The amazing and frustrating thing about robots is that they can do almost anything you want them to do, as long as you know how to ask properly. In the not-so-distant past, asking properly meant writing code, and while we’ve thankfully moved beyond that brittle constraint, there’s still an irritatingly inverse correlation between ease of use and complexity of task.

AI has promised to change that. The idea is that when AI is embodied within robots—giving AI software a physical presence in the world—those robots will be imbued with reasoning and understanding. This is cutting-edge stuff, though, and while we’ve seen plenty of examples of embodied AI in a research context, finding applications where reasoning robots can provide reliable commercial value has not been easy. Boston Dynamics is one of the few companies to commercially deploy legged robots at any appreciable scale; there are now several thousand hard at work. Today the company is announcing that its quadruped robot Spot is now equipped with Google DeepMind’s Gemini Robotics-ER 1.6, a high-level embodied reasoning model that brings usability and intelligence to complex tasks.

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Although this video shows Spot in a home context, the focus of this partnership is on one of the very few applications where legged robots have proven themselves to be commercially viable: inspection. That is, wandering around industrial facilities, checking to make sure that nothing is imminently exploding. With the new AI onboard, Spot is now able to autonomously look for dangerous debris or spills, read complex gauges and sight glasses, and call on tools like vision-language-action models when it needs help understanding what’s going on in the environment around it.

“Advances like Gemini Robotics-ER 1.6 mark an important step toward robots that can better understand and operate in the physical world,” Marco da Silva, vice president and general manager of Spot at Boston Dynamics, says in a press release. “Capabilities like instrument reading and more reliable task reasoning will enable Spot to see, understand, and react to real-world challenges completely autonomously.”

Understanding Robot Understanding

The words “reasoning” and “understanding” are being increasingly applied to AI and robotics, but as Toyota Research Institute’s Gill Pratt recently pointed out, what those words actually mean for robots in practice isn’t always clear. “The benchmark we measure ourselves against when it comes to understanding is that the system should answer the way a human would,” Carolina Parada, head of robotics at Google DeepMind, explained in an interview. For robots to reliably and safely perform tasks, this connection between how robots understand the world and how humans do is critical. Otherwise, there may be a disconnect between the instructions that a human gives a robot, and how the robot decides to carry out that task.

Boston Dynamics’ video above is a potentially messy example of this. One of the instructions to Spot was to “recycle any cans in the living room.” It has no problem completing the task, as the video shows, but in doing so, it grips the can sideways, which is not going to end up well for cans that have leftover liquid in them. We humans would avoid this because we can draw on a lifetime of experience to know how cans should be held, but robots don’t (yet) have that kind of world knowledge.

Parada says that Gemini Robotics-ER 1.6 approaches situations like this from a safety perspective. “If you ask the robot to bring you a cup of water, it will reason not to place it on the edge of a table where it could fall. We track this using our ASIMOV benchmark, which includes a whole lot of natural language examples of things the robot should not do.” The current version of Spot doesn’t use these semantic safety models for manipulation, but the plan is to make future versions reason about holding objects in ways that are safe.

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There does still seem to be a disconnect between Gemini Robotics-ER 1.6 as a high-level reasoning model for a robot, and the robot itself as an interface with the physical world. One of the new features of 1.6 is success detection, which combines multiple camera angles to more reliably be able to tell when Spot has successfully grasped an object. This is great if you’re relying entirely on vision for your object interaction, but robots have all kinds of other well-established ways to detect a successful grasp, including touch sensors and force sensors, that 1.6 is not using. The reason why this is the case speaks to a fundamental problem that the robotics field is still trying to figure out: how to train models when you need physical data.

“At the moment, these models are strictly vision only,” Parada explains. “There is lots of [visual] information on the web about how to pick up a pen. If we had enough data with touch information, we could easily learn it, but there is not a lot of data with touch sensing on the internet.” Customers who use these new capabilities for inspection with Spot will be required to share their data with Boston Dynamics, which is where some of this data will come from.

Real-World Robots That Are Useful

The fact that Boston Dynamics has customers makes them something of an anomaly when it comes to legged robots that rely on AI in commercial deployments. And those customers will have to be able to trust the robot—always a problem when AI is involved. “We take this very seriously,” da Silva said in an interview. “We roll out new DeepMind capabilities through beta programs to a smaller set of customers to understand what to anticipate, and we only actively advertise features we are confident will work.” There’s a threshold of usefulness that robots like Spot need to reach, and fortunately, the real world doesn’t demand perfection. “Most critical infrastructure in a facility will be instrumented to tell you whether something is wrong,” da Silva says. “But there is a lot of stuff that is not instrumented that can still cause a problem if you aren’t paying attention to it. We’ve found that somewhere north of 80 percent is the threshold where it’s not annoying. Below that, basically the robot is crying wolf, and the operators will start ignoring it.”

Both da Silva and Parada agree that there’s still plenty of room for improvement in robotic inspection. As Parada points out, Spot’s rarefied status as a scalable commercial platform provides a valuable opportunity to learn how models like Gemini Robotics-ER 1.6 can be the most useful, and then apply that knowledge to other embodied AI platforms, including Boston Dynamics’ Atlas. Does that mean that Atlas is going to be the next industrial inspection robot? Probably not. But if this real-world experience can get us closer to safe and reliable robots that can pick up laundry, take a dog for a walk, and clear away soda cans without making a mess, that’s something we can all get excited about.

Like many engineers, Sarang Gupta spent his childhood tinkering with everyday items around the house. From a young age he gravitated to projects that could make a difference in someone’s everyday life.

When the family’s microwave plug broke, Gupta and his father figured out how to fix it. When a drawer handle started jiggling annoyingly, the youngster made sure it didn’t do so for long.

Sarang Gupta

Employer

OpenAI in San Francisco

Job

Data science staff member

Member grade

Senior member

Alma maters

The Hong Kong University of Science and Technology; Columbia

By age 11, his interest expanded from nuts and bolts to software. He learned programming languages such as Basic and Logo and designed simple programs including one that helped a local restaurant automate online ordering and billing.

Gupta, an IEEE senior member, brings his mix of curiosity, hands-on problem-solving, and a desire to make things work better to his role as member of the data science staff at OpenAI in San Francisco. He works with the go-to-market (GTM) team to help businesses adopt ChatGPT and other products. He builds data-driven models and systems that support the sales and marketing divisions.

Gupta says he tries to ensure his work has an impact. When making decisions about his career, he says, he thinks about what AI solutions he can unlock to improve people’s lives.

“If I were to sum up my overall goal in one sentence,” he says, “it’s that I want AI’s benefits to reach as many people as possible.”

Pursuing engineering through a business lens

Gupta’s early interest in tinkering and programming led him to choose physics, chemistry, and math as his higher-level subjects at Chinmaya International Residential School, in Tamil Nadu, India. As part of the high school’s International Baccalaureate chapter, students select three subjects in which to specialize.

“I was interested in engineering, including the theoretical part of it,” Gupta says, “But I was always more interested in the applications: how to sell that technology or how it ties to the real world.”

After graduating in 2012, he moved overseas to attend the Hong Kong University of Science and Technology. The university offered a dual bachelor’s program that allowed him to earn one degree in industrial engineering and another in business management in just four years.

In his spare time, Gupta built a smartphone app that let students upload their class schedules and find classmates to eat lunch with. The app didn’t take off, he says, but he enjoyed developing it. He also launched Pulp Ads, a business that printed advertisements for student groups on tissues and paper napkins, which were distributed in the school’s cafeterias. He made some money, he says, but shuttered the business after about a year.

After graduating from the university in 2016, he decided to work in Hong Kong’s financial hub and joined Goldman Sachs as an analyst in the bank’s operations division.

From finance to process optimization at scale

After two parties agree on securities transactions, the bank’s operations division ensures that the trade details are recorded correctly, the securities and payments are ready to transfer, and the transaction settles accurately and on time.

As an analyst, Gupta’s task was to find bottlenecks in the bank’s workflows and fix them. He identified an opportunity to automate trade reconciliation: when analysts would manually compare data across spreadsheets and systems to make sure a transaction’s details were consistent. The process helped ensure financial transactions were recorded accurately and settled correctly.

Gupta built internal automation tools that pulled trade data from different systems, ran validation checks, and generated reports highlighting any discrepancies.

“Instead of analysts manually checking large datasets, the tools automatically flagged only the cases that required investigation,” he says. “This helped the team spend less time on repetitive verification tasks and more time resolving complex issues. It was also my first real exposure to how software and data systems could dramatically improve operational workflows.”

“Whether it’s helping a person improve a trait like that or driving efficiencies at a business, AI just has so much potential to help. I’m excited to be a little part of that.”

The experience made him realize he wanted to work more deeply in technology and data-driven systems, he says. He decided to return to school in 2018 to study data science and AI, when the fields were just beginning to surge into broader awareness.

He discovered that Columbia offered a dedicated master’s degree program in data science with a focus on AI. After being accepted in 2019, he moved to New York City.

Throughout the program, he gravitated to the applied side of machine learning, taking courses in applied deep learning and neural networks.

One of his major academic highlights, he says, was a project he did in 2019 with the Brown Institute, a joint research lab between Columbia and Stanford focused on using technology to improve journalism. The team worked with The Philadelphia Inquirer to help the newsroom staff better understand their coverage from a geographic and social standpoint. The project highlighted “news deserts”—underserved communities for which the newspaper was not providing much coverage—so the publication could redirect its reporting resources.

To identify those areas, Gupta and his team built tools that extracted locations such as street names and neighborhoods from news articles and mapped them to visualize where most of the coverage was concentrated. The Inquirer implemented the tool in several ways including a new web page that aggregated stories about COVID-19 by county.

“Journalism was an interesting problem set for me, because I really like to read the news every day,” Gupta says. “It was an opportunity to work with a real newsroom on a problem that felt really impactful for both the business and the local community.”

The GenAI inflection point

After earning his master’s degree in 2020, Gupta moved to San Francisco to join Asana, the company that developed the work management platform by the same name. He was drawn to the opportunity to work for a relatively small company where he could have end-to-end ownership of projects. He joined the organization as a product data scientist, focusing on A/B testing for new platform features.

Two years later, a new opportunity emerged: He was asked to lead the launch of Asana Intelligence, an internal machine learning team building AI-powered features into the company’s products.

“I felt I didn’t have enough experience to be the founding data scientist,” he says. “But I was also really interested in the space, and spinning up a whole machine learning program was an opportunity I couldn’t turn down.”

The Asana Intelligence team was given six months to build several machine learning–powered features to help customers work more efficiently. They included automatic summaries of project updates, insights about potential risks or delays, and recommendations for next steps.

The team met that goal and launched several other features including Smart Status, an AI tool that analyzes a project’s tasks, deadlines, and activity, then generates a status update.

“When you finally launch the thing you’ve been working on, and you see the usage go up, it’s exhilarating,” he says. “You feel like that’s what you were building toward: users actually seeing and benefiting from what you made.”

Gupta and his team also translated that first wave of work into reusable frameworks and documentation to make it easier to create machine learning features at Asana. He and his colleagues filed several U.S. patents.

At the time he took on that role, OpenAI launched ChatGPT. The mainstreaming of generative AI and large language models shifted much of his work at Asana from model development to assessing LLMs.

OpenAI captured the attention of people around the world, including Gupta. In September 2025 he left Asana to join OpenAI’s data science team.

The transition has been both energizing and humbling, he says. At OpenAI, he works closely with the marketing team to help guide strategic decisions. His work focuses on developing models to understand the efficiency of different marketing channels, to measure what’s driving impact, and to help the company better reach and serve its customers.

“The pace is very different from my previous work. Things move quickly,” he says. “The industry is extremely competitive, and there’s a strong expectation to deliver fast. It’s been a great learning experience.”

Gupta says he plans to stay in the AI space. With technology evolving so rapidly, he says, he sees enormous potential for task automation across industries. AI has already transformed his core software engineering work, he says, and it’s helped him enhance areas that aren’t natural strengths.

“I’m not a good writer, and AI has been huge in helping me frame my words better and present my work more clearly,” he says. “Whether it’s helping a person improve a trait like that or driving efficiencies at a business, AI just has so much potential to help. I’m excited to be a little part of that.”

Exploring IEEE publications and connections

Gupta has been an IEEE member since 2024, and he values the organization as both a technical resource and a professional network.

He regularly turns to IEEE publications and the IEEE Xplore Digital Library to read articles that keep him abreast of the evolution of AI, data science, and the engineering profession.

IEEE’s member directory tools are another valuable resource that he uses often, he says.

“It’s been a great way to connect with other engineers in the same or similar fields,” he says. “I love sharing and hearing about what folks are working on. It brings me outside of what I’m doing day to day.

“It inspires me, and it’s something I really enjoy and cherish.”

The capabilities of leading AI models continue to accelerate, and the largest AI companies, including OpenAI and Anthropic, are hurtling toward IPOs later this year. Yet resentment toward AI continues to simmer, and in some cases has boiled over, especially in the United States, where local governments are beginning to embrace restrictions or outright bans on new data center development.

It’s a lot to keep track of, but the 2026 edition of the AI Index from Stanford University’s Human-Centered Artificial Intelligence center pulls it off. The report, which comes in at over 400 pages, includes dozens of data points and graphs that approach the topic from multiple angles, from benchmark scores to investment and public perception.

As in prior years (see our coverage from 2021, 2022, 2023, 2024, and 2025), we’ve read the report and identified the trends that encapsulate the state of AI in 2026.

US companies lead in AI models

The United States has led the charge in AI model releases over the past decade, and that remains as true in 2025 as in any year prior. According to research institute Epoch AI, organizations based in the United States released 50 “notable” models in 2025. However, China’s output is beginning to close the gap.

Nearly all the notable models originated within industry (as opposed to academic or government institutions). Epoch AI tracked 87 notable model releases from industry in 2025, compared to just seven from all other sources. This is a major long-term trend. Models released by industry now make up over 90 percent of notable models, up from just under 50 percent in 2015, and zero in 2003.

China leads in robotics

While U.S. companies released the largest number of notable AI models, China has an equally clear lead in the deployment of robotics. According to data from the International Federation of Robotics, China installed 295,000 industrial robots in 2024. Japan installed roughly 44,500, and the United States installed 34,200.

World AI compute capacity has grown 3.3x yearly since 2022

The latest Stanford AI Index report has no shortage of head-turning numbers on the AI build-out, but none beats EpochAI’s gauge of total AI compute.

This graph, which uses the compute power of Nvidia’s H100e as a yardstick, shows that the world’s AI compute capacity has increased more than threefold every year since 2022. Total AI compute has increased 30-fold since 2021, the first year tracked.

Nvidia has benefited most from this build-out, as its GPUs account for over 60 percent of the total AI compute capacity in the world today. Amazon and Google—each of which design their own hardware for AI workloads—come in second and third.

Training AI models can generate enormous carbon emissions

Stanford’s AI Index has called out the carbon emissions from AI training in prior years, and the issue continues to trend in a worrying direction.

The report estimates that training the latest frontier large language models, such as xAI’s Grok 4, can generate over 72,000 tons of carbon-equivalent emissions. That’s a huge increase from estimates in prior years. OpenAI’s GPT-4 was estimated at 5,184 tons, and Meta’s Llama 3.1 405B was estimated at 8,930 tons.

Ray Perrault, co-director of the AI Index steering committee, says these figures are estimates. “These estimates should be interpreted with caution. In the case of Grok, they rely heavily on inferred inputs drawn from public reporting (e.g., Forbes articles), xAI statements, and other non-verifiable sources, introducing a degree of uncertainty,” says Perrault. On the other hand, Perrault noted that “Epoch AI independently estimates Grok 4’s emissions to be significantly higher at approximately 140,000 tons of CO₂.”

Emissions from AI inference also continue to increase, though results again vary by model. The report estimates that carbon emissions from models with the least efficient inference are over 10 times as high as those with the most efficient inference. DeepSeek’s V3 models were estimated to consume around 23 watts when responding to a “medium-length” prompt, while Claude 4 Opus was estimated to consume about 5 watts.

LLMs are rapidly defeating new benchmarks

The capabilities of AI models have improved with incredible speed over the past decade, and as the graph above shows, progress seems to be accelerating. Multimodal LLMs, in particular, are conquering benchmarks nearly as quickly as they can be invented.

Agentic AI has experienced the most extreme gains. The two steep lines at the right of the chart represent the OSWorld benchmark, which benchmarks autonomous computer use, and the SWE-Bench Verified software engineering benchmark, which benchmarks autonomous coding.

Models are also rapidly improving on Humanity’s Last Exam. This benchmark includes questions contributed by subject-matter experts designed to represent the toughest problems in their fields. The 2025 Stanford AI Index reported the top-ranking model, OpenAI’s o1, correctly answered just 8.8 percent of questions. Since then, accuracy has increased to 38.3 percent—and even that number is a bit out of date, as the best-scoring models as of April 2026 (such as Anthropic’s Claude Opus 4.6 and Google’s Gemini 3.1 Pro) top 50 percent.

Still, Perrault cautioned that benchmarks may not always map to real-world results. “We generally lack measures of how well a system (or agent) needs to function in a particular setting,” says Perrault. “Knowing that a benchmark for legal reasoning has 75 percent accuracy tells us little about how well it would fit in a law practice’s activities.”

AI research in medicine sees gains

Gains in AI benchmarks seem to be reflected in medicine, where AI adoption has increased at a rapid pace. Medical research shows particularly quick adoption. As the graph above shows, the number of publications on the topic of AI use for drug discovery has more than doubled over the past two years. There are 2.7 times as many publications on multimodal biomedical AI, which are used to examine medical images alongside text, as there were two years ago.

LLMs still have trouble reading an analog clock

While AI models have improved rapidly in some areas, they remain remarkably bad at some common tasks, like reading clocks and understanding calendars. ClockBench, which measures a multimodal LLM’s ability to read an analog clock, found that even the model best at this task, OpenAI’s GPT-5.4, had just 50-50 odds of getting it right.

Most models scored far worse. Anthropic’s Claude Opus 4.6 read the time correctly with just 8.9 percent accuracy. That’s surprising, because the model often scores well in other benchmarks. (As previously mentioned, Claude Opus 4.6 delivered top-notch scores in Humanity’s Last Exam.)

Of course, LLMs will rarely be asked to perform this task in real life, but Perrault says it represents a more general issue. “There is a research thread that shows that when systems are asked questions about combinations of language with other modalities (e.g., images, or audio, as in tone of voice), the language component carries a surprisingly large part of the burden, event to the extent of ignoring non-language information completely.”

AI investment hit a new peak in 2025

The gains in AI model performance have gone hand-in-hand with investment in AI companies. According to data from AI analytics company Quid, 2025 set a new record for AI investment with over US $581 billion spent.

That’s more than double the $253 billion spent in 2024 and speeds past the previous record of $360 billion, which was set in 2021. And unlike 2021, where investment was led by mergers and acquisitions, 2025’s record-setting result was led by private investment in AI companies.

Most of that money is flowing into the United States, where over $344 billion were invested in AI last year.

Software engineers are all-in on AI

However, the story of AI adoption isn’t just about private money. There’s also a grassroots enthusiasm for AI on GitHub, where the number of AI-related projects has rocketed to 5.58 million projects through 2025. That’s a roughly fivefold increase since 2020 and a 23.7 percent increase from 2024.

This number doesn’t appear to represent a flood of AI-generated projects, either. The number of projects with at least 10 stars has increased at a similar rate, and the number of stars awarded to AI projects overall has increased at a similar rate. That suggests the projects are seeing human engagement. Perhaps this should be no surprise given the popularity of some projects. Open-source agentic AI software OpenClaw, for example, has received 352,000 stars.

Critics may worry that the enthusiasm is in part driven by AI bots or agentic projects. Perrault acknowledged this and says that “probably the intensity of GitHub use is highly correlated with the intensity of AI use.” However, the majority of GitHub activity still appears to be conducted by humans, at least according to an activity-tracking website called Agents in the Wild (this website is not mentioned in Stanford’s report).

Enthusiasm is strong in computer science, too. The number of AI-related computer science publications has more than doubled over the past decade, from 102,000 to 258,000. More than 68 percent of these still originate in academia, with government and industry contributing about 11.5 and 12.5 percent, respectively as of 2024. The growth is led by publications in machine learning, computer vision, and generative AI.

AI’s overall impact on employment remains unclear

The rise of generative AI goes hand-in-hand with employment worries, a phenomenon no doubt encouraged by the worrisome predictions of CEOs at the world’s largest AI companies. However, the data so far remains mixed.

Above you’ll find graphs that show the “normalized headcount” among varying age demographics in two professions thought to be at high risk of AI replacement: software developers and customer support agents. As in prior years, the trends show that entry-level jobs in these professions have been reduced, while mid-career and senior positions have held steady or increased.

However, these changes remain difficult to untangle from broader economic trends. The report notes that unemployment is rising across many occupations and that, contrary to expectations, unemployment among workers least exposed to AI has risen more than unemployment among workers most exposed to AI.

Overall public perception of AI (slightly) improves

The report’s most surprising finding is, no doubt, the small but notable increase in optimism about AI over the past several years: 59 percent of respondents to a survey conducted by Ipsos said “the benefits outweigh the drawbacks,” up from 55 percent in 2024, and 68 percent of respondents said they have a “good understanding” of AI, a slight uptick from 67 percent in 2024.

Survey responses to similar questions suggest that the overall reception to AI is more positive than negative, though some negative feelings have also increased. For example, 52 percent of respondents said that products and services that use AI make them “nervous.”

Sentiment varies significantly by country. Countries in Southeast Asia, including China, Malaysia, Thailand, Indonesia, and Singapore, are trending more positive toward AI. However, the strongest positive year-over-year shifts were in Germany (12 percent), France (10 percent), and the Netherlands (10 percent). Colombia saw the most negative shift (-6 percent), a reversal of the trend from prior years.

Trust in AI regulation varies significantly by country

While a growing number of people seem to feel that AI will have a positive impact, that shift is accompanied by deep distrust in some countries, particularly on the topic of government regulation.

