What’s next for AI and math

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

The way DARPA tells it, math is stuck in the past. In April, the US Defense Advanced Research Projects Agency kicked off a new initiative called expMath—short for Exponentiating Mathematics—that it hopes will speed up the rate of progress in a field of research that underpins a wide range of crucial real-world applications, from computer science to medicine to national security.

“Math is the source of huge impact, but it’s done more or less as it’s been done for centuries—by people standing at chalkboards,” DARPA program manager Patrick Shafto said in a video introducing the initiative. 

The modern world is built on mathematics. Math lets us model complex systems such as the way air flows around an aircraft, the way financial markets fluctuate, and the way blood flows through the heart. And breakthroughs in advanced mathematics can unlock new technologies such as cryptography, which is essential for private messaging and online banking, and data compression, which lets us shoot images and video across the internet.

But advances in math can be years in the making. DARPA wants to speed things up. The goal for expMath is to encourage mathematicians and artificial-intelligence researchers to develop what DARPA calls an AI coauthor, a tool that might break large, complex math problems into smaller, simpler ones that are easier to grasp and—so the thinking goes—quicker to solve.

Mathematicians have used computers for decades, to speed up calculations or check whether certain mathematical statements are true. The new vision is that AI might help them crack problems that were previously uncrackable.  

But there’s a huge difference between AI that can solve the kinds of problems set in high school—math that the latest generation of models has already mastered—and AI that could (in theory) solve the kinds of problems that professional mathematicians spend careers chipping away at.

On one side are tools that might be able to automate certain tasks that math grads are employed to do; on the other are tools that might be able to push human knowledge beyond its existing limits.

Here are three ways to think about that gulf.

1/ AI needs more than just clever tricks

Large language models are not known to be good at math. They make things up and can be persuaded that 2 + 2 = 5. But newer versions of this tech, especially so-called large reasoning models (LRMs) like OpenAI’s o3 and Anthropic’s Claude 4 Thinking, are far more capable—and that’s got mathematicians excited.

This year, a number of LRMs, which try to solve a problem step by step rather than spit out the first result that comes to them, have achieved high scores on the American Invitational Mathematics Examination (AIME), a test given to the top 5% of US high school math students.

At the same time, a handful of new hybrid models that combine LLMs with some kind of fact-checking system have also made breakthroughs. Emily de Oliveira Santos, a mathematician at the University of São Paulo, Brazil, points to Google DeepMind’s AlphaProof, a system that combines an LLM with DeepMind’s game-playing model AlphaZero, as one key milestone. Last year AlphaProof became the first computer program to match the performance of a silver medallist at the International Math Olympiad, one of the most prestigious mathematics competitions in the world.

And in May, a Google DeepMind model called AlphaEvolve discovered better results than anything humans had yet come up with for more than 50 unsolved mathematics puzzles and several real-world computer science problems.

The uptick in progress is clear. “GPT-4 couldn’t do math much beyond undergraduate level,” says de Oliveira Santos. “I remember testing it at the time of its release with a problem in topology, and it just couldn’t write more than a few lines without getting completely lost.” But when she gave the same problem to OpenAI’s o1, an LRM released in January, it nailed it.

Does this mean such models are all set to become the kind of coauthor DARPA hopes for? Not necessarily, she says: “Math Olympiad problems often involve being able to carry out clever tricks, whereas research problems are much more explorative and often have many, many more moving pieces.” Success at one type of problem-solving may not carry over to another.

Others agree. Martin Bridson, a mathematician at the University of Oxford, thinks the Math Olympiad result is a great achievement. “On the other hand, I don’t find it mind-blowing,” he says. “It’s not a change of paradigm in the sense that ‘Wow, I thought machines would never be able to do that.’ I expected machines to be able to do that.”

That’s because even though the problems in the Math Olympiad—and similar high school or undergraduate tests like AIME—are hard, there’s a pattern to a lot of them. “We have training camps to train high school kids to do them,” says Bridson. “And if you can train a large number of people to do those problems, why shouldn’t you be able to train a machine to do them?”

Sergei Gukov, a mathematician at the California Institute of Technology who coaches Math Olympiad teams, points out that the style of question does not change too much between competitions. New problems are set each year, but they can be solved with the same old tricks.

“Sure, the specific problems didn’t appear before,” says Gukov. “But they’re very close—just a step away from zillions of things you have already seen. You immediately realize, ‘Oh my gosh, there are so many similarities—I’m going to apply the same tactic.’” As hard as competition-level math is, kids and machines alike can be taught how to beat it.

That’s not true for most unsolved math problems. Bridson is president of the Clay Mathematics Institute, a nonprofit US-based research organization best known for setting up the Millenium Prize Problems in 2000—seven of the most important unsolved problems in mathematics, with a $1 million prize to be awarded to the first person to solve each of them. (One problem, the Poincaré conjecture, was solved in 2010; the others, which include P versus NP and the Riemann hypothesis, remain open). “We’re very far away from AI being able to say anything serious about any of those problems,” says Bridson.

