{"text":[[{"start":11.44,"text":"At first glance, the Saker Scout resembles thousands of other drones that now patrol the skies above Ukraine’s front lines. A black quadcopter, it has a camera enabling a pilot to fly it remotely and deliver a 5kg explosive charge — usually a grenade or an anti-tank warhead."}],[{"start":28.58,"text":"But hidden beneath its carbon-fibre frame is a motherboard no larger than a credit card that may represent the next step in the evolution of war. Its machine learning software can recognise 47 categories of military equipment, from tanks to infantry, drawing virtual boxes around objects and assigning percentile confidence scores to everything it sees."}],[{"start":49.76,"text":"Before launch, the human operator selects which targets matter most, sets the confidence threshold needed for engagement, and defines a “kill box” — a geographic area inside which everything is a target. Once inside that box, the drone can search, identify and, if its confidence score is high enough, attack without waiting for another human command."}],[{"start":70.64,"text":"There is one crucial limitation: the machine can distinguish a tank from a truck, or infantry from artillery, but not whether the target is Ukrainian or Russian. The system therefore relies on a battlefield assumption — everything military inside the “kill box” belongs to the enemy."}],[{"start":null,"text":"

"}],[{"start":null,"text":""}],[{"start":85.68,"text":"“You should be sure there are no civilians, and not your own people,” says Rostyslav Olenchyn of Twist Robotics, the Ukrainian company behind the Saker Scout. “It is a very special weapon and used only in certain cases by special units.”"}],[{"start":99.6,"text":"AI technology has changed how the war in Ukraine is fought. At the start of the conflict it could take 20 minutes to identify a target and launch a strike. Now that process can take less than two minutes, sometimes just seconds."}],[{"start":113.56,"text":"Russia’s full-scale invasion of Ukraine occurred nine months before OpenAI released ChatGPT, a pivotal moment in the AI revolution. The conflict has become a testing ground for the use of AI systems in kinetic warfare, with the lessons shaping not just the strategy of Ukraine and its western allies, but also the US in its war with Iran."}],[{"start":133.6,"text":"According to Olenchyn, the acceleration is not only due to technology but to cost. The computing power needed to run sophisticated visual recognition algorithms can today be bought for around $250. The Saker Scout’s processing power is comparable to a PlayStation 4."}],[{"start":151.44,"text":"But the war also demonstrates AI’s limits. As the technology is rapidly integrated with military systems in both Ukraine and Russia, it has become clear to strategists and frontline operators that although AI has sped up decision-making and made war more lethal, it has not yet given either side a decisive advantage."}],[{"start":171.32,"text":"Still, as frontier AI models reach a point where they are developing faster than humans can control them, military and political leaders will have to grapple with what happens when those limits are removed."}],[{"start":182.48,"text":"AI’s role in warfare"}],[{"start":184.68,"text":"When militaries talk about AI, they are not talking about a single system."}],[{"start":188.88,"text":"They are referring to software being used at different stages of warfare — collecting and processing data, helping commanders identify and prioritise targets and allowing drones to keep operating when communications are jammed."}],[{"start":201.92,"text":"AI-enabled software can process “mass data at a scale, a speed that human staff officers can’t do”, says Anthony King, professor of war studies and director of the University of Exeter’s Strategy and Security Institute. Rather than altering the sharp end of a conflict, King says the primary function of AI has been to improve “situational awareness and intelligence”."}],[{"start":224.12,"text":"The technology can help commanders make battlefield predictions, from the sustainability of a campaign to the rate at which munitions are being depleted. The advantage over human estimates, King says, is that such systems simply have “more evidence to look at”."}],[{"start":239.52,"text":"This matters because modern headquarters absorb huge quantities of information: drone footage, satellite imagery, electronic signals, human reports and data from sensors across the battlefield. Comparing all of that is “very labour-intensive”, says Jack Watling of the Royal United Services Institute, a UK-based defence and security think-tank. Battlefield management systems such as Ukraine’s Delta and the US’s Maven turn those streams into usable information."}],[{"start":268.693,"text":"One US intelligence official told Katrina Manson, author of a book on Project Maven, that the system had helped increase the number of targets US forces could hit from fewer than 100 a day to 1,000 — and with the integration of large language models as many as 5,000."