For anyone unsure: Jevon’s Paradox is that when there’s more of a resource to consume, humans will consume more resource rather than make the gains to use the resource better.
Case in point: AI models could be written to be more efficient in token use (see DeepSeek), but instead AI companies just buy up all the GPUs and shove more compute in.
For the expansive bloat - same goes for phones. Our phones are orders of magnitude better than what they were 10 years ago, and now it’s loaded with bloat because the manufacturer thinks “Well, there’s more computer and memory. Let’s shove more bloat in there!”
Case in point: AI models could be written to be more efficient in token use
They are being written to be more efficient in inference, but the gains are being offset by trying to wring more capabilities out of the models by ballooning token use.
Costs have been dropping by a factor of 3 per year, but token use increased 40x over the same period. So while the efficiency is contributing a bit to the use, the use is exploding even faster.
Jevon’s Paradox is that when there’s more of a resource to consume, humans will consume more resource rather than make the gains to use the resource better.
More specifically, it’s when an improvement in efficiency cause the underlying resource to be used more, because the efficiency reduces cost and then using that resource becomes even more economically attractive.
So when factories got more efficient at using coal in the 19th century, England saw a huge increase in coal demand, despite using less coal for any given task.
For anyone unsure: Jevon’s Paradox is that when there’s more of a resource to consume, humans will consume more resource rather than make the gains to use the resource better.
Case in point: AI models could be written to be more efficient in token use (see DeepSeek), but instead AI companies just buy up all the GPUs and shove more compute in.
For the expansive bloat - same goes for phones. Our phones are orders of magnitude better than what they were 10 years ago, and now it’s loaded with bloat because the manufacturer thinks “Well, there’s more computer and memory. Let’s shove more bloat in there!”
They are being written to be more efficient in inference, but the gains are being offset by trying to wring more capabilities out of the models by ballooning token use.
Which is indeed a form of Jevon’s paradox
Costs have been dropping by a factor of 3 per year, but token use increased 40x over the same period. So while the efficiency is contributing a bit to the use, the use is exploding even faster.
More specifically, it’s when an improvement in efficiency cause the underlying resource to be used more, because the efficiency reduces cost and then using that resource becomes even more economically attractive.
So when factories got more efficient at using coal in the 19th century, England saw a huge increase in coal demand, despite using less coal for any given task.