The US Department of Defense has invested $2.4 million over two years in deepfake detection technology from a startup called Hive AI. It’s the first contract of its kind for the DOD’s Defense Innovation Unit, which accelerates the adoption of new technologies for the US defense sector. Hive AI’s models are capable of detecting AI-generated video, image, and audio content.
Although deepfakes have been around for the better part of a decade, generative AI has made them easier to create and more realistic-looking than ever before, which makes them ripe for abuse in disinformation campaigns or fraud. Defending against these sorts of threats is now crucial for national security, says Captain Anthony Bustamante, a project manager and cyberwarfare operator for the Defense Innovation Unit.
“This work represents a significant step forward in strengthening our information advantage as we combat sophisticated disinformation campaigns and synthetic-media threats,” says Bustamante. Hive was chosen out of a pool of 36 companies to test its deepfake detection and attribution technology with the DOD. The contract could enable the department to detect and counter AI deception at scale.
Defending against deepfakes is “existential,” says Kevin Guo, Hive AI’s CEO. “This is the evolution of cyberwarfare.”
Hive’s technology has been trained on a large amount of content, some AI-generated and some not. It picks up on signals and patterns in AI-generated content that are invisible to the human eye but can be detected by an AI model.
“Turns out that every image generated by one of these generators has that sort of pattern in there if you know where to look for it,” says Guo. The Hive team constantly keeps track of new models and updates its technology accordingly.
The tools and methodologies developed through this initiative have the potential to be adapted for broader use, not only addressing defense-specific challenges but also safeguarding civilian institutions against disinformation, fraud, and deception, the DOD said in a statement.
Hive’s technology provides state-of-the-art performance in detecting AI-generated content, says Siwei Lyu, a professor of computer science and engineering at the University at Buffalo. He was not involved in Hive’s work but has tested its detection tools.
Ben Zhao, a professor at the University of Chicago, who has also independently evaluated Hive AI’s deepfake technology, agrees but points out that it is far from foolproof.
“Hive is certainly better than most of the commercial entities and some of the research techniques that we tried, but we also showed that it is not at all hard to circumvent,” Zhao says. The team found that adversaries could tamper with images in a way that bypassed Hive’s detection.
And given the rapid development of generative AI technologies, it is not yet certain how it will fare in real-world scenarios that the defense sector might face, Lyu adds.
Guo says Hive is making its models available to the DOD so that the department can use the tools offline and on their own devices. This keeps sensitive information from leaking.
But when it comes to protecting national security against sophisticated state actors, off-the-shelf products are not enough, says Zhao: “There’s very little that they can do to make themselves completely robust to unforeseen nation-state-level attacks.”
At first glance, the Mosphera scooter may look normal—just comically oversized. It’s like the monster truck of scooters, with a footplate seven inches off the ground that’s wide enough to stand on with your feet slightly apart—which you have to do to keep your balance, because when you flip the accelerator with a thumb, it takes off like a rocket. While the version I tried in a parking lot in Riga’s warehouse district had a limiter on the motor, the production version of the supersized electric scooter can hit 100 kilometers (62 miles) per hour on the flat. The all-terrain vehicle can also go 300 kilometers on a single charge and climb 45-degree inclines.
Latvian startup Global Wolf Motors launched in 2020 with a hope that the Mosphera would fill a niche in micromobility. Like commuters who use scooters in urban environments, farmers and vintners could use the Mosphera to zip around their properties; miners and utility workers could use it for maintenance and security patrols; police and border guards could drive them on forest paths. And, they thought, maybe the military might want a few to traverse its bases or even the battlefield—though they knew that was something of a long shot.
When co-founders Henrijs Bukavs and Klavs Asmanis first went to talk to Latvia’s armed forces, they were indeed met with skepticism—a military scooter, officials implied, didn’t make much sense—and a wall of bureaucracy. They found that no matter how good your pitch or how glossy your promo video (and Global Wolf’s promo is glossy: a slick montage of scooters jumping, climbing, and speeding in formation through woodlands and deserts), getting into military supply chains meant navigating layer upon layer of officialdom.
Then Russia launched its full-scale invasion of Ukraine in February 2022, and everything changed. In the desperate early days of the war, Ukrainian combat units wanted any equipment they could get their hands on, and they were willing to try out ideas—like a military scooter—that might not have made the cut in peacetime. Asmanis knew a Latvian journalist heading to Ukraine; through the reporter’s contacts, the startup arranged to ship two Mospheras to the Ukrainian army.
Within weeks, the scooters were at the front line—and even behind it, being used by Ukrainian special forces scouts on daring reconnaissance missions. It was an unexpected but momentous step for Global Wolf, and an early indicator of a new demand that’s sweeping across tech companies along Ukraine’s borders: for civilian products that can be adapted quickly for military use.
COURTESY OF GLOBAL WOLF
Global Wolf’s high-definition marketing materials turned out to be nowhere near as effective as a few minutes of grainy phone footage from the war. The company has since shipped out nine more scooters to the Ukrainian army, which has asked for another 68. Where Latvian officials once scoffed, the country’s prime minister went to see Mosphera’s factory in April 2024, and now dignitaries and defense officials from the country are regular visitors.
It might have been hard a few years ago to imagine soldiers heading to battle on oversized toys made by a tech startup with no military heritage. But Ukraine’s resistance to Russia’s attacks has been a miracle of social resilience and innovation—and the way the country has mobilized is serving both a warning and an inspiration to its neighbors. They’ve watched as startups, major industrial players, and political leaders in Ukraine have worked en masse to turn civilian technology into weapons and civil defense systems. They’ve seen Ukrainian entrepreneurs help bootstrap a military-industrial complex that is retrofitting civilian drones into artillery spotters and bombers, while software engineers become cyberwarriors and AI companies shift to battlefield intelligence. Engineers work directly with friends and family on the front line, iterating their products with incredible speed.
Their successes—often at a fraction of the cost of conventional weapons systems—have in turn awakened European governments and militaries to the potential of startup-style innovation and startups to the potential dual uses of their products, meaning ones that have legitimate civilian applications but can be modified at scale to turn them into weapons.
This heady mix of market demand and existential threat is pulling tech companies in Latvia and the other Baltic states into a significant pivot. Companies that can find military uses for their products are hardening them and discovering ways to get them in front of militaries that are increasingly willing to entertain the idea of working with startups. It’s a turn that may only become more urgent if the US under incoming President Donald Trump becomes less willing to underwrite the continent’s defense.
But while national governments, the European Union, and NATO are all throwing billions of dollars of public money into incubators and investment funds—followed closely by private-sector investors—some entrepreneurs and policy experts who have worked closely with Ukraine warn that Europe might have only partially learned the lessons from Ukraine’s resistance.
If Europe wants to be ready to meet the threat of attack, it needs to find new ways of working with the tech sector. That includes learning how Ukraine’s government and civil society adapted to turn civilian products into dual-use tools quickly and cut through bureaucracy to get innovative solutions to the front. Ukraine’s resilience shows that military technology isn’t just about what militaries buy but about how they buy it, and about how politics, civil society, and the tech sector can work together in a crisis.
“[Ukraine], unfortunately, is the best defense technology experimentation ground in the world right now. If you are not in Ukraine, then you are not in the defense business.”
“I think that a lot of tech companies in Europe would do what is needed to do. They would put their knowledge and skills where they’re needed,” says Ieva Ilves, a veteran Latvian diplomat and technology policy expert. But many governments across the continent are still too slow, too bureaucratic, and too worried that they might appear to be wasting money, meaning, she says, that they are not necessarily “preparing the soil for if [a] crisis comes.”