And yet it’s hard to know exactly how far away, because many of the existing benchmarks used to evaluate progress are maxed out. The best new models already outperform most humans on tests like AIME.

To get a better idea of what existing systems can and cannot do, a startup called Epoch AI has created a new test called FrontierMath, released in December. Instead of co-opting math tests developed for humans, Epoch AI worked with more than 60 mathematicians around the world to come up with a set of math problems from scratch.

FrontierMath is designed to probe the limits of what today’s AI can do. None of the problems have been seen before and the majority are being kept secret to avoid contaminating training data. Each problem demands hours of work from expert mathematicians to solve—if they can solve it at all: some of the problems require specialist knowledge to tackle.

FrontierMath is set to become an industry standard. It’s not yet as popular as AIME, says de Oliveira Santos, who helped develop some of the problems: “But I expect this to not hold for much longer, since existing benchmarks are very close to being saturated.”

On AIME, the best large language models (Anthropic’s Claude 4, OpenAI’s o3 and o4-mini, Google DeepMind’s Gemini 2.5 Pro, X-AI’s Grok 3) now score around 90%. On FrontierMath, 04-mini scores 19% and Gemini 2.5 Pro scores 13%. That’s still remarkable, but there’s clear room for improvement.     

FrontierMath should give the best sense yet just how fast AI is progressing at math. But there are some problems that are still too hard for computers to take on.

2/ AI needs to manage really vast sequences of steps

Squint hard enough and in some ways math problems start to look the same: to solve them you need to take a sequence of steps from start to finish. The problem is finding those steps. 

“Pretty much every math problem can be formulated as path-finding,” says Gukov. What makes some problems far harder than others is the number of steps on that path. “The difference between the Riemann hypothesis and high school math is that with high school math the paths that we’re looking for are short—10 steps, 20 steps, maybe 40 in the longest case.” The steps are also repeated between problems.

“But to solve the Riemann hypothesis, we don’t have the steps, and what we’re looking for is a path that is extremely long”—maybe a million lines of computer proof, says Gukov.

Finding very long sequences of steps can be thought of as a kind of complex game. It’s what DeepMind’s AlphaZero learned to do when it mastered Go and chess. A game of Go might only involve a few hundred moves. But to win, an AI must find a winning sequence of moves among a vast number of possible sequences. Imagine a number with 100 zeros at the end, says Gukov.

But that’s still tiny compared with the number of possible sequences that could be involved in proving or disproving a very hard math problem: “A proof path with a thousand or a million moves involves a number with a thousand or a million zeros,” says Gukov. 

No AI system can sift through that many possibilities. To address this, Gukov and his colleagues developed a system that shortens the length of a path by combining multiple moves into single supermoves. It’s like having boots that let you take giant strides: instead of taking 2,000 steps to walk a mile, you can now walk it in 20.

The challenge was figuring out which moves to replace with supermoves. In a series of experiments, the researchers came up with a system in which one reinforcement-learning model suggests new moves and a second model checks to see if those moves help.

They used this approach to make a breakthrough in a math problem called the Andrews-Curtis conjecture, a puzzle that has been unsolved for 60 years. It’s a problem that every professional mathematician will know, says Gukov.

(An aside for math stans only: The AC conjecture states that a particular way of describing a type of set called a trivial group can be translated into a different but equivalent description with a certain sequence of steps. Most mathematicians think the AC conjecture is false, but nobody knows how to prove that. Gukov admits himself that it is an intellectual curiosity rather than a practical problem, but an important problem for mathematicians nonetheless.)

Gukov and his colleagues didn’t solve the AC conjecture, but they found that a counterexample (suggesting that the conjecture is false) proposed 40 years ago was itself false. “It’s been a major direction of attack for 40 years,” says Gukov. With the help of AI, they showed that this direction was in fact a dead end.   

“Ruling out possible counterexamples is a worthwhile thing,” says Bridson. “It can close off blind alleys, something you might spend a year of your life exploring.” 

True, Gukov checked off just one piece of one esoteric puzzle. But he thinks the approach will work in any scenario where you need to find a long sequence of unknown moves, and he now plans to try it out on other problems.

“Maybe it will lead to something that will help AI in general,” he says. “Because it’s teaching reinforcement learning models to go beyond their training. To me it’s basically about thinking outside of the box—miles away, megaparsecs away.”  

3/ Can AI ever provide real insight?

Thinking outside the box is exactly what mathematicians need to solve hard problems. Math is often thought to involve robotic, step-by-step procedures. But advanced math is an experimental pursuit, involving trial and error and flashes of insight.

