In the week leading up to President Donald Trump’s war in Iran, the Pentagon was waging a different battle: a fight with the AI company Anthropic over its flagship AI model, Claude.
That conflict came to a head on Friday, when Trump said that the federal government would immediately stop using Anthropic’s AI tools. Nonetheless, according to a report in the Wall Street Journal, the Pentagon made use of those tools when it launched strikes against Iran on Saturday morning.
Were experts surprised to see Claude on the front lines?
“Not at all,” Paul Scharre, executive vice president at the Center for a New American Security and author of Four Battlegrounds: Power in the Age of Artificial Intelligence, told Vox.
According to Scharre: “We’ve seen, for almost a decade now, the military using narrow AI systems like image classifiers to identify objects in drone and video feeds. What’s newer are large-language models like ChatGPT and Anthropic’s Claude that it’s been reported the military is using in operations in Iran.”
Scharre spoke with Today, Explained co-host Sean Rameswaram about how AI and the military are becoming increasingly intertwined — and what that combination could mean for the future of warfare.
Below is an excerpt of their conversation, edited for length and clarity. There’s much more in the full episode, so listen to Today, Explained wherever you get podcasts, including Apple Podcasts, Pandora, and Spotify.
The people want to know how Claude or ChatGPT might be fighting this war. Do we know?
We don’t know yet. We can make some educated guesses based on what the technology could do. AI technology is really great at processing large amounts of information, and the US military has hit over a thousand targets in Iran.
They need to then find ways to process information about those targets — satellite imagery, for example, of the targets they’ve hit — looking at new potential targets, prioritizing those, processing information, and using AI to do that at machine speed rather than human speed.
Do we know any more about how the military may have used AI in, say, Venezuela on the attack that brought Nicolas Maduro to Brooklyn, of all places? Because we’ve recently found out that AI was used there, too.
What we do know is that Anthropic’s AI tools have been integrated into the US military’s classified networks. They can process classified information to process intelligence, to help plan operations.
We’ve had this sort of tantalizing detail that these tools were used in the Maduro raid. We don’t know exactly how.
We’ve seen AI technology in a broad sense used in other conflicts, as well — in Ukraine, in Israel’s operations in Gaza, to do a couple different things. One of the ways that AI is being used in Ukraine in a different kind of context is putting autonomy onto drones themselves.
When I was in Ukraine, one of the things that I saw Ukrainian drone operators and engineers demonstrate is a little box, like the size of a pack of cigarettes, that you could put onto a small drone. Once the human locks onto a target, the drone can then carry out the attack all on its own. And that has been used in a small way.
We’re seeing AI begin to creep into all of these aspects of military operations in intelligence, in planning, in logistics, but also right at the edge in terms of being used where drones are completing attacks.
How about with Israel and Gaza?
There’s been some reporting about how the Israel Defense Forces have used AI in Gaza — not necessarily large-language models, but machine-learning systems that can synthesize and fuse large amounts of information, geolocation data, cell phone data and connection, social media data to process all of that information very quickly to develop targeting packages, particularly in the early phases of Israel’s operations.
But it raises thorny questions about human involvement in these decisions. And one of the criticisms that had come up was that humans were still approving these targets, but that the volume of strikes and the amount of information that needed to be processed was such that maybe human oversight in some cases was more of a rubber stamp.
The question is: Where does this go? Are we headed in a trajectory where, over time, humans get pushed out of the loop, and we see, down the road, fully autonomous weapons that are making their own decisions about whom to kill on the battlefield?
That’s the direction things are headed. No one’s unleashing the swarm of killer robots today, but the trajectory is in that direction.
We saw reports that a school was bombed in Iran, where [175 people] were killed — a lot of them young girls, children. Presumably that was a mistake made by a human.
Do we think that autonomous weapons will be capable of making that same mistake, or will they be better at war than we are?
This question of “will autonomous weapons be better than humans” is one of the core issues of the debate surrounding this technology. Proponents of autonomous weapons will say people make mistakes all the time, and machines might be able to do better.
Part of that depends on how much the militaries that are using this technology are trying really hard to avoid mistakes. If militaries don’t care about civilian casualties, then AI can allow militaries to simply strike targets faster, in some cases even commit atrocities faster, if that’s what militaries are trying to do.
I think there is this really important potential here to use the technology to be more precise. And if you look at the long arc of precision-guided weapons, let’s say over the last century or so, it’s pointed towards much more precision.
If you look at the example of the US strikes in Iran right now, it’s worth contrasting this with the widespread aerial bombing campaigns against cities that we saw in World War II, for example, where whole cities were devastated in Europe and Asia because the bombs weren’t precise at all, and air forces dropped massive amounts of ordnance to try to hit even a single factory.
The possibility here is that AI could make it better over time to allow militaries to hit military targets and avoid civilian casualties. Now, if the data is wrong, and they’ve got the wrong target on the list, they’re going to hit the wrong thing very precisely. And AI is not necessarily going to fix that.
On the other hand, I saw a piece of reporting in New Scientist that was rather alarming. The headline was, “AIs can’t stop recommending nuclear strikes in war game simulations.”
They wrote about a study in which models from OpenAI, Anthropic, and Google opted to use nuclear weapons in simulated war games in 95 percent of cases, which I think is slightly more than we humans typically resort to nuclear weapons. Should that be freaking us out?
It’s a little concerning. Happily, as near as I could tell, no one is connecting large-language models to decisions about using nuclear weapons. But I think it points to some of the strange failure modes of AI systems.
They tend toward sycophancy. They tend to simply agree with everything that you say. They can do it to the point of absurdity sometimes where, you know, “that’s brilliant,” the model will tell you, “that’s a genius thing.” And you’re like, “I don’t think so.” And that’s a real problem when you’re talking about intelligence analysis.
Do we think ChatGPT is telling Pete Hegseth that right now?
I hope not, but his people might be telling him that.
You start with this ultimate “yes men” phenomenon with these tools, where it’s not just that they’re prone to hallucinations, which is a fancy way of saying they make things up sometimes, but also the models could really be used in ways that either reinforce existing human biases, that reinforce biases in the data, or that people just trust them.
There’s this veneer of, “the AI said this, so it must be the right thing to do.” And people put faith in it, and we really shouldn’t. We should be more skeptical.
