The argument for current LLM AIs leading to AGI has always been that they would spontaneously develop independent reasoning, through an unknown emergent property that would appear as they scale. It hasn't happened, and there's no sign that it will.
That's a dilemma for the big AI companies. They are burning through billions of dollars every month, and will need further hundreds of billions to scale further - but for what in return?
Current LLMs can still do a lot. They've provided Level 4 self-driving, and seem to be leading to general-purpose robots capable of much useful work. But the headwinds look ominous for the global economy, - tit-for-tat protectionist trade wars, inflation, and a global oil shock due to war with Iran all loom on the horizon for 2025.
If current AI players are about to get wrecked, I doubt it's the end for AI development. Perhaps it will switch to the areas that can actually make money - like Level 4 vehicles and robotics.
It's what Altman has constantly said was going to happen. Up to you to decide if he's actually in the industry or not.
When has Sam Altman said LLMs will reach AGI? Can you provide a primary source?
He said it again a few days ago on a Reddit AMA.
Perhaps the most interesting comment from Altman was about the future of AGI - artificial general intelligence. Seen by many as the ‘real’ AI, this is an artificial intelligence model that could rival or even exceed human intelligence. Altman has previously declared that we could have AGI within "a few thousand days".
When asked by a Reddit user whether AGI is achievable with known hardware or it will take something entirely different, Altman replied: “We believe it is achievable with current hardware.”
https://www.techradar.com/computing/artificial-intelligence/chatgpt-5-wont-be-coming-in-2025-according-to-sam-altman-but-superintelligence-is-achievable-with-todays-hardware
That's not Sam Altman saying that LLMs will achieve AGI. LLMs are large language models, OpenAI is continuing to develop LLMs (like GPT-4o) but they're also working on frameworks that use LLMs (like o1). Those frameworks may achieve AGI but not the LLMs themselves. And this is a very important distinction because LLMs are reaching performance parity so we are likely reaching a plateau for LLMs given the existing training data and techniques. There is still optimizations for LLMs like increasing context window sizes etc.