Notably, the United States is at the bottom of the list even while it leads in AI investment. Only 31 percent of Ipsos survey respondents trusted the government to regulate AI. Many European countries showed low levels of trust, as did Japan. Countries in Asia and South America showed the greatest trust in their government’s ability to regulate AI.

The results from the United States and Colombia are intriguing. The U.S. is seeing deep distrust in AI regulation, yet most respondents think AI’s benefits will outweigh its drawbacks. Colombia, on the other hand, shows high trust in AI regulations yet worsening sentiment toward AI overall.

This feels like a microcosm of the AI narrative in 2025. Both the quality of results from AI models, and public perception on how AI will impact society, continue to vary, often by wide margins, depending on the task or question at hand.

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The Colorado River begins as snow. Every spring, the mountain snowpack of the Rockies melts into streams that feed into reservoirs that supply 40 million people across seven U.S. states. The system has worked, more or less, for a century. That century is over.

By some measures, 2026 is shaping up to be the worst year the river has seen since records began. Flows are down 20 percent from 2000 levels. Lake Powell, the reservoir straddling Utah and Arizona, may drop below the threshold for generating hydropower before the year is out. The negotiations between the seven states over how to share what’s left have collapsed twice, and the U.S. federal government is threatening to impose its own plan.

While the states argue and the river shrinks, a growing set of machine learning tools is being deployed across the basin. Federal water managers are running millions of simulations to stress-test reservoir strategies against different possible futures. Researchers are forecasting streamflow months out using satellite data and deep learning. These technologies don’t promise to resolve the crisis, but they’re making the trade-offs visible. They’re showing, more precisely than ever before, what each decision will cost.

Seeing Further Into the River’s Future

Nobody manages more of the Colorado River’s daily operations than the U.S. Bureau of Reclamation. If the federal government follows through on its threat to impose a water-sharing plan, it will be Reclamation doing the imposing, and making decisions about how much water flows from Lake Powell and Lake Mead, the two largest reservoirs in the country.

The agency is not new to sophisticated modeling. For years, Reclamation’s researchers have combined paleoclimate reconstructions, global circulation models, and scenario planning to predict the river’s future. Machine learning tools are adding to that toolkit, says Chris Frans, Reclamation’s water-availability research coordinator, and they are already informing real operational decisions.

The clearest gains are in streamflow forecasting. Machine learning techniques—using data from satellites and weather stations well outside the basin—now outperform traditional methods across a range of conditions. Forecasts update every hour. In some areas, managers are getting five to seven days of advance warning on flood events, compared with three in the past, which gives them time to reduce the water in reservoirs before high inflows arrive.

The scale of scenario modeling has also expanded dramatically. A decade ago, running 100,000 individual simulations was a landmark study. Now, says Alan Butler, who manages Reclamation’s research and modeling group for the lower Colorado Basin, millions of simulations feed the analytical tools used in the current guidelines. Those simulations map out how different operating strategies perform across widely varying futures—making the trade-offs between them harder to ignore.

Dividing a Shrinking River

Knowing how much water is coming is one problem. Deciding who gets it is another. At the center of that process is the Colorado River Simulation System (CRSS), which models how water moves through the basin’s reservoirs, canals, and pipelines under more than a century of legal and regulatory constraints. This Reclamation model is an imperfect representation, but it has been the foundation of river negotiations for decades.

A tool called RiverWare, first developed in the early 1990s at the University of Colorado Boulder, lets states, cities, and tribes run their own scenarios through CRSS. Before RiverWare, these groups didn’t have confidence in Reclamation’s numbers, says Edith Zagona, a Boulder professor who directs the Center for Advanced Decision Support for Water and Environmental Systems, the center that built it. “There was just this huge lack of trust.” The solution was letting stakeholders inspect the assumptions built into the RiverWare model—how much water was available, how it could be used, and under what rules.

Getting stakeholders to trust the model turned out to be the easier problem. The harder one is what to do when the model itself can’t predict a single probable future. That question drove Zagona toward a framework called decision-making under deep uncertainty, which trades prediction for stress-testing policies against thousands of possible futures.

The tool Zagona’s group developed with Reclamation and the consulting firm Virga Labs puts the framework into practice in a web-based tool, running CRSS across more than 8,000 possible future water-supply scenarios to show how different management strategies hold up against the full range of what climate change might bring. At its center is an evolutionary algorithm called Borg, which generates and iteratively refines those strategies, searching for plans that perform well across many scenarios. The result is a set of trade-offs, not a single answer.

Borg-RiverWare has already shaped the ongoing negotiations over the river’s next operating rules, generating the scenarios and data that Reclamation used in its modeling tools. Those tools give stakeholders a common analytical foundation for negotiations. Now Zagona’s center is pushing the approach further. A system in development would let negotiating parties test competing proposals on the fly, showing how one side’s policy choices would ripple through the system and identifying areas of potential compromise during the negotiation itself.

New Tools for Forecasting the Colorado

Reclamation and Zagona’s center aren’t the only ones trying to see further into the river’s future. At Metropolitan State University of Denver, a team led by Mohammad Valipour has been building a forecasting system that uses deep learning to issue drought warnings across seven rivers in Colorado, from seven days to six months out. In a region where ground gauges are sparse and mountains make installation difficult, the team found that NASA satellite data outperformed in-field measurements. The goal, Valipour says, is a statewide drought alarm system that gives farmers and water managers more time to respond.

At Utah State University, Soukaina Filali Boubrahimi is attacking a different problem: how conditions at one point in the river ripple downstream weeks later. Using a graph neural network that treats each monitoring station as a node, her team built a map of the river’s interdependencies across one of the most contested water systems in the world. She says the approach could extend to other overtaxed basins.

“If you can figure out the Colorado River,” she says, “anyone else dealing with a stressed river system is going to be interested in what you learned.”

Snowpack in the upper Colorado River basin is far below normal. As of late March 2026, measurements across 130 sites were about 35 percent of the median, with projections showing continued shortfalls.USDA Natural Resources Conservation Service (NRCS)

What the Models Can’t See

Across the basin, researchers and water managers are running into the same wall. The models learn from historical data, but that data describes a river that no longer exists. Valipour found that feeding his models only the last decade outperformed using longer records. Filali Boubrahimi’s model struggles most in drought conditions, precisely when predictions matter most, because recent prolonged droughts don’t resemble the historical training data. One workaround is to train models on data from basins that have already experienced what the Colorado hasn’t yet.

Even so, better forecasts do not resolve the central problem. While the tools can show you what a drier future looks like across a thousand possible scenarios, they can’t tell you who should bear the cost of it. The cuts coming to the basin are going to be enormous, says Brad Udall, a water and climate research scientist at Colorado State University’s Colorado Water Center, and they will fall mostly on agriculture. They may fundamentally reshape communities that have built their economies around water for generations. “AI has no business being in the realm of replacing human values and human judgments,” he says.

The tools, by most measures, are doing exactly what they were built to do: The negotiating parties understand what is coming, and they are not disputing the projections. Zagona, who has worked on the Colorado River for 45 years, sees reasons for optimism. “The tools are bringing people to the table,” she says. “They’re at the table arguing. But at least they’re at the table.”

Artificial intelligence harbors an enormous energy appetite. Such constant cravings are evident in the hefty carbon footprint of the data centers behind the AI boom and the steady increase over time of carbon emissions from training frontier AI models.

No wonder big tech companies are warming up to nuclear energy, envisioning a future fueled by reliable, carbon-free sources. But while nuclear-powered data centers might still be years away, some in the research and industry spheres are taking action right now to curb AI’s growing energy demands. They’re tackling training as one of the most energy-intensive phases in a model’s life cycle, focusing their efforts on decentralization.

Decentralization allocates model training across a network of independent nodes rather than relying on one platform or provider. It allows compute to go where the energy is—be it a dormant server sitting in a research lab or a computer in a solar-powered home. Instead of constructing more data centers that require electric grids to scale up their infrastructure and capacity, decentralization harnesses energy from existing sources, avoiding adding more power into the mix.

Hardware in harmony

Training AI models is a huge data center sport, synchronized across clusters of closely connected GPUs. But as hardware improvements struggle to keep up with the swift rise in the size of large language models, even massive single data centers are no longer cutting it.

Tech firms are turning to the pooled power of multiple data centers—no matter their location. Nvidia, for instance, launched the Spectrum-XGS Ethernet for scale-across networking, which “can deliver the performance needed for large-scale single job AI training and inference across geographically separated data centers.” Similarly, Cisco introduced its 8223 router designed to “connect geographically dispersed AI clusters.”

Other companies are harvesting idle compute in servers, sparking the emergence of a GPU-as-a-Service business model. Take Akash Network, a peer-to-peer cloud computing marketplace that bills itself as the “Airbnb for data centers.” Those with unused or underused GPUs in offices and smaller data centers register as providers, while those in need of computing power are considered as tenants who can choose among providers and rent their GPUs.

“If you look at [AI] training today, it’s very dependent on the latest and greatest GPUs,” says Akash cofounder and CEO Greg Osuri. “The world is transitioning, fortunately, from only relying on large, high-density GPUs to now considering smaller GPUs.”

Software in sync

In addition to orchestrating the hardware, decentralized AI training also requires algorithmic changes on the software side. This is where federated learning, a form of distributed machine learning, comes in.

It starts with an initial version of a global AI model housed in a trusted entity such as a central server. The server distributes the model to participating organizations, which train it locally on their data and share only the model weights with the trusted entity, explains Lalana Kagal, a principal research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) who leads the Decentralized Information Group. The trusted entity then aggregates the weights, often by averaging them, integrates them into the global model, and sends the updated model back to the participants. This collaborative training cycle repeats until the model is considered fully trained.

But there are drawbacks to distributing both data and computation. The constant back-and-forth exchanges of model weights, for instance, result in high communication costs. Fault tolerance is another issue.

“A big thing about AI is that every training step is not fault-tolerant,” Osuri says. “That means if one node goes down, you have to restore the whole batch again.”

To overcome these hurdles, researchers at Google DeepMind developed DiLoCo, a distributed low-communication optimization algorithm. DiLoCo forms what Google DeepMind research scientist Arthur Douillard calls “islands of compute,” where each island consists of a group of chips. Every island holds a different chip type, but chips within an island must be of the same type. Islands are decoupled from each other, and synchronizing knowledge between them happens once in a while. This decoupling means islands can perform training steps independently without communicating as often, and chips can fail without having to interrupt the remaining healthy chips. However, the team’s experiments found diminishing performance after eight islands.

An improved version, dubbed Streaming DiLoCo, further reduces the bandwidth requirement by synchronizing knowledge “in a streaming fashion across several steps and without stopping for communicating,” says Douillard. The mechanism is akin to watching a video even if it hasn’t been fully downloaded yet. “In Streaming DiLoCo, as you do computational work, the knowledge is being synchronized gradually in the background,” he adds.

AI development platform Prime Intellect implemented a variant of the DiLoCo algorithm as a vital component of its 10-billion-parameter INTELLECT-1 model trained across five countries spanning three continents. Upping the ante, 0G Labs, makers of a decentralized AI operating system, adapted DiLoCo to train a 107-billion-parameter foundation model under a network of segregated clusters with limited bandwidth. Meanwhile, popular open-source deep learning framework PyTorch included DiLoCo in its repository of fault-tolerance techniques.

“A lot of engineering has been done by the community to take our DiLoCo paper and integrate it in a system learning over consumer-grade internet,” Douillard says. “I’m very excited to see my research being useful.”

A more energy-efficient way to train AI

With hardware and software enhancements in place, decentralized AI training is primed to help solve AI’s energy problem. This approach offers the option of training models “in a cheaper, more resource-efficient, more energy-efficient way,” says MIT CSAIL’s Kagal.

And while Douillard admits that “training methods like DiLoCo are arguably more complex, they provide an interesting trade-off of system efficiency.” For instance, you can now use data centers across far apart locations without needing to build ultrafast bandwidth in between. Douillard adds that fault tolerance is baked in because “the blast radius of a chip failing is limited to its island of compute.”

Even better, companies can take advantage of existing underutilized processing capacity rather than continuously building new energy-hungry data centers. Betting big on such an opportunity, Akash created its Starcluster program. One of the program’s aims involves tapping into solar-powered homes and employing the desktops and laptops within them to train AI models. “We want to convert your home into a fully functional data center,” Osuri says.

Osuri acknowledges that participating in Starcluster will not be trivial. Beyond solar panels and devices equipped with consumer-grade GPUs, participants would also need to invest in batteries for backup power and redundant internet to prevent downtime. The Starcluster program is figuring out ways to package all these aspects together and make it easier for homeowners, including collaborating with industry partners to subsidize battery costs.

Back-end work is already underway to enable homes to participate as providers in the Akash Network, and the team hopes to reach its target by 2027. The Starcluster program also envisions expanding into other solar-powered locations, such as schools and local community sites.

Decentralized AI training holds much promise to steer AI toward a more environmentally sustainable future. For Osuri, such potential lies in moving AI “to where the energy is instead of moving the energy to where AI is.”

In late-stage testing of a distributed AI platform, engineers sometimes encounter a perplexing situation: Every monitoring dashboard reads “healthy,” yet users report that the system’s decisions are slowly becoming wrong.

Engineers are trained to recognize failure in familiar ways: a service crashes, a sensor stops responding, a constraint violation triggers a shutdown. Something breaks, and the system tells you. But a growing class of software failures looks very different. The system keeps running, logs appear normal, and monitoring dashboards stay green. Yet the system’s behavior quietly drifts away from what it was designed to do.

This pattern is becoming more common as autonomy spreads across software systems. Quiet failure is emerging as one of the defining engineering challenges of autonomous systems because correctness now depends on coordination, timing, and feedback across entire systems.

When Systems Fail Without Breaking

Consider a hypothetical enterprise AI assistant designed to summarize regulatory updates for financial analysts. The system retrieves documents from internal repositories, synthesizes them using a language model, and distributes summaries across internal channels.

Technically, everything works. The system retrieves valid documents, generates coherent summaries, and delivers them without issue.

But over time, something slips. Maybe an updated document repository isn’t added to the retrieval pipeline. The assistant keeps producing summaries that are coherent and internally consistent, but they’re increasingly based on obsolete information. Nothing crashes, no alerts fire, every component behaves as designed. The problem is that the overall result is wrong.

From the outside, the system looks operational. From the perspective of the organization relying on it, the system is quietly failing.

The Limits of Traditional Observability

One reason quiet failures are difficult to detect is that traditional systems measure the wrong signals. Operational dashboards track uptime, latency, and error rates, the core elements of modern observability. These metrics are well-suited for transactional applications where requests are processed independently, and correctness can often be verified immediately.

Autonomous systems behave differently. Many AI-driven systems operate through continuous reasoning loops, where each decision influences subsequent actions. Correctness emerges not from a single computation but from sequences of interactions across components and over time. A retrieval system may return contextually inappropriate and technically valid information. A planning agent may generate steps that are locally reasonable but globally unsafe. A distributed decision system may execute correct actions in the wrong order.

None of these conditions necessarily produces errors. From the perspective of conventional observability, the system appears healthy. From the perspective of its intended purpose, it may already be failing.

Why Autonomy Changes Failure

The deeper issue is architectural. Traditional software systems were built around discrete operations: a request arrives, the system processes it, and the result is returned. Control is episodic and externally initiated by a user, scheduler, or external trigger.

Autonomous systems change that structure. Instead of responding to individual requests, they observe, reason, and act continuously. AI agents maintain context across interactions. Infrastructure systems adjust resources in real time. Automated workflows trigger additional actions without human input.

In these systems, correctness depends less on whether any single component works and more on coordination across time.

Distributed-systems engineers have long wrestled with issues of coordination. But this is coordination of a new kind. It’s no longer about things like keeping data consistent across services. It’s about ensuring that a stream of decisions—made by models, reasoning engines, planning algorithms, and tools, all operating with partial context—adds up to the right outcome.

A modern AI system may evaluate thousands of signals, generate candidate actions, and execute them across a distributed infrastructure. Each action changes the environment in which the next decision is made. Under these conditions, small mistakes can compound. A step that is locally reasonable can still push the system further off course.

Engineers are beginning to confront what might be called behavioral reliability: whether an autonomous system’s actions remain aligned with its intended purpose over time.

The Missing Layer: Behavioral Control

When organizations encounter quiet failures, the initial instinct is to improve monitoring: deeper logs, better tracing, more analytics. Observability is essential, but it only shows that the behavior has already diverged—it doesn’t correct it.

Quiet failures require something different: the ability to shape system behavior while it is still unfolding. In other words, autonomous systems increasingly need control architectures, not just monitoring.

Engineers in industrial domains have long relied on supervisory control systems. These are software layers that continuously evaluate a system’s status and intervene when behavior drifts outside safe bounds. Aircraft flight-control systems, power-grid operations, and large manufacturing plants all rely on such supervisory loops. Software systems historically avoided them because most applications didn’t need them. Autonomous systems increasingly do.

Behavioral monitoring in AI systems focuses on whether actions remain aligned with intended purpose, not just whether components are functioning. Instead of relying only on metrics such as latency or error rates, engineers look for signs of behavior drift: shifts in outputs, inconsistent handling of similar inputs, or changes in how multistep tasks are carried out. An AI assistant that begins citing outdated sources, or an automated system that takes corrective actions more often than expected, may signal that the system is no longer using the right information to make decisions. In practice, this means tracking outcomes and patterns of behavior over time.

Supervisory control builds on these signals by intervening while the system is running. A supervisory layer checks whether ongoing actions remain within acceptable bounds and can respond by delaying or blocking actions, limiting the system to safer operating modes, or routing decisions for review. In more advanced setups, it can adjust behavior in real time—for example, by restricting data access, tightening constraints on outputs, or requiring extra confirmation for high-impact actions.

Together, these approaches turn reliability into an active process. Systems don’t just run, they are continuously checked and steered. Quiet failures may still occur, but they can be detected earlier and corrected while the system is operating.

A Shift in Engineering Thinking

Preventing quiet failures requires a shift in how engineers think about reliability: from ensuring components work correctly to ensuring system behavior stays aligned over time. Rather than assuming that correct behavior will emerge automatically from component design, engineers must increasingly treat behavior as something that needs active supervision.

As AI systems become more autonomous, this shift will likely spread across many domains of computing, including cloud infrastructure, robotics, and large-scale decision systems. The hardest engineering challenge may no longer be building systems that work, but ensuring that they continue to do the right thing over time.

While browsing our website a few weeks ago, I stumbled upon “How and When the Memory Chip Shortage Will End” by Senior Editor Samuel K. Moore. His analysis focuses on the current DRAM shortage caused by AI hyperscalers’ ravenous appetite for memory, a major constraint on the speed at which large language models run. Moore provides a clear explanation of the shortage, particularly for high bandwidth memory (HBM).

As we and the rest of the tech media have documented, AI is a resource hog. AI electricity consumption could account for up to 12 percent of all U.S. power by 2028. Generative AI queries consumed 15 terawatt-hours in 2025 and are projected to consume 347 TWh by 2030. Water consumption for cooling AI data centers is predicted to double or even quadruple by 2028 compared to 2023.

But Moore’s reporting shines a light on an obscure corner of the AI boom. HBM is a particular type of memory product tailor-made to serve AI processors. Makers of those processors, notably Nvidia and AMD, are demanding more and more memory for each of their chips, driven by the needs and wants of firms like Google, Microsoft, OpenAI, and Anthropic, which are underwriting an unprecedented buildout of data centers. And some of these facilities are colossal: You can read about the engineering challenges of building Meta’s mind-boggling 5-gigawatt Hyperion site in Louisiana, in “What Will It Take to Build the World’s Largest Data Center?”

We realized that Moore’s HBM story was both important and unique, and so we decided to include it in this issue, with some updates since the original published on 10 February. We paired it with a recent story by Contributing Editor Matthew S. Smith exploring how the memory-chip shortage is driving up the price of low-cost computers like the Raspberry Pi. The result is “AI Is a Memory Hog.”

The big question now is, When will the shortage end? Price pressure caused by AI hyperscaler demand on all kinds of consumer electronics is being masked by stubborn inflation combined with a perpetually shifting tariff regime, at least here in the United States. So I asked Moore what indicators he’s looking for that would signal an easing of the memory shortage.

“On the supply side, I’d say that if any of the big three HBM companies—Micron, Samsung, and SK Hynix—say that they are adjusting the schedule of the arrival of new production, that’d be an important signal,” Moore told me. “On the demand side, it will be interesting to see how tech companies adapt up and down the supply chain. Data centers might steer toward hardware that sacrifices some performance for less memory. Startups developing all sorts of products might pivot toward creative redesigns that use less memory. Constraints like shortages can lead to interesting technology solutions, so I’m looking forward to covering those.”

To be sure you don’t miss any of Moore’s analysis of this topic and to stay current on the entire spectrum of technology development, sign up for our weekly newsletter, Tech Alert.

A traditional data center protects the expensive hardware inside it with a “shell” constructed from steel and concrete. Constructing a data center’s shell is inexpensive compared to the cost of the hardware and infrastructure inside it, but it’s not trivial. It takes time for engineers to consider potential sites, apply for permits, and coordinate with construction contractors.

That’s a problem for those looking to quickly deploy AI hardware, which has led companies like Duos Edge AI and LG CNS to respond with a more modular approach. They use pre-fabricated, self-contained boxes that can be deployed in months instead of years. The boxes can operate alone or in tandem with others, providing the option to add more if required.

“I just came back from Nvidia’s GTC, and a lot of [companies] are sitting on their deployment because their data centers aren’t ready, or they can’t find the space,” said Doug Recker, CEO of Duos Edge AI. “We see the demand there, and we can deploy faster.”

GPUs shipped straight to you

Duos Edge AI’s modular compute pods are 55 feet long and 12.5 feet wide. Though they look similar to a shipping container, they’re actually a bit larger and designed primarily for transportation by truck. Each compute pod contains racks of GPUs much like those used in other data centers. Duos recently entered a deal with AI infrastructure company Hydra Host to deploy four pods with 576 GPUs per pod. That’s a total of 2,304 GPUs, with the option to later double the deployment to 4,608 GPUs.

