Every company meeting has that moment. Someone pulls up a slide about Apple, Google, or Amazon and asks: why can’t we move like them? The implication is always the same. If we just had the right tool, the right system, the right shortcut, we could get there faster.

AI gave that fantasy a new vehicle. Faster, cheaper, better. The promise is everywhere. And after sitting through another version of that pitch recently, the question I kept coming back to was: are you willing to pay the actual price?

Because there is always a price. The question is just whether you pay it upfront or later, with interest.

Fast solutions for hard problems

Ozempic exploded because it offered something people desperately wanted: a way out of a hard problem without doing the hard work. For people who genuinely needed it medically, it was a breakthrough. For most, it became a bypass, a way to avoid confronting the diet, the habits, the relationship with food that had created the problem in the first place. The weight went down. The underlying problem stayed.

And then the consequences started showing up.

Not immediately. That is the cruel part. In the short term, Ozempic works. Numbers improve. People feel vindicated. But muscle mass disappears alongside the fat. Metabolism adjusts. When people stop, the weight returns, because the body never learned to maintain itself. The medicine did the job the body was supposed to learn to do. And the body forgot.

So did AI.

The trap

This is not about AI being bad. It is not even about using it or not. Ozempic is not bad either. The parallel is in how we reach for it.

When a company adopts AI to automate away the symptoms of deeper dysfunction, the dysfunction does not disappear. It goes underground. A team that cannot communicate clearly will produce AI outputs that no one can act on. A team that shipped mediocre work will now ship mediocre work at twice the speed. Quality was already a problem. Now it is a faster problem, at higher volume, and harder to catch. A culture that rewards looking busy over doing meaningful work will use AI to look even busier.

The tool amplifies what is already there. If what is already there is unhealthy, you get more of that, quicker.

What I’ve been seeing in organizations is a version of the Ozempic trap. Leaders see faster delivery, fewer people, lower costs, everything AI promises, and reach for it the way someone steps on a scale and reaches for the prescription. The number goes down. The underlying condition remains.

What seems to be going on

Here is what the Ozempic cases teach us about what follows.

Skill atrophy. When you outsource the function, the muscle weakens. A team that stops writing, reasoning, designing, and debugging because AI does it for them does not stay at its current level. It regresses. The skill that made it valuable erodes quietly, invisibly, until the day the system fails or the context changes and there is no one left who knows how to do it manually.

Dependency without resilience. Ozempic patients who never changed their habits become permanently dependent on the drug. Organizations that automate without building underlying competence become permanently dependent on the vendor, the model, the API. When pricing changes, when the model behavior shifts, when the service goes down, there is no fallback. The organization forgot how to walk. A real production AI agent is a lot more complex than a RAG pipeline and a vector database. The dependency goes deeper than most people expect.

The underlying problem compounds. A broken process that AI accelerates is still a broken process, now running at higher speed and scale. Technical debt that gets papered over with generated code is still technical debt, now harder to untangle. A team that never learned to think clearly about a problem does not become clearer by delegating the thinking.

The cost curve bends the wrong way. AI is shipping code faster than any previous generation of tooling. That code has to live somewhere. It has to be read, reasoned about, and maintained — increasingly by AI itself. The more code exists, the more context the model needs to hold. The more context, the higher the cost per operation. If the code being generated is not clean, well structured, and intentional, you are not just accumulating debt. You are accumulating debt denominated in tokens, and the bill compounds with every release. The companies that treat AI as a shortcut to volume will eventually pay for every line multiple times: once to write it, and again, and again to understand what they wrote.

Name what is broken

None of this means you should not use AI. That would be like saying people with real metabolic conditions should refuse medicine that helps them. Given the pace of change, how the world is moving, some organizations genuinely need to restructure around AI to survive. The “medicine” has real uses.

But before the next meeting where someone puts up the Amazon slide, ask what a good doctor would ask. Not just whether the medicine works, but whether the patient is doing the work. Look honestly at the process you are about to accelerate. How far is the actual behavior from what you want it to be? How close are we to the processes that ship optimal results? Name what is broken. Fix it first, or at least face it honestly.

AI is not your Ozempic. It should not be. The difference between a tool that transforms you and a drug that masks the problem is not the tool. It is the work you are willing to do alongside it.

Start there.