Will AI Lead to Cognitive Costs "Near the Cost of Electricity?"

“The cost of intelligence should eventually converge to near the cost of electricity,” says OpenAI head Sam Altman. The economic implications could be quite significant. 

For example, the pricing of services that rely on intelligence (consulting, legal, creative) could approach the marginal cost of electricity, disrupting traditional business models. In other words, at least some cognitive tasks might become commoditized. 

Even if we might not be able to directly compare the “cost” of cognitive activity and equivalent operations conducted by an AI model, we might all agree that AI generally uses more energy and resources upfront than humans to achieve a similar single outcome, but then can scale to produce vastly more output with lower marginal cost. 

In other words, the AI advantage comes when we scale the activities. Looking at the matter in terms of water or electricity consumption, humans use relatively little energy and water in performing knowledge work, but throughput is limited and cost scales roughly linearly with quantity.

If one human produces one unit of work, then 10 units requires 10 humans. AI outperforms at scale. 

From an environmental perspective, a single human brain is “greener” than a single massive AI for one unit of task; however, to match an AI that can do one million tasks, you’d need an army of humans whose combined footprint (millions of computers, offices, and lives) might then rival or exceed the AI’s footprint.

There are other imponderables as well. At least some might speculate that we are entering an age where cognitive labor scales like software: infinite supply, zero distribution cost, and quality improving constantly. 

To be sure, the cost of employing cognitive workers is far more complicated than simple consumption of electricity and water. Still, at scale, AI impact on cognitive work does seemingly create economies of scale. 

By that logic, AI doesn’t just automate tasks; it commoditizes thinking. Of course, that looks only at cognitive input costs, not outputs. We probably are going to have to look at outcomes produced by using AI at scale, such as curing a particular disease or reducing production costs for some product. 

Still, it is shocking to ponder the economic implications of cognitive costs related to the cost of the electricity and water required to produce the models and inferences.