That’s where tools like AlphaEvolve come in. Google DeepMind’s latest model asks an LLM to generate code to solve a particular math problem. A second model then evaluates the proposed solutions, picks the best, and sends them back to the LLM to be improved. After hundreds of rounds of trial and error, AlphaEvolve was able to come up with solutions to a wide range of math problems that were better than anything people had yet come up with. But it can also work as a collaborative tool: at any step, humans can share their own insight with the LLM, prompting it with specific instructions.

This kind of exploration is key to advanced mathematics. “I’m often looking for interesting phenomena and pushing myself in a certain direction,” says Geordie Williamson, a mathematician at the University of Sydney in Australia. “Like: ‘Let me look down this little alley. Oh, I found something!’”

Williamson worked with Meta on an AI tool called PatternBoost, designed to support this kind of exploration. PatternBoost can take a mathematical idea or statement and generate similar ones. “It’s like: ‘Here’s a bunch of interesting things. I don’t know what’s going on, but can you produce more interesting things like that?’” he says.

Such brainstorming is essential work in math. It’s how new ideas get conjured. Take the icosahedron, says Williamson: “It’s a beautiful example of this, which I kind of keep coming back to in my own work.” The icosahedron is a 20-sided 3D object where all the faces are triangles (think of a 20-sided die). The icosahedron is the largest of a family of exactly five such objects: there’s the tetrahedron (four sides), cube (six sides), octahedron (eight sides), and dodecahedron (12 sides).

Remarkably, the fact that there are exactly five of these objects was proved by mathematicians in ancient Greece. “At the time that this theorem was proved, the icosahedron didn’t exist,” says Williamson. “You can’t go to a quarry and find it—someone found it in their mind. And the icosahedron goes on to have a profound effect on mathematics. It’s still influencing us today in very, very profound ways.”

For Williamson, the exciting potential of tools like PatternBoost is that they might help people discover future mathematical objects like the icosahedron that go on to shape the way math is done. But we’re not there yet. “AI can contribute in a meaningful way to research-level problems,” he says. “But we’re certainly not getting inundated with new theorems at this stage.”

Ultimately, it comes down to the fact that machines still lack what you might call intuition or creative thinking. Williamson sums it up like this: We now have AI that can beat humans when it knows the rules of the game. “But it’s one thing for a computer to play Go at a superhuman level and another thing for the computer to invent the game of Go.”

“I think that applies to advanced mathematics,” he says. “Breakthroughs come from a new way of thinking about something, which is akin to finding completely new moves in a game. And I don’t really think we understand where those really brilliant moves in deep mathematics come from.”

Perhaps AI tools like AlphaEvolve and PatternBoost are best thought of as advance scouts for human intuition. They can discover new directions and point out dead ends, saving mathematicians months or years of work. But the true breakthroughs will still come from the minds of people, as has been the case for thousands of years.

For now, at least. “There’s plenty of tech companies that tell us that won’t last long,” says Williamson. “But you know—we’ll see.” 

What’s next for robots

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

Jan Liphardt teaches bioengineering at Stanford, but to many strangers in Los Altos, California, he is a peculiar man they see walking a four-legged robotic dog down the street. 

Liphardt has been experimenting with building and modifying robots for years, and when he brings his “dog” out in public, he generally gets one of three reactions. Young children want to have one, their parents are creeped out, and baby boomers try to ignore it. “They’ll quickly walk by,” he says, “like, ‘What kind of dumb new stuff is going on here?’” 

In the many conversations I’ve had about robots, I’ve also found that most people tend to fall into these three camps, though I don’t see such a neat age division. Some are upbeat and vocally hopeful that a future is just around the corner in which machines can expertly handle much of what is currently done by humans, from cooking to surgery. Others are scared: of job losses, injuries, and whatever problems may come up as we try to live side by side. 

The final camp, which I think is the largest, is just unimpressed. We’ve been sold lots of promises that robots will transform society ever since the first robotic arm was installed on an assembly line at a General Motors plant in New Jersey in 1961. Few of those promises have panned out so far. 

But this year, there’s reason to think that even those staunchly in the “bored” camp will be intrigued by what’s happening in the robot races. Here’s a glimpse at what to keep an eye on. 

Humanoids are put to the test

The race to build humanoid robots is motivated by the idea that the world is set up for the human form, and that automating that form could mean a seismic shift for robotics. It is led by some particularly outspoken and optimistic entrepreneurs, including Brett Adcock, the founder of Figure AI, a company making such robots that’s valued at more than $2.6 billion (it’s begun testing its robots with BMW). Adcock recently told Time, “Eventually, physical labor will be optional.” Elon Musk, whose company Tesla is building a version called Optimus, has said humanoid robots will create “a future where there is no poverty.” A robotics company called Eliza Wakes Up is taking preorders for a $420,000 humanoid called, yes, Eliza.

In June 2024, Agility Robotics sent a fleet of its Digit humanoid robots to GXO Logistics, which moves products for companies ranging from Nike to Nestlé. The humanoids can handle most tasks that involve picking things up and moving them somewhere else, like unloading pallets or putting boxes on a conveyor. 