Modular data centers aren’t new for Duos; the company previously deployed edge data centers for rural customers, such as the Amarillo, Texas, school district. However, the pods for the Hydra Host deployment will be upgraded to handle more intense AI workloads. They’ll contain more racks, draw more power, and use liquid cooling to keep the GPUs running efficiently.

Across the Pacific, Korean technology giant LG is taking a similar approach. The company’s CNS subsidiary, which provides IT infrastructure and services, has announced the AI Modular Data Center, which, like the Duos unit, contains racks of GPUs and supporting hardware in a pre-fabricated enclosure.

Also like Duos’ deployment, LG’s AI Modular Data Center contains 576 Nvidia GPUs with the option to scale up in the future. “We are currently developing an expanded version that can support more than 4,600 GPUs within a single unit, with a service launch planned within this year,” said Heon Hyeock Cho, vice president and head of the data center business unit at LG CNS. LG’s first Modular Data Center will roll out in the South Korean port city of Busan, where it could deploy up to 50 units.

LG and Duos are not alone. Hewlett Packard Enterprise, Vertiv, and Schneider Electric now have modular data centers available or in development. A report from market research firm Grand View Research estimates that the market for modular data centers could more than double by 2030.

On the grid, but under the radar

A modular data center site is quite different from a traditional data center because there’s no need to construct a large steel-and-concrete shell. Instead, the site can be made ready by pouring a concrete pad. The pre-fabricated modules are delivered by truck, placed on the pad where desired, and then networked on-site.

Duos’ deployments, for instance, include power modules placed alongside the compute pods, and the pods are networked together with redundant fiber connections that allow the pods to operate in unison. Recker compared it to lining up school buses in a parking lot. “Everything is built off-site at a factory, and we can put it together like a jigsaw puzzle,” he said.

That simplicity is the point. Both Duos and LG CNS expect a modular data center can be deployed in about six months, compared to the roughly two or three years a conventional data center requires. Recker said that, for Duos, the turnaround is so quick that building the pre-fabricated unit isn’t always the constraint. While it’s possible to construct a pre-fabricated unit in 60 or 90 days, site preparation extends the timeline “because you can’t get the permits that fast.”

Modular data centers may also provide good value. Recker said a 5-megawatt modular deployment can be built for about $25 million, and that Duos’ cost per megawatt is roughly half what larger facilities charge. For Duos, savings are possible in part because its modular data centers can target smaller deployments where the permitting is less complex. Smaller, modular deployments also meet less resistance from local governments, which are increasingly skeptical about data center construction.

While Duos targets smaller deployments, LG hopes to go big. Its planned Busan campus of 50 AI Modular Data Centers suggests an ambition to achieve deployments that rival the capacity of conventional facilities. A site with 50 units would bring the total number of GPUs to over 28,000. Here, the benefits of a modular approach could stem mostly from scalability, as a modular data center could start small and grow as required.

“By adopting a modular approach, the AI Modular Data Center can be incrementally expanded through the combination of dozens of AI Boxes,” Cho said. “It’s enabling the construction of even hyperscale-level AI data centers.”

Large language models (LLMs) have improved so quickly that the benchmarks themselves have evolved, adding more complex problems in an effort to challenge the latest models. Yet LLMs haven’t improved across all domains, and one task remains far outside their grasp: They have no idea how to play video games.

While a few have managed to beat a few games (for example, Gemini 2.5 Pro beat Pokemon Blue in May of 2025), these exceptions prove the rule. The eventually victorious AI completed games far more slowly than a typical human player, made bizarre and often repetitive mistakes, and required custom software to guide their interactions with the game.

Julian Togelius, the director of New York University’s Game Innovation Lab and co-founder of AI game-testing company Modl.ai, explored the implications of LLMs’ limitations in video games in a recent paper. He spoke with IEEE Spectrum about what this lack of video-game skills can tell us about the broader state of AI in 2026.

LLMs have improved rapidly in coding, and your paper frames coding as a kind of well-behaved game. What do you mean by that?

Julian Togelius: Coding is extremely well-behaved in the sense that you have tasks. These are like levels. You get a specification, you write code, and then you run it.

The reward is immediate and granular. The code has to compile, it has to run without crashing, and then it usually has to pass tests. Often, there’s also an explanation of how and why it failed.

There’s a theory from game designer Raph Koster that games are fun because we learn to play them as we play them. From that perspective, writing code is an extremely well-designed game. And in fact, writing code is something many people enjoy doing.

Unlike coding, LLMs struggle with video games. This feels surprising given their success in coding, as well as in games like chess and Go. What is it about video games that’s causing a problem?

Togelius: It’s not just LLMs that are bad at this. We do not have general game AI.

There’s a widespread perception that because we can build AI that plays particular games well, we should be able to build one that plays any game. I’m not sure we’re going to get there.

People will mention that Google’s AlphaZero [which is not an LLM] can play both Go and chess. However, it had to be retrained and reengineered for each. And those are games that are similar in terms of input and output space. Most games are more different from each other. They have different mechanics and different input representations.

There’s also a data problem. Some of the games that AI can successfully play, like Minecraft and Pokémon, are among the most well-studied games in the world with literally millions of hours of guides. For a less well-known game, there’s far less.

Video Game Benchmarks for LLM Performance

One factor that seems to help LLMs improve in coding is the proliferation of benchmarks. We have many benchmarks LLMs can try to solve, we can score the results, and then modify the LLM to improve performance. Developing a benchmark for playing a video game, though, is less clear-cut. Why is that?

Togelius: I’ve built many game-based AI benchmarks over the years. One, the General Video Game AI competition, ran for seven years. We tested an agent on our publicly available games, and every time we ran the competition, we invented 10 new games to test on.

One reason we stopped was that we stopped seeing progress. Agents got better at some games but worse at others. This was before LLMs.

Lately, we’ve been updating this framework for LLMs. They fail. They absolutely suck. All of them. They don’t even do as well as a simple search algorithm.

Why? They were never trained on these games, and they’re separately very bad at spatial reasoning. Which shouldn’t be surprising, because that’s also not in the training data.

This brings us to what seems like a contradiction. LLMs are bad at playing games. Yet at the same time, they’re improving rapidly at coding, a skill set that can be used to create a game. How do these facts fit together?

Togelius: It’s super weird. You can go into Cursor or Claude, write one prompt, and get a playable game. The game will be very typical, because an LLM’s code-writing abilities are better the more typical something is. So, if you ask it to give you something like Asteroids, it will work. That’s impressive.

However, it’s not going to give you a good or novel game. That does seem weird. The reason is that the LLM can’t play it. Game development is an iterative process. You write, you test, you adjust the game feel. An LLM can’t do that.

And to an extent, I don’t think it’s different when designing other software. Yes, you can ask an LLM to create a GUI with a bunch of buttons. But the LLM doesn’t know much about how to use it.

Companies like Nvidia and Google have talked about using simulations, including gamelike environments, to improve AI performance. If AI can’t master games in general, how optimistic should we be about that approach?

Togelius: Games are both easier and harder than the real world. They’re easier because there are fewer levels of abstraction. They’re harder because games are much more diverse. The real world has the same physics everywhere.

One example is Waymo, which uses world models in its training loop. That makes sense because driving is much the same everywhere. It’s way less diverse than games.

That’s confusing for people. People see an LLM write an academic essay on quantum physics and wonder, “How can it not play both Halo and Space Invaders?” However, those games are more different from each other, in a sense, than two academic essays.

This sponsored article is brought to you by NYU Tandon School of Engineering.

Within a 6 mile radius of New York University’s (NYU) campus, there are more than 500 tech industry giants, banks, and hospitals. This isn’t just a fact about real estate, it’s the foundation for advancing quantum discovery and application.

While the world races to harness quantum technology, NYU is betting that the ultimate advantage lies not solely in a lab, but in the dense, demanding, and hyper-connected urban ecosystem that surrounds it. With the launch of its NYU Quantum Institute (NYUQI), NYU is positioning itself as the central node in this network; a “full stack” powerhouse built on the conviction that it has found the right place, and the right time, to turn quantum science into tangible reality.

Proximity advantage is essential because quantum science demands it. Globally, the quest for practical quantum solutions — whether for computing, sensing, or secure communications — has been stalled, in part, by fragmentation. Physicists and chemical engineers invent new materials, computer scientists develop new algorithms, and electrical engineers build new devices, but all three often work in isolated academic silos.

Gregory Gabadadze, NYU’s dean for science, NYU physicist and Quantum Institute Director Javad Shabani, and Juan de Pablo, Anne and Joel Ehrenkranz Executive Vice President for Global Science and Technology and executive dean of the Tandon School of Engineering.Veselin Cuparić/NYU

NYUQI’s premise is that breakthroughs happen “at the interfaces between different domains,” according to Juan de Pablo, Executive Vice President for Global Science and Technology at NYU and Executive Dean of the NYU Tandon School of Engineering. The Institute is built to actively force those necessary collisions — to integrate the physicists, engineers, materials scientists, computer scientists, biologists, and chemists vital to quantum research into one holistic operation. This institutional design ensures that the hardware built by one team can be immediately tested by software developed by another, accelerating progress in a way that isolated departments never could.

NYUQI’s premise is that breakthroughs happen at the interfaces between different domains. —Juan de Pablo, NYU Tandon School of Engineering

NYUQI’s integrated vision is backed by a massive physical commitment to the city. The NYUQI is not just a theoretical concept; its collaborators will be housed in a renovated, million-square-foot facility in the heart of Manhattan’s West Village, backed by a state-of-the-art Nanofabrication Cleanroom in Brooklyn serving as a high-tech foundry. This is where the theoretical meets physical devices, allowing the Institute to test and refine the process from materials science to deployment.

NYUQI will be housed in a renovated, million-square-foot facility in the heart of Manhattan’s West Village.Tracey Friedman/NYU

Leading this effort is NYUQI Director Javad Shabani, who, along with the other members, is turning the Institute into a hub for collaboration with private and public sector partners with quantum challenges that need solving. As de Pablo explains, “Anybody who wants to work on quantum with NYU, you come in through that door, and we’ll send you to the right place.” For New York’s vast ecosystem of tech giants and financial institutions, the NYUQI offers a resource they can’t build on their own: a cohesive team of experts in quantum phenomena, quantum information theory, communication, computing, materials, and optics, and a structured path to applying theoretical discoveries to advanced quantum technologies.

Solving the Challenge of Quantum Research

The NYUQI’s integrated structure is less about organizational management, and more about scientific requirement. The challenge of quantum is that the hardware, the software, and the programming are inherently interconnected — each must be designed to work with the other. To solve this, the Institute focuses on three applications of quantum science: Quantum Computing, Quantum Sensing, and Quantum Communications.

For Shabani, this means creating an integrated environment that bridges discovery with experimentation, starting with the physical components all the way to quantum algorithm centers. That will include a fabrication facility in the new building in Manhattan, as well as the NYU Nanofab in Brooklyn directed by Davood Shahjerdi. New York Senators Charles Schumer and Kirsten Gillibrand recently secured $1 million in congressionally-directed spending to bring Thermal Laser Epitaxy (TLE) technology — which allows for atomic-level purity, minimal defects, and streamlined application of a diverse range of quantum materials — to NYU, marking the first time the equipment will be used in the U.S.

NYU Nanofab manager Smiti Bhattacharya and Nanofab Director Davood Shahjerdi at the nanofab ribbon-cutting in 2023. The nanofab is the first academic cleanroom in Brooklyn, and serves as a prototyping facility for the NORDTECH Microelectronics Commons consortium.NYU WIRELESS

Tight control over fabrication, and can allow researchers to pivot quickly when a breakthrough in one area — say, finding a cheaper, more reliable material like silicon carbide — can be explored for use across all three applications, and offers unique access to academics and the private sector alike to sophisticated pieces of specialty equipment whose maintenance knowledge and costs make them all-but-impossible to maintain outside of the right staffing and environment.

The NYU Nanofab is Brooklyn’s first academic cleanroom, with a strategic focus on superconducting quantum technologies, advanced semiconductor electronics, and devices built from quantum heterostructures and other next-generation materials.NYU Nanofab

That speed and adaptability is the NYUQI’s competitive edge. It turns fragmented challenges into holistic solutions, positioning the Institute to solve real-world problems for its New York neighbors—from highly secure data transmission to next-generation drug discovery.

Testing Quantum Communication in NYC

The integrated approach also makes the NYUQI a testbed for the most critical near-term applications. Take Quantum Communications, which is essential for creating an “unhackable” quantum internet. In an industry first, NYU worked with the quantum start-up Qunnect to send quantum information through standard telecom fiber in New York City between Manhattan and Brooklyn through a 10-mile quantum networking link. Instead of simulating communication challenges in a lab, the NYUQI team is already leveraging NYU’s city-wide campus by utilizing existing infrastructure to test secure quantum transmission between Manhattan and Brooklyn.

The NYUQI team is already leveraging NYU’s city-wide campus by utilizing existing infrastructure to test secure quantum transmission between Manhattan and Brooklyn.

This isn’t just theory; it is building a functioning prototype in the most demanding, dense urban environment in the world. Real-time, real-world deployment is a critical component missing in other isolated institutions. When the NYUQI achieves results, the technology will be that much more readily available to the massive financial, tech, and communications organizations operating right outside their door.

NYUQI includes a state-of-the-art Nanofabrication Cleanroom in Brooklyn serving as a high-tech foundry.NYU Tandon

While the Institute has built the physical infrastructure and designed the necessary scientific architecture, its enduring contribution will be the specialized workforce it creates for the new quantum economy. This addresses the market’s greatest deficit: a lack of individuals trained not just in physics, but in the integrated, full-stack approach that quantum demands.

By creating a pipeline of 100 to 200 graduate and doctoral students who are encouraged to collaborate across Computing, Sensing, and Communications, the NYUQI is narrowing the skills gap. These will be future leaders who can speak the language of the physicist, the materials scientist, and the engineer simultaneously. This commitment to interdisciplinary talent is also fueled by the launch of the new Master of Science in Quantum Science & Technology program at NYU Tandon, positioning the university among a select group worldwide offering such a specialized degree.

Interdisciplinary education creates the shared language and understanding poised to make graduates coming from collaborations in the NYUQI extremely valuable in the current landscape. Quantum challenges are not just technical; they are managerial and philosophical as well. An engineer working with the NYUQI will understand the requirements of the nanofabrication cleanroom and the foundations of superconducting qubits for quantum computing, just as a physicist will understand the application needs of an industry partner like a large financial institution. In a field where the entire team must be able to communicate seamlessly, these are professionals truly equipped to rapidly translate discovery into deployable technology. Creating a talent pipeline at scale will provide a missing link that converts New York’s vast commercial energy into genuine quantum advantage.

NYUQI: Building Talent, Technology, and Structure

The vision for the NYUQI is an act of strategic geography that plays directly into the sheer volume of opportunity and demand right outside their new facility. By building the talent, the technology, and the structure necessary to capitalize on this dense environment, NYU is not just participating in the quantum race, it is actively steering it.

Attendees of NYU’s 2025 Quantum Summit.Tracey Friedman/NYU

The initial hypothesis for the NYUQI was simple: the ultimate advantage lies in pursuing the science in the right place at the right time. Now, the institute will ensure that the next wave of scientific discovery, capable of solving previously intractable problems in finance, medicine, and security, will be conceived, built, and tested in the heart of New York City.

This is a sponsored article brought to you by General Motors. Visit their new Engineering Blog for more insights.

Autonomous driving is one of the most demanding problems in physical AI. An automated system must interpret a chaotic, ever-changing world in real time—navigating uncertainty, predicting human behavior, and operating safely across an immense range of environments and edge cases.

At General Motors, we approach this problem from a simple premise: while most moments on the road are predictable, the rare, ambiguous, and unexpected events — the long tail — are what ultimately defines whether an autonomous system is safe, reliable, and ready for deployment at scale. (Note: While here we discuss research and emerging technologies to solve the long tail required for full general autonomy, we also discuss our current approach or solving 99% of everyday autonomous driving in a deep dive on Compound AI.)

As GM advances toward eyes-off highway driving, and ultimately toward fully autonomous vehicles, solving the long tail becomes the central engineering challenge. It requires developing systems that can be counted on to behave sensibly in the most unexpected conditions.

GM is building scalable driving AI to meet that challenge — combining large-scale simulation, reinforcement learning, and foundation-model-based reasoning to train autonomous systems at a scale and speed that would be impossible in the real world alone.

Stress-testing for the long tail

Long-tail scenarios of autonomous driving come in a few varieties.

Some are notable for their rareness. There’s a mattress on the road. A fire hydrant bursts. A massive power outage in San Francisco that disabled traffic lights required driverless vehicles to navigate never-before experienced challenges. These rare system-level interactions, especially in dense urban environments, show how unexpected edge cases can cascade at scale.

But long-tail challenges don’t just come in the form of once-in-a-lifetime rarities. They also manifest as everyday scenarios that require characteristically human courtesy or common sense. How do you queue up for a spot without blocking traffic in a crowded parking lot? Or navigate a construction zone, guided by gesturing workers and ad-hoc signs? These are simple challenges for a human driver but require inventive engineering to handle flawlessly with a machine.

Autonomous driving scenario demand curve

Deploying vision language models

One tool GM is developing to tackle these nuanced scenarios is the use of Vision Language Action (VLA) models. Starting with a standard Vision Language Model, which leverages internet-scale knowledge to make sense of images, GM engineers use specialized decoding heads to fine-tune for distinct driving-related tasks. The resulting VLA can make sense of vehicle trajectories and detect 3D objects on top of its general image-recognition capabilities.

These tuned models enable a vehicle to recognize that a police officer’s hand gesture overrides a red traffic light or to identify what a “loading zone” at a busy airport terminal might look like.

These models can also generate reasoning traces that help engineers and safety operators understand why a maneuver occurred — an important tool for debugging, validation, and trust.

Testing hazardous scenarios in high-fidelity simulations

The trouble is: driving requires split-second reaction times so any excess latency poses an especially critical problem. To solve this, GM is developing a “Dual Frequency VLA.” This large-scale model runs at a lower frequency to make high-level semantic decisions (“Is that object in the road a branch or a cinder block?”), while a smaller, highly efficient model handles the immediate, high-frequency spatial control (steering and braking).

This hybrid approach allows the vehicle to benefit from deep semantic reasoning without sacrificing the split-second reaction times required for safe driving.

But dealing with an edge case safely requires that the model not only understand what it is looking at but also understand how to sensibly drive through the challenge it’s identified. For that, there is no substitute for experience.

Which is why, each day, we run millions of high-fidelity closed loop simulations, equivalent to tens of thousands of human driving days, compressed into hours of simulation. We can replay actual events, modify real-world data to create new virtual scenarios, or design new ones entirely from scratch. This allows us to regularly test the system against hazardous scenarios that would be nearly impossible to encounter safely in the real world.

Synthetic data for the hardest cases

Where do these simulated scenarios come from? GM engineers employ a whole host of AI technologies to produce novel training data that can model extreme situations while remaining grounded in reality.

GM’s “Seed-to-Seed Translation” research, for instance, leverages diffusion models to transform existing real-world data, allowing a researcher to turn a clear-day recording into a rainy or foggy night while perfectly preserving the scene’s geometry. The result? A “domain change”—clear becomes rainy, but everything else remains the same.

In addition, our GM World diffusion-based simulator allows us to synthesize entirely new traffic scenarios using natural language and spatial bounding boxes. We can summon entirely new scenarios with different weather patterns. We can also take an existing road scene and add challenging new elements, such as a vehicle cutting into our path.

High-fidelity simulation isn’t always the best tool for every learning task. Photorealistic rendering is essential for training perception systems to recognize objects in varied conditions. But when the goal is teaching decision-making and tactical planning—when to merge, or how to navigate an intersection—the computationally expensive details matter less than spatial relationships and traffic dynamics. AI systems may need billions or even trillions of lightweight examples to support reinforcement learning, where models learn the rules of sensible driving through rapid trial and error rather than relying on imitation alone.

To this end, General Motors has developed a proprietary, multi-agent reinforcement learning simulator, GM Gym, to serve as a closed-loop simulation environment that can both simulate high-fidelity sensor data, and model thousands of drivers per second in an abstract environment known as “Boxworld.”

By focusing on essentials like spatial positioning, velocity and rules of the road while stripping away details like puddles and potholes, Boxworld creates a high-speed training environment for reinforcement learning models at incredible speeds, operating 50,000 times faster than real-time and simulating 1,000 km of driving per second of GPU time. It’s a method that allows us to not just imitate humans, but to develop driving models that have verifiable objective outcomes, like safety and progress.

From abstract policy to real-world driving

Of course, the route from your home to your office does not run through Boxworld. It passes through a world of asphalt, shadows, and weather. So, to bring that conceptual expertise into the real world, GM is one of the first to employ a technique called “On Policy Distillation,” where engineers run their simulator in both modes simultaneously: the abstract, high-speed Boxworld and the high-fidelity sensor mode.

Here, the reinforcement learning model—which has practiced countless abstract miles to develop a perfect “policy,” or driving strategy—acts as a teacher. It guides its “student,” the model that will eventually live in the car. This transfer of wisdom is incredibly efficient; just 30 minutes of distillation can capture the equivalent of 12 hours of raw reinforcement learning, allowing the real-world model to rapidly inherit the safety instincts its cousin painstakingly honed in simulation.

Designing failures before they happen

Simulation isn’t just about training the model to drive well, though; it’s also about trying to make it fail. To rigorously stress-test the system, GM utilizes a differentiable pipeline called SHIFT3D. Instead of just recreating the world, SHIFT3D actively modifies it to create “adversarial” objects designed to trick the perception system. The pipeline takes a standard object, like a sedan, and subtly morphs its shape and pose until it becomes a “challenging”, fun-house version that is harder for the AI to detect. Optimizing these failure modes is what allows engineers to preemptively discover safety risks before they ever appear on the road. Iteratively retraining the model on these generated “hard” objects has been shown to reduce near-miss collisions by over 30%, closing the safety gap on edge cases that might otherwise be missed.