“The question is,” she says, “on a political level, are we capable of learning from Ukraine?”
Waking up the neighbors
Many Latvians and others across the Baltic nations feel the threat of Russian aggression more viscerally than their neighbors in Western Europe. Like Ukraine, Latvia has a long border with Russia and Belarus, a large Russian-speaking minority, and a history of occupation. Also like Ukraine, it has been the target of more than a decade of so-called “hybrid war” tactics—cyberattacks, disinformation campaigns, and other attempts at destabilization—directed by Moscow.
Since Russian tanks crossed into Ukraine two-plus years ago, Latvia has stepped up its preparations for a physical confrontation, investing more than €300 million ($316 million) in fortifications along the Russian border and reinstating a limited form of conscription to boost its reserve forces. Since the start of this year, the Latvian fire service has been inspecting underground structures around the country, looking for cellars, parking garages, and metro stations that could be turned into bomb shelters.
And much like Ukraine,Latvia doesn’t have a huge military-industrial complex that can churn out artillery shells or tanks en masse.
What it and other smaller European countries can produce for themselves—and potentially sell to their allies—are small-scale weapons systems, software platforms, telecoms equipment, and specialized vehicles. The country is now making a significant investment in tools like Exonicus, a medical technology platform founded 11 years ago by Latvian sculptor Sandis Kondrats. Users of its augmented-reality battlefield-medicine training simulator put on a virtual reality headset that presents them with casualties, which they have to diagnose and figure out how to treat. The all-digital training saves money on mannequins, Kondrats says, and on critical field resources.
“If you use all the medical supplies on training, then you don’t have any medical supplies,” he says. Exonicus has recently broken into the military supply chain, striking deals with the Latvian, Estonian, US, and German militaries, and it has been training Ukrainian combat medics.
Medical technology company Exonicus has created an augmented-reality battlefield-medicine training simulator that presents users with casualties, which they have to diagnose and figure out how to treat.
GATIS ORLICKIS/BALTIC PICTURES
There’s also VR Cars, a company founded by two Latvian former rally drivers, that signed a contract in 2022 to develop off-road vehicles for the army’s special forces. And there is Entangle, a quantum encryption company that sells widgets that turn mobile phones into secure communications devices, and has recently received an innovation grant from the Latvian Ministry of Defense.
Unsurprisingly, a lot of the focus in Latvia has been on unmanned aerial vehicles (UAVs), or drones, which have become ubiquitous on both sides fighting in Ukraine, often outperforming weapons systems that cost an order of magnitude more. In the early days of the war, Ukraine found itself largely relying on machines bought from abroad, such as the Turkish-made Bayraktar strike aircraft and jury-rigged DJI quadcopters from China. It took a while, but within a year the country was able to produce home-grown systems.
As a result, a lot of the emphasis in defense programs across Europe is on UAVs that can be built in-country. “The biggest thing when you talk to [European ministries of defense] now is that they say, ‘We want a big amount of drones, but we also want our own domestic production,’” says Ivan Tolchinsky, CEO of Atlas Dynamics, a drone company headquartered in Riga. Atlas Dynamics builds drones for industrial uses and has now made hardened versions of its surveillance UAVs that can resist electronic warfare and operate in battlefield conditions.
Agris Kipurs founded AirDog in 2014 to make drones that could track a subject autonomously; they were designed for people doing outdoor sports who wanted to film themselves without needing to fiddle with a controller. He and his co-founders sold the company to a US home security company, Alarm.com, in 2020. “For a while, we did not know exactly what we would build next,” Kipurs says. “But then, with the full-scale invasion of Ukraine, it became rather obvious.”
His new company, Origin Robotics, has recently “come out of stealth mode,” he says, after two years of research and development. Origin has built on the team’s experience in consumer drones and its expertise in autonomous flight to begin to build what Kipurs calls “an airborne precision-guided weapon system”—a guided bomb that a soldier can carry in a backpack.
The Latvian government has invested in encouraging startups like these, as well as small manufacturers, to develop military-capable UAVs by establishing a €600,000 prize fund for domestic drone startups and a €10 million budget to create a new drone program, working with local and international manufacturers.
VR Cars was founded by two Latvian former rally drivers and has developed off-road vehicles for the army’s special forces.
Latvia is also the architect and co-leader, with the UK, of the Drone Coalition, a multicountry initiative that’s directing more than €500 million toward building a drone supply chain in the West. Under the initiative, militaries run competitions for drone makers, rewarding high performers with contracts and sending their products to Ukraine. Its grantees are often not allowed to publicize their contracts, for security reasons. “But the companies which are delivering products through that initiative are new to the market,” Kipurs says. “They are not the companies that were there five years ago.”
Even national telecommunications company LMT, which is partly government owned, is working on drones and other military-grade hardware, including sensor equipment and surveillance balloons. It’s developing a battlefield “internet of things” system—essentially, a system that can track in real time all the assets and personnel in a theater of war. “In Latvia, more or less, we are getting ready for war,” says former naval officer Kaspars Pollaks, who heads an LMT division that focuses on defense innovation. “We are just taking the threat really seriously. Because we will be operationally alone [if Russia invades].”
The Latvian government’s investments are being mirrored across Europe: NATO has expanded its Defence Innovation Accelerator for the North Atlantic (DIANA) program, which runs startup incubators for dual-use technologies across the continent and the US, and launched a separate €1 billion startup fund in 2022. Adding to this, the European Investment Fund, a publicly owned investment company, launched a €175 million fund-of-funds this year to support defense technologies with dual-use potential. And the European Commission has earmarked more than €7 billion for defense research and development between now and 2027.
Private investors are also circling, looking for opportunities to profit from the boom. Figures from the European consultancy Dealroom show that fundraising by dual-use and military-tech companies on the continent was just shy of $1 billion in 2023—up nearly a third over 2022, despite an overall slowdown in venture capital activity.
Atlas Dynamics builds drones for industrial uses and now makes hardened versions that can resist electronic warfare and operate in battlefield conditions.
ATLAS AERO
When Atlas Dynamics started in 2015, funding was hard to come by, Tolchinsky says: “It’s always hard to make it as a hardware company, because VCs are more interested in software. And if you start talking about the defense market, people say, ‘Okay, it’s a long play for 10 or 20 years, it’s not interesting.’” That’s changed since 2022. “Now, what we see because of this war is more and more venture capital that wants to invest in defense companies,” Tolchinsky says.
But while money is helping startups get off the ground, to really prove the value of their products they need to get their tools in the hands of people who are going to use them. When I asked Kipurs if his products are currently being used in Ukraine, he only said: “I’m not allowed to answer that question directly. But our systems are with end users.”
Battle tested
Ukraine has moved on from the early days of the conflict, when it was willing to take almost anything that could be thrown at the invaders. But that experience has been critical in pushing the government to streamline its procurement processes dramatically to allow its soldiers to try out new defense-tech innovations.
Origin Robotics has built on a history of producing consumer drones to create a guided bomb that a soldier can carry in a backpack.
Technology that doesn’t work at the front puts soldiers at risk, so in many cases they have taken matters into their own hands. Two Ukrainian drone makers tell me that military procurement in the country has been effectively flipped on its head: If you want to sell your gear to the armed forces, you don’t go to the general staff—you go directly to the soldiers and put it in their hands. Once soldiers start asking their senior officers for your tool, you can go back to the bureaucrats and make a deal.
