GPT-6: When the "problem-solving process" disappears, AI competition enters a new round

OpenAI's release of GPT-6 could be a heavy blow to China's AI industry.

But the real impact is not that Chinese technology has suddenly fallen behind; it is that the pace of AI iteration is simply too fast, and the rules of the game are being rewritten.

The most noteworthy change in GPT-6 is the "black-boxing" of the reasoning process. Before giving an answer, the model goes through complex internal deliberation—breaking down the problem, verifying hypotheses, and self-correcting—but what users can see is often only the final conclusion.

What appears to be an optimization of user experience is actually an upgrade of the technical barrier.

Over the past two or three years, distillation has played a major role in why latecomers have been able to quickly close the gap. Distillation means having strong models generate large amounts of answer data that include complete reasoning processes, and then training your own model using those data. Answers can be copied, problem-solving approaches can be learned, and training costs are therefore compressed to one-tenth—or even less—of the original version’s cost. The rapid rise of models like DeepSeek and Qwen has benefited from this path to a large extent.

But if, in the future, frontier models only output conclusions and hide the process, the foundation of distillation will be shaken.

You can copy a perfect score test paper, yet you won’t see the reasoning process; you can get the correct answers, but you can’t reproduce the underlying chain of thought. From now on, the ability becomes difficult to replicate. For AI companies relying on a distillation route to achieve "low-cost pursuit," this is undoubtedly bad news.

The deeper challenge is this: once the shortcut of "copying homework" is blocked, the only way out is to build capabilities from scratch. That means constructing high-quality datasets from the ground up, designing more complex training frameworks, and investing in longer R&D cycles—which most directly shows up as compute demand shifting from "optional" to "mandatory."

For each successive generation that frontier models improve, the compute power required by challengers may double. This isn’t linear growth—it’s exponential escalation. For companies that haven’t yet built their own in-house systems and are used to "standing on the shoulders of giants," the window is rapidly narrowing.

From this perspective, the release of GPT-6 actually reinforces the long-term value of compute infrastructure. When training is no longer "optional" but a survival necessity, the position of compute giants like NVIDIA will become even more secure. What they face won’t be periodic swings in demand, but a round of structural advantages driven by "training is non-negotiable."

The underlying logic of AI competition is undergoing a fundamental shift: from "learning other people’s answers" to "paying to train your own capabilities." In this game, it’s no longer about the speed of imitation, but about the money-and-bucks investment, the depth of in-house R&D, and control over computing resources.

You can copy the answers, but in the end, the capability must grow from your own.