The End of the Frontier Threat

There was a time—not long ago—when every startup felt like they were building sandcastles in the path of a tsunami. GPT-4 was smarter than GPT-3.5, and GPT-5 was coming. General-purpose models kept getting better, and the unspoken fear was: Whatever we build today, OpenAI or Google will absorb tomorrow.

But something changed.

GPT-4.5 and LLaMA 4 arrived. They were bigger. They were trained longer. But they weren't noticeably better. Smarter people with more GPUs didn't win by default anymore. Suddenly, scale hit diminishing returns.

The gains now come from reinforcement learning—training a model not just to speak, but to reason. But reinforcement learning only works in places where you can measure success. That's code, math, logic. Recently, it's expanded to search-based tasks, where a model learns to look things up before it answers.

That's it.

So if you're not in those domains—if you're building a legal co-pilot, a sales automation engine, a creative brief generator—you're in luck. Generalist models probably won't get much better at your job. Not soon. Maybe not ever.

Now is the moment to double down on what you know that others don't. Your workflows. Your customers. Your weird, messy, unlabeled data. Train your own model. Tune it. Collect feedback. Iterate. The frontier is slowing down. And in that space, defensibility is making a quiet comeback.