DEEPMIND’S office is tucked away in a nondescript building next to London’s Kings Cross train station. From the outside, it doesn’t look like something that two of the world’s most powerful technology companies, Facebook and Google, would have fought to acquire. Google won, buying DeepMind for £400m ($660m) in January 2014. But why did it want to own a British artificial-intelligence (AI) company in the first place? Google was already on the cutting edge of machine learning and AI, its newly trendy cousin. What value could DeepMind provide?

That question has become a little more pressing. Before October 2015 Google’s gigantic advertising revenues had cast a comfortable shade in which ambitious, zero-revenue projects like DeepMind could shelter. Then Google conjured up a corporate superstructure called Alphabet, slotting itself in as the only profitable firm. For the first time, other businesses had their combined revenues broken out from Google’s on the balance-sheet, placing them under more scrutiny (see article). But understanding DeepMind’s worth is not a simple financial question. Its value is deeper than that.

DeepMind’s most immediate benefit to Google and Alphabet is the advantage it gives in the strategic battle that technology companies are waging over AI (see chart). It hoovers up talent, keeping researchers away from competitors like Facebook, Microsoft and Amazon. The Kings Cross office already houses about 400 computer scientists and neuroscientists, and there is talk of expanding that to 1,000.

Another boost to the mother ship comes in the form of prestige. DeepMind has reached the cover of Nature, a highly regarded academic journal, twice since it was acquired. Gigantic copies of the relevant covers adorn the walls of the office lobby. The first was for a video-game-playing AI programme the second for one that learned to play the ancient Asian board game of Go. Named AlphaGo for its parent, that software went on to make headlines around the world when it beat Lee Sedol, a South Korean champion, in March 2016 (the match is pictured above).

DeepMind’s horizons stretch far beyond talent capture and public attention, however. Demis Hassabis, its CEO and one of its co-founders, describes the company as a new kind of research organisation, combining the long-term outlook of academia with “the energy and focus of a technology startup”—to say nothing of Alphabet’s cash. He founded it in 2010, along with Mustafa Suleyman and Shane Legg. Mr Legg and Mr Hassabis met as neuroscience researchers at University College, London; Mr Suleyman is a childhood friend of Mr Hassabis’s.

The firm’s overall mission, as Mr Hassabis puts it, is to “solve intelligence”. This would allow the firm to create multifunctional, “general” artificial intelligence that can think as broadly and effectively as a human. Being bought by Google had several attractions. One was access to the technology firm’s computing power. Another was Google’s profitability; a weaker buyer would have been more likely to require DeepMind to make money. This way Mr Hassabis can focus on research rather than the detail of running a firm. And by keeping DeepMind in London, at a safe distance from Google’s Silicon Valley base in Mountain View, he can retain more control over the operation.

Were he to succeed in creating a general-purpose AI, that would obviously be enormously valuable to Alphabet. It would in effect give the firm a digital employee that could be copied over and over again in service of multiple problems. Yet DeepMind’s research agenda is not—or not yet—the same thing as a business model. And its time frames are extremely long. Mr Hassabis says the company is following a 20-year road map. DeepMind aims to invent new kinds of AI algorithms, he adds, that are inspired by the way the human brain works. This explains the firm’s large number of neuroscientists. Mr Hassabis claims that seeking inspiration from the brain sets his firm far apart from other machine-learning research units and in particular from “deep learning”, the powerful branch of machine-learning that is being used by the Google Brain unit.

Even if DeepMind never achieves human-level (or indeed, superhuman) artificial intelligence, however, the learning software that it creates along the way can still benefit other Alphabet businesses. This has already happened. In July the company announced that its learning software had found a way to reduce the quantity of electricity that is needed to cool Google data centres, by two-fifths. The software learned about the task by crunching data-centre operation logs, and then optimised the process by running it over and over again in a simulation.

DeepMind is also applying its AI research to solve problems in its own right. Mr Suleyman, who leads these efforts, has expressed an ambition for DeepMind to help manage energy infrastructure, hone health-care systems and improve access to clean water, in return for revenue streams. The company has already started on health care. Its first paid work came in November in the form of a five-year deal with the Royal Free London, an NHS Foundation Trust, to process 1.7m patient records. Earlier this year it gained access to two data sets from other London hospitals: one million retina scans that it can mine and thereby identify early signs of degenerative eye conditions, and head and neck cancer imagery which, fed into its models, will allow DeepMind’s AI to distinguish between healthy and cancerous tissues.

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Skilful programmers and powerful computers are crucial to this applied AI business. But access to data about the real-world environment is also vital. When systems like hospitals, electricity grids and factories are targeted for improvement using AI and machine learning, data about their specific operations are needed.

Alphabet, of course, holds huge volumes of data that can be mined for these purposes. But DeepMind will have to acquire lots more in each of the fields it aims to examine. In the case of a recent project it was involved in on lip-reading, for example, it was the acquisition of an unprecedentedly large data set that made it a success. A group of researchers at the University of Oxford, headed by Andrew Zisserman, a computer-vision researcher, led the work. The BBC gave the researchers hundreds of thousands of hours of newscaster footage, in the absence of which they would not have been able to train their AI systems.

Mr Hassabis downplays the importance of data acquisition to DeepMind’s future. He claims that it is enough for human engineers to build simulations of the problem to be solved; then DeepMind unleashes learning agents within them. But that is not how most machine-learning systems that are currently in operation work. AlphaGo itself first learned on a database of millions of individual moves from 160,000 human-played Go games, before iteratively training against itself and improving. But if DeepMind does need to hoover up lots of personal information, it will have to deal with consumer concerns about corporate access to data.

If it can solve these problems, however, DeepMind will hold immense value as something entirely new for Alphabet: an algorithm factory. That would go far beyond simply being the technology giant’s long-term AI research outfit and talent-holding pool. The data that DeepMind processes can remain the property of the organisations they come from (which should help to allay concerns about privacy), but the software that learns from that data will belong to Alphabet. DeepMind may not ever make significant revenue of its own by applying AI programmes to complex problems. But the knowledge it sends into learning software from those same sets of data may justify the bidding war that brought it into Alphabet’s compass.