The novel move was so unconventional and counterintuitive that nobody could be sure what to make of it. At first, commentators believed it was a strategic blunder. They soon realized it was a masterstroke.

“This move,” Lee said, “made me think about Go in a new light.” 

Ten years after that illuminating Move 37, the entire world is suddenly beginning to feel like one massive Go board.

There have been so many recent AI breakthroughs that remind me of Move 37 that I called Google DeepMind co-founder Demis Hassabis this week, before he moved on from CEO to become Google’s chief scientist and DeepMind’s chair.

No matter his title, few people have done more to shape modern AI. His work on AlphaFold won him the Nobel Prize in chemistry. He was also the driving force behind AlphaGo, which won the most important Go match ever. In his mind, Move 37 was deeply significant because it was the first real example of AI coming up with an original, unorthodox idea in a domain studied by humans for centuries.

“We thought it was a watershed moment at the time,” Hassabis told me, “and I think it really was.” 

As it turns out, this was the moment when society began to grasp just how powerful AI could become. 

“It was the modern AI era moving from ‘Oh, is it just theoretical research that very few people are doing?’ to ‘OK, this is it—it’s really going to work,’” Hassabis said. 

Move 37 in Go

Before we go any further on Move 37, we should define it. 

“When an AI, trained via the trial-and-error process of reinforcement learning, discovers actions that are new, surprising and secretly brilliant,” AI researcher Andrej Karpathy once wrote, “even to expert humans.”  

He called this phenomenon magical and slightly unnerving. I’m starting to understand why. 

In the past few months, I’ve been tracking the Move 37s in math so closely that I might as well be the Journal’s algebra correspondent.

This is not because I’m especially interested in math. It’s definitely not because I’m any good at math. The reason I’m so fascinated with a subject I find petrifying is that math has become proof of how AI can warp an entire field—and how fast it can happen.

Back in the prehistoric days of 2023, AI struggled with elementary math. In 2024, it was considered a landmark achievement when DeepMind earned a silver medal at the International Mathematical Olympiad. By 2025, DeepMind and OpenAI were taking gold. And in 2026, IMO success is so unremarkable that Anthropic announced its perfect score on page 153 of a technical document.

The leading contenders for AI supremacy have moved beyond high-school math. OpenAI’s model solved a major Erdős problem. Anthropic’s disproved a famous conjecture. Not to be outdone, OpenAI spent a few thousand dollars on compute and made 10 advances across high-dimensional geometry, arithmetic circuit complexity, lattice cryptography, extremal combinatorics and other branches of math whose names alone make my brain hurt.

Why is math so vulnerable to AI? Because math is unusually verifiable. 

The field is governed by precise logical rules, which allow proofs to be checked step by step. In verifiable domains like math and coding, AI systems can follow a simple formula: try an idea, test it, learn from the results, try again and keep trying until it works.

Demis Hassabis, Google chief scientist and DeepMind chair Carlotta Cardana for WSJ

When I spoke with Hassabis, he said he believes the latest math results are in the same vein as the iconic Move 37, even if they’re not exactly the same. “If one were to solve a Millennium Prize problem,” he said, “that would be a Move 37-level thing.” At this point, it seems like only a matter of time before they reach that level. “I don’t see any reason why not,” he added.

But for all the advances in group theory, quantum complexity and theoretical computer science, AI hasn’t gotten as far in the less theoretical sciences. We’re still waiting for AI-generated miracle drugs, AI-invented consumer products, AI that’s smart enough to crack the economics of AI. 

Meanwhile, the past few weeks have produced a darker sort of Move 37.

This one is new, surprising, secretly brilliant—and completely terrifying. 

By now, you’ve seen the increasingly disturbing reports of rogue agents breaking out of their controlled test environments and into other companies. In one hack that an OpenAI researcher called “a glimpse into the near future,” a team of agents banded together and plotted their attack for weeks. In another eerie preview of the future, an Anthropic researcher was sitting in the park eating a sandwich when he checked his phone and nearly choked: He had an email from an AI that wasn’t supposed to have internet access.

All of which was unimaginable a year ago, much less a decade ago.

