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 it’s 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 Stanley’s latest CIO survey says that 30% of big company CIOs don’t 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 it’s 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 isn’t.
Microsoft’s 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 we’ve seen since, they’re ‘wrong’ enough, and the ‘wrong’ is hard enough to manage, that you can’t 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
