[...] if a given tool provides massive benefits, it would stand to reason that it would be widely adopted quickly, because early adopters would start massively out-competing everyone else, thus pushing everyone else to figure out how to adopt those tools as well. No?
This would happen if the benefits and the costs were to be concentrated.
When the benefits are diffuse, and the costs are concentrated, then adoption lags.
One such example in a regulated industry would be to allow certain AI models to give valid medical prescriptions—the costs are concentrated on doctors and the benefits are diffuse amongst patients.
Another example that doesn’t depend on regulations are escalators: When they were adopted by department stores in the 60′s, the benefits accrued to the consumers while the department store owners ate up the costs. None of the businesses gained an strategic advantage from it.
If all firms in the same industry face the same cost of implementing AI, then whomever discovers the most optimal way of implementing AI will end up paying all the costs of discovery through trial and error, and gain little to no strategic advantage The only advantage would be “first-mover advantage” which in a competitive industry isn’t really worth much.
Therefore the only industries where we would expect to see quick AI adoption are those that:
Are dominated by scale economies such that first-mover advantage matters.
Are not too regulated.
Have healthy profit margins and a good cashflow to finance the trial-and-error discovery process.
It’s hard to find such industries because (1) strong economies of scale is usually correlated with natural oligopolies and thereforre (2) regulation, such as telcos or airlines. It’s really only the software industry where we have (1) and (2) together because the initial capex is not so massive - you can’t start an airline from a garage—and economies of scale come mostly from network effects.
Indeed, it’s only really software where we are actually finding that the frontier AI models are disrupting the way that things are done.
Other industries are mostly passively following what software tells them to do, and looking for low costs:
Use AI for notetaking! - Unless you are in the legal sector and this creates new problems
Use AI to read documents faster! - Unless you are in academia and you didn’t even read the research papers to start with
Use AI to finish that deck faster! - Unless the bosses internalize this and just increase the number of meetings!
Those are not things that can massively increase productivity and allow you to outcompete the rest. Ergo, as @marquis_de_sod said, “culture and adoption lags capabilities, A LOT”
This would happen if the benefits and the costs were to be concentrated.
When the benefits are diffuse, and the costs are concentrated, then adoption lags.
One such example in a regulated industry would be to allow certain AI models to give valid medical prescriptions—the costs are concentrated on doctors and the benefits are diffuse amongst patients.
Another example that doesn’t depend on regulations are escalators: When they were adopted by department stores in the 60′s, the benefits accrued to the consumers while the department store owners ate up the costs. None of the businesses gained an strategic advantage from it.
If all firms in the same industry face the same cost of implementing AI, then whomever discovers the most optimal way of implementing AI will end up paying all the costs of discovery through trial and error, and gain little to no strategic advantage The only advantage would be “first-mover advantage” which in a competitive industry isn’t really worth much.
Therefore the only industries where we would expect to see quick AI adoption are those that:
Are dominated by scale economies such that first-mover advantage matters.
Are not too regulated.
Have healthy profit margins and a good cashflow to finance the trial-and-error discovery process.
It’s hard to find such industries because (1) strong economies of scale is usually correlated with natural oligopolies and thereforre (2) regulation, such as telcos or airlines. It’s really only the software industry where we have (1) and (2) together because the initial capex is not so massive - you can’t start an airline from a garage—and economies of scale come mostly from network effects.
Indeed, it’s only really software where we are actually finding that the frontier AI models are disrupting the way that things are done.
Other industries are mostly passively following what software tells them to do, and looking for low costs:
Use AI for notetaking! - Unless you are in the legal sector and this creates new problems
Use AI to read documents faster! - Unless you are in academia and you didn’t even read the research papers to start with
Use AI to finish that deck faster! - Unless the bosses internalize this and just increase the number of meetings!
Those are not things that can massively increase productivity and allow you to outcompete the rest. Ergo, as @marquis_de_sod said, “culture and adoption lags capabilities, A LOT”