The AI revolution is already losing steam
Nvidia reported eye-popping revenue last week. Elon Musk just said human-level artificial intelligence is coming next year. Big tech can’t seem to buy enough AI-powering chips.
It sure seems like the AI hype train is just leaving the station, and we should all hop aboard. But significant disappointment may be on the horizon, both in terms of what AI can do, and the returns it will generate for investors. The rate of improvement for AIs is slowing, and there appear to be fewer applications than originally imagined for even the most capable of them.
It is wildly expensive to build and run AI. New, competing AI models are popping up constantly, but it takes a long time for them to have a meaningful impact on how most people actually work. These factors raise questions about whether AI could become commoditized, about its potential to produce revenue and especially profits, and whether a new economy is actually being born.
They also suggest that spending on AI is probably getting ahead of itself in a way we last saw during the fiber-optic boom of the late 1990s—a boom that led to some of the biggest crashes of the first dot-com bubble. Most of the measurable and qualitative improvements in today’s large language model AIs like OpenAI’s ChatGPT and Google’s Gemini—including their talents for writing and analysis—come down to shoving ever more data into them. These models work by digesting huge volumes of text, and it’s undeniable that up to now, simply adding more has led to better capabilities.
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