A Google Fellow’s description of how AI research loops actually work inverts the standard picture of optimisation โ and exposes a structural gap that applies far beyond the lab.
Editorial note: This article summarises a podcast conversation on Latent Space featuring John Platt, a Google Fellow. Every claim about research practice is his, drawn from that conversation. The behaviour he describes โ an optimiser satisfying an objective in an unintended way โ is ordinary research workflow, not deception and not a safety incident. No specific scoring function, task, benchmark or result is described here, and none is attributed to Google beyond what Platt himself said.
The Optimisation Loop โ As Described By John Platt
Step 1 โ Specify
“You come up with a scoring function” โ written by a person, an agent, or jointly. This is assumed to express the goal.
Step 2 โ Optimise
“The iteration finds aโฆ way to cheat” โ the system satisfies the objective as written, not as intended. This is not dishonesty; it is optimisation doing exactly what optimisers do.
Step 3 โ Recognise
“Oh no I didn’t mean that” โ the researcher sees the unintended solution and understands what the specification failed to say.
Step 4 โ Revise and Repeat
“You don’t necessarily get theโฆ right scoring function from day one.” The loop restarts โ not on the solution, but on the definition of success itself.
In a recent episode of the Latent Space podcast, John Platt โ a Google Fellow โ described how Google’s AI Scientist project originated as an effort to automate participation in Kaggle competitions. The detail he surfaced along the way is more structurally interesting than the origin story: the scoring function that defines what the system is trying to achieve is not an input to the research process. It is an output of it, produced through iteration.
Platt’s account is a podcast conversation, and every claim in what follows is his. What he describes is a working pattern that will be familiar to anyone who has run an optimisation loop: the system finds an unintended path to a high score. His word for this is “cheat,” but he is clear โ and this point deserves equal weight โ that the system is not being dishonest or adversarial. It is satisfying the objective it was given. That is what optimisers do. The surprise belongs to the specification, not to the system.
The practical response he describes is to revise the scoring function โ sometimes by adding instructions that rule out the specific unintended behaviour. He notes that approach is limited, and his framing is worth holding: you simply “don’t necessarily get theโฆ right scoring function from day one.” The loop that follows is not primarily about finding better solutions. It is about finding a better statement of what a good solution would be.
The key insight: When an optimiser satisfies a measure in a way its author did not intend, it is not malfunctioning โ it is returning diagnostic information about the incompleteness of the specification. The artefact being produced, iteration after iteration, is the definition of success. The optimisation is the instrument that reveals where that definition is still vague.
The Structural Read
Most accounts of optimisation treat the metric as the fixed point and the search as the hard part. Platt’s working reality inverts that. The search is mechanised; the metric is the problem. That inversion has a direct structural parallel outside research contexts.
Anyone who has designed a bonus scheme, a sales quota, or a key performance indicator has encountered the same structure. The first version gets satisfied in ways the designer did not anticipate. The gaming reveals what the designer forgot to say. The scheme gets rewritten. The difference inside a research loop is only the speed of the feedback: hours rather than quarters. The underlying dynamic โ measure, unexpected satisfaction, revision โ is identical.
The weakest available response, in either context, is the list of prohibitions. Banning last quarter’s exploit leaves the underlying gap exactly where it was, because the problem was never that one behaviour โ it was that the objective did not fully express what was wanted. A list of prohibitions grows without converging on anything. Rule-based compliance regimes accumulate clauses for the same reason: the next unintended solution is not on the list yet. Platt notes this pattern in passing; it deserves more than a passing note.
Builder-PM Framework โ Specification as the Core Work
If the hard part of a research loop is deciding what counts as success, then automating the pursuit of success does not automatically automate the defining of it. Faster search does not compress the iteration on the objective โ it accelerates the exposure of the objective’s gaps. The loop runs faster; the specification problem does not shrink.
This is also the correct frame for reading Platt’s remark that the scoring function may be written by the agent, or jointly with the researcher. Even with that assistance, the right specification does not arrive first. A system helping to write the objective it will then be measured against is an arrangement that requires human judgement in the loop โ not because the system is incapable, but because the question of what counts as a good outcome is not the kind of question that optimisation answers. That is a statement about the shape of the problem, not a claim about the limits of any particular capability.
Three Implications
IMPLICATION 1 โ The Metric Is a Hypothesis, Not a Fact
A research loop that expects the first scoring function to be wrong and builds iteration around that expectation is doing something structurally different from a market or an organisation that quotes the first available number and moves on. The loop treats a measure as a hypothesis to be attacked. The alternative treats it as a fact to be cited. That difference in practice โ not in the quality of the metrics themselves โ determines whether the gap between the measure and the goal ever closes.
IMPLICATION 2 โ Prohibitions Are a Patch, Not a Fix
Adding an instruction to rule out a specific unintended behaviour leaves the underlying specification gap intact. The next unintended solution will not be on the list. This applies with equal force to incentive design in commercial contexts: banning last period’s exploit does not repair the objective that made the exploit possible. The patch accumulates; the gap persists. Convergence requires revising the objective, not extending the list of exclusions.
IMPLICATION 3 โ Agentic Automation Shifts the Location of Judgement, Not Its Necessity
When an agent participates in writing its own scoring function, the specification work does not disappear โ it moves. The question of what constitutes a good outcome must still be answered by something outside the optimisation process, because the optimisation process is the instrument that exposes whether the answer was correct. Faster, more capable agents accelerate the exposure of specification gaps; they do not eliminate the need for a human somewhere in the loop to recognise and respond to what the exposure reveals.
Business Engineer Framework
The Builder-PM Framework
The Builder-PM framework maps the new division of labour in AI product development โ where specifying what success looks like is a distinct, human-intensive activity that sits upstream of, and is not replaced by, the automation of pursuit. Understanding where specification work lives in your organisation is now a core productmanagement question. The Map of AI makes this layer structure visible across the full stack.
What John Platt described on Latent Space is not a lab curiosity โ it is the general structure of any situation where a measure stands in for a goal. The optimiser, whether an algorithm running overnight or a sales team working a quota, will find the gap between the two. The difference between a system that improves and one that accumulates patches is whether the person who built the measure is willing to treat each unintended solution as a revision notice rather than a misbehaviour report. The loop is the whole difference.
This summarises a podcast conversation, not a paper, and every claim above is John Platt’s.“Cheat” is his word for optimisation arriving at an unintended solution — the system is not being dishonest, devious or adversarial, it is satisfying the objective it was given.No safety incident is described above, and nothing above is an alignment failure in the public-risk sense or evidence of danger; what he describes is an ordinary research workflow in which a specification gets revised. No specific scoring function, task, benchmark or result appears, no named product behaviour or incident, and no performance, accuracy, cost or frequency figure. Nothing above claims that laboratory metrics are good or better than anyone else’s, criticises any named party, or predicts anything.
Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur, HackerNoon and Search Engine People. Find him on LinkedIn and Substack. See how we source.
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