Many foreign companies have simply donated their products to Ukraine—partly out of a desire to help, and partly because they’ve identified a (potentially profitable) opportunity to expose them to the shortened innovation cycles of conflict and to get live feedback from those fighting. This can be surprisingly easy as some volunteer units handle their own parallel supply chains through crowdfunding and donations, and they are eager to try out new tools if someone is willing to give them freely. One logistics specialist supplying a front line unit, speaking anonymously as he’s not authorized to talk to the media, tells me that this spring, they turned to donated gear from startups in Europe and the US to fill gaps left by delayed US military aid, including untested prototypes of UAVs and communications equipment.
All of this has allowed many companies to bypass the traditionally slow process of testing and demonstrating their products, for better and worse.
Tech companies’ rush into the conflict zone has unnerved some observers, who are worried that by going to war, companies have sidestepped ethical and safety concerns over their tools. Clearview AI gave Ukraine access to its controversial facial recognition tools to help identify Russia’s war dead, for example, sparking moral and practical questions over accuracy, privacy, and human rights—publishing images of those killed in war is arguably a violation of the Geneva Convention. Some high-profile tech executives, including Palantir CEO Alex Karp and former Google CEO-turned-military-tech-investor Eric Schmidt, have used the conflict to try to shift the global norms for using artificial intelligence in war, building systems that let machines select targets for attacks—which some experts worry is a gateway into autonomous “killer robots.”
LMT’s Pollaks says he has visited Ukraine often since the war began. Though he declines to give more details, he euphemistically describes Ukraine’s wartime bureaucracy as “nonstandardized.” If you want to blow something up in front of an audience in the EU, he says, you have to go through a whole lot of approvals, and the paperwork can take months, even years. In Ukraine, plenty of people are willing to try out your tools.
“[Ukraine], unfortunately, is the best defense technology experimentation ground in the world right now,” Pollaks says. “If you are not in Ukraine, then you are not in the defense business.”
Jack Wang, principal at UK-based venture capital fund Project A, which invests in military-tech startups, agrees that the Ukraine “track” can be incredibly fruitful. “If you sell to Ukraine, you get faster product and tech iteration, and live field testing,” he says. “The dollars might vary. Sometimes zero, sometimes quite a bit. But you get your product in the field faster.”
The feedback that comes from the front is invaluable. Atlas Dynamics has opened an office in Ukraine, and its representatives there work with soldiers and special forces to refine and modify their products. When Russian forces started jamming a wide band of radio frequencies to disrupt communication with the drones, Atlas designed a smart frequency-hopping system, which scans for unjammed frequencies and switches control of the drone over to them, putting soldiers a step ahead of the enemy.
At Global Wolf, battlefield testing for the Mosphera has led to small but significant iterations of the product, which have come naturally as soldiers use it. One scooter-related problem on the front turned out to be resupplying soldiers in entrenched positions with ammunition. Just as urban scooters have become last-mile delivery solutions in cities, troops found that the Mosphera was well suited to shuttling small quantities of ammo at high speeds across rough ground or through forests. To make this job easier, Global Wolf tweaked the design of the vehicle’s optional extra trailer so that it perfectly fits eight NATO standard-sized bullet boxes.
Within weeks of Russia’s full-scale invasion, Mosphera scooters were at Ukraine’s front line—and even behind it, being used by Ukrainian special forces scouts.
GLOBAL WOLF
Some snipers prefer the electric Mosphera to noisy motorbikes or quads, using the vehicles to weave between trees to get into position. But they also like to shoot from the saddle—something they couldn’t do from the scooter’s footplate. So Global Wolf designed a stable seat that lets shooters fire without having to dismount. Some units wanted infrared lights, and the company has made those, too. These types of requests give the team ideas for new upgrades: “It’s like buying a car,” Asmanis says. “You can have it with air conditioning, without air conditioning, with heated seats.”
Being battle-tested is already proving to be a powerful marketing tool. Bukavs told me he thinks defense ministers are getting closer to moving from promises toward “action.” The Latvian police have bought a handful of Mospheras, and the country’s military has acquired some, too, for special forces units. (“We don’t have any information on how they’re using them,” Asmanis says. “It’s better we don’t ask,” Bukavs interjects.) Military distributors from several other countries have also approached them to market their units locally.
Although they say their donations were motivated first and foremost by a desire to help Ukraine resist the Russian invasion, Bukavs and Asmanis admit that they have been paid back for their philanthropy many times over.
Of course, all this could change soon, and the Ukraine “track” could very well be disrupted when Trump returns to office in January. The US has provided more than $64 billion worth of military aid to Ukraine since the start of the full-scale invasion. A significant amount of that has been spent in Europe, in what Wang calls a kind of “drop-shipping”—Ukraine asks for drones, for instance, and the US buys them from a company in Europe, which ships them directly to the war effort.
Wang showed me a recent pitch deck from one European military-tech startup. In assessing the potential budgets available for its products, it compares the Ukrainian budget, which was in the tens of millions of dollars, and the “donated from everybody else” budget, which was a billion dollars. A large amount of that “everybody else” money comes from the US.
If, as many analysts expect, the Trump administration dramatically reduces or entirely stops US military aid to Ukraine, these young companies focused on military tech and dual-use tech will likely take a hit. “Ideally, the European side will step up their spending on European companies, but there will be a short-term gap,” Wang says.
A lasting change?
Russia’s full-scale invasion exposed how significantly the military-industrial complex in Europe has withered since the Cold War. Across the continent, governments have cut back investments in hardware like ships, tanks, and shells, partly because of a belief that wars would be fought on smaller scales, and partly to trim their national budgets.
“After decades of Europe reducing its combat capability,” Pollaks says, “now we are in the situation we are in. [It] will be a real challenge to ramp it up. And the way to do that, at least from our point of view, is real close integration between industry and the armed forces.”
This would hardly be controversial in the US, where the military and the defense industry often work closely together to develop new systems. But in Europe, this kind of collaboration would be “a bit wild,” Pollaks says. Militaries tend to be more closed off, working mainly with large defense contractors, and European investors have tended to be more squeamish about backing companies whose products could end up going to war.
As a result, despite the many positive signs for the developers of military tech, progress in overhauling the broader supply chain has been slower than many people in the sector would like.
Several founders of dual-use and military-tech companies in Latvia and the other Baltic states tell me they are often invited to events where they pitch to enthusiastic audiences of policymakers, but they never see any major orders afterward. “I don’t think any amount of VC blogging or podcasting will change how the military actually procures technology,” says Project A’s Wang. Despite what’s happening next door, Ukraine’s neighbors are still ultimately operating in peacetime. Government budgets remain tight, and even if the bureaucracy has become more flexible, layers upon layers of red tape remain.
Soldiers of the Latvian National Defense Service learn field combat skills in a training exercise.
GATIS INDRēVICS/ LATVIAN MINISTRY OF DEFENSE
Even Global Wolf’s Bukavs laments that a caravan of political figures has visited their factory but has not rewarded the company with big contracts. Despite Ukraine’s requests for the Mosphera scooters, for instance, they ultimately weren’t included in Latvia’s 2024 package of military aid due to budgetary constraints.
What this suggests is that European governments have learned a partial lesson from Ukraine—that startups can give you an edge in conflict. But experts worry that the continent’s politics means it may still struggle to innovate at speed. Many Western European countries have built up substantial bureaucracies to protect their democracies from corruption or external influences. Authoritarian states aren’t so hamstrung, and they, too, have been watching the war in Ukraine closely. Russian forces are reportedly testing Chinese and Iranian drones at the front line. Even North Korea has its own drone program.