}],[{"start":null,"text":""}],[{"start":289.86,"text":"These systems also allow militaries to compress the “kill chain” — the time it takes between identifying a target and executing a strike. “In simple terms, the faster and further away soldiers can do this, the more effect they will have on the enemy,” the British Army says on its website. It enables armies to strike first and increases the number of viable targets — particularly those that are mobile."}],[{"start":null,"text":""}],[{"start":307.84,"text":"Whereas strategists once measured military power by the number of troops, warplanes, tanks and battleships they could deploy, they must now focus on the speed and accuracy of data-processing systems and how to deploy autonomous weapons at scale."}],[{"start":321.76,"text":"On the front line, AI is still largely limited to narrow tasks: a drone stabilising itself in flight or continuing towards a target after its link to the operator is jammed."}],[{"start":332.44,"text":"Kateryna Bondar, a senior fellow at the Center for Strategic and International Studies’ AI centre, says autonomous navigation, much of it adapted from drone-racing software, can help a drone adjust to wind and lock on to a target at speeds of up to 300kph. It has reduced training times for pilots, while strikes are three to four times more likely to succeed if they are AI-assisted."}],[{"start":356.64,"text":"Terminal guidance has proved especially valuable in the final phase of a strike, when jamming often severs the connection between pilot and drone. “It happens often that you’ll lose control of a drone 100 or 200m from the target,” a Ukrainian commander says. Once the pilot locks on to the target, the drone can continue the final approach even if the signal is lost."}],[{"start":377.92,"text":"AI-guided strikes are still relatively rare, says Oleksii Babenko, founder of Vyriy Drone, a Ukrainian drone maker that supplies the military. “Maybe 2 per cent of drones use some [artificial intelligence] so the drone understands what the target is, and finds the target itself . . . still, 2 per cent of five million drones is a lot.”"}],[{"start":398.48,"text":"Ukraine claims that since the beginning of 2026, AI-guided drone strikes have increased 10-fold."}],[{"start":null,"text":""}],[{"start":404.28,"text":"There is also swarming, where machines distribute tasks among themselves. That could mean drones and ground robots co-ordinating to retrieve a wounded soldier, or attack drones dividing up targets to overwhelm a defensive system. Autonomous swarms at scale remain largely experimental, but small groups are now working together on the battlefield."}],[{"start":424.6,"text":"One company developing swarming software is Auterion, a Swiss-founded start-up."}],[{"start":429.6,"text":"Lorenz Meier, Auterion’s chief executive, says that western military forces will increasingly have to rely on largely autonomous swarms because they have not trained enough drone pilots to match Russian or Ukrainian capabilities."}],[{"start":443.1,"text":"“For Nato countries, it is a matter of life and death because they cannot stand up these drone units fast enough,” Meier adds. “They have to go for a higher degree of automation in order to be able to fight effectively.”"}],[{"start":455.28,"text":"Research into autonomous targeting of Russian long-range drones became more urgent after Moscow began sending waves of Shaheds against Ukrainian cities. In response, Ukrainian companies began developing interceptors to identify and attack them."}],[{"start":469.84,"text":"Shaheds have a distinctive shape and fly in skies with few other aircraft, which means AI systems can be trained to recognise them."}],[{"start":477.28,"text":"Russian forces have scaled up their use of AI-powered drones over the past year. In July, a Russian drone used AI to target a petrol station without a human operator making the strike decision. It crashed into a wall and exploded, killing three people — reportedly the first documented deaths caused by a Russian self-targeting drone in Ukraine. In recent weeks, Russia has been using Shaheds equipped with AI targeting systems to hit Ukrainian ships on the Black Sea."}],[{"start":504.2,"text":"Ultimately, Kyiv hopes to raise its AI capabilities to the level of drawing up strategy. Danylo Tsvok oversees a newly established centre tasked with turning Ukraine’s forces into “an AI-driven military”. He has described a hypothetical world in which a commander could tell an AI agent their intention — “I want to defend this position” — and draw up battle plans based on the system’s recommendations. “We think this could be a game changer,” Tsvok says."