Companies like Meta are engaging in "chart crimes" to frame new models in the best possible light. By selectively highlighting winning benchmarks (e.g., in blue), they create a visual impression of superiority, even when the model underperforms in other key areas. This signals that benchmarks are becoming marketing tools rather than objective measures.

While public discourse on AI models often focuses on incremental improvements in common tasks like writing emails, the most profound advancements are happening in specialized fields like science and mathematics. This capability gap creates a disconnect in perceived progress.

Don't trust academic benchmarks. Labs often "hill climb" or game them for marketing purposes, which doesn't translate to real-world capability. Furthermore, many of these benchmarks contain incorrect answers and messy data, making them an unreliable measure of true AI advancement.

AI models will produce a few stunning, one-off results in fields like materials science. These isolated successes will trigger an overstated hype cycle proclaiming 'science is solved,' masking the longer, more understated trend of AI's true, profound, and incremental impact on scientific discovery.

Unlike medicine or biology, which require messy, expensive real-world experiments, pure mathematics offers a cost-effective and prestigious arena for AI labs to demonstrate their models' abstract reasoning power. A proof is a proof, requiring no lab work or physical trials to validate.

Despite student and faculty desire for open discussion on AI's role in education, university administrations are bypassing these conversations. Instead, they are rushing into corporate partnerships and branding strategies, treating AI as a marketing opportunity rather than a profound pedagogical and ethical challenge.

The core fear isn't just automation, but that AI will mechanistically solve existing problems without the creative leap that opens up entirely new fields of research. This could leave the discipline sterile, with a list of solved questions but no new avenues for human-led discovery.

While AI tools can empower talented students, they also enable amateurs to generate seemingly plausible but incorrect proofs. This floods professional mathematicians with requests to verify AI-assisted work from individuals who lack the foundational skills to check it themselves, creating a new form of expert burden.

Labs are incentivized to climb leaderboards like LM Arena, which reward flashy, engaging, but often inaccurate responses. This focus on "dopamine instead of truth" creates models optimized for tabloids, not for advancing humanity by solving hard problems.