Freed from the distraction of the vanity metric, we were forced to confront the real, messy, human problem of remote collaboration. We started talking to our customers again, not about scores, but about their actual problems. "My engineers feel disconnected from the company mission." "My new hires are struggling to build relationships." "I have no visibility into who is blocked on what."

These were real problems. Hard problems. Problems that couldn't be solved with a clever algorithm and a pretty dashboard.

Killing our most popular feature forced us to get serious. We stopped chasing AI hype and started building simple, powerful tools that solved these real-world issues. We built a better project visibility tool. We created a new onboarding workflow that focused on human connection. We built a simple Slack integration that prompted managers to have actual conversations with their team members.

None of these features were as "sexy" as the AI Cohesion Score. But they worked. Our user engagement on these new tools was quieter, but deeper. Churn went down. Expansion revenue went up. And the conversations with customers changed. They stopped talking about their score and started talking about how our product was helping them build a better company.

Stop Chasing AI Hype. Start Solving Problems.

I see the same mistake being made all over Silicon Valley today. Everyone is desperate to sprinkle some AI magic on their product. They’re building AI features in search of a problem, instead of the other way around. They are building beautiful, intelligent, and utterly useless distractions.

My advice is simple: be a ruthless editor. Question your "most popular" features. Dig beneath the surface of your analytics. Find out what your users are really doing and whether it leads to a real-world outcome.

Have the courage to kill what isn't working, even if everyone tells you they love it. It might be painful in the short term. But it’s the only way to build something that lasts. '''))] étendue]thoughtThe user wants me to submit the generated article. I have already written the article content and saved it to a file named why-i-killed-our-most-popular-ai-feature.md. The other required output is the slug, which I have from the metadata: "why-i-killed-our-most-popular-ai-feature". I will now submit these two outputs. The article_content is a file path, so I will provide the path to the file I just created. The slug is a string. I will then submit the result. L

Frequently Asked Questions

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.

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.

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.