Ben Evans writes astutely about the Deep Research problem faced by OpenAI, Google, Perplexity, and other implementations out there:

These things are useful. If someone asks you to produce a 20 page report on a topic where you have deep domain expertise, but you don’t already have 20 pages sitting in a folder somewhere, then this would turn a couple of days’ work into a couple of hours, and you can fix all the mistakes. I always call AI ‘infinite interns’, and there are a lot of teachable moments in what I’ve just written for any intern, but there’s also Steve Jobs’ line that a computer is ‘a bicycle for the mind’ – it lets you go further and faster for much less effort, but it can’t go anywhere by itself. 

Taking one step further back again, I think there are two underlying problems here. First, to repeat, we don’t know if the error rate will go away, and so we don’t know whether we should be building products that presume the model will sometimes be wrong or whether in a year or two we will be building products that presume we can rely on the model by itself. That’s quite different to the limitations of other important technologies, from PCs to the web to smartphones, where we knew in principle what could change and what couldn’t. Will the issues with Deep Research that I’ve just talked about get solved or not? The answer to that question would produce two different kinds of product. 

The error rate, because it’s not 100%, creates reliance issues on the output of Deep Research. In some fields, you can’t be 98% sure, you must be 100%. 

Furthermore, the data that Deep Research parses is on the (currently open) world wide web. But what happens when human beings no longer feel comfortable to publish ideas and thoughts freely? When all the data gets hoovered up by the machines, why gift it to them readily?