The buzz surrounding advanced artificial intelligence (AI) seems to be fading a bit. OpenAI’s latest language model, Orion, is not living up to the high expectations set by its predecessors like GPT-4 and GPT-3. Reports from Bloomberg and The Information suggest that the model is not performing well, especially in areas like coding.

But OpenAI is not the only one facing challenges. Google’s Gemini model and Anthropic’s Claude 3.5 Opus are also not meeting their anticipated performance levels. This trend of diminishing returns in the AI industry indicates that simply making models bigger and more powerful may not be sustainable in the long run.

Margaret Mitchell, chief ethics scientist at Hugging Face, believes that a shift in training approaches may be necessary to achieve human-like levels of intelligence and versatility in AI. The industry’s heavy reliance on scaling – increasing model size and training data – is becoming expensive and unsustainable. Companies are now struggling to obtain high-quality datasets without human input, especially in language-related tasks.

The costs of building and running cutting-edge AI models are on the rise. Anthropic CEO Dario Amodei estimates that by 2027, these models could cost over $10 billion each to develop. With Opus and Gemini facing performance issues and limited advancements, it appears that the rapid progress of the AI industry may be slowing down.

As Noah Giansiracusa, a mathematics professor, points out, the era of rapid AI advancements may have been short-lived and unsustainable. The industry now needs to find new approaches to AI development that can bring significant breakthroughs without breaking the bank.

In conclusion, the AI industry may be experiencing a slowdown as companies grapple with the limitations of current scaling strategies. It is evident that a new direction is needed to drive AI innovation forward in a sustainable and cost-effective manner.