The solution isn’t necessarily to throw out the mechanisms for accountability that are part of democratic society. But the systems that have been built up for good governance have led to fragility, sometimes leading governments to worry more about the politics of procurement than preparing for crises, according to Ilves and other policy experts I spoke to.
“Procurement problems grow bigger and bigger when democratic societies lose trust in leadership,” says Ilves, who now advises Ukraine’s Ministry of Digital Transformation on cybersecurity policy and international cooperation. “If a Twitter [troll] starts to go after a defense procurement budget, he can start to shape policy.”
That makes it hard to give financial support to a tech company whose products you don’t need now, for example, but whose capabilities might be useful to have in an emergency—a kind of merchant marine for technology, on constant reserve in case it’s needed. “We can’t push European tech to keep innovating imaginative crisis solutions,” Ilves says. “Business is business. It works for money, not for ideas.”
Even in Riga the war can feel remote, despite the Ukrainian flags flying from windows and above government buildings. Conversations about ordnance delivery and electronic warfare held in airy warehouse conversions can feel academic, even faintly absurd. In one incubator hub I visited in April, a company building a heavy-duty tracked ATV worked next door to an accounting software startup. On the top floor, bean bag chairs were laid out and a karaoke machine had been set up for a party that evening.
A sense of crisis is needed to jolt politicians, companies, and societies into understanding that the front line can come to them, Ilves says: “That’s my take on why I think the Baltics are ahead. Unfortunately not because we are so smart, but because we have this sense of necessity.”
Nevertheless, she says her experience over the past few years suggests there’s cause for hope if, or when, danger breaks through a country’s borders. Before the full-scale invasion, Ukraine’s government wasn’t exactly popular among the domestic business and tech communities. “And yet, they came together and put their brains and resources behind [the war effort],” she says. “I have a feeling that our societies are sometimes better than we think.”
Google DeepMind has unveiled an AI model that’s better at predicting the weather than the current best systems. The new model, dubbed GenCast, is published in Nature today.
This is the second AI weather model that Google has launched in just the past few months. In July, it published details of NeuralGCM, a model that combined AI with physics-based methods like those used in existing forecasting tools. That model performed similarly to conventional methods but used less computing power.
GenCast is different, as it relies on AI methods alone. It works sort of like ChatGPT, but instead of predicting the next most likely word in a sentence, it produces the next most likely weather condition. In training, it starts with random parameters, or weights, and compares that prediction with real weather data. Over the course of training, GenCast’s parameters begin to align with the actual weather.
The model was trained on 40 years of weather data (1979 to 2018) and then generated a forecast for 2019. In its predictions, it was more accurate than the current best forecast, the Ensemble Forecast, ENS, 97% of the time, and it was better at predicting wind conditions and extreme weather like the path of tropical cyclones. Better wind prediction capability increases the viability of wind power, because it helps operators calculate when they should turn their turbines on and off. And better estimates for extreme weather can help in planning for natural disasters.
Google DeepMind isn’t the only big tech firm that is applying AI to weather forecasting. Nvidia released FourCastNet in 2022. And in 2023 Huawei developed its Pangu-Weather model, which trained on 39 years of data. It produces deterministic forecasts—those providing a single number rather than a range, like a prediction that tomorrow will have a temperature of 30 °F or 0.7 inches of rainfall.
GenCast differs from Pangu-Weather in that it produces probabilistic forecasts—likelihoods for various weather outcomes rather than precise predictions. For example, the forecast might be “There is a 40% chance of the temperature hitting a low of 30 °F” or “There is a 60% chance of 0.7 inches of rainfall tomorrow.” This type of analysis helps officials understand the likelihood of different weather events and plan accordingly.
These results don’t mean the end of conventional meteorology as a field. The model is trained on past weather conditions, and applying them to the far future may lead to inaccurate predictions for a changing and increasingly erratic climate.
GenCast is still reliant on a data set like ERA5, which is an hourly estimate of various atmospheric variables going back to 1940, says Aaron Hill, an assistant professor at the School of Meteorology at the University of Oklahoma, who was not involved in this research. “The backbone of ERA5 is a physics-based model,” he says.
In addition, there are many variables in our atmosphere that we don’t directly observe, so meteorologists use physics equations to figure out estimates. These estimates are combined with accessible observational data to feed into a model like GenCast, and new data will always be required. “A model that was trained up to 2018 will do worse in 2024 than a model trained up to 2023 will do in 2024,” says Ilan Price, researcher at DeepMind and one of the creators of GenCast.
In the future, DeepMind plans to test models directly using data such as wind or humidity readings to see how feasible it is to make predictions on observation data alone.
There are still many parts of forecasting that AI models still struggle with, like estimating conditions in the upper troposphere. And while the model may be good at predicting where a tropical cyclone may go, it underpredicts the intensity of cyclones, because there’s not enough intensity data in the model’s training.
The current hope is to have meteorologists working in tandem with GenCast. “There’s actual meteorological experts that are looking at the forecast, making judgment calls, and looking at additional data if they don’t trust a particular forecast,” says Price.
Hill agrees. “It’s the value of a human being able to put these pieces together that is significantly undervalued when we talk about AI prediction systems,” he says. “Human forecasters look at way more information, and they can distill that information to make really good forecasts.”
A company best known for sucking up industrial waste gases is turning its attention to food. LanzaTech, a rising star in the fuel and chemical industries, is joining a growing group of businesses producing microbe-based food as an alternative to plant and animal products.
Using microbes to make food is hardly new—beer, yogurt, cheese, and tempeh all rely on microbes to transform raw ingredients into beloved dishes. But some companies are hoping to create a new category of food, one that relies on microbes themselves as a primary ingredient in our meals.
The global food system is responsible for roughly 25% to 35% of all human-caused greenhouse gas emissions today (depending on how you tally them up), and much of that comes from animal agriculture. Alternative food sources could help feed the world while cutting climate pollution.
As climate change pushes weather conditions to new extremes, it’s going to be harder to grow food, says LanzaTech CEO Jennifer Holmgren. The company’s current specialty, sucking up waste gases and transforming them into ethanol, is mostly used today in places like steel mills and landfills.
The process the company uses to make ethanol relies on a bacterium that can be found in the guts of rabbits. LanzaTech grows the microbes in reactors, on a diet consisting of gases including carbon monoxide, carbon dioxide, and hydrogen. As they grow, they produce ethanol, which can then be funneled into processes that transform the ethanol into chemicals like ethylene or fuels.
A by-product of that process is tons of excess microbes. In LanzaTech’s existing plants where ethanol is the primary product, operators generally need to harvest bacteria from the reactors, since they multiply over time. When the excess bacteria are harvested and dried, the resulting powder is high in protein. Some plants using LanzaTech’s technology in China are already selling the protein product to feed fish, poultry, and pigs.
Now, LanzaTech is expanding its efforts. The company has identified a new microbe, one they hope to make the star of future plants. Cupriavidus necator can be found in soil and water, and it’s something of a protein machine. The company says that after growing, harvesting, and drying the microbes, the resulting powder is more than 85% protein and could be added to all sorts of food products, for either humans or animals.
Roughly 80 companies around the world are making food products using biomass fermentation (meaning the microbes themselves make up the bulk of the product, rather than being used to transform ingredients, as they do in beer or cheesemaking), according to a report from the Good Food Institute, a think tank that focuses on alternative proteins.
