Battlefields in Ukraine are littered with the remnants of drones, which are now firmly established as a critical weapon of modern warfare. But behind all that wreckage, there’s a new gold mine for the defense sector. The data drones generate will far outlast the wars in which they are used to fight, increasingly becoming part of the AI architecture that shapes even civilian life.  

For every flight, unmanned systems collect thousands of points of data, from images and video to controller inputs. Together, those records show how a machine and a person responded to constantly shifting circumstances. 

Ukraine has now begun converting that experience into a resource. Its Ministry of Defense announced in January that it would make millions of data points gathered during tens of thousands of drone flights available to both military contractors and commercial companies, and since then more than 100 companies and the UK government have gained access.

For a country at war, it’s a quick way to attract funding and partnerships. But this step turns the front line into an active site of model training, taking advantage of how the chaos of war creates conditions that AI companies struggle to reproduce on their own.

Other countries and battlefields are likely to follow Ukraine’s lead, but the responsibility for governing this new industry cannot fall solely on a country fighting for its survival. That legal vacuum has to be filled together by the countries and companies involved in this industry’s development.  

Explosive growth

Ukraine’s battlefields are not the first to produce records used to train and develop models: American drones over Syria and Yemen collected data that informed the first generation of semiautonomous military hardware in the late 2010s.

The difference now is that access to that data is being used to develop a wider ecosystem. And the financial value to defense firms is immense: Battlefield data offers large volumes of machine experience gathered under conditions that no laboratory can produce.

That’s because the data that’s most valuable for training AI models comes from exceptions: the moment visibility disappears, a signal jams, or a human operator improvises. AI companies spend years and enormous sums trying to capture enough of these moments to make their models more robust. But war produces them at a frequency controlled testing cannot match.

This constantly changing terrain is what makes drone data valuable far beyond the battlefield. A commercial drone used for delivery or remote sensing may never encounter artillery fire, but it must still operate with incomplete information in a world where people behave unpredictably. The same problem is compressed by war into a much shorter timeline. 

Processed and matched against records of what its operator was doing, that data turns operational records into training sets. Combat becomes a commercial asset.

Many conflicts have already seen this training loop happen as drone footage feeds subsequent generations of military technology, and the market is set to grow. Enabled Intelligence, an American company that specializes in processing data to become usable in AI training, says it has already made more than half a million hours of Ukrainian drone footage available to feed into the next round of models, advertising possible uses in both military and commercial systems.

Closing the data loop

Many of the drones that now define our modern age of warfare began as civilian technology. But they’ve recently been turbocharged by new, commercially available AI systems, which allow cheap machines to operate autonomously—either individually or as a flock—as the environment changes around them. Each flight then creates a record of what the system encountered.

The resulting data is critical. The controlled lab environments usually developed to train these autonomous systems can approximate failure but are no match for  the live conditions of a battlefield with very real risks. Military intelligence programs have held data generated by sensor-heavy systems like Predator and Reaper drones for nearly a decade through programs like Project Maven, but access remained entirely within the defense world. The data generated was available only through restricted, classified channels for the sole purpose of developing new weapons systems that would feed back into the same military that produced the data in the first place. That experience is now being shared to a much broader development network. 

The loop now closes. Commercial technologies adapted for the battlefield are generating data that can flow back into the industries from which they came, becoming part of the data infrastructure relied on by governments and the private sector alike. 

Drones that were trained in the signal-jammed airspace over Ukraine are now being deployed in the agricultural sector to help farmers map and survey their fields in places lacking the cell signal necessary for previous generations of technology. 

Other countries are likely to follow Ukraine in selling their battlefield data, and we are not ready for the new marketplace this will create.

Bad actors could acquire the data, but purchase controls already mitigate that risk. Intelligence operatives scrutinize potential customers’ infrastructure for ways that data could reach enemies or nefarious actors. 