}],[{"start":null,"text":""}],[{"start":530.44,"text":"For many in Ukraine, struggling for personnel to continue staving off Russia, there is little alternative. “The goal is full autonomy,” says Maksym Savanevsky, in charge of marketing at The Fourth Law, a Ukrainian company that has developed drones equipped with terminal guidance systems. “We call it the Manhattan project of the 21st century because it will change the battlefield dramatically.”"}],[{"start":554.16,"text":"But, as the Ukraine war shows, stalemate is just as likely as victory. Olivier Schmitt, professor and head of research at the Institute for Military Operations at the Royal Danish Defence College, says there is a “discrepancy between the tactical level, which is changing really fast, and the pace or rhythm of the campaign, which is basically stalled because no one is able to break through”."}],[{"start":578.44,"text":"The continuing US conflict with Iran demonstrates that even when targeting works effectively, it may not produce victory. Iran is a “textbook case study of this”, says Franz-Stefan Gady, a defence analyst at the Institute for International Strategic Studies."}],[{"start":593.64,"text":"The idea is that “we’re going to strike all these targets quicker and faster than the adversary and then we hope that the enemy at some point is going to collapse. You see that there’s really no plan B if that doesn’t work out.” Schmitt adds that the US “have been really good at their targeting, and [Iran’s] military system has not collapsed”."}],[{"start":612.308,"text":"“AI will help you accomplish what you want to accomplish,” Schmitt says. “It doesn’t mean that what you want to accomplish is going to win you the war.”"}],[{"start":612.808,"text":"The accountability gap"}],[{"start":620.92,"text":"The concept of an “AI-driven military” is deeply troubling to some experts, who point out that battlefields are seldom black-and-white theatres in which clean decisions are made."}],[{"start":632,"text":"Paddy Walker, an expert on AI ethics, says modern warfare is characterised by ambiguity which is difficult to code for or train an LLM to handle. “The hybrid, the grey zone, these are much more complicated battlefields . . . you’ve got to be processing the context.”"}],[{"start":648.449,"text":"AI tools can help identify Shahed drones, but there are limits to their effectiveness. Artem Martynenko, a lieutenant colonel in the Ukrainian military and an original architect of Delta, Ukraine’s battlefield management system, says “accurately, autonomously detecting a soldier remains a problem”, let alone distinguishing country or status. The problem is made worse by a battlefield where positions are often intermingled and concealment is essential to survival."}],[{"start":null,"text":""}],[{"start":678.092,"text":"Heidy Khlaaf, chief AI scientist at the AI Now Institute, an independent research institute, says such systems struggle with “uniform-like clothing”, vehicles, weapons and movement because they fall into a “broader and less predictable space”. Even lighting can lead a machine to confuse a civilian vehicle with a military one."}],[{"start":null,"text":""}],[{"start":null,"text":""}],[{"start":696.16,"text":"“The rule book says you always need a human making decisions,” says one former employee of Palantir, which built Maven, and who is a veteran of the US military’s drone warfare programme. “The challenge is, the computer is spitting out a list of 1,000 targets. And you’re not moving fast enough. The problem is that the human becomes the bottleneck. And that analyst, he doesn’t always care. He wants to go home or go get a cheeseburger.”"}],[{"start":721.34,"text":"The more such systems are used to find and classify targets, the more significant their limits become. The information they use can be fragmentary, outdated and ambiguous. Even curated data, such as a drone feed, can be incomplete, corrupted by sensor failure or environmental interference, or mislabelled, says Khlaaf, who studies safety in autonomous weapons."}],[{"start":743.44,"text":"The risk was illustrated in Iran earlier this year. AI-supported targeting systems enabled the US to hit 13,000 targets in the first 38 days of the conflict. This included a girls’ school where more than 150 people, among them 120 children, were killed. The building had been part of a military compound, but a separate entrance and a wall were built around 2016, when the school was set up, according to local officials."}],[{"start":769.32,"text":"King at the University of Exeter says the strike showed the danger of systems “based on statistical correlation”: if the information fed into them is wrong or outdated, the result can be catastrophic. Mistakes in targeting are not new, but greater speed and scale can increase the risk of errors."}],[{"start":null,"text":""}],[{"start":786.8,"text":"The faster the system can identify or recommend targets, the less time humans may have to scrutinise them. “If you bake in heuristic mistakes [flawed assumptions], whether that’s the AI’s mistakes or human, then of course [that] can accelerate the rate of error,” says Watling of the Royal United Services Institute."