Human progress is an evolutionary process of conjecture, criticism and error correction. We generate explanations, test them against reality, discard what fails and build on what survives. Thinking machines dramatically expand our capacity to do this.
But this is not an argument for random experimentation. The best conjectures are guided by good explanations: explanations that are hard to vary without breaking their ability to account for what we observe. Experimentation matters because it allows us to test those explanations and discover where they are wrong.
The mistake is assuming we already know which uses of compute, tokens or energy are worthwhile. Value is often only obvious after the experiment runs. What looks frivolous, inefficient or wrong today may contain the explanation that unlocks something important tomorrow.
The goal should therefore be abundance: cheap energy, abundant compute and widely accessible intelligence. Abundance lowers the cost of being wrong, making it cheaper to test better explanations, correct errors and discover new knowledge.
Trying to eliminate every mistake by restricting experimentation risks destroying the very mechanisms that produce progress. Preserve error correction, reward good explanations and make it cheap to test them.