This week, in an interview with Bloomberg, MIT economist Daron Acemoglu re-stated his generally pessimistic view that AI would only impact 5% of jobs, while warning of a stock crash. It’s the ExoBrain view that this perspective grossly underestimates AI’s transformative potential and completely overlooks the realities of 21st century knowledge work, productivity, innovation, and the new and complex dynamics of AI.
Acemoglu’s 5% relates to his estimate of the proportion of jobs “ripe for being taken over or heavily assisted by AI technologies”. This number is likely drawn from his previous work on task automation, for example in a 2022 paper he looks at how automation works at an occupational level. What this analysis does not do is factor in a range of more complex AI impacts, it’s blind to a future that will likely be materially different from the past. AI-powered science, technology and healthcare innovation could lift the upper bounds of economic growth, moving us on from the zero-sum model favoured by many economists. AI may enable new extremely efficient, high-revenue-per-employee businesses, remove external constraints on business-level automation, and also accelerate its own creation and adoption, speeding up economic change and so on. It’s the ExoBrain (and the market’s) bet that we will shift from a labour constrained to compute constrained economic system in the coming years, which is precisely why the datacentre build-out it is accelerating in every corner of the globe. These and many more complex dynamics make a task automation assessment – at an occupational level – somewhat inadequate.
History does show that industries and workforces evolve in response to technological revolutions. A recent study suggests 80% of software developers will need to upskill by 2027 due to AI’s impact. Rather than job losses, this points to a transformation of roles and the creation of new opportunities.
Ultimately Acemoglu’s methodology fails to capture AI’s true potential, unlocking, not simply automating, knowledge work. A leading expert in workplace efficiency, Cal Newport, argues that it’s fundamentally wasteful for highly skilled professionals to spend significant portions of their workday on administrative tasks, team chat and email. Yet this misallocation of expertise is the norm in today’s knowledge working environments. He cites the absurdity of a world-renowned vaccine researcher with decades of experience spending a third of their time fielding requests from HR, building management, finance, and on and on. Speaking recently on the 80,000 hours podcast, Newport proposes that whilst the 20th century saw incomparable productivity and wealth increases through industrial process automation, despite the ‘IT revolution’ knowledge work is still stuck in 1900. Task switching and information overload take trillions of dollars of GDP from the economy every year in every role, team and organisation, and assessing task automation at an occupational level fails to take this into account. Please raise your hand (or comment on this post) if you believe that your knowledge workplace is as efficient as it could be…
Takeaways: It’s time to recalibrate our expectations of AI’s transformative power and avoid placing too much store in over-simplified economic models. AI’s natural ability to hoover up low-level knowledge work, even at today’s levels of capability, will start to have impact as the costs drop further and the user interfaces improve (see the other topics this week!), the next phase will be far more interesting. Businesses should look beyond job displacement and focus on AI’s potential to enhance productivity, drive innovation, and create altogether new value streams. If AI could automate just 5% of the noise and task switching away from our daily lives, that in itself would unlock more output, more problem solving, and more economic growth. If AI can add to the zero sum (as we’re already seeing it do with breakthroughs such as AlphaFold doing work that was almost impossible prior to its creation) then the impacts will be vast. The real question isn’t whether AI will impact 5% or 95% of today’s jobs, but how we can harness its potential to drive progress across all levels and sectors of the economy and society.