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A lot of these charts are really about what happens when the utopian dreams of AI maximalism meet the messy reality of consumer behaviour and enterprise IT budgets - it takes longer than you think, and its complicated (this is also one reason why I think doomers are naive). The typical enterprise IT sales cycle is longer than the time since Chat GPT3.5 was launched, and Morgan Stanleys latest CIO survey says that 30% of big company CIOs dont expect to deploy anything before 2026. They might be being too cautious, but the cloud adoption chart above (especially the expectation data) suggests the opposite. Remember, also, that the Bain Production data only means that this is being used for something, somewhere, not that its taken over your workflows.

Stepping back, though, the very speed with which ChatGPT went from a science project to 100m users might have been a trap (a little as NLP was for Alexa). LLMs look like they work, and they look generalised, and they look like a product - the science of them delivers a chatbot and a chatbot looks like a product. You type something in and you get magic back! But the magic might not be useful, in that form, and it might be wrong. It looks like product, but it isnt.

Microsofts failed and forgotten attempt to bolt this onto Bing and take on Google at the beginning of last year is a good microcosm of the problem. LLMs look like better databases, and they look like search, but, as weve seen since, theyre wrong enough, and the wrong is hard enough to manage, that you cant just give the user a raw prompt and a raw output - you need to build a lot of dedicated product around that, and even then its not clear how useful this is. Firing LLM web search out of the gate was falling into that trap. Satya Nadella said he wanted to make Google dance, but ironically the best way to compete with Bing Copilot might have been sit it out - to wait, watch, learn, and work this through before launching anything (if Wall Street had allowed that, of course).

The rush to bolt this into search came from competitive pressure, and stock market pressure, but more fundamentally from the sense that this is the next platform shift and you have to grab it with both hands. Thats much broader than Google. The urgency is accelerated by that standing on the shoulders of giants moment - you dont have time to to wait for people to buy devices - and from the way these things look like finished products. And meanwhile, the firehose of cash that these companies produced in the last decade has collided with the enormous capital-intensity of cutting-edge LLMs like matter meeting anti-matter.

In other words - These things are the future and will change everything, right now, and they need all this money, and we have all this money.

As a lot of people have now pointed out, all of that adds up to a stupefyingly large amount of capex (and a lot of other investment too) being pulled forward for a technology thats mostly still only in the experimental budgets.

Link:

The AI summer - Benedict Evans