The 'Andy Warhol Coke' era, where everyone could access the best AI for a low price, is over. As inference costs for more powerful models rise, companies are introducing expensive tiered access. This will create significant inequality in who can use frontier AI, with implications for transparency and regulation.

The ambition to fully reverse-engineer AI models into simple, understandable components is proving unrealistic as their internal workings are messy and complex. Its practical value is less about achieving guarantees and more about coarse-grained analysis, such as identifying when specific high-level capabilities are being used.

Just as biology deciphers the complex systems created by evolution, mechanistic interpretability seeks to understand the "how" inside neural networks. Instead of treating models as black boxes, it examines their internal parameters and activations to reverse-engineer how they work, moving beyond just measuring their external behavior.

As AI models are used for critical decisions in finance and law, black-box empirical testing will become insufficient. Mechanistic interpretability, which analyzes model weights to understand reasoning, is a bet that society and regulators will require explainable AI, making it a crucial future technology.

John Jumper contends that science has always operated with partial understanding, citing early crystallography and Roman engineering. He suggests demanding perfect 'black box' clarity for AI is a peculiar and unrealistic standard not applied to other scientific tools.

Fears of a single AI company achieving runaway dominance are proving unfounded, as the number of frontier models has tripled in a year. Newcomers can use techniques like synthetic data generation to effectively "drink the milkshake" of incumbents, reverse-engineering their intelligence at lower costs.

To avoid a future where a few companies control AI and hold society hostage, the underlying intelligence layer must be commoditized. This prevents "landlords" of proprietary models from extracting rent and ensures broader access and competition.

For AI systems to be adopted in scientific labs, they must be interpretable. Researchers need to understand the 'why' behind an AI's experimental plan to validate and trust the process, making interpretability a more critical feature than raw predictive power.

The idea that one company will achieve AGI and dominate is challenged by current trends. The proliferation of powerful, specialized open-source models from global players suggests a future where AI technology is diverse and dispersed, not hoarded by a single entity.