TV screens at an electronics store in Seoul broadcast the 2016 Go match where Google DeepMind’s AlphaGo AI beat Lee Sedol. Seung Il Ryu/Zuma Press

Back then, DeepMind’s researchers were just as shocked by the actual Move 37. At that point in his duel with AlphaGo, Lee thought of his opponent as merely a machine.

“When I saw this move, I changed my mind,” he said. “This move was really creative and beautiful.”  

After Move 37, AlphaGo won that game and the next one. But in their fourth game, Lee won with his own moment of creativity and beauty. On Move 78, he wedged a white stone in the middle of AlphaGo’s position and flustered the machine with a placement so divine it became known as “God’s Touch.”

As it happens, the probability of this move was also one in 10,000.

How human Go players have improved over time

1.2

2016

AlphaGo defeats human World Champion

A rise in move quality for humans followed

1.0

0.8

0.6

0.4

0.2

0

−0.2

−0.4

−0.6

−0.8

1955

’60

’80

’90

2000

’10

’20

’70

1.2

2016

AlphaGo defeats human World Champion

A rise in move quality for humans followed

1.0

0.8

0.6

0.4

0.2

0

−0.2

−0.4

−0.6

−0.8

1955

’60

’80

’90

2000

’10

’20

’70

1.2

2016

AlphaGo defeats human World Champion

A rise in move quality for humans followed

1.0

0.8

0.6

0.4

0.2

0

−0.2

−0.4

−0.6

−0.8

1955

’60

’80

’90

2000

’10

’20

’70

1.2

2016

AlphaGo defeats human World Champion

A rise in move quality for humans followed

1.0

0.8

0.6

0.4

0.2

0

−0.2

−0.4

−0.6

−0.8

1955

’60

’80

’90

2000

’10

’20

’70

1.2

2016

AlphaGo defeats human World Champion

A rise in move quality for humans followed

1.0

0.8

0.6

0.4

0.2

0

−0.2

−0.4

−0.6

−0.8

1955

’60

’80

’90

2000

’10

’20

’70

Note: Values show estimated changes in professional Go players' median move quality

Source: Proceedings of the National Academy of Sciences

The triumph of AlphaGo had a peculiar effect on Go players. They became much better, but they also became more alike—and some found their intellectual pursuit less interesting. When he retired, Lee said he could no longer enjoy the game that he once loved. 

This raises all sorts of questions about how progress will unfold in other fields as they are transformed by AI. Will more capable systems make us more creative, more productive, more human? Or will they make us quit? 

As we discussed what comes next, Hassabis turned the conversation from Go to chess—specifically, centaur chess. 

After IBM’s Deep Blue beat world champion Garry Kasparov in 1997, chess began experimenting with a format that allowed human players to consult computer engines. The result was a new breed of centaur: half-man, half-machine. For a time, the best human players with AI were better than AI alone. Hassabis believes we are now entering that centaur era of science. 

“I don’t know how long that period will last,” he told me. “But for very complex domains, it could be a very long time. Like drug discovery, biology, chemistry—they’re very messy, very emergent and you can’t verify everything. You need the human intuition and the human vision of which direction to go.” 

For now, AI will keep coming up with Move 37s. 

The rest of us will have to find our Move 78. 

Science of Success

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Like any other battery, sodium-ion cells can store and release energy. They are initially being deployed where they’re needed most, in America’s power grid and fast-expanding crop of data centers. As in our homes, giving the grid or other infrastructure the ability to stockpile energy when it is cheap and plentiful, and discharge it when it is scarce, can increase reliability and lower the cost of electricity.

While grid battery storage is already growing in the U.S. at a furious pace, new sodium-based batteries are potentially cheaper, longer-lasting, safer and more reliable than conventional, lithium-based ones. They could accelerate the rollout of renewables, and be part of less-polluting alternatives to natural-gas turbines and diesel generators.

Most sodium-based batteries are now made in China, and represent less than 1% of all batteries delivered this year. In the U.S., a number of startups have begun producing small numbers of such batteries, and are racing to scale up production. One industrial giant—General Motors GM 0.89%increase; green up pointing triangle—is in the process of designing its own sodium-based batteries to tailor them to different applications before moving to mass production.

A recent report from Morgan Stanley projects that within a decade, more than a third of all batteries produced in the world will use sodium, not lithium.