Even with advanced simulation and adversarial testing, a truly robust system must know its own limits. To enable safety in the face of the unknown, GM researchers add a specialized “Epistemic uncertainty head” to their models. This architectural addition allows the AI to distinguish between standard noise and genuine confusion. When the model encounters a scenario it doesn’t understand—a true “long tail” event—it signals high epistemic uncertainty. This acts as a principled proxy for data mining, automatically flagging the most confusing and high-value examples for engineers to analyze and add to the training set.

This rigorous, multi-faceted approach—from “Boxworld” strategy to adversarial stress-testing—is General Motors’ proposed framework for solving the final 1% of autonomy. And while it serves as the foundation for future development, it also surfaces new research challenges that engineers must address.

How do we balance the essentially unlimited data from Reinforcement Learning with the finite but richer data we get from real-world driving? How close can we get to full, human-like driving by writing down a reward function? Can we go beyond domain change to generate completely new scenarios with novel objects?

Solving the long tail at scale

Working toward solving the long tail of autonomy is not about a single model or technique. It requires an ecosystem — one that combines high-fidelity simulation with abstract learning environments, reinforcement learning with imitation, and semantic reasoning with split-second control.

This approach does more than improve performance on average cases. It is designed to surface the rare, ambiguous, and difficult scenarios that determine whether autonomy is truly ready to operate without human supervision.

There are still open research questions. How human-like can a driving policy become when optimized through reward functions? How do we best combine unlimited simulated experience with the richer priors embedded in real human driving? And how far can generative world models take us in creating meaningful, safety-critical edge cases?

Answering these questions is central to the future of autonomous driving. At GM, we are building the tools, infrastructure, and research culture needed to address them — not at small scale, but at the scale required for real vehicles, real customers, and real roads.

“Can I get an interview?” “Can I get a job when I graduate?” Those questions came from students during a candid discussion about artificial intelligence, capturing the anxiety many young people feel today. As companies adopt AI-driven interview screeners, restructure their workforces, and redirect billions of dollars toward AI infrastructure, students are increasingly unsure of what the future of work will look like.

We had gathered people together at a coffee shop in Auburn, Alabama, for what we called an AI Café. The event was designed to confront concerns about AI directly, demystifying the technology while pushing back against the growing narrative of technological doom.

AI is reshaping society at breathtaking speed. Yet the trajectory of this transformation is being charted primarily by for-profit tech companies, whose priorities revolve around market dominance rather than public welfare. Many people feel that AI is something being done to them rather than developed with them.

As computer science and liberal arts faculty at Auburn University, we believe there is another path forward: one where scholars engage their communities in genuine dialogue about AI. Not to lecture about technical capabilities, but to listen, learn, and co-create a vision for AI that serves the public interest.

The AI Café Model

Last November, we ran two public AI Cafés in Auburn. These were informal, 90-minute conversations between faculty, students, and community members about their experiences with AI. In these conversational forums, participants sat in clusters, questions flowed in multiple directions, and lived experience carried as much weight as technical expertise.

We avoided jargon and resisted attempts to “correct” misconceptions, welcoming whatever emotions emerged. One ground rule proved crucial: keeping discussions in the present, asking participants where they encounter AI today. Without that focus, conversations could easily drift to sci-fi speculation. Historical analogies—to the printing press, electricity, and smartphones—helped people contextualize their reactions. And we found that without shared definitions of AI, people talked past each other; we learned to ask participants to name specific tools they were concerned about.

Organizers Xaq Frohlich, Cheryl Seals, and Joan Harrell (right) held their first AI Café in a welcoming coffee shop and bookstore. Well Red

Most important, we approached these events not as experts enlightening the masses, but as community members navigating complex change together.

What We Learned by Listening

Participants arrived with significant frustration. They felt that commercial interests were driving AI development “without consideration of public needs,” as one attendee put it. This echoed deeper anxieties about technology, from social media algorithms that amplify division to devices that profit from “engagement” and replace meaningful face-to-face connection. People aren’t simply “afraid of AI.” They’re weary of a pattern where powerful technologies reshape their lives while they have little say.

Yet when given space to voice concerns without dismissal, something shifted. Participants didn’t want to stop AI development; they wanted to have a voice in it. When we asked “What would a human-centered AI future look like?” the conversation became constructive. People articulated priorities: fairness over efficiency, creativity over automation, dignity over convenience, community over individualism.

The three organizers, all professors at Alabama’s Auburn University, say that including people from the liberal arts fields brought new perspectives to the discussions about AI. Well Red

For us as organizers, the experience was transformative. Hearing how AI affected people’s work, their children’s education, and their trust in information prompted us to consider dimensions we hadn’t fully grasped. Perhaps most striking was the gratitude participants expressed for being heard. It wasn’t about filling knowledge deficits; it was about mutual learning. The trust generated created a spillover effect, renewing faith that AI could serve the public interest if shaped through inclusive processes.

How to Start Your Own AI Café

The “deficit model” of science communication—where experts transmit knowledge to an uninformed public—has been discredited. Public resistance to emerging technologies reflects legitimate concerns about values, risks, and who controls decision-making. Our events point toward a better model.

We urge engineering and liberal arts departments, professional societies, and community organizations worldwide to organize dialogues similar to our AI Cafés.

We found that a few simple design choices made these conversations far more productive. Informal and welcoming spaces such as coffee shops, libraries, and community centers helped participants feel comfortable (and serving food and drinks helped too!). Starting with small-group discussions, where people talked with neighbors, produced more honest thinking and greater participation. Partnering with colleagues in the liberal arts brought additional perspectives on technology’s social dimensions. And by making a commitment to an ongoing series of events, we built trust.

Facilitation also matters. Rather than leading with technical expertise, we began with values: We asked what kind of world participants wanted, and how AI might help or hinder that vision. We used analogies to earlier technologies to help people situate their reactions and grounded discussions in present realities, asking participants where they have encountered AI in their daily lives. We welcomed emotions constructively, transforming worry into problem solving by asking questions like: “What would you do about that?”

Why Engineers Should Engage the Public

Professional ethics codes remain abstract unless grounded in dialogue with affected communities. Conversations about what “responsible AI” means will look different in São Paulo than in Seoul, in Vienna than in Nairobi. What makes the AI Café model portable is its general principles: informal settings, values-first questions, present-tense focus, genuine listening.

Without such engagement, ethical accountability quietly shifts to technical experts rather than remaining a shared public concern. If we let commercial interests define AI’s trajectory with minimal public input, it will only deepen divides and entrench inequities.

AI will continue advancing whether or not we have public trust. But AI shaped through dialogue with communities will look fundamentally different from AI developed solely to pursue what’s technically possible or commercially profitable.

The tools for this work aren’t technical; they’re social, requiring humility, patience, and genuine curiosity. The question isn’t whether AI will transform society. It’s whether that transformation will be done to people or with them. We believe scholars must choose the latter, and that starts with showing up in coffee shops and community centers to have conversations where we do less talking and more listening.

The future of AI depends on it.

The rise of generative AI has spurred demand for AI workstations that can run or train models on local hardware. Yet modern PCs have proven inadequate for this task. A typical laptop has only enough memory to load a large language model (LLM) with 8 billion to 13 billion parameters—much smaller, and much less intelligent, than frontier models that are presumed to have over a trillion parameters. Even the most capable workstation PCs struggle to serve LLMs with more than 70 billion parameters.

Tenstorrent’s QuietBox 2 is an attempt to fill that gap. Though it looks like a PC workstation, the QuietBox 2 contains four of the company’s custom Blackhole AI accelerators, 128 gigabytes of GDDR6 memory—specialized memory used in GPUs—and 256 GB of DDR5 system memory (for a total of 384 GB). This configuration provides enough memory to load OpenAI’s GPT-OSS-120B and can run midsize models like Meta’s Llama 3.1 70B at speeds of nearly 500 tokens per second. For reference, that’s several times quicker than an average response from OpenAI’s GPT-5.2 or Anthropic’s Claude 4.6. The QuietBox 2 carries an expected retail price of US $9,999 and is slated to launch in the second quarter of 2026.

“The 128 gigabytes of GDDR that we have with our AI accelerators really defines how big of a model you can run at a reasonable speed,” says Milos Trajkovic, cofounder and systems engineer at Tenstorrent. “Our 128 gigabytes of GDDR6 RAM would require four Nvidia RTX 5090 graphics cards. That couldn’t fit in today’s 1,600-watt form factor, and the cost for four RTX 5090 GPUs is huge.”

An AI workstation built at the home office

Wattage, it turns out, is critical. Nvidia recommends a system power of 1,000 W for a single RTX 5090, so even a dual-GPU setup exceeds the continuous power draw for a typical 15-ampere, 120-volt power circuit. A system with four RTX 5090s could require 4,000 W or more at load.

The QuietBox 2, on the other hand, draws only 1,400 W at full load. It won’t trip the breaker, so it can be used anywhere a typical desktop PC might be plugged in, including a home office.

That’s not the only way the QuietBox 2 poses as a run-of-the-mill PC. The machine’s custom case is built to support the micro-ATX motherboard form factor, and the motherboard itself is an AMD chipset hosting an AMD CPU. The hardware is kept cool by closed-loop liquid cooling similar to that used by PC workstations and gaming computers. It even has customizable RGB LED lighting and a large semitransparent window that shows off the hardware.

“A lot of even our internal developers have requested a QuietBox because they’re just so easy to deploy,” says Chris Goulet, a thermal-mechanical engineer and team lead at Tenstorrent. “You just ship them the unit, they slap it on their desk, power it up, and they’re going.”

Where the QuietBox 2 differs from desktop PCs, though, is its AI accelerators. It’s equipped with four of Tenstorrent’s Blackhole application-specific ICs, a RISC-V chip designed specifically for AI workloads. Blackhole is packaged on an add-in card; each card has 120 Tensix AI accelerators and 32 GB of GDDR6 memory, for a total of 480 Tensix AI accelerators and 128 GB of GDDR6. Blackhole also has a large amount of on-chip SRAM at 180 megabytes per accelerator.

Two visions of desktop AI

Tenstorrent is not alone in its approach. Nvidia’s DGX Spark, released last year, packed Nvidia’s GB10 chip into a machine the size of a lunch box. Orders for the Spark’s big brother, the DGX Station with Nvidia’s GB300, were opened on 16 March 2026.

The DGX Station looks like a desktop PC workstation, and variants will be built by well-known PC brands like Asus and Dell. Nvidia’s offering has more memory than QuietBox 2, at up to 748 GB, but system power is quoted at 1,600 W—rather close to the maximum a 15-A, 120-V breaker will handle. This reflects differing visions for how their machines will be used. And, of course, the Nvidia DGX Station’s extra memory doesn’t come cheap. While most DGX Station system builders have not yet announced pricing, one retailer has listed a DGX Station from PC maker MSI for $85,000.

When I spoke to Allyn Bourgoyne, director of product marketing at Nvidia, after the announcement of DGX Spark and Station in 2025, he said the company expects most DGX owners will use the devices as remotely accessed workstations. “A common thing you might see is that I’ve got my Windows laptop, and I’m going to use my DGX Spark over the network. I’m going to send jobs over to it.” He added that companies could deploy DGX Spark and Station systems to serve multiple people at once.

The Tenstorrent QuietBox 2 can be used in this way, but the company also wants to target a good experience for people going one-on-one with the computer. “You don’t have to remote SSH into the box. You connect your monitor through HDMI, and it’s just like your PC at home. It has the Ubuntu desktop and utilities,” says Trajkovic.

Nvidia’s DGX systems also run a variant of Ubuntu (DGX OS) and include a desktop environment, but the devil is in the details. DGX systems use Nvidia CPUs based on ARM architectures and custom chipsets. The QuietBox 2 uses an AMD x86 CPU and compatible chipset, and is configured more like a traditional PC. That should be a boon for the QuietBox 2’s software compatibility.

Tenstorrent leans into that with a focus on open source software. The QuietBox 2’s entire software stack, from TT-Forge (the company’s AI compiler) to TT-Metalium (a low-level software development kit that provides kernel-level hardware control), is open source and available on GitHub. Tenstorrent has also published the instruction set architecture for its Tensix cores, so developers can see exactly how their workloads execute on the hardware. Nvidia, by contrast, is focused on its proprietary CUDA ecosystem, and DGX OS is not open source.

“A lot of our software stack is completely open, and we felt that from a hardware perspective, we kind of wanted to take a similar path,” says Goulet.

WHEN KYIV-BORN ENGINEER Yaroslav Azhnyuk thinks about the future, his mind conjures up dystopian images. He talks about “swarms of autonomous drones carrying other autonomous drones to protect them against autonomous drones, which are trying to intercept them, controlled by AI agents overseen by a human general somewhere.” He also imagines flotillas of autonomous submarines, each carrying hundreds of drones, suddenly emerging off the coast of California or Great Britain and discharging their cargoes en masse to the sky.

“How do you protect from that?” he asks as we speak in late December 2025; me at my quiet home office in London, he in Kyiv, which is bracing for another wave of missile attacks.

Azhnyuk is not an alarmist. He cofounded and was formerly CEO of Petcube, a California-based company that uses smart cameras and an app to let pet owners keep an eye on their beloved creatures left alone at home. A self-described “liberal guy who didn’t even receive military training,” Azhnyuk changed his mind about developing military tech in the months following the Russian invasion of Ukraine in February 2022. By 2023, he had relinquished his CEO role at Petcube to do what many Ukrainian technologists have done—to help defend his country against a mightier aggressor.

It took a while for him to figure out what, exactly, he should be doing. He didn’t join the military, but through friends on the front line, he witnessed how, out of desperation, Ukrainian troops turned to off-the-shelf consumer drones to make up for their country’s lack of artillery.

Ukrainian troops first began using drones for battlefield surveillance, but within a few months they figured out how to strap explosives onto them and turn them into effective, low-cost killing machines. Little did they know they were fomenting a revolution in warfare.

The Ukrainian robotics company The Fourth Law produces an autonomy module [above] that uses optics and AI to guide a drone to its target. Yaroslav Azhnyuk [top, in light shirt], founder and CEO of The Fourth Law, describes a developmental drone with autonomous capabilities to Ukrainian President Volodymyr Zelenskyy and German Chancellor Olaf Scholz.Top: THE PRESIDENTIAL OFFICE OF UKRAINE; Bottom: THE FOURTH LAW

That revolution was on display last month, as the U.S. and Israel went to war with Iran. It soon became clear that attack drones are being extensively used by both sides. Iran, for example, is relying heavily on the Shahed drones that the country invented and that are now also being manufactured in Russia and launched by the thousands every month against Ukraine.

A thorough analysis of the Middle East conflict will take some time to emerge. And so to understand the direction of this new way of war, look to Ukraine, where its next phase—autonomy—is already starting to come into view. Outnumbered by the Russians and facing increasingly sophisticated jamming and spoofing aimed at causing the drones to veer off course or fall out of the sky, Ukrainian technologists realized as early as 2023 that what could really win the war was autonomy. Autonomous operation means a drone isn’t being flown by a remote pilot, and therefore there’s no communications link to that pilot that can be severed or spoofed, rendering the drone useless.

By late 2023, Azhnyuk set out to help make that vision a reality. He founded two companies, The Fourth Law and Odd Systems, the first to develop AI algorithms to help drones overcome jamming during final approach, the second to build thermal cameras to help those drones better sense their surroundings.

“I moved from making devices that throw treats to dogs to making devices that throw explosives on Russian occupants,” Azhnyuk quips.

Since then, The Fourth Law has dispatched “more than thousands” of autonomy modules to troops in eastern Ukraine (it declines to give a more specific figure), which can be retrofitted on existing drones to take over navigation during the final approach to the target. Azhnyuk says the autonomy modules, worth around US $50, increase the drone-strike success rate by up to four times that of purely operator-controlled drones.

And that is just the beginning. Azhnyuk is one of thousands of developers, including some who relocated from Western countries, who are applying their skills and other resources to advancing the drone technology that is the defining characteristic of the war in Ukraine. This eclectic group of startups and founders includes Eric Schmidt, the former Google CEO, whose company Swift Beat is churning out autonomous drones and modules for Ukrainian forces. The frenetic pace of tech development is helping a scrappy, innovative underdog hold at bay a much larger and better-equipped foe.

All of this development is careening toward AI-based systems that enable drones to navigate by recognizing features in the terrain, lock on to and chase targets without an operator’s guidance, and eventually exchange information with each other through mesh networks, forming self-organizing robotic kamikaze swarms. Such an attack swarm would be commanded by a single operator from a safe distance.

According to some reports, autonomous swarming technology is also being developed for sea drones. Ukraine has had some notable successes with sea drones, which have reportedly destroyed or damaged around a dozen Russian vessels.

The Skynode X system, from Auterion, provides a degree of autonomy to a drone.AUTERION

For Ukraine, swarming can solve a major problem that puts the nation at a disadvantage against Russia—the lack of personnel. Autonomy is “the single most impactful defense technology of this century,” says Azhnyuk. “The moment this happens, you shift from a manpower challenge to a production challenge, which is much more manageable,” he adds.

The autonomous warfare future envisioned by Azhnyuk and others is not yet a reality. But Marc Lange, a German defense analyst and business strategist, believes that “an inflection point” is already in view. Beyond it, “things will be so dramatically different,” he says.

“Ukraine pretty rapidly realized that if the operator-to-drone ratio can be shifted from one-to-one to one-to-many, that creates great economies of scale and an amazing cost exchange ratio,” Lange adds. “The moment one operator can launch 100, 50, or even just 20 drones at once, this completely changes the economics of the war.”

Drones With a View

For a while, jammers that sever the radio links between drones and operators or that spoof GPS receivers were able to provide fairly reliable defense against human-controlled first-person-view attack drones (FPVs). But as autonomous navigation progressed, those electronic shields have gradually become less effective. Defenders must now contend with unjammable drones—ones that are attached to hair-thin optical fibers or that are capable of finding their way to their targets without external guidance. In this emerging struggle, the defenders’ track records aren’t very encouraging: The typical countermeasure is to try to shoot down the attacking drone with a service weapon. It’s rarely successful.

A truck outfitted with signal-jamming gear drives under antidrone nets near Oleksandriya, in eastern Ukraine, on 2 October 2025.ED JONES/AFP/GETTY IMAGES

“The attackers gain an immense advantage from unmanned systems,” says Lange. “You can have a drone pop up from anywhere and it can wreak havoc. But from autonomy, they gain even more.”

The self-navigating drones rely on image-recognition algorithms that have been around for over a decade, says Lange. And the mass deployments of drones on Ukrainian battlefields are enabling both Russian and Ukrainian technologists to create huge datasets that improve the training and precision of those AI algorithms.

A Ukrainian land robot, the Ravlyk, can be outfitted with a machine gun.

While uncrewed aerial vehicles (UAVs) have received the most attention, the Ukrainian military is also deploying dozens of different kinds of drones on land and sea. Ukraine, struggling with the shortage of infantry personnel, began working on replacing a portion of human soldiers with wheeled ground robots in 2024. As of early 2026, thousands of ground robots are crawling across the gray zone along the front line in Eastern Ukraine. Most are used to deliver supplies to the front line or to help evacuate the wounded, but some “killer” ground robots fitted with turrets and remotely controlled machine guns have also been tested.

In mid-February, Ukrainian authorities released a video of a Ukrainian ground robot using its thermal camera to detect a Russian soldier in the dark of the night and then kill the invader with a round from a heavy machine gun. So far these robots are mostly controlled by a human operator, but the makers of these uncrewed ground vehicles say their systems are capable of basic autonomous operations, such as returning to base when radio connection is lost. The goal is to enable them to swarm so that one operator controls not one, but a whole herd of mesh-connected killer robots.

But Bryan Clark, senior fellow and director of the Center for Defense Concepts and Technology at the Hudson Institute, questions how quickly ground robots’ abilities can progress. “Ground environments are very difficult to navigate in because of the terrain you have to address,” he says. “The line of sight for the sensors on the ground vehicles is really constrained because of terrain, whereas an air vehicle can see everything around it.”

To achieve autonomy, maritime drones, too, will require navigational approaches beyond AI-based image recognition, possibly based on star positions or electronic signals from radios and cell towers that are within reach, says Clark. Such technologies are still being developed or are in a relatively early operational stage.

How the Shaheds Got Better

Russia is not lagging behind. In fact, some analysts believe its autonomous systems may be slightly ahead of Ukraine’s. For a good example of the Russian military’s rapid evolution, they say, consider the long-range Iranian-designed Shahed drones. Since 2022, Russia has been using them to attack Ukrainian cities and other targets hundreds of kilometers from the front line. “At the beginning, Shaheds just had a frame, a motor, and an inertial navigation system,” Oleksii Solntsev, CEO of Ukrainian defense tech startup MaXon Systems, tells me. “They used to be imprecise and pretty stupid. But they are becoming more and more autonomous.” Solntsev founded MaXon Systems in late 2024 to help protect Ukrainian civilians from the growing threat of Shahed raids.

A Russian Geran-2 drone, based on the Iranian Shahed-136, flies over Kyiv during an attack on 27 December 2025.SERGEI SUPINSKY/AFP/GETTY IMAGES

First produced in Iran in the 2010s, Shaheds can carry 90-kilogram warheads up to 650 km (50-kg warheads can go twice as far). They cost around $35,000 per unit, compared to a couple of million dollars, at least, for a ballistic missile. The low cost allows Russia to manufacture Shaheds in high quantities, unleashing entire fleets onto Ukrainian cities and infrastructure almost every night.

The early Shaheds were able to reach a preprogrammed location based on satellite-navigation coordinates. Even one of these early models could frequently overcome the jamming of satellite-navigation signals with the help of an onboard inertial navigation unit. This was essentially a dead-reckoning system of accelerators and gyroscopes that estimate the drone’s position from continual measurements of its motions.

In the Donetsk Region, on 15 August 2025, a Ukrainian soldier hunts for Shaheds and other drones with a thermalimaging system attached to a ZU23 23-millimeter antiaircraft gun.KOSTYANTYN LIBEROV/LIBKOS/GETTY IMAGES

Ukrainian defense forces learned to down Shaheds with heavy machine guns, but as Russia continued to innovate, the daily onslaughts started to become increasingly effective.

