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OpenAI’s Flagship Model Just Hit a Wall—Can They Keep AI Advancing?
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Every technology has its S-curve. First, there’s the sprint. Growth explodes as breakthroughs come faster, improvements compound, and the boundaries of possibility expand with each iteration. Then, inevitably, comes the plateau.
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OpenAI is hitting that plateau. Its latest model, code-named Orion, is reportedly an improvement over GPT-4, but the leap isn’t as dramatic as we’ve seen in previous upgrades, like from GPT-3 to GPT-4. Orion might even underperform in areas like coding, where expectations for progress are high.
So, what happens now? This is where strategy matters more than sheer force. OpenAI has created a dedicated team to answer the tough question: “How do we keep advancing when the raw ingredients—massive new datasets, extreme computing power—are running thin?”
Their answer is nuanced and reflects the evolution of technology at scale. They’re not just throwing more data or compute at the problem. They’re rethinking the process: using synthetic data, where models train on AI-generated datasets, and fine-tuning their approach after the initial training rounds.
Think of it like the early days of chess-playing AI, where brute force met its limits and true breakthroughs came from teaching models to learn creatively, rather than simply memorizing positions. Synthetic data could open doors that real data has locked. And better fine-tuning can extract more from every byte of data already available.
The lesson here is simple: in the beginning, progress is easy because the path is new. Eventually, we reach the edges, where raw power no longer guarantees results. That’s when we shift from expansion to refinement, from more to better.
For OpenAI—and for anyone building in the long term—the question isn’t whether progress slows. It always does. The question is what you do next.