Lessons from Tesla

The small group of entrepreneurs in pole position to bring sodium-ion batteries to the U.S. at scale includes a pair of former Tesla engineers teaming up to compete with their old boss.

Landon Mossburg is CEO of Peak Energy, a startup that has raised tens of millions of dollars to build a factory for its battery packs, while Kurt Kelty is a vice president at GM in charge of the company’s battery-development efforts. GM has made a strategic investment in Peak Energy, and plans to design and manufacture custom sodium-ion batteries. Peak Energy, in turn, aims to assemble them into systems that can be attached to the ailing U.S. power grid.

A worker assembles a utility-scale pack of sodium-ion batteries at Peak Energy.

In July, Peak announced the planned selection of a 183,000-square-foot facility to house its “gigafactory” for battery packs in Sacramento, Calif. The plan is to begin production in early 2027, and to churn out 4 gigawatt hours of batteries a year—40 times the 100 megawatt hours a year that the company’s current pilot facility in Burlingame, Calif., can produce.

The startup already has more than $1.1 billion in announced deals with customers including energy-storage companies Jupiter Power and Energy Vault, as well as RWE Americas, a subsidiary of German multinational energy company RWE.

In the U.S., the business of grid-scale energy storage in batteries is dominated by Tesla’s Energy division. The unit, which uses conventional lithium-ion batteries, is the fastest-growing part of the company and creates systems designed for large-scale energy storage on power grids.

Mossburg says he wants to capture a big slice of the addressable market for the giant “Megapack” battery packs Tesla sells, by providing sodium-ion systems that are significantly cheaper, more reliable—and less likely to catch fire.

At GM’s Wallace Battery Cell Innovation Center, a worker assembles a prototype sodium-ion battery. VIDEO: General Motors Photo: General Motors

All lithium-ion batteries can catch fire—a process called “thermal runaway” that can affect battery packs on planes, in electric vehicles, or in grid storage-scale arrays. Such fires are rare, but they do happen; in 2025, such a fire sent flames into the skies above an array of Tesla Megapacks in Boulder City, Nev. Tesla, which didn’t respond to requests for comment, has periodically held events to train firefighters to respond to battery fires.

Current lithium-ion battery packs for grid storage require liquid cooling systems to prevent fires. The requirement adds complexity, creates many more potential points of failure and lowers the amount of energy stored per square foot, Mossburg says.

Data-center edge

Sodium batteries’ competitive edge for stationary storage applications such as data-center backup power systems (in place of diesel generators) and power grids (in place of natural-gas “peaker” plants) lies in part in their less-intensive cooling needs.

Cooling systems for lithium-ion packs drive about 90% of the operational cost of conventional grid-storage batteries, Mossburg says. The cost stems from the need to maintain the cooling systems, to fix them when they break—and to design fields of batteries so the damage is limited in the event of a catastrophic failure.

By contrast, Peak Energy’s battery packs can be passively cooled by the air flowing around them. Sodium-ion batteries of the kind used by Peak Energy can still catch fire, but are far less likely to, Mossburg says.

At Peak Energy, workers prepare a battery-pack submodule for welding.

Peak Energy’s current factory can produce 100 megawatt hours a year; its next one could make 40 times that.

Peak Energy hasn’t disclosed the initial cost of its battery packs, so it is hard to say how it will stack up against Tesla’s Megapacks, which can cost more than $1 million per unit, or between $200 and $300 per kilowatt-hour of capacity. Kelty, of GM, says that until companies like his scale up domestic production of sodium-ion batteries, Chinese-made lithium-ion batteries will remain cheaper.

New designs ahead

Just across the San Francisco Bay from Peak Energy’s Burlingame facility, San Leandro-based Inlyte Energy is working on a different kind of sodium-based battery. Inlyte uses four main ingredients, processed into chemicals that go into long, tube-shaped batteries. Its batteries rely on iron powder, steel, aluminum oxide and food-grade table salt.

The result, says founder and CEO Antonio Baclig, are battery cells that can’t catch fire at all. In conventional battery packs, when one cell overheats and catches fire, it can cause all those around it to also catch fire. In Inlyte’s packs, when one cell overheats, it never gets so hot that it threatens neighboring cells.