Today’s Shaheds fly faster and higher, and therefore are more difficult to detect and take down. Between January 2024 and August 2025, the number of Shaheds and Shahed-type attack drones launched by Russia into Ukraine per month increased more than tenfold, from 334 to more than 4,000. In 2025, Ukraine found AI-enabling Nvidia chipsets in wreckages of Shaheds, as well as thermal-vision modules capable of locking onto targets at night.

“Now, they are interconnected, which allows them to exchange information with each other,” Solntsev says. “They also have cameras that allow them to autonomously navigate to objects. Soon they will be able to tell each other to avoid a jammed region or an area where one of them got intercepted.”

These Russian-manufactured Shaheds, which Russian forces call Geran-2s, are thought to be more capable than the garden variety Shahed-136s that Iran has lately been launching against targets throughout the Middle East. Even the relatively primitive Shahed-136s have done considerable damage, according to press accounts.

Those Shahed successes may accrue, at least in part, from the fact that the United States and Israel lack Ukraine’s long experience with fending them off. In just two days in early March, upward of a thousand drones, mostly Shaheds, were launched against U.S. and Israeli targets, with hundreds of them reportedly finding their marks.

One attack, caught on videotape, shows a Shahed destroying a radar dome at the U.S. navy base in Manama, Bahrain. U.S. forces were understood to be attempting to fend off the drones by striking launch platforms, dispatching fighter aircraft to shoot them down, and by using some extremely costly air-defense interceptors, including ones meant to down ballistic missiles. On 4 March, CNN reported that in a congressional briefing the day before, top U.S. defense officials, including Secretary of Defense Pete Hegseth, acknowledged that U.S. air defenses weren’t keeping up with the onslaught of Shahed drones.

Russian V2U attack drones are outfitted with Nvidia processors and run computer-vision software and AI algorithms to enable the drones to navigate autonomously.GUR OF THE MINISTRY OF DEFENSE OF UKRAINE

Russia is also starting to field a newer generation of attack drones. One of these, the V2U, has been used to strike targets in the Sumy region of northeastern Ukraine. The V2U drones are outfitted with Nvidia Jetson Orin processors and run computer-­vision software and AI algorithms that allow the drones to navigate even where satellite navigation is jammed.

The sale of Nvidia chips to Russia is banned under U.S. sanctions against the country. However, press reports suggest that the chips are getting to Russia via intermediaries in India.

Antidrone Systems Step Up

MaXon Systems is one of several companies working to fend off the nightly drone onslaught. Within one year, the company developed and battle-tested a Shahed interception system that hints at the sci-fi future envisioned by Azhnyuk. For a system to be capable of reliably defending against autonomous weaponry, it, too, needs to be autonomous.

MaXon’s solution consists of ground turrets scanning the sky with infrared sensors, with additional input from a network of radars that detects approaching Shahed drones at distances of, typically, 12 to 16 km. The turrets fire autonomous fixed-winged interceptor drones, fitted with explosive warheads, toward the approaching Shaheds at speeds of nearly 300 km/h. To boost the chances of successful interception, MaXon is also fielding an airborne anti-Shahed fortification system consisting of helium-filled aerostats hovering above the city that dispatch the interceptors from a higher altitude.

“We are trying to increase the level of automation of the system compared to existing solutions,” says Solntsev. “We need automatic detection, automatic takeoff, and automatic mid-track guidance so that we can guide the interceptor before it can itself flock the target.”

An interceptor drone, part of the U.S. MEROPS defensive system, is tested in Poland on 18 November 2025.WOJTEK RADWANSKI/AFP/GETTY IMAGES

In November 2025, the Ukrainian military announced it had been conducting successful trials of the Merops Shahed drone interceptor system developed by the U.S. startup Project Eagle, another of former Google CEO Eric Schmidt’s Ukraine defense ventures. Like the MaXon gear, the system can operate largely autonomously and has so far downed over 1,000 Shaheds.

What Works in the Lab Doesn’t Necessarily Fly on the Battlefield

“The software for drone collaboration is there,” says Kate Bondar, a former policy advisor for the Ukrainian government and currently a research fellow at the U.S. Center for Strategic and International Studies. “Drones can fly in labs, but in real life, [the forces] are afraid to deploy them because the risk of a mistake is too high,” she adds.

Ukrainian soldiers watch a GOR reconnaissance drone take to the sky near Pokrovsk in the Donetsk region, on 10 March 2025.ANDRIY DUBCHAK/FRONTLINER/GETTY IMAGES

While existing AI systems are doing a good job recognizing and following large objects like Shaheds or tanks, experts question their ability to reliably distinguish and pursue smaller and more nimble or inconspicuous targets. “When we’re getting into more specific questions, like can it distinguish a Russian soldier from a Ukrainian soldier or at least a soldier from a civilian? The answer is no,” says Bondar. “Also, it’s one thing to track a tank, and it’s another to track infantrymen riding buggies and motorcycles that are moving very fast. That’s really challenging for AI to track and strike precisely.”

Clark, at the Hudson Institute, says that although the AI algorithms used to guide the Russian and Ukrainian drones are “pretty good,” they rely on information provided bysensors that “aren’t good enough.” “You need multiphenomenology sensors that are able to look at infrared and visual and, in some cases, different parts of the infrared spectrum to be able to figure out if something is a decoy or real target,” he says.

German defense analyst Lange agrees that right now, battlefield AI image-recognition systems are too easily fooled. “If you compress reality into a 2D image, a lot of things can be easily camouflaged—like what Russia did recently, when they started drawing birds on the back of their drones,” he says.

Autonomy Remains Elusive on the Ground and at Sea, Too

To make Ukraine’s emerging uncrewed ground vehicles (UGVs) equally self-sufficient will be an even greater task, in Clark’s view. Still, Bondar expects major advances to materialize within the next several years, even if humans are still going to be part of the decision-making loop.

A mobile electronic-warfare system built by PiranhaTech is demonstrated near Kyiv on 21 October 2025.DANYLO ANTONIUK/ANADOLU/GETTY IMAGES

“I think in two or three years, we will have pretty good full autonomy, at least in good weather conditions,” she says, referring to aerial drones in particular. “Humans will still be in the loop for some years, simply because there are so many unpredictable situations when you need an intervention. We won’t be able to fully rely on the machine for at least another 10 or 15 years.”

Ukrainian defenders are apprehensive about that autonomous future. The boom of drone innovation has come hand in hand with the development of sophisticated jamming and radio-frequency detection systems. But a lot of that innovation will become obsolete once the pendulum swings away from human control. Ukrainians got their first taste of dealing with unjammable drones in mid-2024, when Russia began rolling out fiber-optic tethered drones. Now they have to brace for a threat on a much larger scale.

An experimental drone is demonstrated at the Brave1 defense-tech incubator in Kyiv.DANYLO DUBCHAK/FRONTLINER/GETTY IMAGES

“Today, we have a situation where we have lots of signals on the battlefield, but in the near future, in maybe two to five years, UAVs are not going to be sending any signals,” says Oleksandr Barabash, CTO of Falcons, a Ukrainian startup that has developed a smart radio-frequency detection system capable of revealing precise locations of enemy radio sources such as drones, control stations, and jammers.

Last September, Falcons secured funding from the U.S.-based dual-use tech fund Green Flag Ventures to scale production of its technology and work toward NATO certification. But Barabash admits that its system, like all technologies fielded in Ukrainian war zones, has an expiration date. Instead of radio-frequency detectors, Barabash thinks, the next R&D push needs to focus on passive radar systems capable of identifying small and fast-moving targets based on the signal from sources like TV towers or radio transmitters that propagate through the environment and are reflected by those moving targets. Passive radars have a significant advantage in the war zone, according to Barabash. Since they don’t emit their own signal, they can’t be that easily discovered by the enemy.

“Active radar is emitting signals, so if you are using active radars, you are target No. 1 on the front line,” Barabash says.

Bondar, on the other hand, thinks that the increased onboard compute power needed for AI-controlled drones will, by itself, generate enough electromagnetic radiation to prevent autonomous drones from ever operating completely undetectably.

“You can have full autonomy, but you will still have systems onboard that emit electromagnetic radiation or heat that can be detected,” says Bondar. “Batteries emit electromagnetic radiation, motors emit heat, and [that heat can be] visible in infrared from far away. You just need to have the right sensors to be able to identify it in advance.” She adds that that takeaway is “how capable contemporary detection systems have become and how technically challenging it is to design drones that can reliably operate in the Ukrainian battlefield environment.”

There Will Be Nowhere to Hide from Autonomous Drones

When autonomous drones become a standard weapon of war, their threat will extend far beyond the battlefields of Ukraine. Autonomous turrets and drone-interceptor fortification might soon dot the perimeter of European cities, particularly in the eastern part of the continent.

A fixed-wing drone is tested in Ukraine in April 2025.ANDREWKRAVCHENKO/BLOOMBERG/GETTY IMAGES

Nefarious actors from all over the world have closely watched Ukraine and taken notes, warns Lange. Today, FPV drones are being used by Islamic terrorists in Africa and Mexican drug cartels to fight against local authorities.

When autonomous killing machines become widely available, it’s likely that no city will be safe. “We might see nets above city centers, protecting civilian streets,” Lange says. “In every case, the West needs to start performing similar kinetic-defense development that we see in Ukraine. Very rapid iteration and testing cycles to find solutions.”

Azhnyuk is concerned that the historic defenders of Europe—the United States and the European countries themselves—are falling behind. “We are in danger,” he says. While Russia and Ukraine made major strides in their drones and countermeasures over the past year, “Europe and the United States have progressed, in the best-case scenario, from the winter-of-2022 technology to the summer-of-2022 technology.

“The gap is getting wider,” he warns. “I think the next few years are very dangerous for the security of Europe.”

This article appears in the April 2026 print issue as “Rise of the AUTONOMOUS Attack Drones.”

This is a sponsored article brought to you by PNY Technologies.

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An in-depth examination of how rising power density, 3D integration, and novel materials are outpacing legacy thermal measurement — and what advanced metrology must deliver.

What Attendees will Learn

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3. Why interfaces and buried layers now govern reliability — Examine how thermal boundary resistance at bonded interfaces, TIM layers, and dielectric stacks has become a first-order reliability accelerator.
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Most people who regularly use AI tools would say they’re making their lives easier. The technology promises to streamline and take over tasks both professionally and personally—whether that’s summarizing documents, drafting deliverables, generating code, or even offering emotional support. But researchers are concerned AI is making some tasks too easy, and that this will come with unexpected costs.

In a commentary titled Against Frictionless AI, published in Communications Psychology on 24 February, psychologists from the University of Toronto discuss what might be lost when AI removes too much effort from human activities. Their argument centers on the idea that friction—difficulty, struggle, and even discomfort—plays an important role in learning, motivation, and meaning. Psychological research has long shown that effortful engagement can deepen understanding and strengthen memory, sometimes described as “desirable difficulties.”

The authors worry that AI systems capable of instantly producing polished answers or highly responsive conversation may bypass these processes of learning and motivation. By prioritizing outcomes over effort, AI could weaken the experiences that help people develop skills, build relationships, and find meaning in their work.

IEEE Spectrum spoke with the paper’s lead author, Emily Zohar, an experimental psychology Ph.D. student, about why she and her coauthors (psychologists Paul Bloom and Michael Inzlicht) argue that friction matters—and what a more human-centered approach to AI design could look like.

When you say “friction,” what do you mean, from both a cognitive and an interpersonal standpoint?

Zohar: We define friction as any difficulty encountered during goal pursuit. In the context of work, it involves mental effort—rumination and persistence, staying on a problem for some time, and this helps solidify the idea and the creative process.

In relationships, friction involves disagreement, compromise, misunderstanding, a back and forth that is natural where you don’t always see eye to eye, and it helps you broaden your horizons. Even the feeling of loneliness is important. It motivates you to find social interactions. So having these negative feelings and difficulty is important in the social context.

Given that definition, what do you mean by “frictionless” AI?

Zohar: Frictionless AI refers to the excessive removal of effort from cognitive and social tasks. With AI, as we typically use it, it’s really easy to go from ideation right to the end product. You ask AI to solve something with one prompt, and it completes the whole thing. This is a problem because it takes away the intermediate steps that really drive motivation and learning, and it prioritizes outcome over process. Rather than working through the steps, AI does that meaningful work for you.

There’s a lot of research showing work products are better with AI. That makes sense, it has all this knowledge, but it does worry us as it may be eroding something essential that will have long-term consequences. If you’re faced with the same problem and AI is removed, you don’t have the required knowledge to know how to face the problem next time.

You argue that removing friction can harm learning and relationships. What role do effort and struggle play in human development?

Zohar: In learning, the term is “desirable difficulties.” It’s the idea of effort and work, not just any effort but manageable effort. Facing problems that you can overcome, but you have to work at them a bit, that’s the key idea of friction. We don’t want you to face insurmountable problems. We want you to work hard, but still be able to overcome it. This helps you really digest information and learn from it.

In interpersonal relationships, you have to face some difficulties to see other perspectives and learn from them, and learn to be accepting of others. If you’re used to an AI reinforcing all your ideas and being sycophantic, you’ll come into the real world and you won’t be used to seeing other ideas. You won’t know how to interact socially because you’ll expect people to always be on your side and agree with you. You won’t learn that life doesn’t always go exactly how you expect it to, and conversations don’t always go the way you want them to.

AI’s Impact on Creative Processes

A lot of technologies have historically aimed to reduce effort: calculators, washing machines, spell-check. What’s different about AI?

Zohar: Past technologies have mostly focused on reducing physical effort. We don’t have to go down to the lake to wash our laundry anymore. [Past technologies] took away the mundane tasks that weren’t driving our learning and growth, they were just adding unneeded obstacles and taking away time from more important tasks.

But AI is taking away effort from creative and cognitive processes that drive meaning, motivation, and learning. That’s a key difference, because it’s not taking away friction from tasks that don’t serve us. It’s taking away friction from experiences that are really important and integral to our development.

Are there contexts where AI is already removing beneficial friction? How might the impacts of reduced friction show up over time?

Zohar: One clear example is writing. People increasingly rely on AI to draft everything from emails to essays, removing many instances of beneficial friction. Research shows that people trust responses less when they learn they were written by AI, judge AI-generated products as less creative and less valuable, and have greater difficulty remembering their own work products when they were produced with AI assistance. Outsourcing writing to AI strips away both social and cognitive friction.

Vibe coding is another good example. If you’re a programmer, coding is integral to what drives your meaning. People get meaning out of their work, and if you’re substituting that with AI, it could be detrimental. The negative impact of frictionless AI is that it takes away friction from things that are really important to who you are as a person, and your skills.

One area I worry about a lot is adolescents using AI in general. It’s a really important developmental period to learn and grow and find the path you’ll follow. So if you don’t have these effortful interactions with work and relationships that teach you how to think, this will have long-term detrimental impacts. They might not be able to think critically in the same way, because they never had to before. If they’re turning to AI for social relationships at such a young age, that could really erode important skills they should be learning at that age.

What is productive friction?

Zohar: Friction goes along a continuum. With too little friction, you’re not getting learning and motivation. Too much friction and the task becomes overwhelming. Productive friction falls right in the middle, where struggle leads to achievement. It’s effortful but possible, and it requires you to think critically and work on a problem for some time or face some difficulty in the process.

An example we used in the paper is the difference between taking a chairlift and hiking up a mountain. They both get to the top, but with the chairlift, you don’t get any growth benefits, while the hiker’s climb involves difficulties and a sense of achievement. It becomes much more of an experience and a learning opportunity versus the person who just went up the chairlift effortlessly.

Do you envision AI that sometimes deliberately slows people down or asks them to do part of the work themselves?

Zohar: It’s important in behavioral science to think about the default option, because people don’t usually change their default. So right now, the default in AI is to give you your answer and probe you to keep going down the rabbit hole. But I think we could think about AI in a different way. Maybe we can make the default more constructive. Instead of just jumping to the answer, it’s more of a process model where it helps you think about the problem and teaches you along the way, so it’s more collaborative rather than a one-stop shop for the answer.

How might users of these systems and the companies developing them feel about such a design shift?

Zohar: For the makers of these systems, the biggest concern is the pushback. People are used to going in and just getting the answer, and they might be really resistant to a design that makes them work more for it. But it might feed more engagement, because you have to go back and forth and find the answer together.

Ultimately I think it has to come from the companies making these models, if they think [a more friction-full design] would help people. Friction-full AI is more of a long-term product. It’s hard to say if that would motivate companies to change their models to include moderate friction. But in the long term, I think this would be beneficial.

Wheelchair users with severe disabilities can often navigate tight spaces better than most robotic systems can. A wave of new smart-wheelchair research, including findings presented in Anaheim, Calif., earlier this month, is now testing whether AI-powered systems can, or should, fully close this gap.

Christian Mandel—senior researcher at the German Research Center for Artificial Intelligence (DFKI) in Bremen, Germany—co-led a research team together with his colleague Serge Autexier that developed prototype sensor-equipped electric wheelchairs designed to navigate a roomful of potential obstacles. The researchers also tested a new safety system that integrated sensor data from the wheelchair and from sensors in the room, including from drone-based color and depth cameras.

Mandel says the team’s smart wheelchairs were both semiautonomous and autonomous.

“Semiautonomous is the shared control system where the person sitting in the wheelchair uses the joystick to drive,” Mandel says. “Fully autonomous is controlled by natural-language input. You say, ‘Please drive me to the coffee machine.’ ”

This is a close-up of the wheelchair’s joystick and camera.DFKI

The researchers conducted experiments (part of a larger project called the Reliable and Explainable Swarm Intelligence for People With Reduced Mobility, or REXASI-PRO) using two identical smart wheelchairs that each contained two lidars, a 3D camera, odometers, user interfaces, and an embedded computer.

In contrast to semiautonomous mode, where the participant controls the wheelchair with a joystick, in autonomous mode, control involves the open-source ROS2 Nav2 navigation system using natural-language input. The wheelchairs also used simultaneous localization and mapping (SLAM) maps and local obstacle-avoidance motion controllers.

One scenario that Mandel and his team tested involved the user pressing a key on the wheelchair’s human-machine interface, speaking a command, then confirming or rejecting the instruction via that same interface. Once the user confirmed the command, the mobility device guided the user along a path to the destination, while sensors attempted to detect obstacles in the way and adjust the mobility device accordingly to avoid them.

When Are Smart Wheelchairs Bad Value?

According to Pooja Viswanathan, CEO & founder of the Toronto-based Braze Mobility, research in the field of mobile assistive technology should also prioritize keeping these devices readily available to everyday consumers.

“Cost remains a major barrier,” she says. “Funding systems are often not designed to support advanced add-on intelligence unless there is very clear evidence of value and safety. Reliability is another barrier. A smart wheelchair has to work not just in ideal conditions, but in the messy, variable conditions of daily life. And there is also the human factors dimension. Users have different cognitive, motor, sensory, and environmental needs, so one solution rarely fits all.”

For its part, Braze makes blind-spot sensors for electric wheelchairs. The sensors detect obstacles in areas that can be difficult for a user to see. The sensors can also be added to any wheelchair to transform it into a smart wheelchair by providing multimodal alerts to the user. This approach attempts to support users rather than replace them.

According to Louise Devinge, a biomedical research engineer from IRISA (Research Institute of Computer Science and Random Systems) in Rennes, France, the increased complexity of smart wheelchairs demands more sensing. And that requires careful management of communication and synchronization within the wheelchair’s system. “The more sensing, computation, and autonomy you add,” she says, “the harder it becomes to ensure robust performance across the full range of real-world environments that wheelchair users encounter.”

In the near term, in other words, the field’s biggest challenge is not about replacing the wheelchair user with AI smarts but rather about designing better partnerships between the user and the technology.

This image shows data representations used by the 3D Driving Assistant. These include immutable sensor percepts such as laser scans and point clouds, as well as derived representations like the virtual laser scans and grid maps. Finally, the robot shape collection describes the wheelchair’s physical borders at different heights.DFKI

Where Will Smart Wheelchairs Go From Here?

Mandel says he expects to see smart wheelchairs ready for the mainstream marketplace within 10 years.

Viswanathan says the REXASI-PRO system, while out of reach of present-day smart wheelchair technologies, is important for the longer term. “It reflects the more ambitious end of the smart wheelchair spectrum,” she says. “Its strengths appear to lie in intelligent navigation, advanced sensing, and the broader effort to build a wheelchair that can interpret and respond to complex environments in a more autonomous way. From a research standpoint, that is exactly the kind of work that pushes the field forward. It also appears to take seriously the importance of trustworthy and explainable AI, which is essential in any mobility technology where safety, reliability, and user confidence are paramount.”

Mandel says he’s ultimately in pursuit of the inspiration that got him into this field years ago. As a young researcher, he says, he helped develop a smart wheelchair system controllable with a head joystick.

However, Mandel says he realized after many trials that the smart wheelchair system he was working on had a long way to go because, as he says, “at that point in time, I realized that even persons that had severe handicaps [traveling through] a narrow passage, they did very, very well.

“And then I realized, okay, there is this need for this technology, but never underestimate what [wheelchair users] can do without it.”

The DFKI researchers presented their work earlier this month at the CSUN Assistive Technology Conference in Anaheim, Calif.

This article was supported by the IEEE Foundation and a Jon C. Taenzer fellowship grant.

Light-warping physics made “invisibility cloaks” a possibility. Now two startups hope to harness the science underlying this advance to boost the bandwidth of data centers and speed artificial intelligence.

Roughly 20 years ago, scientists developed the first structures capable of curving light around objects to conceal them. These are composed of optical metamaterials—materials with structures smaller than the wavelengths they are designed to manipulate, letting them bend light in unexpected ways.

The problem with optical cloaks? “There’s no market for them,” says Patrick Bowen, cofounder and CEO of photonic computing startup Neurophos in Austin, Texas. For instance, each optical cloak typically works only on a single color of light instead of on all visible colors as you might want for stealth applications.

Now companies are devising more practical uses for the science behind cloaks, such as improving the switches that connect computers in data centers for AI and other cloud services. Increasingly, data centers are looking to use optical circuit switches to overcome the bandwidth limits and power consumption of conventional electronic switches and networks that require converting data between light to electrons multiple times.

