Laboratory automation is real: liquid handlers, plate readers, high-throughput sequencing, and cloud labs already change who can run which protocols. The question is not whether any steps can be automated, but whether biology’s wet core will become as programmable as software. This essay argues for structural resistance—not impossibility of progress, but asymmetric difficulty.
What automation has already won#
Where inputs are standardised and readouts are digital, automation thrives:
- Sequencing pipelines with stable library preps
- Plate-based assays with fixed volumes and timings
- Imaging with controlled optics and barcodes
These domains still need humans for exception handling, but the happy path is machine-runnable. That success tempts a generalisation: the rest of the lab is only a few robots away.
Three sources of resistance#
1. Physical contingency#
Biological materials vary. A “standard” cell line drifts; a reagent lot changes; humidity alters evaporation in a 384-well plate. Software failures are reproducible from logs; many wet failures are not, or not cheaply.
| Software analogue | Wet-lab reality |
|---|---|
| Deterministic CPU | Variable living systems |
| Bit-identical copies | Batch effects |
| Free rollback | Consumed samples |
| Stack traces | Ambiguous phenotypes |
2. Tacit skill and perception#
Pipetting technique, colony picking, “how viscous does this look?”, and knowing when a gel “looks wrong” remain partly embodied. Vision systems and force sensors improve, but the long tail of lab craft is expensive to encode.
3. Open-ended protocols#
Discovery science changes the protocol mid-course. Automation prefers closed workflows. When the hypothesis shifts, the robot’s program is suddenly the wrong abstraction. Cloud labs help with scheduled, pre-specified work; they help less when the experiment is a conversation with nature.
Full automation is easiest when you already know the answer’s shape. Biology often does not.
Partial automation is still transformative#
Resistance is not a counsel of despair. High leverage exists in:
- Instrument APIs and data models that make partial pipelines composable
- Exception-aware orchestration—humans as supervisors of fleets, not pipetters of every well
- Standardisation where science allows without pretending all biology is ELISA
The economic picture may resemble aviation more than pure software: high automation with mandatory human judgment at critical junctures.
A caution on forecasts#
Claims that “self-driving labs will close the loop on all of biology” should be treated as speculation unless scoped to a protocol class with clear success metrics. Historical automation in chemistry and manufacturing offers analogies, but cells are not parts bins.
Closing position#
Wet-lab biology will keep absorbing automation at the edges and in standardised cores. The centre of gravity—messy samples, shifting questions, living variance—will continue to demand human responsibility. Designing tools that respect that fact is more useful than promising a fully unmanned discovery engine.
Notes and references#
Layout demonstration. Empirical rates of automation adoption vary by subfield; readers should consult domain surveys rather than treat this taxonomy as data.