The last time your author opined about the state of artificial intelligence1 I predicted that commercial success required two things: first, that AI researchers focus on solving a specific business problem, and second, that enough data exists for that specific business problem.
The premise for this prediction was that researchers needed to develop an intuition of the business process involved so they could encode that intuition into their models.
In other words, that a general-purpose solution would not crack every business problem.
This might have been true temporarily, but it’s doomed to be wrong more permanently.
I missed a reoccurring pattern in the history of AI: that eventually enough computational power wins.
In the same way chess-playing engines that tried to encode heuristics about the game eventually lost to models that had enough computational power, these AI models for “specific business problems” have all just lost to the hundred billion parameters of GPT-3.

I am not known for being overly bullish on technology, but I struggle to think of everyday sorts of business examples where such a large language model would not do well.
It is true that in the above example the model did terribly on questions requiring basic arithmetic (converting rent per square foot per month to rent per square metre per year, for example), but these limitations are missing the point.
Computers are known to be adequate arithmetic-performing machines (hence the name), and surely future models would correct this and other deficiencies.
Artificial intelligence is now generally useful for business, and I am probably not thinking broadly enough about where it will end up.

One decent guess, however, might be augmented intelligence – the idea that AI is best deployed as a tool to increase the power and productivity of human operators rather than replace them. 2
Large language models like GPT-3 could be used to scale the work of a human or handle their dull, boring work, much like I might use a programming language to scale my work or automate away my dull, repetitive tasks.
We already have products like GitHub’s Copilot, which can sit alongside a programmer and make helpful suggestions of entire functions or algorithms, increasing the programmer’s productivity.
It’s not hard to imagine lawyers, doctors, accountants, marketers, salespersons, political speechwriters, et cetera, having similar AI assistants.
In fact, many already do!

This should recall to mind that technology is a lever.
Artificial intelligence algorithms will amplify the work that a single person can do – if that person is connected to the hive mind.

Let’s leave AI aside for a second and consider more pedestrian technologies.
In two decades smart phones, search engines, and social media went from bei