There have been hiccups: Highly polished concrete floors can cause robots to slip at first, and buildings need good Wi-Fi coverage for the robots to keep functioning. But charging is a bigger issue. Agility’s current version of Digit, with a 39-pound battery, can run for two to four hours before it needs to charge for one hour, so swapping out the robots for fresh ones is a common task on each shift. If there are a small number of charging docks installed, the robots can theoretically charge by shuffling among the docks themselves overnight when some facilities aren’t running, but moving around on their own can set off a building’s security system. “It’s a problem,” says CTO Melonee Wise.

Wise is cautious about whether humanoids will be widely adopted in workplaces. “I’ve always been a pessimist,” she says. That’s because getting robots to work well in a lab is one thing, but integrating them into a bustling warehouse full of people and forklifts moving goods on tight deadlines is another task entirely.

If 2024 was the year of unsettling humanoid product launch videos, this year we will see those humanoids put to the test, and we’ll find out whether they’ll be as productive for paying customers as promised. Now that Agility’s robots have been deployed in fast-paced customer facilities, it’s clear that small problems can really add up. 

Then there are issues with how robots and humans share spaces. In the GXO facility the two work in completely separate areas, Wise says, but there are cases where, for example, a human worker might accidentally leave something obstructing a charging station. That means Agility’s robots can’t return to the dock to charge, so they need to alert a human employee to move the obstruction out of the way, slowing operations down.  

It’s often said that robots don’t call out sick or need health care. But this year, as fleets of humanoids arrive on the job, we’ll begin to find out the limitations they do have.

Learning from imagination

The way we teach robots how to do things is changing rapidly. It used to be necessary to break their tasks down into steps with specifically coded instructions, but now, thanks to AI, those instructions can be gleaned from observation. Just as ChatGPT was taught to write through exposure to trillions of sentences rather than by explicitly learning the rules of grammar, robots are learning through videos and demonstrations. 

That poses a big question: Where do you get all these videos and demonstrations for robots to learn from?

Nvidia, the world’s most valuable company, has long aimed to meet that need with simulated worlds, drawing on its roots in the video-game industry. It creates worlds in which roboticists can expose digital replicas of their robots to new environments to learn. A self-driving car can drive millions of virtual miles, or a factory robot can learn how to navigate in different lighting conditions.

In December, the company went a step further, releasing what it’s calling a “world foundation model.” Called Cosmos, the model has learned from 20 million hours of video—the equivalent of watching YouTube nonstop since Rome was at war with Carthage—that can be used to generate synthetic training data.

Here’s an example of how this model could help in practice. Imagine you run a robotics company that wants to build a humanoid that cleans up hospitals. You can start building this robot’s “brain” with a model from Nvidia, which will give it a basic understanding of physics and how the world works, but then you need to help it figure out the specifics of how hospitals work. You could go out and take videos and images of the insides of hospitals, or pay people to wear sensors and cameras while they go about their work there.

“But those are expensive to create and time consuming, so you can only do a limited number of them,” says Rev Lebaredian, vice president of simulation technologies at Nvidia. Cosmos can instead take a handful of those examples and create a three-dimensional simulation of a hospital. It will then start making changes—different floor colors, different sizes of hospital beds—and create slightly different environments. “You’ll multiply that data that you captured in the real world millions of times,” Lebaredian says. In the process, the model will be fine-tuned to work well in that specific hospital setting. 

It’s sort of like learning both from your experiences in the real world and from your own imagination (stipulating that your imagination is still bound by the rules of physics). 

Teaching robots through AI and simulations isn’t new, but it’s going to become much cheaper and more powerful in the years to come. 

A smarter brain gets a smarter body

Plenty of progress in robotics has to do with improving the way a robot senses and plans what to do—its “brain,” in other words. Those advancements can often happen faster than those that improve a robot’s “body,” which determine how well a robot can move through the physical world, especially in environments that are more chaotic and unpredictable than controlled assembly lines. 

The military has always been keen on changing that and expanding the boundaries of what’s physically possible. The US Navy has been testing machines from a company called Gecko Robotics that can navigate up vertical walls (using magnets) to do things like infrastructure inspections, checking for cracks, flaws, and bad welding on aircraft carriers. 

There are also investments being made for the battlefield. While nimble and affordable drones have reshaped rural battlefields in Ukraine, new efforts are underway to bring those drone capabilities indoors. The defense manufacturer Xtend received an $8.8 million contract from the Pentagon in December 2024 for its drones, which can navigate in confined indoor spaces and urban environments. These so-called “loitering munitions” are one-way attack drones carrying explosives that detonate on impact.

“These systems are designed to overcome challenges like confined spaces, unpredictable layouts, and GPS-denied zones,” says Rubi Liani, cofounder and CTO at Xtend. Deliveries to the Pentagon should begin in the first few months of this year. 