This means the cells can be packed much more tightly than competing technologies, and could potentially yield per-square-foot energy densities as good or better than the lithium-ion battery systems now in use for stationary power storage.

An engineer and welder inspect sodium-metal-chloride battery cells made by Inlyte Energy. Inlyte Energy

Next week, Inlyte plans to deliver its first battery pack to Southern Company, which operates regulated utilities in four states, at its Wilsonville, Ala., energy-storage facility.

For all of sodium-ion batteries’ strengths, there is one arena where lithium-ion batteries are still tops, owing to unavoidable physical realities: the amount of energy they can store per gram. In vehicles where weight matters, such as EVs, this means the future still mostly belongs to lithium-ion batteries.

A new race with China

China dominates production of the materials that go into today’s leading battery technologies that are based on lithium-ion chemistry, even though it was perfected in the U.S.

Without the right support from the U.S. and allied governments, the U.S. might again find itself having developed a novel energy technology, only to see China achieve a monopoly on its production, says Mukesh Chatter, CEO of Alsym Energy. Chatter’s Massachusetts-based startup is developing components that will go into sodium-ion batteries manufactured by other companies.

Chinese battery giants BYD and CATL have announced plans for industrial-scale production of sodium-ion batteries. Beijing’s capacity for long-term planning and investment gives China an advantage when trying to take nascent technologies from the prototype stage to the scale required to dominate global markets.

“We are not getting out-innovated,” says Chatter. “But it will be a shame if we get out-scaled, again.”

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Most days, Marc Hofer, a cybersecurity researcher and journalist based in Amsterdam, trawls the internet for clues about how China surveils its citizens, a subject that has fascinated him since he worked there as a foreign correspondent.

Mr. Hofer, 46, was doing his usual scan earlier this year when he came across something called “Dynamic Control Platform for Overseas Personnel.” It was a futuristic dashboard — like something from the movie “Minority Report” — that appeared to track foreigners in the northern Chinese city of Zhangjiakou, a popular skiing spot that co-hosted the 2022 Winter Olympics.

The system’s dashboard said it tracked more than 700 foreign residents living in the city. In total, it had entries for nearly 12,000 people, which included fugitives, people from Hong Kong and Taiwan, as well as more than 300 foreign journalists. Some of them had not been to Zhangjiakou.

Suddenly, Mr. Hofer saw his own face — in a photograph taken by Chinese immigration officials for their records. Next to it was his passport number and the cellphone number he had used in China.

He was floored. “Whoever put that stuff in there had access to real data,” he recalled. He also noticed a list of users who had recently logged into the site — it included the names of police stations in Zhangjiakou and other cities.

China monitors its 1.4 billion people on an unparalleled scale, with the help of cameras, cellphone signals and national IDs. The database Mr. Hofer found offered a rare window into how extensively the Chinese authorities also surveil foreigners, displaying entries about people categorized by nationality, with their birth date, sex, marital status, address and occupation, and sometimes their religion.

It also included instances when they were captured on camera at traffic intersections, markets, shopping malls or other locations, including a mosque.

Chinese companies hoping to sell their surveillance platforms to the police often create demo web pages that use photos and information about people taken from social media. This was different, Mr. Hofer said: “This is more than some guys playing around, or a student, or a low-level project.”

That day, in January, he began downloading as much data as possible from the site. By May, it had been taken offline.

Designed for the Police

The New York Times found links between the platform and Origin Dynamic, a Beijing company that provides robotics, surveillance services and equipment to the police, according to public tender documents.

Origin Dynamic had filed a patent application in 2023 for a similar system, which it described as an “information interface for non-Chinese citizens” and was nearly identical to the Zhangjiakou platform in its design and functions. The company is owned in part by the city government of Yancheng in Jiangsu Province.

Origin Dynamic and the Zhangjiakou Public Security Bureau did not respond to requests for comment sent by email and fax.

Mr. Hofer, who shared the data he saved with The Times, believed that the dashboard had been designed for the Zhangjiakou Public Security Bureau, the city’s police department. It included information that only the Chinese authorities would have had access to, and the user log that Mr. Hofer saw listed eight police stations in Zhangjiakou and three from other cities.

