The take-up of AI
Benedict Evans
I really admire Benedict Evans’ work, and his latest article is another great one. He looks at how AI uptake is measured, and more importantly, what those metrics actually mean. At the end he asks:
"the real question, as I’ve hinted at a couple of times, is how much LLMs will be used mostly as actual user-facing general-purpose chatbots at all or whether they will mostly be embedded inside other things"
I’ve been using LLMs more and more, and for many use cases treating one like “search on steroids” is enough. It delivers results fast — formatted tables, sources, links — and there’s that moment of relief: “Yes! The AI did all the work!”. Then comes the crash: broken links or claims that don’t hold up, and suddenly you’re verifying everything manually.
But that’s just Level-0 stuff. Where LLMs become really interesting is with deeper, multi-stage prompts. That’s also where many people lose traction. If you don’t put in the work — learn how the latest models behave; discover what prompt styles get results — you won’t get close to their potential.
A good example that comes to mind: CV-checking tools that promise to help you beat the ATS (Applicant Tracking System). Yes, you could write a detailed prompt yourself, feed in the job description and your CV, get feedback from GPT or Claude directly. But a lot of people prefer something simpler: upload your CV, click a button, see a slick chart and some step-by step improvement instructions. Easy. Fast.
At the moment my prediction is that for many, AI chatbots are still only wonderful and unexpected novelties. Only the hardcore users are using them close to full potential. I don’t have hard data to prove it, but I believe we’re still far from a world where AI fully, or even substantially replaces people. As William Gibson put it:
"The future is already here – it's just not evenly distributed"
I think it's the same for any tech in the business world. However revolutionary and productivity-enhancing a new technology is it takes time for it to mature and be adopted by organisations. Anyone who has been through a protracted digital transformation or system migration knows just how hard it can be, even if everyone agrees the change is worth doing.
Of course I could be completely wrong and we'll get to AGI in 2027, machines cross the Rubicon, self-improve, foom, we’re done. But for now I still think we are somewhere before the peak of inflated expectations on the hype cycle and people are way too excited.
Time will tell —unless the foom gets us first!
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