In late 2024, I was working with a company that had a sales problem, yet some leaders focused on building AI features.

Interestingly, the core problem of the company — revenue — was not front and center for the leadership team. AI took center stage, and most important engineering resources got pulled from the core product to build ‘innovative AI projects’.

They had read about companies transforming their products with AI. They believed their project would look like those stories.

Nobody asked: what is happening to companies like ours that did this?

That is survivorship bias. And it is everywhere in AI right now.

Survivorship bias is when we study only what survived and overlook what didn't.

In Outliers, Malcolm Gladwell writes about Steve Jobs, Bill Gates, and Mark Zuckerberg, all dropped out of university. Someone looks at that and concludes: dropping out is a key to success in tech. What is missing? The thousands of dropouts who went nowhere. Nobody writes articles about them.

ChatGPT is the Bill Gates of AI. The dropout who became a billionaire.

But there are dropouts everywhere. McDonald's tested an AI drive-through in partnership with IBM. In one incident, the AI ordered a customer 18,000 cups of water. The program was quietly shut down.

Projects like these are far more common than the billion-dollar success stories,
They are just less likely to be discussed.

This data can help us avoid survivorship bias. When an AI project is imagined, most teams reference the success stories. These numbers show what happens when we look at the full picture instead.

US businesses have invested between $35 and $40 billion in generative AI. Very little to show for it.

These are the numbers that don't make it into the conversation. And this is why the better question is not "how do we add AI?" but

"what burden can AI actually remove?"

The company in my opening had a revenue problem. Users were churning. AI could have helped

Instead, the team built an AI feature that looked impressive in a demo but had nothing to do with the user's actual problem.

AI should start with the user's burden. Think about Nadia, a product manager.

Her week splits into Goal Time (customer conversations, discovery, trade-off decisions) and Tool Time (status updates, formatting PRDs, pulling dashboard data, searching Confluence).

In 2024, I did not ask these questions. The team I worked with started with excitement, not with the company's real problem. We saw what AI could do in other companies. We did not study what it failed to do in companies like ours.

Three years into AI, we have enough data to make better decisions. These are not reasons to avoid AI. They are reasons to start with your company's objective — the user's real problem and work backward from there.

The companies that will get value from AI are the ones that skip the highlight reel and ask: what burden can we actually remove? What friction is costing us users? And is the technology ready to solve that specific problem today?

That is the outside view. And according to my experience, it is the only one that works.

💬 Claude — strong momentum, but now hitting limits as growth raises questions around consistency and reliability

🧠 ChatGPT — after stepping away for a month while testing Claude, I’m back. It still performs better for clear English variations and structured writing

⚙️ Open Claw — testing it this week for local automation on my system. Early focus is workflow control, not just outputs

I’ll share what holds up and what breaks in next week’s update

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