}],[{"start":805.32,"text":"The AI-assisted target generation process for Israel’s bombing campaign in Gaza has also raised ethical questions. There was “political top-down pressure to do a lot of targeting, to demonstrate force”, says Schmitt, of the Royal Danish Defence College. It meant that in a military context, “officers had between 15 and 20 seconds to assess whether a target was legitimate or not”."}],[{"start":828.08,"text":"With such time constraints, “it’s difficult to understand how it would be possible to ensure that the target that they have in the crosshairs is a legitimate military target, which is what’s required by law,” says Jessica Dorsey, assistant professor of international law at Utrecht University."}],[{"start":844.8,"text":"The stakes rise when humans are the target, and not just buildings or infrastructure. Under international humanitarian law, whether a person can be targeted depends on their status, actions and circumstances, requiring legal assessments that computers cannot make. A soldier’s status can change quickly, for example if they surrender or become wounded."}],[{"start":864.8,"text":"Schmitt says Israel’s campaign in Gaza shows not just how the technology works, but “how the social and political environments pressure humans to use the technology in a very specific way”. If political leaders are demanding results and officers have only seconds to review targets, he says, they are more likely to accept the recommendation in front of them."}],[{"start":884.52,"text":"And there remains limited understanding of how such systems reach their conclusions. The US state department says autonomous systems must be “transparent to and auditable by their relevant defense personnel”. Palantir says its tools allow operators to search back through the lines of data and documents that led to a specific suggestion."}],[{"start":null,"text":""}],[{"start":902.8,"text":"But auditability of the data is not the same as understanding why a large language model recommended a course of action."}],[{"start":909.16,"text":"With AI decision-support systems, Khlaaf says, “there’s no way for us to investigate why a certain decision was being made. These models “implicitly automate what would traditionally be considered several decision points in the kill chain”. Palantir says they have built systems which enable their LLMs to use tools and operate step by step, making the models’ decisions more auditable."}],[{"start":933.36,"text":"If an autonomous system kills the wrong people, it may also be hard to determine whether the failure lies in flawed data, technical failures or human error. There is a risk the complexity could lead to a tendency to label incidents as accidents or to blame individual users rather than the state that built the system."}],[{"start":951.12,"text":"Madeleine Elish, a researcher at Google DeepMind, describes this as the “moral crumple zone” — when a human operator with limited control over an automated system bears the brunt of responsibility when it fails."}],[{"start":963.08,"text":"Legally, however, the buck stops with battlefield commanders. “Humans are responsible for the way that war is waged,” Dorsey says."}],[{"start":963.58,"text":"What this means for warfare"}],[{"start":971.36,"text":"The rapid integration of AI into warfare is happening as these models become advanced in ways even their creators cannot fully predict."}],[{"start":980.84,"text":"The recent incident involving a swarm of OpenAI agents breaking out of a testing environment and hacking into AI developer Hugging Face showed frontier models pushing against attempts to constrain their behaviour. Some researchers believe so-called recursive self-improvement, or the ability of models to build their successors, could enable advances at a speed and intensity as yet unforeseen."}],[{"start":1003.44,"text":"Anthropic says today’s most advanced models are not reliable enough to autonomously select and engage targets. The company’s attempts to put limits on the military use of AI have put it at odds with the Pentagon."}],[{"start":1015.56,"text":"Meanwhile, the company’s threat-intelligence report, released this month, found non-state actors using Claude to develop weapons, including an autonomous drone swarm designed to select human targets and detonate without a human in the loop. It documented Claude being abused by actors across the world — from China, Russia, Ukraine and Iran to Yemen, Sudan and Taiwan."}],[{"start":1037.36,"text":"Soon the question may no longer be how far militaries are prepared to incorporate AI into the kill chain, but whether governments and AI companies will be able to set those limits at all."}],[{"start":1047.72,"text":"Additional work by Sam Joiner, Peter Andringa and Ian Bott"}],[{"start":1055.64,"text":""}]],"url":"https://audio.ftcn.net.cn/album/a_1789744644_4176.mp3"}