Whoever writes the paper, the tools have to be trustworthy

If we are going to hand more of the writing, the citing and eventually the analysis to machines, then the tools doing that work have to verify before they assert, keep provenance attached, and leave ownership with the human whose name is on the paper.

Do we actually need to write research papers at all?

If we are going to lean on the creativity and the cross-domain thinking of human researchers, then surely that should be where the human time goes.

BioSingularity. The substrate determines the ceiling

Biology becoming computationally tractable depends less on model size than on the data substrate underneath. The ceiling is infrastructure.

Ground the model, or it invents the evidence

arXiv now bans authors for unchecked LLM output. The real fix is trusted, curated, subject-specific models rather than more policy.

The new era of going “Fast and Far” in research

A Nobel Prize in Chemistry went, in effect, to software. The old trade-off between small fast teams and large slow ones has stopped holding.

What parts of the academic knowledge creation & dissemination pipeline can we automate with AI?

Four working prototypes testing which parts of the research pipeline, from writing to review to publishing, can actually be automated.

OpenAccess.ai: How Cheap Can We Make Academic Publishing?

Article processing charges run to thousands per paper. OpenAccess.ai tests how much of the editorial stack can genuinely be automated.

Preprints.ai: How Much of Peer Review Can We Automate?

A large share of peer review is mechanical checking. Preprints.ai tests how much of it can be scored automatically, at preprint scale.

OpenScience.ai: Using Open Data to Generate Research That Doesn't Yet Exist

What claims are already latent in ClinVar, GTEx, STRING and Open Targets that nobody has written up yet? OpenScience.ai explores that gap.