The costs of running generative AI are mind-blowing considering energy, training, and materials, while the revenue case is still largely missing. In just one example, Silicon Valley venture capital firm Sequoia estimates that in 2023, the tech industry spent $50 billion on chips from Nvidia to train AI but generated only $3 billion in revenue. A similar mania in the 1999-2000 tech top was rewarded with a brutal capital drubbing. See WSJ The AI revolution is already losing steam:
“…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 fibre-optic boom of the late 1990s—a boom that led to some of the biggest crashes of the first dot-com bubble.”
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