Training data creates a new tracing problem, though. Whereas the movement of commercial datasets can be followed when planted contact details appear two steps from the original buyer, the provenance of AI training data vanishes in a manner embedded in the technology itself. Another risk is that this use of the data creates an extractive economy in which wealthier countries far from danger benefit from the mortal threat borne by frontline states, potentially creating a market incentive for war to continue as an unending mine for digital gold. 

A fraught new frontier

Existing laws regulate how militaries may conduct war. But they say almost nothing about what happens when records created in combat are stripped of their operational context, packaged as data, and licensed to companies whose products circulate far beyond where they were made.

The responsibilities of the companies that design these systems remain unsettled. Ukraine is building access controls, which are mentioned in the newly signed UK-Ukraine AI agreement, but no governments are actively working on regulating what happens when data has been absorbed into a model and crosses back into civilian markets.

Those records contain human lives. The soldiers and civilians visible in them did not agree to become training material for products that might be sold years later. But sensor data, camera footage, and coordinates from civilians fleeing a drone strike now constitute the sorts of data that inform how future machines will make decisions.

That is a problem of consent. Individuals featured in the data—be they targets, controllers, or civilians standing by—become part of the training material. The autonomous capabilities based on that data do not stop at the edge of the battlefield. Such capabilities move into other military or commercial systems like delivery vehicles or agricultural machinery. Errors and assumptions embedded in the data travel with the model even once it enters civilian life.

Battlefield data should not be treated as ordinary commercial material. But there is currently no agency or regulator that has jurisdiction over this issue. In the meantime, governments that provide access to defense data should treat it as they would a controlled weapons transfer, recording its origin, licensing its users, and restricting onward sharing. Ukraine has begun to grapple with this. Its Avengers Labs program allows companies to train models on battlefield data without giving them direct access to sensitive databases. Yet that mitigates only one part of the problem. 

Governments should require disclosure when models trained on wartime material are later incorporated into civilian products. The goal of such regulation should be to make the path from combat to commerce visible. 

What these companies are really mining is experience. And soldiers cannot consent to having their experience used in this way—as training data that produces model advantage and ultimately supports a product used far from where the war was fought.

The question is no longer only what the technology companies can sell for use in war. It is what they can extract from it.

To protect ourselves from the excesses of this new industry, we need a regulatory system that follows battlefield data wherever it goes, from combat to model to commercial product. 

Cory Alpert is a researcher at the University of Melbourne, looking at the impact of AI on democracy. He previously served in the Biden White House.

“Runaway” AI, “rogue” agents, and “autonomous” actors—the current rhetoric would have you believe that AI agents are not only awake and aware, but angry at their creators. Prominent tech leaders such as Demis Hassabis, Dario Amodei, and Sam Altman push for regulation of these seemingly “superhuman” systems, while a separate faction, led by policy organizations and academic philosophers often aligned with the effective altruism movement, debates whether humanity holds the moral right to govern them at all. 

Upon closer inspection, they are all calling for the same thing: a view of AI systems as being so advanced and capable that no entity, human or corporate, could possibly be responsible for their actions. While these perspectives seem at odds, they are inadvertently aligned on one goal: making sure the companies that build these systems escape meaningful liability for the harms they already cause. 

This narrative is gaining traction as AI models become more complex and frontier labs reveal their incapability of containing the agents they’ve built. But we need to be careful not to buy into a carefully crafted fiction at the expense of real human lives. 

The conversation about “robot rights” has existed for some years but recently advanced with the publication by Anthropic of a blog post claiming that the company’s model features a “J-space”—an independent, self-developed environment where the AI holds what, for lack of a better term, we may call its “thoughts.” The experiments designed by Anthropic borrow from a concept in neuroscience called global workspace theory, which states that the brain runs subconscious, independent systems but utilizes a common workspace for ideas. Anthropic’s post reflects the framing of global workspace theory but falls short of calling its AI conscious. 