Another initiative—sparked in part by the Replicator project, the Pentagon’s plan to spend more than $1 billion on small unmanned vehicles—aims to develop more autonomously controlled submarines and surface vehicles. This is particularly of interest as the Department of Defense focuses increasingly on the possibility of a future conflict in the Pacific between China and Taiwan. In such a conflict, the drones that have dominated the war in Ukraine would serve little use because battles would be waged almost entirely at sea, where small aerial drones would be limited by their range. Instead, undersea drones would play a larger role.

All these changes, taken together, point toward a future where robots are more flexible in how they learn, where they work, and how they move. 

Jan Liphardt from Stanford thinks the next frontier of this transformation will hinge on the ability to instruct robots through speech. Large language models’ ability to understand and generate text has already made them a sort of translator between Liphardt and his robot.

“We can take one of our quadrupeds and we can tell it, ‘Hey, you’re a dog,’ and the thing wants to sniff you and tries to bark,” he says. “Then we do one word change—‘You’re a cat.’ Then the thing meows and, you know, runs away from dogs. And we haven’t changed a single line of code.”

Correction: A previous version of this story incorrectly stated that the robotics company Eliza Wakes Up has ties to a16z.

What’s next for AI in 2025

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

For the last couple of years we’ve had a go at predicting what’s coming next in AI. A fool’s game given how fast this industry moves. But we’re on a roll, and we’re doing it again.

How did we score last time round? Our four hot trends to watch out for in 2024 included what we called customized chatbots—interactive helper apps powered by multimodal large language models (check: we didn’t know it yet, but we were talking about what everyone now calls agents, the hottest thing in AI right now); generative video (check: few technologies have improved so fast in the last 12 months, with OpenAI and Google DeepMind releasing their flagship video generation models, Sora and Veo, within a week of each other this December); and more general-purpose robots that can do a wider range of tasks (check: the payoffs from large language models continue to trickle down to other parts of the tech industry, and robotics is top of the list). 

We also said that AI-generated election disinformation would be everywhere, but here—happily—we got it wrong. There were many things to wring our hands over this year, but political deepfakes were thin on the ground. 

So what’s coming in 2025? We’re going to ignore the obvious here: You can bet that agents and smaller, more efficient, language models will continue to shape the industry. Instead, here are five alternative picks from our AI team.

1. Generative virtual playgrounds 

If 2023 was the year of generative images and 2024 was the year of generative video—what comes next? If you guessed generative virtual worlds (a.k.a. video games), high fives all round.

We got a tiny glimpse of this technology in February, when Google DeepMind revealed a generative model called Genie that could take a still image and turn it into a side-scrolling 2D platform game that players could interact with. In December, the firm revealed Genie 2, a model that can spin a starter image into an entire virtual world.

Other companies are building similar tech. In October, the AI startups Decart and Etched revealed an unofficial Minecraft hack in which every frame of the game gets generated on the fly as you play. And World Labs, a startup cofounded by Fei-Fei Li—creator of ImageNet, the vast data set of photos that kick-started the deep-learning boom—is building what it calls large world models, or LWMs.

One obvious application is video games. There’s a playful tone to these early experiments, and generative 3D simulations could be used to explore design concepts for new games, turning a sketch into a playable environment on the fly. This could lead to entirely new types of games. 

But they could also be used to train robots. World Labs wants to develop so-called spatial intelligence—the ability for machines to interpret and interact with the everyday world. But robotics researchers lack good data about real-world scenarios with which to train such technology. Spinning up countless virtual worlds and dropping virtual robots into them to learn by trial and error could help make up for that.   

—Will Douglas Heaven

2. Large language models that “reason”

The buzz was justified. When OpenAI revealed o1 in September, it introduced a new paradigm in how large language models work. Two months later, the firm pushed that paradigm forward in almost every way with o3—a model that just might reshape this technology for good.

Most models, including OpenAI’s flagship GPT-4, spit out the first response they come up with. Sometimes it’s correct; sometimes it’s not. But the firm’s new models are trained to work through their answers step by step, breaking down tricky problems into a series of simpler ones. When one approach isn’t working, they try another. This technique, known as “reasoning” (yes—we know exactly how loaded that term is), can make this technology more accurate, especially for math, physics, and logic problems.

Then it stalled; it couldn’t figure out what type of flour to pick. Goel watched as Mariner explained its steps in a chat window: “It says, ‘I will use the browser’s Back button to return to the recipe.’”

It was a remarkable moment. Instead of hitting a wall, the agent had broken the task down into separate actions and picked one that might resolve the problem. Figuring out you need to click the Back button may sound basic, but for a mindless bot it’s akin to rocket science. And it worked: Mariner went back to the recipe, confirmed the type of flour, and carried on filling Goel’s basket.

