Google Cloud、科学特化型 AI モデルの販売を開始

*Google is adding SandboxAQ’s ‘large quantitative models’ to its cloud marketplace, pairing Gemini with AI trained on scientific equations and laboratory data.*

The large language models that power most of the AI industry are very good at words and surprisingly unreliable at numbers. Google’s latest move is an admission that, for science, a different kind of model is needed.

The company said it will start offering specialist AI models from SandboxAQ through Google Cloud, adding what SandboxAQ calls large quantitative models to the cloud marketplace. The aim is to widen enterprise and research access to AI built for drug discovery, materials science, and semiconductor manufacturing, the announcement said.

The distinction is the whole point. Large language models are trained on text and excel at generating it. Large quantitative models, by SandboxAQ’s description, are trained on numerical data and scientific equations rather than prose, which is meant to make them better suited to problems in chemistry, biology, and physics, fields where the right answer is a number or a structure, not a fluent paragraph.

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On Google Cloud, researchers will be able to combine these with Gemini, using the language model for reasoning and interface and the quantitative model for the underlying science.

Google paired the marketplace move with Gemini for Science, a bundle of tools and experiments aimed at the research workflow itself. It draws on projects the company has been building for a while, including its AI co-scientist, the AlphaEvolve coding agent, an empirical research assistant, and NotebookLM, and is pitched as a way to speed up the routine, laborious steps of the scientific method rather than to replace the scientist.

That framing is consistent with where Google has put its scientific weight. DeepMind’s protein-structure work has already reshaped parts of drug development, and a separate effort produced an AI that found more new materials in a year than science had catalogued in its entire history. The common thread is that the highest-value AI in the sciences tends to be narrow and trained on real measurements, not general and trained on the internet.

The commercial logic is straightforward. Google is competing with the other hyperscalers to be the default place enterprises run AI, and scientific and industrial R&D is a high-value segment that general chatbots do not serve well.

Selling specialist models through the marketplace, the same channel through which it already offers a wide catalogue of third-party systems, lets Google capture that demand without having to build every domain model itself.

It also fits a broader scramble to turn AI into actual laboratory results. DeepMind’s own drug-discovery spinoff Isomorphic Labs is moving toward trials, and rivals across the industry are racing to convert algorithmic promise into treatments and materials that work outside a benchmark. Putting quantitative models in front of enterprise researchers is Google’s bid to be the infrastructure underneath that race.

Google said the capabilities are already in use by partners in private preview for real-world R&D, though it has been sparing with specifics on which organisations and what results.

The marketplace listing is the substantive change: a category of AI that was largely confined to specialist labs becomes something a research team can rent. Whether it produces discoveries or simply faster spreadsheets is the question the private previews are meant to answer.