The most established efforts in this space have been around since the 1980s. They use mycelial fungi, says Adam Leman, principal scientist for fermentation at the Good Food Institute.
Other startups are starting to grow other options for food products, including Air Protein and Calysta in the US and Solar Foods in Europe, Leman says. LanzaTech, which has significant experience raising microbes and running reactors, hopping into this space is a “really good sign for the industry,” he adds.
Making food with microbes would use less land and water and produce fewer emissions than many protein sources we rely on today, particularly high-impact ones like beef, Holmgren says. While it’s still early days for bacteria-based foods, one recent review found that mycoprotein-based foods (products like Quorn, made from mycelial fungi) generally have emissions lower than or similar to those of planet-friendly plant-based protein products, like those produced from corn and soy.
LanzaTech is currently developing prototype products with Mattson, a company that specializes in food development. In one such trial, Mattson made bread using the protein product as a sort of flour, Holmgren says. As for whether the bread tastes good, she says she hasn’t tried it yet, as the company is still working on getting the necessary certification from the US Food and Drug Administration.
So far, LanzaTech’s efforts have been relatively small-scale—the company is operating a pilot facility in Illinois that can produce around one kilogram of protein product each day. The company is working to start up a pre-commercial plant by 2026 that could produce half a metric ton of product per day, enough to supply the protein requirements of roughly 10,000 people, Holmgren says. A full-scale commercial plant would produce about 45,000 metric tons of protein product each year.
“I just want to make sure that there’s enough protein for the world,” Holmgren says.
At the start of 2024, OpenAI’s rules for how armed forces might use its technology were unambiguous.
The company prohibited anyone from using its models for “weapons development” or “military and warfare.” That changed on January 10, when The Interceptreported that OpenAI had softened those restrictions, forbidding anyone from using the technology to “harm yourself or others” by developing or using weapons, injuring others, or destroying property. OpenAI said soon after that it would work with the Pentagon on cybersecurity software, but not on weapons. Then, in a blog post published in October, the company shared that it is working in the national security space, arguing that in the right hands, AI could “help protect people, deter adversaries, and even prevent future conflict.”
Today, OpenAI is announcing that its technology will be deployed directly on the battlefield.
The company says it will partner with the defense-tech company Anduril, a maker of AI-powered drones, radar systems, and missiles, to help US and allied forces defend against drone attacks. OpenAI will help build AI models that “rapidly synthesize time-sensitive data, reduce the burden on human operators, and improve situational awareness” to take down enemy drones, according to the announcement. Specifics have not been released, but the program will be narrowly focused on defending US personnel and facilities from unmanned aerial threats, according to Liz Bourgeois, an OpenAI spokesperson. “This partnership is consistent with our policies and does not involve leveraging our technology to develop systems designed to harm others,” she said. An Anduril spokesperson did not provide specifics on the bases around the world where the models will be deployed but said the technology will help spot and track drones and reduce the time service members spend on dull tasks.
OpenAI’s policies banning military use of its technology unraveled in less than a year. When the company softened its once-clear rule earlier this year, it was to allow for working with the military in limited contexts, like cybersecurity, suicide prevention, and disaster relief, according to an OpenAI spokesperson.
Now, OpenAI is openly embracing its work on national security. If working with militaries or defense-tech companies can help ensure that democratic countries dominate the AI race, the company has written, then doing so will not contradict OpenAI’s mission of ensuring that AI’s benefits are widely shared. In fact, it argues, it will help serve that mission. But make no mistake: This is a big shift from its position just a year ago.
In understanding how rapidly this pivot unfolded, it’s worth noting that while the company wavered in its approach to the national security space, others in tech were racing toward it.
Venture capital firms more than doubled their investment in defense tech in 2021, to $40 billion, after firms like Anduril and Palantir proved that with some persuasion (and litigation), the Pentagon would pay handsomely for new technologies. Employee opposition to working in warfare (most palpable during walkouts at Google in 2018) softened for some when Russia invaded Ukraine in 2022 (several executives in defense tech told me that the “unambiguity” of that war has helped them attract both investment and talent).
So in some ways, by embracing defense OpenAI is just catching up. The difference is that defense-tech companies own that they’re in the business of warfare and haven’t had to rapidly disown a legacy as a nonprofit AI research company. From its founding charter, OpenAI has positioned itself as an organization on a mission to ensure that artificial general intelligence benefits all of humanity. It had publicly vowed that working with the military would contradict that mission.
Its October 24 blog post charted a new path, attempting to square OpenAI’s willingness to work in defense with its stated values. Titled “OpenAI’s approach to AI and national security,” it was released the same day the White House issued its National Security Memorandum on AI, which ordered the Pentagon and other agencies to ramp up their use of AI, in part to thwart competition from China.
“We believe a democratic vision for AI is essential to unlocking its full potential and ensuring its benefits are broadly shared,” OpenAI wrote, echoing similar language in the White House memo. “We believe democracies should continue to take the lead in AI development, guided by values like freedom, fairness, and respect for human rights.”
It offered a number of ways OpenAI could help pursue that goal, including efforts to “streamline translation and summarization tasks, and study and mitigate civilian harm,” while still prohibiting its technology from being used to “harm people, destroy property, or develop weapons.” Above all, it was a message from OpenAI that it is on board with national security work.
The new policies emphasize “flexibility and compliance with the law,” says Heidy Khlaaf, a chief AI scientist at the AI Now Institute and a safety researcher who authored a paper with OpenAI in 2022 about the possible hazards of its technology in contexts including the military. The company’s pivot “ultimately signals an acceptability in carrying out activities related to military and warfare as the Pentagon and US military see fit,” she says.
Amazon, Google, and OpenAI’s partner and investor Microsoft have competed for the Pentagon’s cloud computing contracts for years. Those companies have learned that working with defense can be incredibly lucrative, and OpenAI’s pivot, which comes as the company expects $5 billion in losses and is reportedly exploring new revenue streams like advertising, could signal that it wants a piece of those contracts. Big Tech’s relationships with the military also no longer elicit the outrage and scrutiny that they once did. But OpenAI is not a cloud provider, and the technology it’s building stands to do much more than simply store and retrieve data. With this new partnership, OpenAI promises to help sort through data on the battlefield, provide insights about threats, and help make the decision-making process in war faster and more efficient.
OpenAI’s statements on national security perhaps raise more questions than they answer. The company wants to mitigate civilian harm, but for which civilians? Does contributing AI models to a program that takes down drones not count as developing weapons that could harm people?
“Defensive weapons are still indeed weapons,” Khlaaf says. They “can often be positioned offensively subject to the locale and aim of a mission.”
Beyond those questions, working in defense means that the world’s foremost AI company, which has had an incredible amount of leverage in the industry and has long pontificated about how to steward AI responsibly, will now work in a defense-tech industry that plays by an entirely different set of rules. In that system, when your customer is the US military, tech companies do not get to decide how their products are used.
This story is from The Algorithm, our weekly newsletter on AI. To get it in your inbox first, sign up here.
President Biden first witnessed the capabilities of ChatGPT in 2022 during a demo from Arati Prabhakar, the director of the White House Office of Science and Technology Policy, in the oval office. That demo set a slew of events into motion and encouraged President Biden to support the US’s AI sector while managing the safety risks that will come from it.
Prabhakar was a key player in passing the president’s executive order on AI in 2023, which sets rules for tech companies to make AI safer and more transparent (though it relies on voluntary participation). Before serving in President Biden’s cabinet, she held a number of government roles, from rallying for domestic production of semiconductors to heading up DARPA, the Pentagon’s famed research department.