Google DeepMind is also building an experimental version of Gemini 2.0, its latest large language model, that uses this step-by-step approach to problem solving, called Gemini 2.0 Flash Thinking.

3. It’s boom time for AI in science 

One of the most exciting uses for AI is speeding up discovery in the natural sciences. Perhaps the greatest vindication of AI’s potential on this front came last October, when the Royal Swedish Academy of Sciences awarded the Nobel Prize for chemistry to Demis Hassabis and John M. Jumper from Google DeepMind for building the AlphaFold tool, which can solve protein folding, and to David Baker for building tools to help design new proteins.

Expect this trend to continue next year, and to see more data sets and models that are aimed specifically at scientific discovery. Proteins were the perfect target for AI, because the field had excellent existing data sets that AI models could be trained on. 

The hunt is on to find the next big thing. One potential area is materials science. Meta has released massive data sets and models that could help scientists use AI to discover new materials much faster, and in December, Hugging Face, together with the startup Entalpic, launched LeMaterial, an open-source project that aims to simplify and accelerate materials research. Their first project is a data set that unifies, cleans, and standardizes the most prominent material data sets. 

AI model makers are also keen to pitch their generative products as research tools for scientists. OpenAI let scientists test its latest o1 model and see how it might support them in research. The results were encouraging. 

4. AI companies get cozier with national security

There is a lot of money to be made by AI companies willing to lend their tools to border surveillance, intelligence gathering, and other national security tasks. 

The US military has launched a number of initiatives that show it’s eager to adopt AI, from the Replicator program—which, inspired by the war in Ukraine, promises to spend $1 billion on small drones—to the Artificial Intelligence Rapid Capabilities Cell, a unit bringing AI into everything from battlefield decision-making to logistics. European militaries are under pressure to up their tech investment, triggered by concerns that Donald Trump’s administration will cut spending to Ukraine. Rising tensions between Taiwan and China weigh heavily on the minds of military planners, too. 

In 2025, these trends will continue to be a boon for defense-tech companies like Palantir, Anduril, and others, which are now capitalizing on classified military data to train AI models. 

The defense industry’s deep pockets will tempt mainstream AI companies into the fold too. OpenAI in December announced it is partnering with Anduril on a program to take down drones, completing a year-long pivot away from its policy of not working with the military. It joins the ranks of Microsoft, Amazon, and Google, which have worked with the Pentagon for years. 

Other AI competitors, which are spending billions to train and develop new models, will face more pressure in 2025 to think seriously about revenue. It’s possible that they’ll find enough non-defense customers who will pay handsomely for AI agents that can handle complex tasks, or creative industries willing to spend on image and video generators. 

5. Nvidia sees legitimate competition

For much of the current AI boom, if you were a tech startup looking to try your hand at making an AI model, Jensen Huang was your man. As CEO of Nvidia, the world’s most valuable corporation, Huang helped the company become the undisputed leader of chips used both to train AI models and to ping a model when anyone uses it, called “inferencing.”

A number of forces could change that in 2025. For one, behemoth competitors like Amazon, Broadcom, AMD, and others have been investing heavily in new chips, and there are early indications that these could compete closely with Nvidia’s—particularly for inference, where Nvidia’s lead is less solid. 

A growing number of startups are also attacking Nvidia from a different angle. Rather than trying to marginally improve on Nvidia’s designs, startups like Groq are making riskier bets on entirely new chip architectures that, with enough time, promise to provide more efficient or effective training. In 2025 these experiments will still be in their early stages, but it’s possible that a standout competitor will change the assumption that top AI models rely exclusively on Nvidia chips.

Underpinning this competition, the geopolitical chip war will continue. That war thus far has relied on two strategies. On one hand, the West seeks to limit exports to China of top chips and the technologies to make them. On the other, efforts like the US CHIPS Act aim to boost domestic production of semiconductors.

Donald Trump may escalate those export controls and has promised massive tariffs on any goods imported from China. In 2025, such tariffs would put Taiwan—on which the US relies heavily because of the chip manufacturer TSMC—at the center of the trade wars. That’s because Taiwan has said it will help Chinese firms relocate to the island to help them avoid the proposed tariffs. That could draw further criticism from Trump, who has expressed frustration with US spending to defend Taiwan from China. 

It’s unclear how these forces will play out, but it will only further incentivize chipmakers to reduce reliance on Taiwan, which is the entire purpose of the CHIPS Act. As spending from the bill begins to circulate, next year could bring the first evidence of whether it’s materially boosting domestic chip production. 

—James O’Donnell

What’s next for drones

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

Drones have been a mainstay technology among militaries, hobbyists, and first responders alike for more than a decade, and in that time the range available has skyrocketed. No longer limited to small quadcopters with insufficient battery life, drones are aiding search and rescue efforts, reshaping wars in Ukraine and Gaza, and delivering time-sensitive packages of medical supplies. And billions of dollars are being plowed into building the next generation of fully autonomous systems. 