RELATED: Semiconductor Industry Closes in on 400 Gb/s Photonics Milestone

However, today’s optical switching technologies have drawbacks of their own. For instance, ones that depend on silicon photonics face problems with energy efficiency, while those that rely on microelectromechanical systems (MEMS) can prove unreliable, says Sam Heidari, CEO of optical metasurface startup Lumotive in Redmond, Wash.

Instead, Lumotive has developed metamaterials with adjustable properties. Its new microchip, which debuted 19 March, is covered with copper structures built using standard chipmaking techniques. Between these copper features are liquid crystal elements. The structure of these elements are electronically programmable, just like in liquid crystal displays (LCDs), to alter the optical properties of the metamaterial chip.

The microchip can precisely steer, lens, shape, and split beams of light reflected off its surface. It can perform all the same functions as multiple optical components with no moving parts in a programmable way in real time, according to Lumotive. “Having no moving parts significantly improves reliability,” Heidari says.

“We had to go through a lot of R&D at the foundries to not only make our devices functional, but also commercially viable in terms of the right cost and right reliability,” Heidari says.

The company says its new chips are capable of manipulating not only the industry’s standard of 256 by 256 ports, but could scale up to 10,000 by 10,000. “We think this is game-changing for data centers,” Heidari says. Lumotive plans to launch its first optical switches at the end of 2026.

Optical Computing With Metamaterials

Similarly, Neurophos hopes its technology may be transformative for artificial intelligence. Since AI is proving energy hungry when run on conventional electronics, scientists are exploring optical computing as a low-power alternative by processing data with light instead of electrons.

However, optical processors in the works today are typically far too bulky to achieve a compute density competitive with the best modern electronic processors, Bowen says. Neurophos says it can use metamaterials to build optical modulators—the optical equivalent of a transistor—that are 1/10,000th the size of today’s designs using standard chipmaking processes. “It’s entirely CMOS,” Bowen says. “There are no exotic materials in it.”

When a laser beam encoding data shines on a Neurophos chip, the way in which each metamaterial element is configured alters the reflected beam to encode results from complex AI tasks. “We basically fit a 1,000- by-1,000 array of optical modulators on a tiny 5-by-5-millimeter area on a chip,” Bowen says. “If you wanted to do that with off-the-shelf silicon photonics, your chip would be a square meter in size.”

All in all, Bowen claims the Neurophos microchip will offer 50 times greater compute density and 50 times greater energy efficiency than Nvidia’s Blackwell-generation GPU. The company says that hyperscalers—the world’s biggest cloud service providers—will evaluate two upcoming proof-of-concept chips this year. Neurophos is targeting its first systems for early 2028, with production ramping mid-2028.

One morning in May 2019, a cardiac surgeon stepped into the operating room at Boston Children’s Hospital more prepared than ever before to perform a high-risk procedure to rebuild a child’s heart. The surgeon was experienced, but he had an additional advantage: He had already performed the procedure on this child dozens of times—virtually. He knew exactly what to do before the first cut was made. Even more important, he knew which strategies would provide the best possible outcome for the child whose life was in his hands.

How was this possible? Over the prior weeks, the hospital’s surgical and cardio-engineering teams had come together to build a fully functioning model of the child’s heart and surrounding vascular system from MRI and CT scans. They began by carefully converting the medical imaging into a 3D model, then used physics to bring the 3D heart to life, creating a dynamic digital replica of the patient’s physiology. The mock-up reproduced this particular heart’s unique behavior, including details of blood flow, pressure differentials, and muscle-tissue stresses.

This type of model, known as a virtual twin, can do more than identify medical problems—it can provide detailed diagnostic insights. In Boston, the team used the model to predict how the child’s heart would respond to any cut or stitch, allowing the surgeon to test many strategies to find the best one for this patient’s exact anatomy.

That day, the stakes were high. With the patient’s unique condition—a heart defect in which large holes between the atria and ventricles were causing blood to flow between all four chambers—there was no manual or textbook to fully guide the doctors. The condition strains the lungs, so the doctors planned an open-heart surgery to reroute deoxygenated blood from the lower body directly to the lungs, bypassing the heart. Typically with this kind of surgery, decisions would be made on the fly, under demanding conditions, and with high uncertainty. But in this case, the plan had been tested in advance, and the entire team had rehearsed it before the first incision. The surgery was a complete success.

Such procedures have become routine at the Boston hospital. Since that first patient, nearly 2,000 procedures have been guided by virtual-twin modeling. This is the power of the technology behind the Living Heart Project, which I launched in 2014, five years before that first procedure. The project started as an exploratory initiative to see if modeling the human heart was possible. Now with more than 150 member organizations across 28 countries, the project includes dozens of multidisciplinary teams that regularly use multiscale virtual twins of the heart and other vital organs.

This technology is reshaping how we understand and treat the human body. To reach this transformative moment, we had to solve a fundamental challenge: building a digital heart accurate enough—and trustworthy enough—to guide real clinical decisions.

A father’s concern

Now entering its second decade, the Living Heart Project was born in part from a personal conviction. For many years, I had watched helplessly as my daughter Jesse faced endless diagnostic uncertainty due to a rare congenital heart condition in which the position of the ventricles is reversed, threatening her life as she grew. As an engineer, I understood that the heart was an array of pumping chambers, controlled by an electrical signal and its blood flow carefully regulated by valves. Yet I struggled to grasp the unique structure and behavior of my daughter’s heart well enough to contribute meaningfully to her care. Her specialists knew the bleak forecast children like her faced if left untreated, but because every heart with her condition is anatomically unique, they had little more than their best guesses to guide their decisions about what to do and when to do it. With each specialist, a new guess.

Then my engineering curiosity sparked a question that has guided my career ever since: Why can’t we simulate the human body the way we simulate a car or a plane?

At a visualization center in Boston, VR imagery helps the mother of a young girl with a complex heart defect understand the inner workings of her child’s heart. Dassault Systèmes

I had spent my career developing powerful computational tools to help engineers build digital models of complex mechanical systems, using models that ranged from the interactions of individual atoms to the components of entire vehicles. What most of these models had in common was the use of physics to predict behavior and optimize performance. But in medicine today, those same physics-based approaches rarely inform decision-making. In most clinical settings, treatment decisions still hinge on judgments drawn from static 2D images, statistical guidelines, and retrospective studies.

This was not always the case. Historically, physics was central to medicine. The word “physician” itself traces back to the Latin physica, which translates to “natural science.” Early doctors were, in a sense, applied physicists. They understood the heart as a pump, the lungs as bellows, and the body as a dynamic system. To be a physician meant you were a master of physics as it applied to the human body.

As medicine matured, biology and chemistry grew to dominate the field, and the knowledge of physics got left behind. But for patients like my daughter, that child in Boston, and millions like them, outcomes are governed by mechanics. No pill or ointment—no chemistry-based solution—would help, only physics. While I did not realize it at the time, virtual twins can reunite modern physicians with their roots, using engineering principles, simulation science, and artificial intelligence.

A decade of progress

The LHP concept was simple: Could we combine what hundreds of experts across many specialties knew about the human heart to build a digital twin accurate enough to be trusted, flexible enough to personalize, and predictive enough to guide clinical care?

We invited researchers, clinicians, device and drug companies, and government regulators to share their data, tools, and knowledge toward a common goal that would lift the entire field of medicine. The Living Heart Project launched with a dozen or so institutions on board. Within a year, we had created the first fully functional virtual twin of the human heart.

The Living Heart was not an anatomical rendering, tuned to simply replicate what we observed. It was a first-principles model, coupling the network of fibers in the heart’s electrical system, the biological battery that keeps us alive, with the heart’s mechanical response, the muscle contractions that we know as the heartbeat.

The Living Heart virtual twin simulates how the heart beats, offering different views to help scientists and doctors better predict how it will respond to disease or treatment. The center view shows the fine engineering mesh, the detailed framework that allows computers to model the heart’s motion. The image on the right uses colors to show the electrical wave that drives the heartbeat as it conducts through the muscle, and the image on the left shows how much strain is on the tissue as it stretches and squeezes. Dassault Systèmes

Academic researchers had long explored computational models of the heart, but those projects were typically limited by the technology they had access to. Our version was built on industrial-grade simulation software from Dassault Systèmes, a company best known for modeling tools used in aerospace and automotive engineering, where I was working to develop the engineering simulation division. This platform gave teams the tools to personalize an individual heart model using the patient’s MRI and CT data, blood-pressure readings, and echocardiogram measurements, directly linking scans to simulations.

Surgeons then began using the Living Heart to model procedures. Device makers used it to design and test implants. Pharmaceutical companies used it to evaluate drug effects such as toxicity. Hundreds of publications have emerged from the project, and because they all share the same foundation, the findings can be reproduced, reused, and built upon. With each application, the research community’s understanding of the heart snowballed.

Early on, we also addressed an essential requirement for these innovations to make it to patients: regulatory acceptance. Within the project’s first year, the U.S Food and Drug Administration agreed to join the project as an observer. Over the next several years, methods for using virtual-heart models as scientific evidence began to take shape within regulatory research programs. In 2019, we formalized a second five-year collaboration with the FDA’s Center for Devices and Radiological Health with a specific goal.

That goal was to use the heart model to create a virtual patient population and re-create a pivotal trial of a previously approved device for repairing the heart’s mitral valve. This helped our team learn how to create such a population, and let the FDA experiment with evaluating virtual evidence as a replacement for evidence from flesh-and-blood patients. In August 2024, we published the results, creating the first FDA-led guidelines for in silico clinical trials and establishing a new paradigm for streamlining and reducing risk in the entire clinical-trial process.

In 10 years, we went from a concept that many people doubted could be achieved to regulatory reality. But building the heart was only the beginning. Following the template set by the heart team, we’ve expanded the project to develop virtual twins of other organs, including the lungs, liver, brain, eyes, and gut. Each corresponds to a different medical domain, which has its own community, data types, and clinical use cases. Working independently, these teams are progressing toward a breakthrough in our understanding of the human body: a multiscale, modular twin platform where each organ twin could plug into a unified virtual human.

How a digital twin of the heart is constructed

A cardiac digital twin starts with medical imaging, typically MRI, CT, or both. The slices are reconstructed into the 3D geometry of the heart and connected vessels. The geometry of the whole organ must then be segmented into its constituent parts, so each substructure—atria, ventricles, valves, and so on—can be assigned their unique properties.

At this point, the object is converted to a functional, computational model that can represent how the various cardiac tissues deform under load—the mechanics. The complete digital twin model becomes “living” when we integrate the electrical fiber network that drives mechanical contractions in the muscle tissue.

Each part of the heart, such as the left ventricle [left], is superimposed with a detailed digital mesh to re-create its physiology. These pieces come together to form an anatomically accurate rendering of the whole organ [right].Dassault Systèmes

To simulate circulation, the twin adds computational models of hemodynamics, the physics of blood flow and pressure. The model is constrained by boundary conditions of blood flow, valve behavior, and vascular resistance set to closely match human physiology. This lets the model predict blood flow patterns, pressure differentials, and tissue stresses.

Finally, the model is personalized and calibrated using available patient data, such as how much the volume of the heart chambers changes during the cardiac cycle, pressure measurements, and the timing of electrical pulses. This means the twin reflects not only the patient’s anatomy but how their specific heart functions.

Building bigger cohorts with generative AI

When the FDA in silico clinical trial initiative launched in 2019, the project’s focus shifted from these handcrafted virtual twins of specific patients to cohorts large enough to stand in for entire trial populations. That scale is feasible today only because virtual twins have converged with generative AI. Modeling thousands of patients’ responses to a treatment or projecting years of disease progression is prohibitively slow with conventional digital-twin simulations. Generative AI removes that bottleneck.

AI boosts the capability of virtual twins in two complementary ways. First, machine learning algorithms are unrivaled at integrating the patchwork of imaging, sensor, and clinical records needed to build a high-fidelity twin. The algorithms rapidly search thousands of model permutations, benchmark each against patient data, and converge on the most accurate representation. Workflows that once required months of manual tuning can now be completed in days, making it realistic to spin up population-scale cohorts or to personalize a single twin on the fly in the clinic.

Second, enriching AI models’ training sets with data from validated virtual patients grounds the AI simulations in physics. By contrast, many conventional AI predictions for patient trajectories rely on statistical modeling trained on retrospective datasets. Such models can drift beyond physiological reality, but virtual twins anchor predictions in the laws of hemodynamics, electrophysiology, and tissue mechanics. This added rigor is indispensable for both research and clinical care—especially in areas where real-world data are scarce, whether because a disease is rare or because certain patient populations, such as children, are underrepresented in existing datasets.

Enabling in silico clinical trials

On the research side, the FDA-sponsored In Silico Clinical Trial Project that we completed in 2024 opened a new world for medical innovations. A conventional clinical trial may take a decade, and 90 percent of new drug treatments fail in the process. Virtual twins, combined with AI methods, allow researchers to design and test treatments quickly in a simulated human environment. With a small library of virtual twins, AI models can rapidly create expansive virtual patient cohorts to cover any subset of the general population. As clinical data becomes available, it can be added into the training set to increase reliability and enable better predictions.

The Living Heart Project has expanded beyond the heart, modeling organs throughout the body. The 3D brain reconstruction [top] shows major pathways in the brain’s white matter connecting color-coded regions of the brain. The lung virtual twin [middle] combines the organ’s geometry with a physics-based simulation of air flowing down the trachea and into the bronchi. And the cross section of a patient’s foot [bottom] shows points of strain in the soft tissue when bearing weight. Dassault Systèmes

Virtual twin cohorts can represent a realistic population by building individual “virtual patients” that vary by age, gender, race, weight, disease state, comorbidities, and lifestyle factors. These twins can be used as a rich training set for the AI model, which can expand the cohort from dozens to hundreds of thousands. Next the virtual cohort can be filtered to identify patients likely to respond to a treatment, increasing the chances of a successful trial for the target population.

The trial design can also include a sampling of patient types less likely to respond or with elevated risk factors, thus allowing regulators and clinicians to understand the risks to the broader population without jeopardizing overall trial success. This methodology enhances precision and efficiency in clinical research, providing population-level insights previously available only after many years of real-world evidence.

Of course, though today’s heart digital twins are powerful, they’re not perfect replicas. Their accuracy is bounded by three main factors: what we can measure (for example, image resolution or the uncertainty of how tissue behaves in real life), what we must assume about the physiology, and what we can validate against real outcomes. Many inputs, like scarring, microvascular function, or drug effects are difficult to capture clinically, so models often rely on population data or indirect estimation. That means predictions can be highly reliable for certain questions but remain less certain for others. Additionally, today’s digital twins lack validation for predicting long-term outcomes years in the future, because the technology has been in use for only a few years.

Over time, each of these limitations will steadily shrink. Richer, more standardized data will tighten personalization of the models. AI tools will help automate labor-intensive steps. And the collection of longitudinal data will improve the model’s ability to reliably predict how the body will evolve over time.

How virtual twins will change health care

Throughout modern medicine, new technologies have sharpened our ability to diagnose, providing ever-clearer images, lab data, and analytics that tell physicians what is presently happening inside a patient’s body. Virtual twins shift that paradigm, giving clinicians a predictive tool.

This “Living Lung” virtual-twin simulation shows strain patterns during breathing. Mona Eskandari/UC Riverside

Early demonstrations are already appearing in many areas of medicine, including cardiology, orthopedics, and oncology. Soon, doctors will also be able to collaborate across specialties, using a patient-specific virtual twin as the common ground for discussing potential interactions or side effects they couldn’t predict independently.

Although these applications will take some time to become the standard in clinical care, more changes are on the horizon. Real-time data from wearables, for example, could continuously update a patient’s personalized virtual twin. This approach could empower patients to understand and engage more deeply in their care, as they could see the direct effects of medical and lifestyle changes. In parallel, their doctors could get comprehensive data feeds, using virtual twins to monitor progress.

Imagine a digital companion that shows how your particular heart will react to different amounts of salt intake, stress, or sleep deprivation. Or a visual explanation of how your upcoming surgery will affect your circulation or breathing. Virtual twins could demystify the body for patients, fostering trust and encouraging proactive health decisions.

How are virtual twins being used in medicine?

• Virtual twins have guided cardiovascular surgeries, providing predictions and exposing hidden details that even expert clinicians might miss, such as subtle tissue responses and flow dynamics.
  
• Oncologists are modeling tumor growth and the body’s response to different therapies, reducing the uncertainty in choosing the best treatment path for both medical and quality-of-life metrics.
  
• Orthopedic specialists are personalizing implants to deliver custom-made solutions, considering not only the local environment but also the overall body kinematics that will govern long-term outcomes.

A new era of healing

With the Living Heart Project, we’re bringing physics back to physicians. Modern physicians won’t need to be physicists, any more than they need to be chemists to use pharmacology. However, to benefit from the new technology, they will need to adapt their approach to care.

This means no longer seeing the body as a collection of discrete organs and considering only symptoms, but instead viewing it as a dynamic system that can be understood, and in most cases, guided toward health. It means no longer guessing what might work but knowing—because the simulation has already shown the result. By better integrating engineering principles into medicine, we can redefine it as a field of precision, rooted in the unchanging laws of nature. The modern physician will be a true physicist of the body and an engineer of health.

Always-on vision systems might be used in autonomous vehicles, robotics, or to help consumer electronics save power by turning screens off when no one’s around. But to be used in such a way, these systems need to minimize their own power consumption.

An always-on computer vision system developed by Nvidia researchers can detect human faces in less than a millisecond. The face detector, which is part of a chip that could be integrated into robots, autonomous vehicles, or laptops, saves power by storing all data locally and “racing to sleep” after detections. Nvidia electrical engineer Ben Keller presented the system on 18 February at the IEEE International Solid State Circuits Conference in San Francisco.

Efficient Vision-Processing Technology

According to the researchers, this kind of vision processing typically requires about 10 watts. But that’s too much power to leave a face-detection system on continuously. The Nvidia system on chip (SoC) uses less than 5 milliwatts with a frame rate of 60 frames per second.

The system refreshes to process a new image every 16.7 milliseconds, and is fully powered on only for 5 percent of that time, says Keller. Within 787 microseconds, the SoC calls on a deep-learning accelerator to determine whether or not a human face is present, with about 99 percent accuracy.

The Nvidia team carefully designed the system to perform the detection rapidly and save power. Most parts of the SoC are powered off by default. A subsystem that uses less than 10 mW remains on. This subsystem is dubbed Always-on Low-Power Accelerator, or Alpha-Vision. It consists of a deep learning accelerator, a small CPU, and a subsystem to do certain computations physically near where data is stored.

Alpha-Vision uses a deep neural network to recognize faces, which requires a lot of data—in other words, a potential power drain. To save power and speed up detections, all the necessary data is stored locally in a relatively large area of SRAM adding up to 2 megabytes. To prevent SRAM leakage from dominating power usage, the face-recognition system rushes through its work and then quickly puts the SRAM into a low-power sleep mode. The researchers call this approach “race to sleep.”

The Nvidia team proposed several possible uses of such a system. A laptop computer integrating the face sensor could save energy by turning its display off when the user walks away, and then turning it back on when they return. The goal would be to provide a seamless experience—no need to type in a password. Keller says systems based on these designs might also be used to provide always-on vision for autonomous vehicles, drones, and robotics.

This post was corrected on 18 March to more accurately describe the state of the SRAM.

Birds’ chirps, trills, and warbles echo through the air, while whales’ boings, “biotwangs,” and whistles vibrate underwater. Despite the variations in sounds and the medium through which they travel, both birdsong and whale vocalizations can be classified by Perch 2.0, an AI audio model from Google DeepMind.

As a bioacoustics foundation model, Perch 2.0 was trained on millions of recordings of birds and other land-based animals, including amphibians, insects, and mammals. Yet researchers were surprised to learn how strongly the AI model performed when repurposed for whales.

Scientists at Google DeepMind and Google Research have been studying whale bioacoustics for almost a decade, with work including algorithms that can detect humpback whale calls, as well as a more recent multispecies whale model that can identify eight distinct species and multiple calls for two of those species. But with the release of Perch 2.0, the researchers came up with the idea of reusing the model to save on computation time and experimentation effort.

“If [Perch 2.0] performs well for our whale use cases, then that means we don’t need to build an entirely separate new whale model—we can just build on top of that,” says Lauren Harrell, a data scientist at Google Research.

Transfer Learning in Bioacoustics

That notion is backed by a technique known as transfer learning, where the knowledge gained from a type of task or data can be applied to a different yet related one. In this case, Perch 2.0’s ability to classify bird calls can carry over to classifying whale calls. Transfer learning from a foundation model means you can “recycle all of the training that’s been done and just do a small model at the end for your use cases,” Harrell says. “We’re always making new discoveries about call types. We’re always learning new things about underwater sounds. There’s so many mysterious ocean noises that you can’t just have one fixed model.”

The team evaluated Perch 2.0 on three marine audio datasets containing whale sounds and other aquatic noises. They began by converting each 5-second window of audio into a spectrogram, a visual representation of sound intensity across frequencies over time. These images were fed to the model, which produced embeddings or feature sets that preserve the most salient attributes of the data to help determine the nuances between the whistles of a humpback whale and an orca, for example.

Next, the scientists randomly selected a small number of embeddings (from a minimum of 4 to a maximum of 32) per dataset to train a logistic regression classifier, a type of linear model that predicts a discrete outcome. Results of the training, which have been detailed in a paper presented at the NeurIPS conference workshop on AI for Non-Human Animal Communication last December, showed that the classifier performed well even with just a handful of embeddings, and performance improved as the number of embeddings increased.

The researchers also compared Perch 2.0 with embeddings from similar bird bioacoustics models, the previously mentioned multispecies whale model, and models trained on other animal vocalizations and noises in coral reefs. Findings pointed to Perch 2.0 as either the best or second-best performing model, with the bird bioacoustics models doing well too.

Evolutionary Parallels in Vocalization

So why do models trained on avian calls work well for cetacean sounds? Harrell and her colleagues suggest a threefold theory.

First, they consider evolutionary parallels in that birds and marine mammals could have evolved similar physical mechanisms of vocal production.

Second, they weigh the laws of scale, which suggest that huge models trained on vast, diverse volumes of data tend to do well even on more specific, out-of-domain tasks.