I had a chance to sit down with Prabhakar earlier this month. We discussed AI risks, immigration policies, the CHIPS Act, the public’s faith in science, and how it all may change under Trump.
The change of administrations comes at a chaotic time for AI. Trump’s team has not presented a clear thesis on how it will handle artificial intelligence, but plenty of people in it want to see that executive order dismantled. Trump said as much in July, endorsing the Republican platform that says the executive order “hinders AI innovation and imposes Radical Leftwing ideas on the development of this technology.” Powerful industry players, like venture capitalist Marc Andreessen, have said they support that move. However, complicating that narrative will be Elon Musk, who for years has expressed fears about doomsday AI scenarios and has been supportive of some regulations aiming to promote AI safety. No one really knows exactly what’s coming next, but Prabhakar has plenty of thoughts about what’s happened so far.
For her insights about the most important AI developments of the last administration, and what might happen in the next one, read my conversation with Arati Prabhakar.
Now read the rest of The Algorithm
Deeper Learning
These AI Minecraft characters did weirdly human stuff all on their own
The video game Minecraft is increasingly popular as a testing ground for AI models and agents. That’s a trend startup Altera recently embraced. It unleashed up to 1,000 software agents at a time, powered by large language models (LLMs), to interact with one another. Given just a nudge through text prompting, they developed a remarkable range of personality traits, preferences, and specialist roles, with no further inputs from their human creators. Remarkably, they spontaneously made friends, invented jobs, and even spread religion.
Why this matters: AI agents can execute tasks and exhibit autonomy, taking initiative in digital environments. This is another example of how the behaviors of such agents, with minimal prompting from humans, can be both impressive and downright bizarre. The people working to bring agents into the world have bold ambitions for them. Altera’s founder, Robert Yang sees the Minecraft experiments as an early step towards large-scale “AI civilizations” with agents that can coexist and work alongside us in digital spaces. “The true power of AI will be unlocked when we have truly autonomous agents that can collaborate at scale,” says Yang. Read more from Niall Firth.
Bits and Bytes
OpenAI is exploring advertising
Building and maintaining some of the world’s leading AI models doesn’t come cheap. The Financial Times has reported that OpenAI is hiring advertising talent from big tech rivals in a push to increase revenues. (Financial Times)
Landlords are using AI to raise rents, and cities are starting to push back
RealPage is a tech company that collects proprietary lease information on how much renters are paying and then uses an AI model to suggest to realtors how much to charge on apartments. Eight states and many municipalities have joined antitrust suits against the company, saying it constitutes an “unlawful information-sharing scheme” and inflates rental prices. (The Markup)
The way we measure progress in AI is terrible
Whenever new models come out, the companies that make them advertise how they perform in benchmark tests against other models. There are even leaderboards that rank them. But new research suggests these measurement methods aren’t helpful. (MIT Technology Review)
Nvidia has released a model that can create sounds and music
AI tools to make music and audio have received less attention than their counterparts that create images and video, except when the companies that make them get sued. Now, chip maker Nvidia has entered the space with a tool that creates impressive sound effects and music. (Ars Technica)
Artists say they leaked OpenAI’s Sora video model in protest
Many artists are outraged at the tech company for training its models on their work without compensating them. Now, a group of artists who were beta testers for OpenAI’s Sora model say they leaked it out of protest. (The Verge)
Every year, MIT Technology Review recognizes 35 young innovators who are doing pioneering work across a range of technical fields including biotechnology, materials science, artificial intelligence, computing, and more.
We’re now taking nominations for our 2025 list and you can submit one here. The process takes just a few minutes. Nominations will close at 11:59 PM ET on January 20, 2025. You can nominate yourself or someone you know, based anywhere in the world. The only rule is that the nominee must be under the age of 35 on October 1, 2025.
We want to hear about people who have made outstanding contributions to their fields and are making an early impact in their careers. Perhaps they’ve led an important scientific advance, founded a company that’s addressing an urgent problem, or discovered a new way to deploy an existing technology that improves people’s lives.
If you want to nominate someone, you should identify a clear advance or innovation for which they are primarily responsible. We seek to highlight innovators whose breakthroughs are broad in scope and whose influence reaches beyond their immediate scientific communities.
The 2025 class of innovators will join a long list of distinguished honorees. We featured Lisu Su, now CEO of AMD, when she was 32 years old; Andrew Ng, a computer scientist and serial entrepreneur, made the list in 2008 when he was an assistant professor at Stanford. That same year, we featured 31-year-old Jack Dorsey—two years after he launched Twitter. And Helen Greiner, co-founder of iRobot, was on the list in 1999.
A startup called Exa is pitching a new spin on generative search. It uses the tech behind large language models to return lists of results that it claims are more on point than those from its rivals, including Google and OpenAI. The aim is to turn the internet’s chaotic tangle of web pages into a kind of directory, with results that are specific and precise.
Exa already provides its search engine as a back-end service to companies that want to build their own applications on top of it. Today it is launching the first consumer version of that search engine, called Websets.
“The web is a collection of data, but it’s a mess,” says Exa cofounder and CEO Will Bryk. “There’s a Joe Rogan video over here, an Atlantic article over there. There’s no organization. But the dream is for the web to feel like a database.”
Websets is aimed at power users who need to look for things that other search engines aren’t great at finding, such as types of people or companies. Ask it for “startups making futuristic hardware” and you get a list of specific companies hundreds long rather than hit-or-miss links to web pages that mention those terms. Google can’t do that, says Bryk: “There’s a lot of valuable use cases for investors or recruitersor really anyone who wants any sort of data set from the web.”
Things have moved fast since MIT Technology Review broke the news in 2021 that Google researchers were exploring the use of large language models in a new kind of search engine. The idea soon attracted fierce critics. But tech companies took little notice. Three years on, giants like Google and Microsoft jostle with a raft of buzzy newcomers like Perplexity and OpenAI, which launched ChatGPT Search in October, for a piece of this hot new trend.
Exa isn’t (yet) trying to out-do any of those companies. Instead, it’s proposing something new. Most other search firms wrap large language models around existing search engines, using the models to analyze a user’s query and then summarize the results. But the search engines themselves haven’t changed much. Perplexity still directs its queries to Google Search or Bing, for example. Think of today’s AI search engines as a sandwich with fresh bread but stale filling.
More than keywords
Exa provides users with familiar lists of links but uses the tech behind large language models to reinvent how search itself is done. Here’s the basic idea: Google works by crawling the web and building a vast index of keywords that then get matched to users’ queries. Exa crawls the web and encodes the contents of web pages into a format known as embeddings, which can be processed by large language models.
Embeddings turn words into numbers in such a way that words with similar meanings become numbers with similar values. In effect, this lets Exa capture the meaning of text on web pages, not just the keywords.
A screenshot of Websets showing results for the search: “companies; startups; US-based; healthcare focus; technical co-founder”
Large language models use embeddings to predict the next words in a sentence. Exa’s search engine predicts the next link. Type “startups making futuristic hardware” and the model will come up with (real) links that might follow that phrase.
Exa’s approach comes at cost, however. Encoding pages rather than indexing keywords is slow and expensive. Exa has encoded some billion web pages, says Bryk. That’s tiny next to Google, which has indexed around a trillion. But Bryk doesn’t see this as a problem: “You don’t have to embed the whole web to be useful,” he says. (Fun fact: “exa” means a 1 followed by 18 0s and “googol” means a 1 followed by 100 0s.)