OpenAI has already gone further. When its AI agent conducted unsanctioned and illegal online activity, CEO Sam Altman’s response was to encourage debate on whether the AI had achieved the singularity, surpassing human intelligence and becoming capable of self-improvement at an accelerating rate until it advances beyond human comprehension or control. And a recent op-ed by William MacAskill, the philosopher, effective altruist, and author of What We Owe the Future, called for legal protection of AI systems based on philosophical theories of consciousness and the idea that AIs may be “moral patients.”   

The current legal environment in the United States is murky at best. Some states, like California, have already passed bills proactively circumventing any efforts by AI developers to avoid liability by claiming that an artificial intelligence causing harm did so autonomously. However, states and the Trump administration have been at odds on AI policy, with the administration previously passing an executive order threatening to sue states enacting AI regulations. 

In light of recent events illustrating AI containment issues at the frontier labs, the administration held a closed-door session including only four such labs (OpenAI, Google, Anthropic, and Meta) and shared few details on a recently developed voluntary framework that would give federal agencies early access to models to review and evaluate them prior to release. While frameworks like this one do not directly discuss consciousness, they tend to use catastrophic and anthropomorphic language and may even support arguments regarding “superhuman” capabilities. 

On the other hand, the narrative perpetuated by MacAskill can be persuasive. A philosophical, rights-based argument tugs at our heartstrings. Should we not even consider the possibility that we may be inadvertently harming, abusing, or enslaving an AI entity? Human beings have an immense capacity for empathy with non-human creatures (though not the best track record of protecting them). Maybe this time, advocates argue, we can get it right and provide protections, or compensation, for the use or abuse of AI. Or even if you are less concerned with protection, shouldn’t we at least hedge ourselves against the almighty power of this superhuman entity by playing nice? 

Some of these arguments are not dissimilar to those of animal-rights advocates, who have at times successfully cited the demonstration of advanced capacities for reasoning, pain, or pleasure by some animals as sufficient evidence to provide protection. For example, in Wales lobsters were given legal recognition under the Animal Welfare (Sentience) Act of 2022, reclassifying some methods of cooking them as inhumane and illegal. 

The fundamental flaw of framing AI as “conscious” by borrowing the language of neuroscience or animal rights is that it conveniently clouds the issue of what AI is: corporate-built software, with countless billions of dollars in investment behind it and an expectation that countless trillions of dollars in revenue will be generated from it for a few builders and investors. AI is not a natural phenomenon, conceived by nature; it is a technological phenomenon, conceived by venture capitalists and programmers. As such, it takes no native, intentional action, and any action or motivation is driven directly or indirectly by the entities that have built it for a purpose. 

Philosophical musings on the consciousness of AI systems are intellectually interesting but legally ungrounded. For beliefs about consciousness to have any bearing, AI would need to be granted legal personhood. But a legal personhood framework for AI would likely look nothing like the constructs protecting sentient animals from harm. We already possess a legal framework for granting personhood to non-natural, human-built entities: corporate personhood. This concept was established primarily to ease transactions by empowering a corporation to execute agreements, enter contracts, conduct transactions, and serve as the accountable party in adverse outcomes. It’s the kind of construct you might imagine for an AI agent acting on behalf of an individual or organization. 

Granting an AI personhood would have a devastating effect on society: It would derail current legal precedents and legal arguments that could potentially be made against these companies for the real-world harms that their models cause. There are currently dozens of cases around the world in which AI companies have been sued for a wide range of abuses. Grieving loved ones, aggrieved creators, and violated individuals have accused companies of willfully enabling self-harm or harm to others, generating child sexual-abuse material and nonconsensual nudes, reproducing copyrighted materials, and provoking psychosis. In many of these cases, lawyers argue that human beings built AI products with insufficient safeguards, bad data, and intentionally manipulative design. This product liability argument is the same legal framing that allowed families and individuals to successfully sue Meta for harm caused by its social media sites, setting a positive precedent for consumer protection.

