The proliferation of AI leaderboards incentivizes companies to optimize models for specific benchmarks. This creates a risk of "acing the SATs" where models excel on tests but don't necessarily make progress on solving real-world problems. This focus on gaming metrics could diverge from creating genuine user value.

Static benchmarks are easily gamed. Dynamic environments like the game Diplomacy force models to negotiate, strategize, and even lie, offering a richer, more realistic evaluation of their capabilities beyond pure performance metrics like reasoning or coding.

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.

The primary bottleneck in improving AI is no longer data or compute, but the creation of 'evals'—tests that measure a model's capabilities. These evals act as product requirement documents (PRDs) for researchers, defining what success looks like and guiding the training process.

Instead of relying on hyped benchmarks, the truest measure of the AI industry's progress is the physical build-out of data centers. Tracking permits, power consumption, and satellite imagery reveals the concrete, multi-billion dollar bets being placed, offering a grounded view that challenges both extreme skeptics and believers.

Do not blindly trust an LLM's evaluation scores. The biggest mistake is showing stakeholders metrics that don't match their perception of product quality. To build trust, first hand-label a sample of data with binary outcomes (good/bad), then compare the LLM judge's scores against these human labels to ensure agreement before deploying the eval.

As reinforcement learning (RL) techniques mature, the core challenge shifts from the algorithm to the problem definition. The competitive moat for AI companies will be their ability to create high-fidelity environments and benchmarks that accurately represent complex, real-world tasks, effectively teaching the AI what matters.

Good Star Labs found GPT-5's performance in their Diplomacy game skyrocketed with optimized prompts, moving it from the bottom to the top. This shows a model's inherent capability can be masked or revealed by its prompt, making "best model" a context-dependent title rather than an absolute one.

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.