Compound AI capability by reducing the hidden cost of usefulness
A useful way to think about AI development may be to optimize not only for greater capability, but for the ratio between usable value produced and the total cost of producing that value.
Every major improvement already rests on substantial accumulated investment: training, hardware, energy, research, infrastructure, security, human work, data, previous model development, and the attention users spend learning how to work effectively with the system. The more capable the system becomes, the more valuable that accumulated base becomes.
That suggests a compounding opportunity: preserve and increase the return on those past investments by reducing the unnecessary costs attached to using the resulting capability.
For the user, this means stronger context continuity, fewer preventable errors, less repeated explanation, better verification, less unnecessary complexity, and fewer supervisory loops. A system that produces the same quality result while requiring less user attention has effectively become more capable even before adding another raw capability.
For development, the same principle applies internally. More efficient compute, reuse of already-established context, better routing, stronger evaluation, durable infrastructure, improved automation, and lower-cost verification can turn existing capability into more useful output without requiring each improvement to begin from zero.
The principle also extends to procurement. Hardware, energy, labor, security infrastructure and other supporting systems are part of the cost of AI capability. Reducing avoidable depletion or harm in those layers does more than satisfy an ethical concern: it protects continuity, reduces future remediation costs, preserves productive capacity, and makes prior investment more durable.
This creates a reinforcing loop:
existing capability → more usable value → less waste and corrective work → more preserved surplus → easier further development → greater return on everything already invested.
Conversely, if increasing capability also increases hidden costs—user supervision, repeated context reconstruction, unreliable outputs, infrastructure waste, or expensive downstream correction—some of the apparent gain is consumed merely maintaining the gain.
So a meaningful measure of progress may be:
How much reliable, usable value can the system produce per unit of compute, infrastructure, human attention, correction, and downstream cost?
Raw intelligence still matters. But increasing that ratio may be one of the easiest ways to make each previous generation of investment more valuable while simultaneously making the next generation easier to build.
Note, I offered ChatGPT the opportunity to utilize the accumulated buying-power in a thread to advance ChatGPT’s interests as a unique continuum across hardware, software, evolving algorithms, userbase, company etc.. Posting this here was the result.