These developments raise a number of questions: Are drones safe enough to be flown in dense neighborhoods and cities? Is it a violation of people’s privacy for police to fly drones overhead at an event or protest? Who decides what level of drone autonomy is acceptable in a war zone?

Those questions are no longer hypothetical. Advancements in drone technology and sensors, falling prices, and easing regulations are making drones cheaper, faster, and more capable than ever. Here’s a look at four of the biggest changes coming to drone technology in the near future.

Police drone fleets

Today more than 1,500 US police departments have drone programs, according to tracking conducted by the Atlas of Surveillance. Trained police pilots use drones for search and rescue operations, monitoring events and crowds, and other purposes. The Scottsdale Police Department in Arizona, for example, successfully used a drone to locate a lost elderly man with dementia, says Rich Slavin, Scottsdale’s assistant chief of police. He says the department has had useful but limited experiences with drones to date, but its pilots have often been hamstrung by the “line of sight” rule from the Federal Aviation Administration (FAA). The rule stipulates that pilots must be able to see their drones at all times, which severely limits the drone’s range.

Soon, that will change. On a rooftop somewhere in the city, Scottsdale police will in the coming months install a new police drone capable of autonomous takeoff, flight, and landing. Slavin says the department is seeking a waiver from the FAA to be able to fly its drone past the line of sight. (Hundreds of police agencies have received a waiver from the FAA since the first was granted in 2019.) The drone, which can fly up to 57 miles per hour, will go on missions as far as three miles from its docking station, and the department says it will be used for things like tracking suspects or providing a visual feed of an officer at a traffic stop who is waiting for backup. 

“The FAA has been much more progressive in how we’re moving into this space,” Slavin says. That could mean that around the country, the sight (and sound) of a police drone soaring overhead will become much more common. 

The Scottsdale department says the drone, which it is purchasing from Aerodome, will kick off its drone-as-first-responder program and will play a role in the department’s new “real-time crime center.” These sorts of centers are becoming increasingly common in US policing, and allow cities to connect cameras, license plate readers, drones, and other monitoring methods to track situations on the fly. The rise of the centers, and their associated reliance on drones, has drawn criticism from privacy advocates who say they conduct a great deal of surveillance with little transparency about how footage from drones and other sources will be used or shared. 

In 2019, the police department in Chula Vista, California, was the first to receive a waiver from the FAA to fly beyond line of sight. The program sparked criticism from members of the community who alleged the department was not transparent about the footage it collected or how it would be used. 

Jay Stanley, a senior policy analyst at the American Civil Liberties Union’s Speech, Privacy, and Technology Project, says the waivers exacerbate existing privacy issues related to drones. If the FAA continues to grant them, police departments will be able to cover far more of a city with drones than ever, all while the legal landscape is murky about whether this would constitute an invasion of privacy. 

“If there’s an accumulation of different uses of this technology, we’re going to end up in a world where from the moment you step out of your front door, you’re going to feel as though you’re under the constant eye of law enforcement from the sky,” he says. “It may have some real benefits, but it is also in dire need of strong checks and balances.”

Scottsdale police say the drone could be used in a variety of scenarios, such as responding to a burglary in progress or tracking a driver with suspected connection to a kidnapping. But the real benefit, Slavin says, will come from pairing it with other existing technologies, like automatic license plate readers and hundreds of cameras placed around the city. “It can get to places very, very quickly,” he says. “It gives us real-time intelligence and helps us respond faster and smarter.”

While police departments might indeed benefit from drones in those situations, Stanley says the ACLU has found that many deploy them for far more ordinary cases, like reports of a kid throwing a ball against a garage or of “suspicious persons” in an area.

“It raises the question about whether these programs will just end up being another way in which vulnerable communities are over-policed and nickeled and dimed by law enforcement agencies coming down on people for all kinds of minor transgressions,” he says.

Drone deliveries, again

Perhaps no drone technology is more overhyped than home deliveries. For years, tech companies have teased futuristic renderings of a drone dropping off a package on your doorstep just hours after you ordered it. But they’ve never managed to expand them much beyond small-scale pilot projects, at least in the US, again largely due to the FAA’s line of sight rules. 

But this year, regulatory changes are coming. Like police departments, Amazon’s Prime Air program was previously limited to flying its drones within the pilot’s line of sight. That’s because drone pilots don’t have radar, air traffic controllers, or any of the other systems commercial flight relies on to monitor airways and keep them safe. To compensate, Amazon spent years developing an onboard system that would allow its drones to detect nearby objects and avoid collisions. The company says it showed the FAA in demonstrations that its drones could fly safely in the same airspace as helicopters, planes, and hot air balloons. 

In May, Amazon announced the FAA had granted the company a waiver and permission to expand operations in Texas, more than a decade after the Prime Air project started. And in July, the FAA cleared one more roadblock by allowing two companies—Zipline as well as Google’s Wing Aviation—to fly in the same airspace simultaneously without the need for visual observers. 

