Metadata →
AI inference costs have dropped sharply, but total AI spending has grown even faster, showing Jevons’ paradox in action. Cheaper AI makes new uses possible, so demand rises instead of falling. This growth may either drive real value like railroads or create waste like telecoms, and it’s still unclear which path AI will follow.
Highlights
id1011854762
In 1865, a 29-year-old English economist named William Stanley Jevons published a book with an observation that contradicted every intuition about efficiency. Britain’s steam engines had become dramatically more fuel-efficient over the previous decades. The logical expectation was that coal consumption would fall — the same work, less fuel. Jevons showed the opposite had happened. Coal consumption had increased tenfold. The efficiency gains hadn’t reduced demand. They had made coal useful in contexts where it previously wasn’t economical, and the resulting proliferation of steam engines overwhelmed the per-unit savings.
Paradoja de Jevons
id1011854882
The cost of running a large language model has collapsed. Epoch AI’s analysis of inference pricing shows a median decline of 50x per year across performance benchmarks, accelerating to 200x per year for data since January 2024. GPT-4-equivalent performance that cost 0.40. The total reduction over three years is roughly a thousandfold.
Reducción sistemática y profunda del costo de la inferencia espeja la reconocida ley de Moore, y habilita la ocurrencia de la paradoja de Jevons aplicada al mundo de la IA.
id1011855320
Jevons’ paradox doesn’t just mean more spending. It means the spending becomes harder to track, harder to attribute, and harder to justify — because the same efficiency that enables proliferation prevents measurement. When AI is in everything, the cost of AI is the cost of everything. The data confirms this. Only 51 percent of organizations can confidently measure their AI return on investment. The other half are spending more, deploying more, and unable to say whether any of it is working.
Esta es una reflexión importante que afecta el furor de utilizar herramientas de IA para todo, la “claude manía”… somos realmente más eficientes y efectivos? Cómo lo sabemos? Qué cambios y métricas observamos? Es importante tener este problema en consideración al utilizar IA Generativa, para no caer víctima del hype y subirse a una rueda de hámster sólo para hacerla correr más rápido, sin desplazarse a ningún lugar.