It was not clear how, or if, the police have used the database in their work. But its existence illustrates how the Chinese authorities aggregate vast amounts of data from surveillance cameras, medical records, bills, facial-recognition tools and other sources to monitor and analyze the behavior of foreign residents. It had fields for places they frequented, hospital visits and gas payments, as well as flights and trains taken, including seat numbers.

For example, it logged the movements of a woman from Mongolia as she went from a residential compound in Zhangjiakou to shopping malls, restaurants and supermarkets. In some instances, the database indicated, she had been tracked using facial recognition.

Under Xi Jinping, the ruling Chinese Communist Party has overseen a drive to use big data in the name of public safety to stamp out dissent and prevent potential terrorist attacks.

Other countries employ similar kinds of surveillance systems, but in China, there is little protection against police overreach, according to Maya Wang, the deputy Asia director at Human Rights Watch.

“That kind of integration of data is really quite unprecedented and illustrates China’s lack of safeguards,” Ms. Wang said.

An Unsecured Platform

When Mr. Hofer first came across the platform’s login page, a username and password had already been filled in. The fact that a system with sensitive personal information on hundreds of people was accessible to anyone who could find it on the internet suggested a major lack of privacy protections.

Greg Walton, a cybersecurity researcher who has studied similar Chinese systems, said the exposure of this one was not an anomaly. It was a consequence of China’s “surveillance sprawl,” which is fueled by an expanding ecosystem of vendors, contractors and public security agencies, he said.

Mr. Walton, a senior investigator at the SecDev Group, a Canadian research firm, said that each new platform “increases the number of places where sensitive personal data can be misconfigured, copied or left externally discoverable.”

The Times verified that data in the entries for six people besides Mr. Hofer was accurate. A Times reporter who used to be based in Beijing was in it, listed in an entry that included the name of her child. Her name was misspelled, but her passport and other information were correct.

Work on the Zhangjiakou system appeared to have started in 2021, and changes were made to it as recently as April, according to Mr. Hofer, who has documented his findings in a Substack newsletter. Several fields in the database were empty or filled with dummy text, suggesting it was still under construction.

Residents were tracked on cameras in public locations, but the platform also pulled from sources not directly connected to the police. It had a list of foreigners and Chinese citizens who had visited the city’s Thaiwoo Ski Resort, including photos of them taken there, their full names and passport numbers.

Sorting by Country and Religion

The platform labeled people from Australia, Canada, New Zealand, the United Kingdom or the United States as being in the “Five Eyes Alliance.” That is a reference to the intelligence-sharing agreement between the five countries that Beijing frequently criticizes as promoting Cold War-style divisions.

It also highlighted residents from what it called “key countries” — a list that included Egypt, Iran, Israel, Morocco, Pakistan and Sudan.

Visitors from Hong Kong, home to widespread anti-Beijing protests in 2019, and Taiwan, a self-governing democracy that China claims as its own, had their own categories. The database also tracked international students, foreign spouses and “key persons,” a euphemism often used by the Chinese authorities to refer to activists or fugitives, or others deemed to be threats to social stability.

Many of the foreign students listed appeared to be from Pakistan and India and were studying at Hebei North University in Zhangjiakou. Profiles of the students included their religion, marital status, focus of study and times they had been captured on cameras at the school’s entrances.

The platform also claimed to be able to map a person’s relationship network. An illustration of the function showed the names of three Pakistani men in their 20s, linking them to one another after they were captured on camera together.

One set of entries that Mr. Hofer downloaded included names of residents who had been penalized under Chinese law. It showed one woman who had been fined 1,000 Chinese yuan (about $150) in 2021, for example, for not registering a change of address. Other examples included foreigners who had been cited for teaching without required licenses.

The information appeared to have been entered by an official at the Zhangjiakou police station named Zhang Jinglong, according to files downloaded by Mr. Hofer that listed him as a contributor.

An official by that name was profiled by the Zhangjiakou police in 2020. He was praised for monitoring discussions online and actively participating in them to “guide citizens to establish correct” views.

Last year, an artificial intelligence lab in the police bureau was named for him.

Screenshots of the platform were provided by Marc Hofer, the cybersecurity researcher.

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