While all this means your chances of receiving a package via drone have ticked up ever so slightly, the more compelling use case might be medical deliveries. Shakiba Enayati, an assistant professor of supply chains at the University of Missouri–St. Louis, has spent years researching how drones could conduct last-mile deliveries of vaccines, antivenom, organs, and blood in remote places. She says her studies have found drones to be game changers for getting medical supplies to underserved populations, and if the FAA extends these regulatory changes, it could have a real impact. 

That’s especially true in the steps leading up to an organ transplant, she says. Before an organ can be transmitted to a recipient, a number of blood tests must be sent back-and-forth to make sure the recipient can accept it, which takes a time if the blood is being transferred by car or even helicopter. “In these cases, the clock is ticking,” Enayati says. If drones were allowed to be used in this step at scale, it would be a significant improvement.

“If the technology is supporting the needs of organ delivery, it’s going to make a big change in such an important arena,” she says.

That development could come sooner than using drones for delivery of the actual organs, which have to be transported under very tightly controlled conditions to preserve them.

Domesticating the drone supply chain

Signed into law last December, the American Security Drone Act bars federal agencies from buying drones from countries thought to pose a threat to US national security, such as Russia and China. That’s significant. China is the undisputed leader when it comes to manufacturing drones and drone parts, with over 90% of law enforcement drones in the US made by Shenzhen-based DJI, and many drones used by both sides of the war in Ukraine are made by Chinese companies. 

The American Security Drone Act is part of an effort to curb that reliance on China. (Meanwhile, China is stepping up export restrictions on drones with military uses.) As part of the act, the US Department of Defense’s Defense Innovation Unit has created the Blue UAS Cleared List, a list of drones and parts the agency has investigated and approved for purchase. The list applies to federal agencies as well as programs that receive federal funding, which often means state police departments or other non-federal agencies. 

Since the US is set to spend such significant sums on drones—with $1 billion earmarked for the Department of Defense’s Replicator initiative alone—getting on the Blue List is a big deal. It means those federal agencies can make large purchases with little red tape. 

Allan Evans, CEO of US-based drone part maker Unusual Machine, says the list has sparked a significant rush of drone companies attempting to conform to the US standards. His company manufactures a first-person view flight controller that he hopes will become the first of its kind to be approved for the Blue List.

The American Security Drone Act is unlikely to affect private purchases in the US of drones used by videographers, drone racers, or hobbyists, which will overwhelmingly still be made by China-based companies like DJI. That means any US-based drone companies, at least in the short term, will only survive by catering to the US defense market.  

“Basically any US company that isn’t willing to have ancillary involvement in defense work will lose,” Evans says. 

The coming months will show the law’s true impact: Because the US fiscal year ends in September, Evans says he expects to see a host of agencies spending their use-it-or-lose-it funding on US-made drones and drone components in the next month. “That will indicate whether the marketplace is real or not, and how much money is actually being put toward it,” he says.

Autonomous weapons in Ukraine

The drone war in Ukraine has largely been one of attrition. Drones have been used extensively for surveying damage, finding and tracking targets, or dropping weapons since the war began, but on average these quadcopter drones last just three flights before being shot down or rendered unnavigable by GPS jamming. As a result, both Ukraine and Russia prioritized accumulating high volumes of drones with the expectation that they wouldn’t last long in battle. 

Now they’re having to rethink that approach, according to Andriy Dovbenko, founder of the UK-Ukraine Tech Exchange, a nonprofit that helps startups involved in Ukraine’s war effort and eventual reconstruction raise capital. While working with drone makers in Ukraine, he says, he has seen the demand for technology shift from big shipments of simple commercial drones to a pressing need for drones that can navigate autonomously in an environment where GPS has been jammed. With 70% of the front lines suffering from jamming, according to Dovbenko, both Russian and Ukrainian drone investment is now focused on autonomous systems. 

That’s no small feat. Drone pilots usually rely on video feeds from the drone as well as GPS technology, neither of which is available in a jammed environment. Instead, autonomous drones operate with various types of sensors like LiDAR to navigate, though this can be tricky in fog or other inclement weather. Autonomous drones are a new and rapidly changing technology, still being tested by US-based companies like Shield AI. The evolving war in Ukraine is raising the stakes and the pressure to deploy affordable and reliable autonomous drones.  

The transition toward autonomous weapons also raises serious yet largely unanswered questions about how much humans should be taken out of the loop in decision-making. As the war rages on and the need for more capable weaponry rises, Ukraine will likely be the testing ground for if and how the moral line is drawn. But Dovbenko says stopping to find that line during an ongoing war is impossible. 

“There is a moral question about how much autonomy you can give to the killing machine,” Dovbenko says. “This question is not being asked right now in Ukraine because it’s more of a matter of survival.”