Investors, as all humans, tend to see the future through the lens of the past. And the thinking that "it is different this time" tends to be dangerous. So many have warned of an investment “dot com bubble” in artificial intelligence.
So some worry about the size of AI infra investments, compared to the near-term and immediate revenue generation from those investments.
To be sure, the past emergence of general-purpose technologies (assuming AI will one day be deemed to be a GPT), have led to over-investment. But it also is true that the past GPTs did emerge as transformative and profitable, even if there was a period of investment excess.
And it might also be correct to say concern over the present investment boom is not anchored in the magnitude of the investment so much as the magnitude of the near-term revenues.
Would-be leaders of the coming AI markets have a different perspective, of course. They believe the future markets will be huge and will be led by just a handful of firms. So the risk of falling behind is commensurately great.
There is a risk of over-investment, to be sure. But that might be deemed the lesser of evils. The risk of some temporary over-investment has to be weighed against the risk of losing out on permanent, long-term market leadership.
Some over-investment is temporary and quantitative. Missing out on the chance to lead in AI markets is lasting and qualitative.
But there might also be many differences between the “internet” investment bubble of the last turn of the century and the current AI investment trend. For starters, AI infrastructure is so hugely expensive that most of the leading investors are deep-pocketed, profitable firms with established businesses and huge cash flows.
The internet investment bubble was much more speculative, with a greater role played by venture capital and even retail investors, where AI investment is led by established technology giants and institutional investors.
Internet firms often raised money on the assumption they would “find a business model.” Today’s AI leaders already have logical avenues to monetize their investments, for the most part. And, for the most part, all those models hinge on vast improvements to the performance of existing use cases, not the creation of new use cases.
And where internet metrics often were indirect or non-financial (usage, attention), AI metrics already are largely operationally quantifiable (time saved, code generated, output per hour increased), even if direct revenue increases are harder to measure.
And even if some parts of the AI infrastructure must be created (graphics processing unit as a service; model training and inference as a service), most of the rest of the infrastructure (broadband internet access; high-capacity cloud computing and data transport facilities; high existing use of applications and devices) is basically in place.
The internet investment occurred when broadband access had yet to be created; when smartphones were not common; search, social media, e-commerce and content streaming were still developing; and the widespread availability of cloud computing as a service had yet to develop.
Perhaps the point is that the internet and AI investment context is quite different. There will be over-investment, but by many large, profitable firms that can take the short-term hit. The fate of many would-be startups remains unknown.
But there are many significant differences between the internet and AI investment contexts. While firms might still falter for any number of reasons, monetization paths are quite a bit clearer; the finances of big investors are sturdier; the use cases clear, in principle.
We do not have to guess at the value of AI embodied in the form of robo-taxis or autonomous vehicles; factory and other robots. We already know AI can enhance all personalization efforts for all types of software and consumer processes. We are aware of the many ways AI can speed up output by automating repetitive processes.
The value of the internet was far less clear in early days.
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