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Last week, on the heels of DeepMind’s breakthrough in using artificial intelligence to predict protein folding came the news that the UK-based AI company is still costing its parent company Alphabet Inc hundreds of millions of dollars in losses each year. A t…

Jan 12, 2021

Last week, on the heels of DeepMinds breakthrough in using artificial intelligence to predict protein folding came the news that the UK-based AI company is still costing its parent company Alphabet Inc hundreds of millions of dollars in losses each year.
A tech company losing money is nothing new. The tech industry is replete with examples of companies who burned investor money long before becoming profitable. But DeepMind is not a normal company seeking to grab a share of a specific market. It is an AI research lab that has had to repurpose itself into a semi-commercial outfit to ensure its survival.
And while its owner, which is also the parent company of Google, is currently happy with footing the bill for DeepMinds expensive AI research, it is not guaranteed that it will continue to do so forever.
DeepMinds profits and losses
DeepMinds AlphaFold project used artificial intelligence to help advance the complicated challenge of protein folding.
According to its annual report filed with the UKs Companies House register, DeepMind has more than doubled its revenue, raking in £266 million in 2019, up from £103 million in 2018. But the companys expenses continue to grow as well, increasing from £568 million in 2018 to £717 in 2019. The overall losses of the company grew from £470 million in 2018 to £477 million in 2019.
At first glance, this isnt bad news. Compared to the previous years, DeepMinds revenue growth is accelerating while its losses are plateauing.
DeepMinds revenue and losses from 2016 to 2019
But the report contains a few more significant facts. The document mentions Turnover research and development remuneration from other group undertakings. This means DeepMinds main customer is its owner. Alphabet is paying DeepMind to apply its AI research and talent to Googles services and infrastructure. In the past, Google has used DeepMinds services for tasks such as managing the power grid of its data centers and improving the AI of its voice assistant.
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What this also means that there isnt yet a market for DeepMinds AI, and if there is, it will only be available through Google.
The document also mentions that the growth of costs mainly relates to a rise in technical infrastructure, staff costs, and other related charges.
This is an important point. DeepMinds technical infrastructure runs mainly on Googles huge cloud services and its special AI processors, the Tensor Processing Unit (TPU). DeepMinds main area of research is deep reinforcement learning, which requires access to very expensive compute resources. Some of the companys projects in 2019 included work on an AI system that played StarCraft 2 and another that played Quake 3, both of which cost millions of dollars in training.
A spokesperson for DeepMind told the media that the costs mentioned in the document also included work on the AlphaFold, the companys celebrated protein-folding AI, another very expensive project.
There are no public details on how much Google charges DeepMind for access to its cloud AI services, but it is most likely renting its TPUs at a discount. This means that without the support and backing of Google, the companys expenses would have been much higher.
Staff costs is another important issue. While participation in machine learning courses has increased in the past few years, scientists that can engage in the kind of cutting-edge AI research DeepMind is involved in are very scarce. And by some accounts, top AI talent command seven-digit salaries.
The growing interest in deep learning and its applicability to commercial settings has created an arms race between tech companies to acquire top AI talent. Most of the industrys top AI scientists and pioneers are working either full- or half-time at large companies such as Google, Facebook, Amazon, and Microsoft. The fierce competition for snatching top AI talent has had two consequences. First, like every other field where supply doesnt meet demand, it has resulted in a steep incline in the salaries of AI scientists. And second, it has driven many AI scientists from academic institutions that cant afford stellar salaries to wealthy tech companies that can. Some scientists continue to stay in academia for the sake of continuing scientific research, but they are too few and far between.
And without the backing of a large tech company like Google, research labs like DeepMind cant afford to hire new researchers for their projects.
So, while DeepMind shows signs of slowly turning around its losses, its growth has made it even more dependent on Googles financial resources and large cloud infrastructure.
Google is still satisfied with DeepMind
DeepMinds developed an AI system called AlphaStar that can beat the best players at the real-time strategy game StarCraft 2
According to DeepMinds annual report, Google Ireland Holdings Unlimited, one of the investment branches of Alphabet, waived the repayment of intercompany loans and all accrued interest amounting to £1.1 billion.
DeepMind has also received written assurances from Google that it will continue to provide adequate financial support to the AI firm for a period of at least twelve months.
For the time being, Google seems to be satisfied with the progress DeepMind has made, which is also reflected in remarks made by Google and Alphabet executives.
In Julys quarterly earnings call with investors and analysts, Alphabet CEO Sundar Pichai said, Im very happy with the pace at which our R&D on AI is progressing. And for me, its important that we are state-of-the-art as a company, and we are leading. And to me, Im excited at the pace at which our engineering and R&D teams are working both across Google and DeepMind.
But the corporate world and scientific research move at different paces.
Scientific research is measured in decades. Much of the AI technology used today in commercial applications has been in the making since the 1970s and 1980s. Likewise, a lot of the cutting-edge research and techniques presented at AI conferences today will probably not find their way into the mass market in the coming years. DeepMinds ultimate goal, developing artificial general intelligence (AGI), is by the most optimistic estimates at least decades away.
On the other hand, the patience of shareholders and investors is measured in months and years. Companies that cant turn over a profit in years or at least show hopeful signs of growth fall afoul of investors. DeepMind currently has none of those. It doesnt have measurable growth, because its only client is Google itself. And its not clear whenif ever some of its technology will be ready for commercialization.
Google CEO Sundar Pichai is satisfied with the pace of AI research and development at DeepMind
And heres where DeepMinds dilemma lies. At heart, it is a research lab that wants to push the limits and of science and make sure advances in AI are beneficial to all humans. Its owners goal, however, is to build products that solve specific problems and turn in profits. The two goals are diametrically opposed, pulling DeepMind in two different directions: maintaining its scientific nature or transforming into a product-making AI company. The company has already had trouble finding balance scientific research and product development in the past.
And DeepMind is not alone. OpenAI, DeepMinds implicit rival, has been facing a similar identity crisis, transforming from an AI research lab to a Microsoft-backed for-profit company that rents its deep learning models.
Therefore, while DeepMind doesnt need to worry about its unprofitable research yet, but as it becomes more and more enmeshed in the corporate dynamics of its owner, it should think deeply about its future and the future of scientific AI research.
This article was originally published by Ben Dickson on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here.
Published January 12, 2021 — 09:54 UTC