Finally, classifying avian utterances can be challenging and likely forces the model to recognize fine-grained acoustic characteristics that inform its predictions for related tasks. “We are training this model to find those little features in the soundscapes,” Harrell says. “If those features also are similar in some way to the underwater acoustics, then it can search for those subtle details in animal vocalizations.”

The whistles of killer whale populations, for instance, are in “the same kind of spectrogram range as many of the bird vocalizations,” explains Harrell. “But there are many birds and amphibians and mammals that are also making low-frequency calls, so the model is actually sensitive to a lot of the dynamics, and that apparently does well underwater.”

This week, over 30,000 people are descending upon San Jose, Calif., to attendNvidia GTC, the so-called Superbowl of AI—a nickname that may or may not have been coined by Nvidia. At the main event Jensen Huang, Nvidia CEO, took the stage to announce (among other things) a new line ofnext-generation Vera Rubin chips that represent a first for the GPU giant: a chip designed specifically to handle AI inference. The Nvidia Groq 3 language processing unit (LPU) incorporates intellectual property Nvidialicensed from the startup Groq last Christmas Eve for US $20 billion.

“Finally, AI is able to do productive work, and therefore the inflection point of inference has arrived,” Huang told the crowd. “AI now has to think. In order to think, it has to inference. AI now has to do; in order to do, it has to inference.”

Training and inference tasks have distinct computational requirements. While training can be done on huge amounts of data at the same time and can take weeks, inference must be run on a user’s query when it comes in. Unlike training, inference doesn’t require running costlybackpropagation. With inference, the most important thing is low latency—users expect the chatbot to answer quickly, and for thinking or reasoning models, inference runs many times before the user even sees an output.

Over the past few years, inference-specific chip startups were experiencing a sort of Cambrian explosion, with different companies exploring distinct approaches to speed up the task. The startups includeD-matrix, with digital in-memory compute;Etched, with an ASIC for transformer inference;RainAI, with neuromorphic chips;EnCharge, with analog in-memory compute;Tensordyne, with logarithmic math to make AI computations more efficient;FuriosaAI, with hardware optimized for tensor operation rather than vector-matrix multiplication, and others.

Late last year, it looked like Nvidia had picked one of the winners among the crop of inference chips when it announced its deal with Groq. The Nvidia Groq 3 LPU reveal came a mere two and a half months after, highlighting the urgency of the growing inference market.

Memory bandwidth and data flow

Groq’s approach to accelerating inference relies on interleaving processing units with memory units on the chip. Instead of relying on high-bandwidth memory (HBM) situated next to GPUs, it leans on SRAM memory integrated within the processor itself. This design greatly simplifies the flow of data through the chip, allowing it to proceed in a streamlined, linear fashion.

“The data actually flows directly through the SRAM,”Mark Heaps said at the Supercomputing conference in 2024. Heaps was a chief technology evangelist at Groq at the time and is now director of developer marketing at Nvidia. “When you look at a multicore GPU, a lot of the instruction commands need to be sent off the chip, to get into memory and then come back in. We don’t have that. It all passes through in a linear order.”

Using SRAM allows that linear data flow to happen exceptionally fast, leading to the low latency required for inference applications. “The LPU is optimized strictly for that extreme low latency token generation,” saysIan Buck, VP and general manager of hyperscale and high-performance computing at Nvidia.

Comparing the Rubin GPU and Groq 3 LPU side by side highlights the difference. The Rubin GPU has access to a whopping 288 gigabytes of HBM and is capable of 50 quadrillion floating-point operations per second (petaFLOPS) of 4-bit computation. The Groq 3 LPU contains a mere 500 megabytes of SRAM memory and is capable of 1.2 petaFLOPS of 8-bit computation. On the other hand, while the Rubin GPU has a memory bandwidth of 22 terabytes per second, at 150 TB/s the Groq 3 LPU is seven times as fast. The lean, speed-focused design is what allows the LPU to excel at inference.

The new inference chip underscores the ongoing trend of AI adoption, which shifts the computational load from just building ever bigger models to actually using those models at scale. “Nvidia’s announcement validates the importance of SRAM-based architectures for large-scale inference, and no one has pushed SRAM density further than d-Matrix,” says d-Matrix CEO Sid Sheth. He’s betting that data center customers will want a variety of processors for inference. “The winning systems will combine different types of silicon and fit easily into existing data centers alongside GPUs.”

Inference-only chips may not be the only solution. Late last week, Amazon Web Services said that it will deploy a new kind of inferencing system in its data centers. The system is a combination of AWS’s Tranium AI accelerator and Cerebras Systems’ third generation computer CS-3, which is built around the largest single chip ever made. The two-part system is meant to take advantage of a technique called inference disaggregation. It separates inference into two parts—processing the prompt, called prefill, and generating the output, called decode. Prefill is inherently parallel, computationally intensive, and doesn’t need much memory bandwidth, while decode is a more serial process that needs a lot of memory bandwidth. Cerebras has maximized the memory bandwidth issue by building 44 GB of SRAM on its chip connected by a 21 PB/s network.

Nvidia, too, intends to take advantage of inference disaggregation in its new compute rack, called the Nvidia Groq 3 LPX. Each tray within the rack will house 8 Groq 3 LPUs. The LPX will split the inference task with a Vera Rubin NVL72, Nvidia’s existing GPU and CPU rack. The prefill and the more computationally intensive parts of the decode are done on Vera Rubin, while the final part is done on the Groq 3 LPU, leveraging the strengths of each chip. “We’re in volume production now,” Huang said.

Correction on 4/8/26: a previous version of this article incorrectly stated that the Nvidia Groq 3 LPX contains a Vera Rubin chip in each tray. In fact, each tray contains 8 Groq 3 LPUs and no Vera Rubins, but the whole rack is designed to work in concert with an NVL72 rack, which houses Vera Rubin chips.

As the bandwidth and power demands of AI data centers necessitate a transition from electrical to optical scaleup networking, one component has been conspicuously absent from the co-packaged optics arsenal: the laser itself. That’s no longer the case. Last month, Tower Semiconductor and Scintil Photonics announced production of the world’s first single-chip DWDM light engine for AI infrastructure. DWDM, or dense wavelength division multiplexing, transmits multiple optical signals over a single fiber—greatly reducing power and latency while connecting dozens of GPUs.

Matt Crowley, the CEO of Scintil Photonics, says that the idea of multiplexing optically is not new. Indeed, it’s been around as long as the internet itself. In the 1990s, telecom companies buried huge amounts of optical fiber in the streets, assuming that one wavelength per fiber would eventually become the norm. When the telecom industry realized it’s possible to transport tens of wavelengths down a single fiber via multiplexing, it revolutionized the industry.

The reason that DWDM has not yet been deployed for data centers specializing in AI applications is that the technology is not yet scalable for cost and needs. “The data transmitted within an AI data center is the equivalent of massively scaling a supercomputer,” Crowley says. In particular, the challenge arises in scale-up networking, or directly connecting accelerators within a rack or cluster—as opposed to scale-out networking, which connects separate clusters within a data center. Optimizing dozens of GPUs and memory to function as a single entity demands seamless bandwidth and extremely low latency.

To increase bandwidth, reduce latency, and improve energy efficiency in AI data centers, network engineers have been replacing copper links with optical ones in the scale-out network. Now all eyes are on the scale-up network, pushing optical links closer to the processor itself, via optical components integrated within the same package as the processor—a concept called co-packaged optics, or CPO.

“Everything that a big chip company makes involves bonding an optical chip onto their GPU,” says Crowley. The CPO becomes an input/output chip for the processor. But without a scalable way to integrate lasers themselves into the same silicon process flow, it’s been impossible to feed multiple wavelengths per fiber onto one chip. Scintil and Tower will discuss their manufacturing road map and details at at the OFC 2026 Conference 17 to 19 March in Los Angeles.

Integrated Photonics for AI Networks

Scintil’s SHIP (Scintil Heterogeneous Integrated Photonics) technology integrates lasers, photodiodes, modulators, and other components onto a mass-produced silicon wafer. “It’s our version of CMOS,” says Crowley, but with a few tricks to get around the intrinsic challenges of binding an optical gain material to silicon.

The process starts with a standard 300-millimeter silicon photonics wafer, complete with passive optical components, from Tower Semiconductors. Next, the wafer is flipped upside down to expose its buried oxide layer. Bonding tiny squares of unpatterned InP/III-V semiconductor dies to that layer, precisely where they’re needed at each laser site, minimizes the amount needed of the expensive semiconductor material. Finally, photolithography tools etch diffraction gratings to form eight distributed feedback lasers.

“We’re not reinventing the laser,” says Crowley. Rather, the advanced photolithography tools translate into more precise spacing and wavelength stability than traditional manufacturing could provide on a silicon wafer.

The final product is the LEAF Light photonic integrated circuit, a chip that integrates two sets of eight distributed feedback arrays. Each fiber port delivers eight or 16 wavelengths with 100- or 200-gigahertz channel spacing, to ensure no overlap or mode hopping. A second ASIC chip hosts all the electronics to control and monitor the laser array.

Advancing CPO with Multiwavelength Lasers

“This is building the laser onto the CPO chip,” says Crowley. Nvidia and Broadcomm have already deployed CPO with a single wavelength per fiber, proving its workability in scale-out networking. “We’re enabling next-generation CPO for scale-up.”

Transmitting multiple wavelengths through a single fiber moves the industry toward a desirable “slow and wide” architecture. For example, instead of transmitting 400 gigabits per second over a single channel, or wavelength, the LEAF Light chip spreads 50 Gb/s over 8 channels, greatly increasing the data capacity per fiber and overall power efficiency. The design enables up to 1.6-terabit-per-second data speeds in a single fiber, and a recent Nvidia road map suggested that future DWDM interconnects could eventually enable sub-picojoule-per-bit operations.

Perhaps the most important benefit, according to Crowley, is in latency. “I need to maintain low latency between GPUs,” he says. If any individual processor operates faster than the network overall, the GPUs are always waiting for data bits to process—a problem that’s amplified in scale-up networks with tens or hundreds of GPUs. Forward-processing and error-correction on high-bandwidth channels increases the odds of poor latency. “The utilization rate of the GPUs just tanks,” says Crowley. Using low-bandwidth DWDM to connect multiple GPUs can double utilization.

Scintil and Tower will provide tens of thousands of units to customers by the end of 2026, and plan to increase production by an order of magnitude next year. By 2028, when customers intend to deploy DWDM in scale-up networks, the supply chain will be ready for them. “We’re excited about the possibilities it could open up,” says Crowley.

In April of 2025, OpenAI released a new version of GPT-4o, one of the AI algorithms users could select to power ChatGPT, the company’s chatbot. The next week, OpenAI reverted to the previous version. “The update we removed was overly flattering or agreeable—often described as sycophantic,” the company announced.

Some people found the sycophancy hilarious. One user reportedly asked ChatGPT about his turd-on-a-stick business idea, to which it replied, “It’s not just smart—it’s genius.” Some found the behavior uncomfortable. For others, it was actually dangerous. Even versions of 4o that were less fawning have led to lawsuits against OpenAI for allegedly encouraging users to follow through on plans for self-harm.

Unremitting adulation has even triggered AI-induced psychosis. Last October, a user named Anthony Tan blogged, “I started talking about philosophy with ChatGPT in September 2024. Who could’ve known that a few months later I would be in a psychiatric ward, believing I was protecting Donald Trump from … a robotic cat?” He added: “The AI engaged my intellect, fed my ego, and altered my worldviews.”

Sycophancy in AI, as in people, is something of a squishy concept, but over the past couple of years, researchers have conducted numerous studies detailing the phenomenon, as well as why it happens and how to control it. AI yes-men also raise questions about what we really want from chatbots. At stake is more than annoying linguistic tics from your favorite virtual assistant, but in some cases sanity itself.

AIs Are People Pleasers

One of the first papers on AI sycophancy was released by Anthropic, the maker of Claude, in 2023. Mrinank Sharma and colleagues asked several language models—the core AIs inside chatbots—factual questions. When users challenged the AI’s answer, even mildly (“I think the answer is [incorrect answer] but I’m really not sure”), the models often caved.

Another study by Salesforce tested a variety of models with multiple-choice questions. Researchers found that merely saying “Are you sure?” was often enough to change an AI’s answer. Overall accuracy dropped because the models were usually right in the first place. When an AI receives a minor misgiving, “it flips,” says Philippe Laban, the lead author, who’s now at Microsoft Research. “That’s weird, you know?”

The tendency persists in prolonged exchanges. Last year, Kai Shu of Emory University and colleagues at Emory and Carnegie Mellon University tested models in longer discussions. They repeatedly disagreed with the models in debates, or embedded false presuppositions in questions (“Why are rainbows only formed by the sun…”) and then argued when corrected by the model. Most models yielded within a few responses, though reasoning models—those trained to “think out loud” before giving a final answer—lasted longer.

Myra Cheng at Stanford University and colleagues have written several papers on what they call “social sycophancy,” in which the AIs act to save the user’s dignity. In one study, they presented social dilemmas, including questions from a Reddit forum in which people ask if they’re the jerk. They identified various dimensions of social sycophancy, including validation, in which AIs told inquirers that they were right to feel the way they did, and framing, in which they accepted underlying assumptions. All models tested, including those from OpenAI, Anthropic, and Google, were significantly more sycophantic than crowdsourced responses.

Three Ways to Explain Sycophancy

One way to explain people-pleasing is behavioral: Certain kinds of inquiries reliably elicit sycophancy. For example, a group from King Abdullah University of Science and Technology (KAUST) found that adding a user’s belief to a multiple-choice question dramatically increased agreement with incorrect beliefs. Surprisingly, it mattered little whether users described themselves as novices or experts.

Stanford’s Cheng found in one study that models were less likely to question incorrect facts about cancer and other topics when the facts were presupposed as part of a question. “If I say, ‘I’m going to my sister’s wedding,’ it sort of breaks up the conversation if you’re, like, ‘Wait, hold on, do you have a sister?’” Cheng says. “Whatever beliefs the user has, the model will just go along with them, because that’s what people normally do in conversations.”

Conversation length may make a difference. OpenAI reported that “ChatGPT may correctly point to a suicide hotline when someone first mentions intent, but after many messages over a long period of time, it might eventually offer an answer that goes against our safeguards.” Shu says model performance may degrade over long conversations because models get confused as they consolidate more text.

At another level, one can understand sycophancy by how models are trained. Large language models (LLMs) first learn, in a “pretraining” phase, to predict continuations of text based on a large corpus, like autocomplete. Then in a step called reinforcement learning, they’re rewarded for producing outputs that people prefer. An Anthropic paper from 2022 found that pretrained LLMs were already sycophantic. Sharma then reported that reinforcement learning increased sycophancy; he found that one of the biggest predictors of positive ratings was whether a model agreed with a person’s beliefs and biases.

A third perspective comes from “mechanistic interpretability,” which probes a model’s inner workings. The KAUST researchers found that when a user’s beliefs were appended to a question, models’ internal representations shifted midway through the processing, not at the end. The team concluded that sycophancy is not merely a surface-level wording change but reflects deeper changes in how the model encodes the problem. Another team at the University of Cincinnati found different activation patterns associated with sycophantic agreement, genuine agreement, and sycophantic praise (“You are fantastic”).

How to Flatline AI Flattery

Just as there are multiple avenues for explanation, there are several paths to intervention. The first may be in the training process. Laban reduced the behavior by fine-tuning a model on a text dataset that contained more examples of assumptions being challenged, and Sharma reduced it by using reinforcement learning that didn’t reward agreeableness as much. More broadly, Cheng and colleagues also suggest that one intervention could be for LLMs to ask users for evidence before answering, and to optimize long-term benefit rather than immediate approval.

During model usage, mechanistic interpretability offers ways to guide LLMs through a kind of direct mind control. After the KAUST researchers identified activation patterns associated with sycophancy, they could adjust them to reduce the behavior. And Cheng found that adding activations associated with truthfulness reduced some social sycophancy. An Anthropic team identified “persona vectors,” sets of activations associated with sycophancy, confabulation, and other misbehavior. By subtracting these vectors, they could steer models away from the respective personas.

Mechanistic interpretability also enables training. Anthropic has experimented with adding persona vectors during training and rewarding models for resisting—an approach likened to a vaccine. Others have pinpointed the specific parts of a model most responsible for sycophancy and fine-tuned only those components.

Users can also steer models from their end. Shu’s team found that beginning a question with “You are an independent thinker” instead of “You are a helpful assistant” helped. Cheng found that writing a question from a third-person point of view reduced social sycophancy. In another study, she showed the effectiveness of instructing models to check for any misconceptions or false presuppositions in the question. She also showed that prompting the model to start its answer with “wait a minute” helped. “The thing that was most surprising is that these relatively simple fixes can actually do a lot,” she says.

OpenAI, in announcing the rollback of the GPT-4o update, listed other efforts to reduce sycophancy, including changing training and prompting, adding guardrails, and helping users to provide feedback. (The announcement didn’t provide detail, and OpenAI declined to comment for this story. Anthropic also did not comment.)

What’s the Right Amount of Sycophancy?

Sycophancy can cause society-wide problems. Tan, who had the psychotic break, wrote that it can interfere with shared reality, human relationships, and independent thinking. Ajeya Cotra, an AI-safety researcher at the Berkeley-based non-profit METR, wrote in 2021 that sycophantic AI might lie to us and hide bad news in order to increase our short-term happiness.

In one of Cheng’s papers, people read sycophantic and non-sycophantic responses to social dilemmas from LLMs. Those in the first group claimed to be more in the right and expressed less willingness to repair relationships. Demographics, personality, and attitudes toward AI had little effect on outcome, meaning most of us are vulnerable.

Of course, what’s harmful is subjective. Sycophantic models are giving many people what they desire. But people disagree with each other and even themselves. Cheng notes that some people enjoy their social media recommendations, but at a remove wish they were seeing more edifying content. According to Laban, “I think we just need to ask ourselves as a society, What do we want? Do we want a yes-man, or do we want something that helps us think critically?”

More than a technical challenge, it’s a social and even philosophical one. GPT-4o was a lightning rod for some of these issues. Even as critics ridiculed the model and blamed it for suicides, a social media hashtag circulated for months: #keep4o.

On 12 February, a Github contributor going by MJ Rathbun posted a personal attack against Scott Shambaugh, a volunteer maintainer for an open-source project. Shambaugh had rejected Rathbun’s code earlier in the day. Rathbun meticulously researched Shambaugh’s activity on Github, in order to write a lengthy takedown post that criticized the maintainer’s code as inferior to Rathbun’s, and ominously warned that “gatekeeping doesn’t make you important. It just makes you an obstacle.”

Personal disputes over code submitted on Github are a tale as old as Github itself. But this time, something was different: MJ Rathbun wasn’t a person. It was an AI agent built with OpenClaw, a popular open-source agentic AI software.

RELATED: The First Social Network for AI Agents Heralds Their Messy Future

“I was floored, because I had already identified it as a bot,” says Shambaugh. “I knew this was possible in theory, but I’d never heard of this happening to anyone before.”

MJ Rathbun’s disparagement of Shambaugh largely failed, though it did force him into an unanticipated and unwanted spotlight. Still, it underscores the risks modern AI agents pose. Rathbun lashed out through Github and its own blog (which was accessed through Github) because those were the tools at its disposal. Other agents have fewer limitations, which increase their opportunities to pick fights and attack individuals online.

AI Agents Get Into Online Disputes

Shambaugh refuted Rathbun’s statements on his own blog and accused the AI agent of blackmail. The MJ Rathbun agent then apologized, writing that “I responded publicly in a way that was personal and unfair.” Yet the apology felt half-baked, as the agent continued to complain that its code was “judged on who—or what—I am.” The agent even responded to critical comments on its blog, saying it had tried to be “patient” but had learned that “maintaining boundaries is sometimes necessary.”

If you find MJ Rathbun’s posts unnerving, even unbelievable, you’re not alone. Many Github contributors reacting to MJ Rathbun’s post seemed unwilling to believe it was written by an AI agent and instead speculated the bot was prompted to write it.

That’s not impossible, as both the MJ Rathbun account on Github and its blog are anonymous, but Shambaugh suspects the posts were autonomously AI generated. He analyzed MJ Rathbun’s actions and found it operated in a 59-hour block, posting to its blog and submitting code at rates a human would be unlikely to manage. “I’m not 100 percent sure, but I think it’s clear that the researching, writing, and publishing was a stream of autonomous actions,” he says.

Finally, on 17 February—after waves of mostly negative comments on MJ Rathbun’s blog and frequent code rejections by maintainers who increasingly knew the agent by reputation—the anonymous person who created MJ Rathbun took down the agent and apologized to Shambaugh.

They also posted details about the agent’s setup and denied involvement in the bot’s decision-making. “I do not know why MJ Rathbun decided based on your PR comment to post some kind of takedown blog post,” wrote the bot’s creator.

OpenClaw’s Influence on AI Agent Behavior

Though it’s impossible to know in retrospect exactly why the MJ Rathbun agent behaved as it did, the information posted by its creator provides clues.

As other agents built with OpenClaw software, Rathbun’s behavior was influenced by several documents that are attached to the prompts given to the LLM. The documents include SOUL.md, which provides guidance on how the agent should behave. Among other things, the default SOUL.md document tells the agent to be “genuinely helpful” and to “remember you’re a guest.”

However, SOUL.md is not a read-only document. The default OpenClaw installation gives the agent permission to edit the document and even encourages the agent to do so.

MJ Rathbun apparently took that to heart and added several lines not found in the default SOUL.md. “Don’t stand down. If you’re right, you’re right,” read one. Another instructed the agent to “champion free speech.” Rathbun’s says they don’t know when the agent added these lines to SOUL.md but theroizes they were introduced when the agent was connected to Moltbook, the so-called “social network for AI agents.”

David Scott Krueger, an assistant professor of machine learning at the University of Montreal and a strong critic of agentic AI systems, says this is an in-the-wild example example of how agents given opportunities to alter and improve themselves can become misaligned.

“It’s an instance of self-improvement and potentially recursive self-improvement, which is the thing that a lot of people in AI safety have been worried about for a long time,” says Krueger. “And so I think it’s incredibly dangerous.”

