AI for Science

• Where do scientific paradigms live?
• What role does human understanding and agency play?
• What’s an emerging playbook for AI-for-science?

• Paradigms: shift in science or in underpinning information infrastructure?
• Understanding: what do we still demand (and from whom)?
• Omni-models vs specialists: what’s gained/lost?

• We already struggle to see the whole of science across fields and institutions.
• LLM interfaces can lower the cost of moving across disciplines — but increase the risk of “plausible” error.
• So the skill that matters is scepticism: what would convince us we’re wrong, and where is the verification boundary?

• A model family \(f_\theta\) that maps inputs to outputs.
• A learning objective that scores how well \(f_\theta\) matches data (and priors/constraints).
• Optimisation + scale: we fit \(\theta\) with large compute and large datasets.

• AlphaFold
• GraphCast/Aardvark

• If the training data are rigorous equations (PDE/ODE solvers), we have a clearer sense of “ground truth.”
• These models may not map onto human intuitions — but they can still be scientifically useful.
• That makes physics a promising sandbox for a science of AI: what is learned, what generalises, and how do we verify?

• LLMs emulate human behaviour
• Unsurprising that the work better “in collaboration”
• Also with rapid access to information infrastructure.

• Denario
• Claude Code
• Codex

• Science as technology
• Science as understanding

• Covid19 Epidemiological Modelling
• Does your model account for Facemasks?
• Does your model account for Hotel Closures?

We might not have data, but we can do some arithmetic

• Compare with technical debt.
• Highlighted by Sculley et al. (2015).

• Decompose your complex problem/task into parts.
• Each part manageable (e.g. by a small team)
• Recompose to solve total problem

• Highly successful approach to complex tasks.
• Tuned to the human bandwidth limitation.
• But the whole system still hard to understand.

• Technical debt is the inability to maintain your complex software system.
• Intellectual debt is the inability to explain your software system.

• Agentic AI could pay down technical and intellectual debt.
• But it can create agentic debt: delegation without authority/authorship

• Delegation of workflows without crisp boundaries.
• Agentic debt is about unsafe or illegible delegation.

• Agentic Orchestration compress reading/writing/coding into a single interface.
• Tool use turns text into action: search, code execution, lab automation, simulation pipelines.
• This shifts the bottleneck to verification and scientific judgement.

• Operational understanding: can I use it safely and know when it fails?
• Mechanistic understanding: do I have an interpretable causal/mechanistic story?
• Paradigm understanding: can the community reproduce, contest, and extend it?
• Social understanding: are the ideas understood in the wider public and other fields?

• The “judgement layer” in organisations is often tacit: norms, exceptions, escalation paths, and context that rarely makes it into documentation.
• It lives in handoffs and approvals: what gets challenged, what gets waived, and what triggers a halt.
• Ceding this tacit knowlege without making it explicit is how we accumulate agentic debt.

• Human communication: walking pace (2000 bits/minute)
• Machine communication: light speed (billions of bits/second)
• Our sharing walks, machine sharing …

• Databases / tables (OEIS): store patterns and prior results.
• Solvers (CAS, SAT/SMT): mechanised search and case analysis.
• Modern triad: proof assistants, machine learning, large language models.

• Digitally verifiable: proof assistants (e.g. Lean) and machine-checkable artefacts.
• Operationally reliable: code, simulators, pipelines — repeatable, but not always interpretable.
• Tacit + contextual: protocols, judgement, and field knowledge (bio/geo/social science).
• LLMs can compress and transmit tacit knowledge — but they push the bottleneck to verification boundaries.

• We can hold humans to account for judgement (even when it’s wrong).
• We can’t hold models to account; they don’t bear responsibility or liability.
• So we must design systems where a named actor signs off, with traceable evidence.

• What do we want trainees to be able to explain, verify, and challenge?
• Where is the verification boundary in AI-for-science systems?
• Which artefacts are the new paradigm stores?