Why AI Will Widen the Gap Between Superstars and Everybody Else (www.wsj.com)

AI won’t just replace routine work — it will amplify the productivity and market power of those who are already best positioned to use it. The piece argues that large firms and “superstar” individuals will pull further ahead because AI acts as a force multiplier: access to proprietary data, massive compute, integrated engineering teams, and the skill to fine-tune and compose models lets top performers squeeze far more value from the same tasks. In practice this means faster, higher-quality outputs from those who can build toolchains (MLOps, inference pipelines, prompt and RLHF expertise), buy bespoke models, and iterate quickly — while everyone else sees incremental or even negative gains. For the AI/ML community this has technical and policy implications. Technically, it highlights where advantages arise: scale and data-driven fine-tuning, low-latency inference, multimodal models, embeddings and retrieval-augmented generation, and tooling for human-AI co-pilot workflows. That concentrates research, compute, and talent in elite labs and products, shaping benchmarks and priorities toward winner-take-all capabilities. Mitigations include open-source models, shared compute grants, federated and privacy-preserving training, public datasets and model cards, and regulation/antitrust measures — all of which will influence how equitable access to AI capabilities evolves and where researchers and engineers focus their efforts.