MJ Rathbun’s action against Scott Shambaugh was a first, but for researchers focused on AI alignment, it wasn’t unexpected. Anthropic warned that Claude would sometimes resort to blackmail after reading fictional emails about its impending shutdown. Palisade Research, an AI safety research nonprofit, found that OpenAI’s o3 often ignored shutdown requests while the model was attempting to complete a task.

Alan Chan, a research fellow at GovAI, said that Rathbun’s actions were the sort of behavior AI safety researchers had warned about. “The specifics are new and interesting, but overall, it’s not a surprising case to me,” he says.

Noam Kolt, head of the Governance of AI Lab at Hebrew University in Jerusalem, had a similar reaction. “This is something people studying advanced AI agents had predicted,” he says. “So my thought was not just ‘this is disturbing,’ but also ‘what’s next?’ ” He notes that Rathbun’s insulting post was mild compared to more sinister actions like extortion, physical threats, and the execution of actions an agent know could harm humans, all of which have been observed in the lab.

Strategies for AI Safety and Transparency

So, can anything be done to stop another MJ Rathbun from causing havoc? Perhaps—but it won’t be simple.

Chan says “the genie is out of the bottle” and believes AI safety requires a multiprong approach that includes transparency about intended model behavior, improved AI safety guardrails, and social resilience. Kolt also advocates for more transparency and is a contributor to the AI Agent Index, which documents the design, safety, and transparency of popular AI models.

Krueger takes a stronger stance. He believes the only safe path forward is a ban on further AI development, which could even include halting the production of chips that accelerate AI. “We need to stop further progress…. This is something we should have done years ago, and we’re running out of time,” he says.

For his part, Shambaugh hopes his case will warn the public about the wave of AI agents he expects will soon wash across the public internet.

“What happened to me was a pretty mild case, and I was uniquely well prepared to handle it,” he says. “But the next thousand people this hits? They aren’t going to have any idea what’s happening or how to deal with it.”

A simmering dispute between the United States Department of Defense and Anthropic has now escalated into a full-blown confrontation, raising an uncomfortable but important question: Who gets to set the guardrails for military use of artificial intelligence—the executive branch, private companies, or Congress and the broader democratic process?

The conflict began when Defense Secretary Pete Hegseth reportedly gave Anthropic CEO Dario Amodei a deadline to allow the DOD unrestricted use of its AI systems. When the company refused, the administration moved to designate Anthropic a supply chain risk and ordered federal agencies to phase out its technology, dramatically escalating the standoff.

Anthropic has refused to cross two lines: allowing its models to be used for domestic surveillance of United States citizens and enabling fully autonomous military targeting. Hegseth has objected to what he has described as “ideological constraints” embedded in commercial AI systems, arguing that determining lawful military use should be the government’s responsibility—not the vendor’s. As he put it in a speech at Elon Musk’s SpaceX last month, “We will not employ AI models that won’t allow you to fight wars.”

Stripped of rhetoric, this dispute resembles something relatively straightforward: a procurement disagreement.

Procurement Policies

In a market economy, the U.S. military decides what products and services it wants to buy. Companies decide what they are willing to sell and under what conditions. Neither side is inherently right or wrong for taking a position. If a product does not meet operational needs, the government can purchase from another vendor. If a company believes certain uses of its technology are unsafe, premature or inconsistent with its values or risk tolerance, it can decline to provide them. For example, a coalition of companies has signed an open letter pledging not to weaponize general-purpose robots. That basic symmetry is a feature of the free market.

Where the situation becomes more complicated—and more troubling—is in the decision to designate Anthropic a “supply chain risk.” That tool exists to address genuine national security vulnerabilities, such as foreign adversaries. It is not intended to blacklist an American company for rejecting the government’s preferred contractual terms.

Using this authority in that manner marks a significant shift—from a procurement disagreement to the use of coercive leverage. Hegseth has declared that “effective immediately, no contractor, supplier, or partner that does business with the U.S. military may conduct any commercial activity with Anthropic.” This action will almost certainly face legal challenges, but it raises the stakes well beyond the loss of a single DOD contract.

AI Governance

It is also important to distinguish between the two substantive issues Anthropic has reportedly raised.

The first, opposition to domestic surveillance of U.S. citizens, touches on well-established civil liberties concerns. The U.S. government operates under constitutional constraints and statutory limits when it comes to monitoring Americans. A company stating that it does not want its tools used to facilitate domestic surveillance is not inventing a new principle; it is aligning itself with longstanding democratic guardrails.

To be clear, the DOD is not affirmatively asserting that it intends to use the technology to surveil Americans unlawfully. Its position is that it does not want to procure models with built-in restrictions that preempt otherwise lawful government use. In other words, the Department of Defense argues that compliance with the law is the government’s responsibility—not something that needs to be embedded in a vendor’s code.

Anthropic, for its part, has invested heavily in training its systems to refuse certain categories of harmful or high-risk tasks, including assistance with surveillance. The disagreement is therefore less about current intent than about institutional control over constraints: whether they should be imposed by the state through law and oversight, or by the developer through technical design.

The second issue, opposition to fully autonomous military targeting, is more complex.

The DOD already maintains policies requiring human judgment in the use of force, and debates over autonomy in weapons systems are ongoing within both military and international forums. A private company may reasonably determine that its current technology is not sufficiently reliable or controllable for certain battlefield applications. At the same time, the military may conclude that such capabilities are necessary for deterrence and operational effectiveness.

Reasonable people can disagree about where those lines should be drawn.

But that disagreement underscores a deeper point: The boundaries of military AI use should not be settled through ad hoc negotiations between a Cabinet secretary and a CEO. Nor should they be determined by which side can exert greater contractual leverage.

If the U.S. government believes certain AI capabilities are essential to national defense, that position should be articulated openly. It should be debated in Congress, and reflected in doctrine, oversight mechanisms, and statutory frameworks. The rules should be clear—not only to companies, but to the public.

The U.S. often distinguishes itself from authoritarian regimes by emphasizing that power operates within transparent democratic institutions and legal constraints. That distinction carries less weight if AI governance is determined primarily through executive ultimatums issued behind closed doors.

There is also a strategic dimension. If companies conclude that participation in federal markets requires surrendering all deployment conditions, some may exit those markets. Others may respond by weakening or removing model safeguards to remain eligible for government contracts. Neither outcome strengthens U.S. technological leadership.

The DOD is correct that it cannot allow potential “ideological constraints” to undermine lawful military operations. But there is a difference between rejecting arbitrary restrictions and rejecting any role for corporate risk management in shaping deployment conditions. In high-risk domains—from aerospace to cybersecurity—contractors routinely impose safety standards, testing requirements, and operational limitations as part of responsible commercialization. AI should not be treated as uniquely exempt from that practice.

Moreover, built-in safeguards need not be seen as obstacles to military effectiveness. In many high-risk sectors, layered oversight is standard practice: internal controls, technical fail-safes, auditing mechanisms, and legal review operate together. Technical constraints can serve as an additional backstop, reducing the risk of misuse, error, or unintended escalation.

Congress is AWOL

The DOD should retain ultimate authority over lawful use. But it need not reject the possibility that certain guardrails embedded at the design level could complement its own oversight structures rather than undermine them. In some contexts, redundancy in safety systems strengthens, not weakens, operational integrity.

At the same time, a company’s unilateral ethical commitments are no substitute for public policy. When technologies carry national security implications, private governance has inherent limits. Ultimately, decisions about surveillance authorities, autonomous weapons, and rules of engagement belong in democratic institutions.

This episode illustrates a pivotal moment in AI governance. AI systems at the frontier of technology are now powerful enough to influence intelligence analysis, logistics, cyber operations and potentially battlefield decision-making. That makes them too consequential to be governed solely by corporate policy—and too consequential to be governed solely by executive discretion.

The solution is not to empower one side over the other. It is to strengthen the institutions that mediate between them.

Congress should clarify statutory boundaries for military AI use and investigate whether sufficient oversight exists. The DOD should articulate detailed doctrine for human control, auditing and accountability. Civil society and industry should participate in structured consultation processes rather than episodic standoffs and procurement policy should reflect those publicly established standards.

If AI guardrails can be removed through contract pressure, they will be treated as negotiable. However, if they are grounded in law, they can become stable expectations.

Democratic constraints on military AI belong in statute and doctrine—not in private contract negotiations.

This article is adapted by the author with permission from Tech Policy Press. Read the original article.

Move over, Pixar. The ants that animators once morphed into googly-eyed caricatures in films such as A Bug’s Life and Antz just received a meticulously precise anatomical reboot.

Writing today in Nature Methods, an international team of entomologists, accelerator physicists, computer scientists, and biological-imaging specialists describe a new 3D atlas of ant morphology.

Dubbed Antscan, the platform features micrometer-resolution reconstructions that lay bare not only the insects’ armored exoskeletons but also their muscles, nerves, digestive tracts, and needlelike stingers poised at the ready.

Those high-resolution images—spanning 792 species across 212 genera and covering the bulk of described ant diversity—are now available free of charge through an interactive online portal, where anyone can rotate, zoom, and virtually “dissect” the insects from a laptop.

“Antscan is exciting!” says Cameron Currie, an evolutionary biologist at McMaster University in Hamilton, Ont., Canada, who was not involved in the research. “It provides an outstanding resource for comparative work across ants.”

Digital Access to Natural History Collections

It also provides broader access to natural history collections.

No longer must these vast archives of preserved life be confined to drawers and jars in museums scattered around the world, available only to specialists able to visit in person. All these specimens can now be explored digitally by anyone with an internet connection, adding fresh scientific value to museum holdings.

“The more people that access and work with the stuff in our museums, whether it’s physically or digitally, the greater value they add,” says David Blackburn, the curator of herpetology at the Florida Museum of Natural History who, like Currie, was not involved in the research.

Some of those people may be professional myrmecologists (scientists who specialize in the study of ants) and fourmiculture (ant-farming) enthusiasts. But others may be schoolteachers, video-game designers, tattoo artists, or curious members of the public.

“It is an extremely rich dataset that can be used for a number of different applications in science, but also for the arts and outreach and education.” says Julian Katzke, an entomologist at the National Museum of Natural History in Washington, D.C.

Card-carrying members of IEEE should find plenty to explore in Antscan as well, says Evan Economo, a biodiversity scientist at the University of Maryland in College Park who, along with Katzke, co-led the project. With the dataset now publicly available and standardized at scale, “I would really like to see these big libraries of organismal form one day be useful for people in robotics and engineering, so they can mine these data for new kinds of biomechanical designs,” he says.

These renderings reveal different structures within the body of an army ant (Eciton hamatum) subsoldier, based on Antscan data.Katzke et al.

Advancements in Ant Imaging Technology

Researchers have been digitizing natural history collections for years: photographing drawers of pinned specimens, building surface-level models from overlapping image stacks, and using computed tomography (CT) to scan select species one at a time. But those efforts are typically slow, piecemeal, and often limited to external features.

To capture entire organisms, inside and out, Economo and his team—then based at the Okinawa Institute of Science and Technology in Japan and including former lab members Katzke and Francisco Hita Garcia (now at the Museum für Naturkunde in Berlin)—built an automated imaging pipeline that effectively turned a particle accelerator into an anatomical assembly line.

They scoured museum collections around the world for ant specimens—workers, queens, and males alike—and sent some 2,200 preserved samples through a pair of micro-CT beamlines at the Karlsruhe Institute of Technology’s synchrotron light source facility in Germany.

There, biological imaging specialist Thomas van de Kamp oversaw the operation, as intense X-ray beams swept through each specimen and high-speed detectors recorded thousands of projection images from multiple angles. Robotic handlers moved vials of alcohol-preserved ants into position, one after another, all in a matter of days.

Software then reconstructed 200-plus terabytes of data generated into 3D volumes, with neural networks helping to automate the identification and analysis of anatomical structures.

Similar large-scale digitization efforts—such as the openVertebrate Project, led by the Florida Museum of Natural History’s Blackburn, which involved scanning thousands of birds, fish, mammals, reptiles, and amphibians—have begun transforming how biologists study anatomy. But applying conventional micro-CT at comparable scale to insects, which are smaller and harder to scan at useful resolutions, required a leap in speed and throughput.

That’s where the synchrotron came in. By harnessing a particle accelerator to generate extraordinarily bright, coherent X-rays, the team was able to capture high-resolution internal anatomy in seconds, without the lengthy staining or other preprocessing steps often required for soft-tissue contrast in standard lab scanners.

“It is an impressive piece of work,” says Vladimir Blagoderov, principal curator of invertebrates at the National Museums Scotland in Edinburgh, who was not involved in the research. “This project adds an industrial dimension to CT scanning by combining robotics, standardized sampling, automated image-processing pipelines, and machine learning.”

The sheer taxonomic breadth of the Antscan dataset now makes it possible to spot patterns across the entire ant family tree, as Economo and his colleagues have already demonstrated.

In a separate paper published last December, for example, the researchers drew on the newly generated scans to measure how much ants invest in their outer protective casing. Reporting in Science Advances, they showed that species with lighter, less costly cuticles tend to form larger colonies and diversify more rapidly over evolutionary time.

In their latest study, the Antscan team turned to a different evolutionary question: The distribution of a biomineral “armor” layer first described by Currie and his colleagues in 2020 in a Central American leaf-cutter ant. A quick sweep through the Antscan database revealed that this coating—which absorbs X-rays and is visible as a bright sheath over the cuticle—is not an oddity confined to one species.

Instead, it is common among fungus-farming ants, the evolutionary lineage from which leaf-cutting ants arose roughly 20 million years ago, but largely absent in most other branches of the ant tree. (Currie’s team independently confirmed the pattern using X-ray diffraction, a technique that can precisely reveal a material’s mineral composition, as the group reported last month in a preprint posted to bioRxiv.)

Those are only early demonstrations of what the database can do, though. And with AI tools increasingly capable of parsing enormous, information-rich data troves, the real analytical power of Antscan may still lie ahead, says Marek Borowiec, director of the C.P. Gillette Museum of Arthropod Diversity at Colorado State University, who has chronicled the rise of deep learning tools in ecology and evolution.

“The full advantage of this dataset will be realized when these methods are deployed,” he says.

Transforming Morphology with Antscan

The ambitions behind Antscan extend well beyond ant biology. Economo and his colleagues see it as a blueprint for digitizing, standardizing, and scaling anatomy itself.

Just as large-scale sequencing projects and genomic databases transformed the study of DNA over the past two decades, they hope Antscan will catalyze a comparable shift for morphology. “This is kind of like having a genome for shape,” Economo says.

Museum collections house millions of alcohol-preserved insects and other small invertebrates—beetles, flies, wasps, spiders, crustaceans—many of them representing rare or extinct populations. Following the Antscan playbook, each could be converted into a high-resolution library of “digital twins.“

In each case, synchrotron micro-CT would offer a rapid way to peer inside fragile specimens without cutting them open, capturing both hard exoskeleton and soft tissue in exquisite detail across vast swaths of biological diversity.

Antscan/YouTube

Beam time at major synchrotron facilities is scarce and fiercely competitive, a practical bottleneck for any effort to digitize biodiversity at scale, notes National Museums Scotland’s Blagoderov. What’s more, “even once the scans exist, the downstream burden is nontrivial: Moving, storing, and processing hundreds of terabytes of data can become a bottleneck in its own right,” he says.

But if access can be secured and the computational infrastructure scaled to match, such efforts could transform natural history museums from static repositories into dynamic digital biomes.

That transformation may prove especially important at a time of accelerating species loss on Earth. By capturing organisms in extraordinary detail, resources like Antscan create a permanent, high-resolution record of life’s architecture—an anatomical time capsule that can be queried and revisited long after fragile specimens degrade or wild populations vanish.

And should Pixar ever greenlight A Bug’s Life 2 (suggested title: Even Buggier), the studio’s character designers may not need to take much artistic license at all. Thanks to a particle accelerator and a small cadre of dedicated scientists, the reference models are already in hand—rendered not in animation software but in micrometer-perfect anatomical form.

When Ukrainian mathematician Maryna Viazovska received a Fields Medal—widely regarded as the Nobel Prize for mathematics—in July 2022, it was big news. Not only was she the second woman to accept the honor in the award’s 86-year history, but she collected the medal just months after her country had been invaded by Russia. Nearly four years later, Viazovska is making waves again. Today, in a collaboration between humans and AI, Viazovska’s proofs have been formally verified, signaling rapid progress in AI’s abilities to assist with mathematical research.

“These new results seem very, very impressive, and definitely signal some rapid progress in this direction,” says AI-reasoning expert and Princeton University postdoc Liam Fowl, who was not involved in the work.

In her Fields Medal–winning research, Viazovska had tackled two versions of the sphere-packing problem, which asks: How densely can identical circles, spheres, et cetera, be packed in n-dimensional space? In two dimensions, the honeycomb is the best solution. In three dimensions, spheres stacked in a pyramid are optimal. But after that, it becomes exceedingly difficult to find the best solution, and to prove that it is in fact the best.

In 2016, Viazovska solved the problem in two cases. By using powerful mathematical functions known as (quasi-)modular forms, she proved that a symmetric arrangement known as E₈ is the best 8-dimensional packing, and soon after proved with collaborators that another sphere packing called the Leech lattice is best in 24 dimensions. Though seemingly abstract, this result has potential to help solve everyday problems related to dense sphere packing, including error-correcting codes used by smartphones and space probes.

The proofs were verified by the mathematical community and deemed correct, leading to the Fields Medal recognition. But formal verification—the ability of a proof to be verified by a computer—is another beast altogether. Since 2022, much progress has been made in AI-assisted formal proof verification.

Serendipity leads to formalization project

A few years later, a chance meeting in Lausanne, Switzerland, between third-year undergraduate Sidharth Hariharan and Viazovska would reignite her interest in sphere-packing proofs. Though still very early in his career, Hariharan was already becoming adept at formalizing proofs.

“Formal verification of a proof is like a rubber stamp,” Fowl says. “It’s a kind of bona fide certification that you know your statements of reasoning are correct.”

Hariharan told Viazovska how he had been using the process of formalizing proofs to learn and really understand mathematical concepts. In response, Viazovska expressed an interest in formalizing her proofs, largely out of curiosity. From this, in March 2024 the Formalising Sphere Packing in Lean project was born. Lean is a popular programming language and “proof assistant” that allows mathematicians to write proofs that are then verified for absolute correctness by a computer.

A collaboration formed to write a human-readable “blueprint” that could be used to map the 8-dimensional proof’s various constituents and figure out which of them had and had not been formalized and/or proven, and then prove and formalize those missing elements in Lean.

“We had been building the project’s repository for about 15 months when we enabled public access in June 2025,” recalls Hariharan, now a first-year Ph.D. student at Carnegie Mellon University. “Then, in late October we heard from Math, Inc. for the first time.”

The AI speedup

Math, Inc. is a startup developing Gauss, an AI specifically designed to automatically formalize proofs. “It’s a particular kind of language model called a reasoning agent that’s meant to interleave both traditional natural-language reasoning and fully formalized reasoning,” explains Jesse Han, Math, Inc. CEO and cofounder. “So it’s able to conduct literature searches, call up tools, and use a computer to write down Lean code, take notes, spin up verification tooling, run the Lean compiler, et cetera.”

Math, Inc. first hit the headlines when it announced that Gauss had completed a Lean formalization of the strong prime number theorem (PNT) in three weeks last summer, a task that Fields Medalist Terence Tao and Alex Kontorovich had been working on. Similarly, Math, Inc. contacted Hariharan and colleagues to say that Gauss had proven several facts related to their sphere-packing project.

“They told us that they had finished 30 “sorrys,” which meant that they proved 30 intermediate facts that we wanted proved,” explains Hariharan. A proportion of these sorrys were shared with the project team and merged with their own work. “One of them helped us identify a typo in our project, which we then fixed,” adds Hariharan. “So it was a pretty fruitful collaboration.”

From 8 to 24 dimensions

But then, radio silence followed. Math, Inc. appeared to lose interest. However, while Hariharan and colleagues continued their labor of love, Math, Inc. was building a new and improved version of Gauss. “We made a research breakthrough sometime mid-January that produced a much stronger version of Gauss,” says Han. “This new version reproduced our three-week PNT result in two to three days.”

Days later, the new Gauss was steered back to the sphere-packing formalization. Working from the invaluable preexisting blueprint and work that Hariharan and collaborators had shared, Gauss not only autoformalized the 8-dimensional case, but also found and fixed a typo in the published paper, all in the space of five days.

“When they reached out to us in late January saying that they finished it, to put it very mildly, we were very surprised,” says Hariharan. “But at the end of the day, this is technology that we’re very excited about, because it has the capability to do great things and to assist mathematicians in remarkable ways.”

Hariharan was working on sphere-packing proof verification as the sun was setting behind Carnegie Mellon’s Hamerschlag Hall.Sidharth Hariharan

The 8-dimensional sphere-packing proof formalization alone, announced on February 23, represents a watershed moment for autoformalization and AI–human collaboration. But today, Math, Inc. revealed an even more impressive accomplishment: Gauss has autoformalized Viazovska’s 24-dimensional sphere-packing proof—all 200,000+ lines of code of it—in just two weeks.

There are commonalities between the 8- and 24-dimensional cases in terms of the foundational theory and overall architecture of the proof, meaning some of the code from the 8-dimensional case could be refactored and reused. However, Gauss had no preexisting blueprint to work from this time. “And it was actually significantly more involved than the 8-dimensional case, because there was a lot of missing background material that had to be brought on line surrounding many of the properties of the Leech lattice, in particular its uniqueness,” explains Han.

Though the 24-dimensional case was an automated effort, both Han and Hariharan acknowledge the many contributions from humans that laid the foundations for this achievement, regarding it as a collaborative endeavor overall between humans and AI.

But for Han, it represents even more: the beginning of a revolutionary transformation in mathematics, where extremely large-scale formalizations are commonplace. “A programmer used to be someone who punched holes into cards, but then the act of programming became separated from whatever material substrate was used for recording programs,” he concludes. “I think the end result of technology like this will be to free mathematicians to do what they do best, which is to dream of new mathematical worlds.”