Britain's
AI for Science
Landscape

We’ve charted the UK’s current AI for Science landscape: the extant organisations, programmes, pieces of infrastructure, and funders most relevant to the integration of AI into research.

—AI‑for‑science organisations analysed

What this is

On our map, you can see where the entities we tracked are based, what they do, in what fields, and who funds them. This map is a living resource and we will update it regularly.

Explore the map

What counts

The map doesn’t try to chart the wider shift towards AI‑enabled work across UK research. That work is substantial, but it isn’t organised around a mappable unit. This is a guide to the ecosystem’s organisations, not a census of the research.

Read the methodology

How it will evolve

The dataset may include inaccuracies. Please help us correct them using the form below to submit corrections or new entries.

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The stories below cover the tranche of — organisations most relevant to AI in science; for a more comprehensive picture of all —, including more of the enabling organisations around them, explore the map.

01

Of today’s organisations and programmes, more than twice as many were founded in the five years from 2020 compared with the five years before.

In recent years, the UK has seen a rising tide of organisations working in or supporting AI in science. Of those active today, an average of — a year were founded from — to —, — times as many as in —. The counts for the most recent years are lower bounds because recently founded organisations take time to identify, and — is a part year.

Colour bars by

Organisations and programmes active today, by year founded

Scroll to see all years

The first bar includes organisations founded in 2010 or earlier. Click a segment to see its organisations.

AI4S boomed after the pandemic.

— organisations and programmes active today were founded in —: — companies and university spinouts, and — research bodies, programmes, and other supporting organisations.

The landscape today

This is a snapshot of the current lay of the land: all — organisations and programmes that we analysed. Of these: — are research bodies (the university units, institutes, or independent labs conducting science); — are companies and spinouts; — are the programmes, funders, networks, and shared resources that support the work; and — are facilities – the shared laboratories, instruments, and supercomputers that researchers use.

Colour dots by

Click a dot to identify an organisation, or a category heading to see the full list.

02

The newest wave is heavy in fellowships, networks, and programmes, which barely featured before 2020.

The newest wave looks different from previous ones. New companies have held roughly steady, though their latest count will rise as recent start-ups come to light. The growth came from fellowships and training programmes, consortia and networks, and government programmes and facilities: — were founded in —, against — in —. As you would expect, companies dominate on overall count.

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Organisations on the map, by type and main funder

03

Venture capital builds models and tools; public and philanthropic money builds the shared foundations.

Government is the main funder of organisations that train people, set strategy, fund research, or run compute and facilities; philanthropy is the next biggest. Models and tools are where venture capital leads. Data is more mixed – with generation and curation a mixed bag, but primarily government funding the infrastructure.

Organisations by contribution mode · coloured by their main funder

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What organisations do, by their main funder

Read across a row to see what the organisations mainly paid for by that kind of funder do. Click a cell to see them. Organisations usually work in several parts of the value chain, so a row adds up to more than 100%.

— of the — organisations led by venture or investment capital build models, tools, or platforms. Government-led organisations and programmes are spread across the whole stack: — train people, — generate or look after data, — run compute or facilities, and — are councils, agencies, and programmes that fund others in turn. Of the three main funders, organisations and programmes led by philanthropy are the most focused on people: — of them train people, more than for government or venture capital. Philanthropy almost never leads compute or facilities (—).

The three biggest high‑relevance philanthropies support different parts of the AI for Science ecosystem

Wellcome Trust

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Schmidt Sciences and XTX Markets

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What the three largest philanthropic backers support · entries on the map at High or Medium relevance, whether philanthropy leads or co‑funds

04

A few new companies, several founded by people from DeepMind, have raised more in three years than most older ones in a decade.

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A handful of companies hold most recorded funding

Recorded funding per UK‑headquartered company · £m

Reported round: a predicted amount, not money yet raised

Recorded funding per UK‑headquartered company, by year founded

01

Most money is concentrated in very few companies

As usual, a few companies are backed to be the big winners. The top — companies have raised — of recorded funding. The largest belongs to —, a compute provider – but

The upper chart shows this on a logarithmic scale. The lower chart shows the same companies with the added dimension of time: with dots higher on the Y axis having raised more (non‑log scale).

02

Much of the money is new

The — companies founded since — raised — of all recorded funding, and — of the ten largest amounts – despite having had much less time to raise.

—

03

Big raises, small teams

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That may simply be because they are new and have not yet hired. It may also be an early sign of what AI agents make possible for a small team.

New, £—m or more raised, 25 or fewer staff
04

Many of the best‑funded new companies were founded by former DeepMind employees

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Founded by people from DeepMind
Reported round, not yet raised

The DeepMind diaspora

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Companies founded by former DeepMind employees, sized by cumulative money raised

Scroll sideways to see the whole timeline.

05

Companies cluster in the Golden Triangle; government is a particularly important investor for the rest of the country.

Most of the high‑relevance AI4S organisations on our map are in London, Oxford, and Cambridge and their campuses: — of the — UK‑headquartered organisations and programmes, and — of the — companies and spinouts, with only — companies based elsewhere. Beyond the Golden Triangle, the field is largely public: government is the main funder of — of the — organisations there, and of — of the — in Edinburgh, the next largest cluster.

Where each type of funder’s UK‑headquartered organisations are located

—

of the — government‑funded, UK‑headquartered organisations are outside the Golden Triangle. —

06

Government and philanthropy fund the open layer of AI for science.

Government and philanthropy support openly available research, data, models, and tools: — of the — government‑funded and — of the — philanthropy‑funded organisations with assessable outputs make them mostly or fully open, against — of the — venture‑backed ones.

Availability of main outputs, by main funding model

Three main funding groups. Expand to see university, corporate, and mixed funding.

where complete comparable figures are unavailable.

Government and philanthropy support a similar proportion of open outputs.

— of — government‑funded organisations and — of — philanthropy‑funded organisations make their main work mostly or fully open. Primarily government‑backed entities with mixed or mostly proprietary outputs are often successful contractors and grant‑funded spinouts that sell software or hardware, or biofoundries that run experiments for others.

07

Beyond the big three, each cluster has a character of its own.

Use the map below to explore Britain’s AI‑for‑science clusters: automated chemistry and materials in Liverpool, drug discovery and bioimaging in Dundee, environmental science in Exeter, fusion at Culham, and national compute and data in Edinburgh.

The profiles cover clusters with at least — entries on the map, counting Medium‑relevance entries as well as High.

08

Most of the organisations doing AI for science are in biology, and each science uses AI in its own way.

The sciences have taken up AI in different ways, shaped by their data, their methods, and their markets.

Organisations in each field, by year founded

01

Companies lean further towards biology

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02

Chemistry and materials science design

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03

The physical sciences simulate

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04

AI agents are new; robots are in the lab

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Interactive map

Explore AI‑for‑science organisations across the UK.

Explore the interactive map
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