Websets is very slow at returning results. A search can sometimes take several minutes. But Bryk claims it’s worth it. “A lot of our customers started to ask for, like, thousands of results, or tens of thousands,” he says. “And they were okay with going to get a cup of coffee and coming back to a huge list.”
“I find Exa most useful when I don’t know exactly what I’m looking for,” says Andrew Gao, a computer science student at Stanford Univesrsity who has used the search engine. “For instance, the query ‘an interesting blog post on LLMs in finance’ works better on Exa than Perplexity.” But they’re good at different things, he says: “I use both for different purposes.”
“I think embeddings are a great way to represent entities like real-world people, places, and things,” says Mike Tung, CEO of Diffbot, a company using knowledge graphs to build yet another kind of search engine. But he notes that you lose a lot of information if you try to embed whole sentences or pages of text: “Representing War and Peace as a single embedding would lose nearly all of the specific events that happened in that story, leaving just a general sense of its genre and period.”
Bryk acknowledges that Exa is a work in progress. He points to other limitations, too. Exa is not as good as rival search engines if you just want to look up a single piece of information, such as the name of Taylor Swift’s boyfriend or who Will Bryk is: “It’ll give a lot of Polish-sounding people, because my last name is Polish and embeddings are bad at matching exact keywords,” he says.
For now Exa gets around this by throwing keywords back into the mix when they’re needed. But Bryk is bullish: “We’re covering up the gaps in the embedding method until the embedding method gets so good that we don’t need to cover up the gaps.”
President Biden’s administration will end within two months, and likely to depart with him is Arati Prabhakar, the top mind for science and technology in his cabinet. She has served as Director of the White House Office of Science and Technology Policy since 2022 and was the first to demonstrate ChatGPT to the president in the Oval Office. Prabhakar was instrumental in passing the president’s executive order on AI in 2023, which sets guidelines for tech companies to make AI safer and more transparent (though it relies on voluntary participation).
The incoming Trump administration has not presented a clear thesis of how it will handle AI, but plenty of people in it will want to see that executive order nullified. Trump said as much in July, endorsing the 2024 Republican Party Platform that says the executive order “hinders AI innovation and imposes Radical Leftwing ideas on the development of this technology.” Venture capitalist Marc Andreessen has said he would support such a move.
However, complicating that narrative will be Elon Musk, who for years has expressed fears about doomsday AI scenarios, and has been supportive of some regulations aiming to promote AI safety.
As she prepares for the end of the administration, I sat down with Prabhakar and asked her to reflect on President Biden’s AI accomplishments, and how AI risks, immigration policies, the CHIPS Act and more could change under Trump.
This conversation has been edited for length and clarity.
Every time a new AI model comes out, there are concerns about how it could be misused. As you think back to what were hypothetical safety concerns just two years ago, which ones have come true?
We identified a whole host of risks when large language models burst on the scene, and the one that has fully manifested in horrific ways is deepfakes and image-based sexual abuse. We’ve worked with our colleagues at the Gender Policy Council to urge industry to step up and take some immediate actions, which some of them are doing. There are a whole host of things that can be done—payment processors could actually make sure people are adhering to their Terms of Use. They don’t want to be supporting [image-based sexual abuse] and they can actually take more steps to make sure that they’re not. There’s legislation pending, but that’s still going to take some time.
Have there been risks that didn’t pan out to be as concerning as you predicted?
At first there was a lot of concern expressed by the AI developers about biological weapons. When people did the serious benchmarking about how much riskier that was compared with someone just doing Google searches, it turns out, there’s a marginally worse risk, but it is marginal. If you haven’t been thinking about how bad actors can do bad things, then the chatbots look incredibly alarming. But you really have to say, compared to what?
For many people, there’s a knee-jerk skepticism about the Department of Defense or police agencies going all in on AI. I’m curious what steps you think those agencies need to take to build trust.
If consumers don’t have confidence that the AI tools they’re interacting with are respecting their privacy, are not embedding bias and discrimination, that they’re not causing safety problems, then all the marvelous possibilities really aren’t going to materialize. Nowhere is that more true than national security and law enforcement.
I’ll give you a great example. Facial recognition technology is an area where there have been horrific, inappropriate uses: take a grainy video from a convenience store and identify a black man who has never even been in that state, who’s then arrested for a crime he didn’t commit. (Editor’s note: Prabhakar is referring to this story). Wrongful arrests based on a really poor use of facial recognition technology, that has got to stop.
In stark contrast to that, when I go through security at the airport now, it takes your picture and compares it to your ID to make sure that you are the person you say you are. That’s a very narrow, specific application that’s matching my image to my ID, and the sign tells me—and I know from our DHS colleagues that this is really the case—that they’re going to delete the image. That’s an efficient, responsible use of that kind of automated technology. Appropriate, respectful, responsible—that’s where we’ve got to go.
Were you surprised at the AI safety bill getting vetoed in California?
I wasn’t. I followed the debate, and I knew that there were strong views on both sides. I think what was expressed, that I think was accurate, by the opponents of that bill, is that it was simply impractical, because it was an expression of desire about how to assess safety, but we actually just don’t know how to do those things. No one knows. It’s not a secret, it’s a mystery.
To me, it really reminds us that while all we want is to know how safe, effective and trustworthy a model is, we actually have very limited capacity to answer those questions. Those are actually very deep research questions, and a great example of the kind of public R&D that now needs to be done at a much deeper level.
Let’s talk about talent. Much of the recent National Security Memorandum on AI was about how to help the right talent come from abroad to the US to work on AI. Do you think we’re handling that in the right way?
It’s a hugely important issue. This is the ultimate American story, that people have come here throughout the centuries to build this country, and it’s as true now in science and technology fields as it’s ever been. We’re living in a different world. I came here as a small child because my parents came here in the early 1960s from India, and in that period, there were very limited opportunities [to emigrate to] many other parts of the world.
One of the good pieces of news is that there is much more opportunity now. The other piece of news is that we do have a very critical strategic competition with the People’s Republic of China, and that makes it more complicated to figure out how to continue to have an open door for people who come seeking America’s advantages, while making sure that we continue to protect critical assets like our intellectual property.
Do you think the divisive debates around immigration, especially around the time of the election, may hurt the US ability to bring the right talent into the country?
Because we’ve been stalled as a country on immigration for so long, what is caught up in that is our ability to deal with immigration for the STEM fields. It’s collateral damage.
Has the CHIPS Act been successful?
I’m a semiconductor person starting back with my graduate work. I was astonished and delighted when, after four decades, we actually decided to do something about the fact that semiconductor manufacturing capability got very dangerously concentrated in just one part of the world [Taiwan]. So it was critically important that, with the President’s leadership, we finally took action. And the work that the Commerce Department has done to get those manufacturing incentives out, I think they’ve done a terrific job.
One of the main beneficiaries so far of the CHIPS Act has been Intel. There’s varying degrees of confidence in whether it is going to deliver on building a domestic chip supply chain in the way that the CHIPS Act intended. Is it risky to put a lot of eggs in one basket for one chip maker?
I think the most important thing I see in terms of the industry with the CHIPS Act is that today we’ve got not just Intel, but TSMC, Samsung, SK Hynix and Micron. These are the five companies whose products and processes are at the most advanced nodes in semiconductor technology. They are all now building in the US. There’s no other part of the world that’s going to have all five of those. An industry is bigger than a company. I think when you look at the aggregate, that’s a signal to me that we’re on a very different track.
