Back in 2022, after my first encounter with ChatGPT, I suggested that it was likely to wipe out large categories of “bullshit jobs”, but unlikely to create mass unemployment. In retrospect, that was probably an overestimate of the likely impact. But three years later, it seems as if an update might be appropriate.
In the last three years, I have found a few uses for LLM technology. First, I use a product called Rewind, which transcribes the content of Zoom meetings and produces a summary (you may want to check local law on this). Also, I have replaced Google with Kagi, a search engine which will, if presented with a question, produced a detailed answer with links to references, most of which are similar to those I would have found on an extensive Google search, avoiding ads and promotions. Except in the sense that anything on the Internet may be wrong, the results aren’t subject to the hallucinations for which ChatGPT is infamous.
Put high-quality search and accurate summarization together and you have the technology for a literature survey. And that’s what OpenAI now offers as DeepResearch I’ve tried it a few times, and it’s as good as I would expect from a competent research assistant or a standard consultant’s report. If I were asked to do a report on a topic with which I had limited familiarity, I would certainly check out what DeepResearch had to say.
Here, for example, is the response to a request to assess whether Canada should adopt the euro. I didn’t give much in the way of prompting except to request an academic style. If we ignore developments post-Trump (which wouldn’t have been found by a search of academic and semi-academic publications) it’s pretty good, at least as a starting point. And even as regards Trump, the response includes the observation “If, hypothetically, … political relations with the U.S. soured badly, the notion of joining a stable currency area might gain some public traction. ”
So, just as Github Copilot has led to big changes in coding jobs, I’d expect to see DeepResearch having a significant impact on a lot of research projects. That in turn implies an increase in productivity, which had been lagging in the absence of major new developments in IT for the decade or so before the recent AI boom.
None of that implies the kind of radical change often tossed about in discussions of AI, or even the kind of disruption seen when an existing analog technology is suddenly subject to digital challenge – the classic example was the “quartz crisis” in the watch industry. Coding has been progressively automated over time with the development of tools like online code libraries.
Similarly, research processes have changed year by year over my lifetime. When I was starting out, reference aids like citation indexes and the Journal of Economic Literature were brand new, and only accessible in libraries. Photocopying articles was sufficiently expensive and painful that you only did it for the important stuff (Daniel Ellsberg’s account of the work involved in producing multiple copies of the Pentagon papers gives you an idea). Today, a well organised research assistant with reasonable background knowledge could do the same job as Deep Research in a couple of days, and without needing to leave home (to level the playing field with AI, I’m assuming the RA is free to plagiarise at will, as long as the source is cited).
The other big question is whether efforts like this will generate profits comparable to the tens of billions of dollars being invested. I’ve always doubted this – once it became clear that LLMs were possible, it was obviously possible to copy the idea. This has been proved, reasonably conclusively by DeepSeek (no relation to DeepResearch), an LLM developed by a medium-sized Chinese company at a claimed cost of $500m. It charges a lot less then ChatGPT while providing an adequate substitute for most of the current applications of LLMs.
The likely collapse of the current AI boom will have some big economic repercussions. Most importantly and fortunately, it will kill (in fact, is already killing) the projections of massively expanded electricity demand from data centres. In combination with the failure of Tesla, it will bring down the market valuations of most of the “Magnificent Seven” tech stocks that now dominate the US share market. That will be bad for the US economy, but to the extent that it weakens Trump, a good thing for the world and for the long-term survival of US democracy.
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