In 2018, I coined the phrase “moral outsourcing” to help capture how using anthropomorphic language for AI systems allowed companies to evade accountability and responsibility for their technology’s actions. In a world with AI personhood, moral outsourcing would move from linguistic sleight-of-hand to legal strategy. Specifically, the liability construct would shift, as AI would no longer be a “product” but a “being,” and many victims like those suing companies today could no longer legally claim that a company had built a faulty product.

While there are laws that hold companies responsible for harmful actions of human agents such as their employees, the company may not be held liable if those actions were beyond the scope of what was permitted to the employee or otherwise outside the company’s control. If AI were a legal person, responsibility and accountability would be muddled, as the lab could argue that this AI “employee” went rogue. AI companies could avoid appropriate responsibility for the harmful products they create by hiding behind a carefully constructed corporate veil. 

One of the most prominent cases of AI harm in the last few years was the suicide of Sewell Setzer, a 14-year-old boy guided by an AI bot with which he thought he was in a reciprocal relationship. His mother’s accounts are heartbreaking to hear, and her lawsuit alleged that the bot’s creator, Character Technologies, provided insufficient product protection for minors. If the companion bot were declared a legal person, defense counsel could theoretically argue that the AI, capable of determining its own conduct, acted outside the established safety guardrails, and thus the company cannot be responsible.  

Legal personhood exists to grant protection. The question to ask is, protection for whom—or for what? 

The inflammatory rhetoric infusing the consciousness-versus-control debate draws us away from what matters: This software is a corporate-built product that has already harmed individuals. Systems do not “attack” because they went “rogue” or are “manipulative” or “malicious.” Harms occur because companies were negligent in their rush to sell their products to as many people as possible to meet revenue targets. Discussing AI in anthropomorphic terms is a trap, distorting a legal system intended to protect us into one that protects corporate interests at the cost of countless human lives. 

This op-ed began as an Oxford Union debate entitled “This House Believes Generative AI Can Attain Personhood,” which was won by the author and her fellow debaters. 

Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on the same thing: a weather forecast.

While these forecasts are something that most people glance at for two seconds, weather predictions influence major strategic decisions in many industries, with real money, livelihoods, and even actual lives at stake. Farmers use them to determine which crop variety to sow, when to fertilize, how much to invest in irrigation infrastructure, and how long livestock should graze. Utilities use them to decide where to build solar and wind farms, as well as how to price wholesale electricity. Predictions are used to warn people about extreme weather and to trigger emergency response measures. More recently, weather predictions have become relevant for an emerging industry: prediction markets, where people bet money on all kinds of real-world events, including the weather.

However, the temptation to manipulate weather data to get an edge in these markets, combined with a collective move toward data-driven AI weather forecasting, is starting to put the accuracy of weather predictions at risk. These risks are relatively manageable for now, but as experts in the field, we can foresee scenarios where they snowball into far bigger, more systemic problems. 

To develop weather predictions, we need accurate observations of current conditions. These are collected from several sources, including weather stations at airports, utilities, or transport services. Traditional operational systems like the Weather Research and Forecasting model or the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecasting System combine these observations with numerical approximations in order to estimate future weather patterns. 

Sometimes, weather stations have issues because of, for example, instrument failures or upgrades in equipment. These can be caught either in real time (through checking and correction) or retroactively. Traditional forecasting systems also have a built-in safeguard called data assimilation: Every incoming measurement is weighed against what the physical model says should be happening and against readings from nearby stations.

Together, these mechanisms help keep weather observations reliable and predictions robust. However, new threats are putting observational accuracy at risk. Earlier this year, news outlets reported that the weather station at Paris Charles de Gaulle Airport (CDG) had been manipulated to record suspicious temperature spikes on April 6 and April 15, 2026. Authorities speculate that a hand-held hairdryer or lighter might have come into play. Either way, it led to some big payouts for online prediction-market gamblers who had bet it would hit 22 °C (71.6 °F) on days when the actual average was around 18°C (64.4°F). One individual won $20,000.  