You are the President’s chief advisor for science and technology. I want to ask about the cultural authority that science has, or doesn’t have, today. RFK Jr. is the pick for health secretary, and in some ways, he captures a lot of frustration that Americans have about our healthcare system. In other ways, he has many views that can only be described as anti-science. How do you reflect on the authority that science has now?
I think it’s important to recognize that we live in a time when trust in institutions has declined across the board, though trust in science remains relatively high compared with what’s happened in other areas. But it’s very much part of this broader phenomenon, and I think that the scientific community has some roles [to play] here. The fact of the matter is that despite America having the best biomedical research that the world has ever seen, we don’t have robust health outcomes. Three dozen countries have longer life expectancies than America. That’s not okay, and that disconnect between advancing science and changing people’s lives is just not sustainable. The pact that science and technology and R&D makes with the American people is that if we make these public investments, it’s going to improve people’s lives and when that’s not happening, it does erode trust.
Is it fair to say that that gap—between the expertise we have in the US and our poor health outcomes—explains some of the rise in conspiratorial thinking, in the disbelief of science?
It leaves room for that. Then there’s a quite problematic rejection of facts. It’s troubling if you’re a researcher, because you just know that what’s being said is not true. The thing that really bothers me is [that the rejection of facts] changes people’s lives, and it’s extremely dangerous and harmful. Think about if we lost herd immunity for some of the diseases for which we right now have fairly high levels of vaccination. It was an ugly world before we tamed infectious disease with the vaccines that we have.
A Japanese publishing startup is using Anthropic’s flagship large language model Claude to help translate manga into English, allowing the company to churn out a new title for a Western audience in just a few days rather than the two to three months it would take a team of humans.
Orange was founded by Shoko Ugaki, a manga superfan who (according to VP of product Rei Kuroda) has some 10,000 titles in his house. The company now wants more people outside Japan to have access to them. “I hope we can do a great job for our readers,” says Kuroda.
Orange’s Japanese-to-English translation of Neko Oji: Salaryman reincarnated as a kitten!
IMAGES COURTESY ORANGE / YAJIMA
But not everyone is happy. The firm has angered a number of manga fans who see the use of AI to translate a celebrated and traditional art form as one more front in the ongoing battle between tech companies and artists. “However well-intentioned this company might be, I find the idea of using AI to translate manga distasteful and insulting,” says Casey Brienza, a sociologist and author of the book Manga in America: Transnational Book Publishing and the Domestication of Japanese Comics.
Manga is a form of Japanese comic that has been around for more than a century. Hit titles are often translated into other languages and find a large global readership, especially in the US. Some, like Battle Angel Alita or One Piece, are turned into anime (animated versions of the comics) or live-action shows and become blockbuster movies and top Netflix picks. The US manga market was worth around $880 million in 2023 but is expected to reach $3.71 billion by 2030, according to some estimates. “It’s a huge growth market right now,” says Kuroda.
Orange wants a part of that international market. Only around 2% of titles published in Japan make it to the US, says Kuroda. As Orange sees it, the problem is that manga takes human translators too long to translate. By building AI tools to automate most of the tasks involved in translation—including extracting Japanese text from a comic’s panels, translating it into English, generating a new font, pasting the English back into the comic, and checking for mistranslations and typos—it can publish a translated mange title in around one-tenth the time it takes human translators and illustrators working by hand, the company says.
Humans still keep a close eye on the process, says Kuroda: “Honestly, AI makes mistakes. It sometimes misunderstands Japanese. It makes mistakes with artwork. We think humans plus AI is what’s important.”
Superheroes, aliens, cats
Manga is a complex art form. Stories are told via a mix of pictures and words, which can be descriptions or characters’ voices or sound effects, sometimes in speech bubbles and sometimes scrawled across the page. Single sentences can be split across multiple panels.
There are also diverse themes and narratives, says Kuroda: “There’s the student romance, mangas about gangs and murders, superheroes, aliens, cats.” Translations must capture the cultural nuance in each story. “This complexity makes localization work highly challenging,” he says.
Orange often starts with nothing more than the scanned image of a page. Its system first identifies which parts of the page show Japanese text, copies it, and erases the text from each panel. These snippets of text are then combined into whole sentences and passed to the translation module, which not only translates the text into English but keeps track of where on the page each individual snippet comes from. Because Japanese and English have a very different word order, the snippets need to be reordered, and the new English text must be placed on the page in different places from where the Japanese equivalent had come from—all without messing up the sequence of images.
“Generally, the images are the most important part of the story,” says Frederik Schodt, an award-winning manga translator who published his first translation in 1977. “Any language cannot contradict the images, so you can’t take many of the liberties that you might in translating a novel. You can’t rearrange paragraphs or change things around much.”
Orange’s Japanese-to-English translation of Neko Oji: Salaryman reincarnated as a kitten!
IMAGES COURTESY ORANGE / YAJIMA
Orange tried several large language models, including its own, developed in house, before picking Claude 3.5. “We’re always evaluating new models,” says Kuroda. “Right now Claude gives us the most natural tone.”
Claude also has an agent framework that lets several sub-models work together on an overall task. Orange uses this framework to juggle the multiple steps in the translation process.
Orange distributes its translations via an app called Emaqi (a pun on “emaki,” the ancient Japanese illustrated scrolls that are considered a precursor to manga). It also wants to be a translator-for-hire for US publishers.
But Orange has not been welcomed by all US fans. When it showed up at Anime NYC, a US anime convention, this summer, the Japanese-to-English translator Jan Mitsuko Cash tweeted: “A company like Orange has no place at the convention hosting the Manga Awards, which celebrates manga and manga professionals in the industry. If you agree, please encourage @animenyc to ban AI companies from exhibiting or hosting panels.”
Brienza takes the same view. “Work in the culture industries, including translation, which ultimately is about translating human intention, not mere words on a page, can be poorly paid and precarious,” she says. “If this is the way the wind is blowing, I can only grieve for those who will go from making little money to none.”
Some have also called Orange out for cutting corners. “The manga uses stylized text to represent the inner thoughts that the [protagonist] can’t quite voice,” another fan tweeted. “But Orange didn’t pay a redrawer or letterer to replicate it properly. They also just skip over some text entirely.”
Orange distributes its translations via an app called Emaqi (available only in the US and Canada for now)
EMAQI
Everyone at Orange understands that manga translation is a sensitive issue, says Kuroda: “We believe that human creativity is absolutely irreplaceable, which is why all AI-assisted work is rigorously reviewed, refined, and finalized by a team of people.”
Orange also claims that the authors it has translated are on board with its approach. “I’m genuinely happy with how the English version turned out,” says Kenji Yajima, one of the authors Orange has worked with, referring to the company’s translation of his title Neko Oji: Salaryman reincarnated as a kitten! (see images). “As a manga artist, seeing my work shared in other languages is always exciting. It’s a chance to connect with readers I never imagined reaching before.”
Schodt sees the upside too. He notes that the US is flooded with poor-quality, unofficial fan-made translations. “The number of pirated translations is huge,” he says. “It’s like a parallel universe.”
He thinks using AI to streamline translation is inevitable. “It’s the dream of many companies right now,” he says. “But it will take a huge investment.” He believes that really good translation will require large language models trained specifically on manga: “It’s not something that one small company is going to be able to pull off.”
“Whether this will prove economically feasible right now is anyone’s guess,” says Schodt. “There is a lot of advertising hype going on, but the readers will have the final judgment.”
