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San Francisco: A quietly growing belief in Silicon Valley could have immense implications: the breakthroughs from large AI models — the ones expected to bring human-level artificial intelligence in the near future — may be slowing down.Since the frenzied launch of two years ago, AI believers have maintained that improvements in generative AI would accelerate exponentially as tech giants kept adding fuel to the fire in the form of data for training and computing muscle.The reasoning was that delivering on the technology’s promise was simply a matter of resources — pour in enough computing power and data, and artificial general intelligence (AGI) would emerge, capable of matching or exceeding human-level performance.Progress was advancing at such a rapid pace that leading industry figures, including Elon Musk, called for a moratorium on AI research.Yet the major tech companies, including Musk’s own, pressed forward, spending tens of billions of dollars to avoid falling behind. , ChatGPT’s Microsoft-backed creator, recently raised $6.6 billion to fund further advances. xAI, Musk’s AI company, is in the process of raising $6 billion, according to CNBC, to buy 100,000 Nvidia chips, the cutting-edge electronic components that power the big models.However, there appears to be problems on the road to AGI. Industry insiders are beginning to acknowledge that (LLMs) aren’t scaling endlessly higher at breakneck speed when pumped with more power and data.