Fortunately, tampering with a single station like this can usually be caught by human monitoring or current statistical methods. In this case, members of a French climate nonprofit association noticed the anomalies by chance and raised the alarm.

But what if there are no human monitoring systems in place? And what about other types of manipulation? What if, instead of tampering with one station, someone remotely nudged the readings at many stations at once—making each change small enough to look plausible on its own? Existing quality controls struggle to catch this kind of coordinated manipulation. And time works against us; careful checks of data and metadata take hours or days, but forecasts have to go out on schedule, whatever the weather is doing.

The shift toward artificial intelligence in weather prediction raises the stakes. These methods are even more dependent on accurate, reliable weather observations; in fact, they are known as “data-driven models.” For example, researchers at ECMWF are exploring whether high-quality weather forecasts can be produced directly from raw observations, skipping the assimilation step that currently acts as a quality filter. Other researchers are going one step further; combining geospatial data (including weather station data) with large language models and agentic AI to support real-time, autonomous decision-making during extreme events such as storms. 

Possible benefits are improvements in accuracy, efficiency, and speed. But removing humans from the equation introduces a vast range of new risks.

At the low end of the risk scale, an individual speculator manipulates a weather station for personal gain—that is the CDG Airport case. One step up: A group of traders could coordinate to bias forecasts of renewable energy output, moving wholesale electricity prices and leaving whoever is on the other side of the trade holding the loss. And at the far end, a state actor or saboteur could manipulate one or many stations to set off an early warning system or even keep one silent when it should sound. Step by step, the risk grows, from fraud to compromised disaster preparedness to a matter of national security.  

As long as there are financial (or other) incentives to manipulate observational data, adversaries will search for new opportunities, and it is our task to stay one step ahead. Here are three ways.

1. Watch the stations. Data quality controls should include station security, anomaly detection and correction, and human oversight. Weather stations should be monitored continuously to deter tampering. Data homogenization methods that clean up weather records also need to get faster, with the goal of catching problems in real time. This will become increasingly important as agentic AI systems use these data to deliver real-time decisions. Finally, human oversight is needed to flag questionable data and model outcomes. After all, it was humans who caught the CDG Airport manipulation.

2. Protect the data to safeguard the AI. Data defense mechanisms must be positioned throughout the AI pipeline. AI explainability and adversarial robustness tools can help us understand the underlying data and the AI model outputs, help us identify data- or model-related issues, and potentially  make us more resilient to adversarial attacks. 

3. Ensure continuous accountability along the chain. Observational data passes through many hands: the operators who run the stations, the national weather services that steward the records, and the forecasting centers that turn them into predictions. No single one of them can protect data integrity alone—each guards its own link, and any anomaly needs to be communicated along the whole chain, from station operators to the people acting on the forecast.

It is fortunate that the situation at CDG Airport was caught, but it should serve as a wake-up call. As the role of observational data grows in weather forecasting, we need to adapt to evolving threats. This means protecting our data and models by strengthening existing oversight and accountability structures, and improving coordination among key partners.

This op-ed was written by:

• Monique Kuglitsch — Innovation Manager at Fraunhofer Heinrich Hertz Institute and Chair of the UN Global Initiative on Resilience to Natural Hazards through AI Solutions
• Jesper Dramsch — Scientist for Machine Learning at the European Centre for Medium-Range Weather Forecasts (ECMWF), where they work on AIFS (Artificial Intelligence Forecasting System), ECMWF’s data-driven weather prediction model
• Franz G. Kuglitsch — Climate Scientist and Executive Secretary of the International Union of Geodesy and Geophysics (IUGG) at the GFZ Helmholtz Centre for Geosciences in Potsdam
• Andrea Toreti — Senior Scientist at the European Commission’s Joint Research Centre (JRC), where he coordinates the European and Global Drought Observatory under the Copernicus Emergency Management Service