Editorial forecasts frame the mid-2020s as a window where integrated tooling matters: models that read literature at scale, propose candidates for synthesis, and slot into reproducibility pipelines — always alongside statistical hygiene humans insist on.
Where friction remains
Hallucinated citations and cherry-picked narratives still undermine naive automation. Leading labs embed verification steps — duplicate experiments, adversarial reviews of AI-written summaries, and structured datasets that downstream models cannot casually distort.
The scientist’s job evolves toward steering ensembles — defining rewards, constraints, and falsifiable checkpoints machines alone ignore.
Adjacent breakthroughs
Parallel advances in imaging, sequencing, and fabrication compound AI leverage: discovery accelerates when sensing captures sharper ground truth for models to learn from.