# AI sharply increased the number of targets the military could strike

Markdown copy of https://realalpha.app/s/ai-sharply-increased-the-number-of-targets-the-military/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Katrina Manson](https://realalpha.app/people/katrina-manson/) · Katrina Manson is author of Project Maven: A Marine Colonel, His Team, and the Dawn of AI Warfare. · Investigated the US military's AI use and chronicled the Pentagon's Project Maven program in her book.
- Host: [Gus Docker](https://realalpha.app/people/gus-docker/) · Host, Future of Life Institute Podcast
- Show: Future of Life Institute Podcast
- Episode: [Can Humans Still Control AI-Assisted Kill Chains? (with Katrina Manson)](https://realalpha.app/v/nPsqoXabzzc/), Sep 25, 2026
- Moment: 8:42 to 15:26 of the episode (7 min), playing on https://realalpha.app/s/ai-sharply-increased-the-number-of-targets-the-military/; the episode from there: https://realalpha.app/v/nPsqoXabzzc/?t=522

## Quote

> “With the help of large language models as well, because that **sped up processes**.”
> — [Katrina Manson](https://realalpha.app/people/katrina-manson/) at [10:42](https://realalpha.app/v/nPsqoXabzzc/?t=642) · Future of Life Institute Podcast

## Takeaways

1. Computer vision helped raise targeting capacity to 1,000 a day.
2. Large language models were expected to push that figure to 5,000.
3. More time for decisions may instead lead to more decisions.

## About this segment

Katrina Manson traces how AI boosted targeting capacity, from fewer than 100 targets a day to thousands. She also explains why faster decisions can increase risks rather than improve judgment.

From “Can Humans Still Control AI-Assisted Kill Chains? (with Katrina Manson)”, Sep 25, 2026. [Watch the full episode](https://realalpha.app/v/nPsqoXabzzc/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[8:45](https://realalpha.app/v/nPsqoXabzzc/?t=525) say? And what do opponents say? So for the people who have spent many, many years now, a decade, trying to create the system to breathe it into life, and are incredibly proud of it, for them, I think it comes down to a few different things. One is the idea that US intelligence has always lagged people on the front lines who were actually risking their lives. And so one of the people who led Project Maven, the chief of it, who ran it day to day and really pushed for it, was called Drew Kukor.

[9:17](https://realalpha.app/v/nPsqoXabzzc/?t=557) He was a colonel in the Marines, and he had been frustrated in Afghanistan. by the complete inability of the US military to really be able to check its own information, to know, as one of his colleagues told me, whether they were going to knock on the door and be greeted by cups of tea or machine guns. And so they simply weren't storing their information in a way that they could recall it. And this is a very basic digital filing system, essentially, that he had been working on for years in order simply to access information about the environment they were in. But I think it went much further to this idea that AI was going to speed up kill chains. At the beginning, they were in no way talking about targeting explicitly, often even inside the Pentagon themselves. But once it became Maven's smart system, and this group started

[10:03](https://realalpha.app/v/nPsqoXabzzc/?t=603) working with the 18th Airborne Corps, they really were trying to pair weapons to objects that became targets and hit them. And using this system, they started to get to a much higher rate of being able to hit things, which if you're in the military counts as success. And so there were some exercises that were trying to get to a thousand decisions an hour. When I went to speak to folks at the National Geospatial Intelligence Agency, I learned that with the help of computer vision, before the US military could hit under a hundred targets, I think in a day, and with the help of with computer vision, they could hit a thousand targets in a day. With the help of large language models as well, because that sped up processes. For example, making the request for a legal judgment or a legal assessment of why something may or may not be legal, they thought they could

[10:58](https://realalpha.app/v/nPsqoXabzzc/?t=658) get to 5,000 targets in a day. And if you look at what the Pentagon has said about using this system in Operation Epic Fury in Iran, they've used this system for those first 1,000 targets in the opening 24 hours. They've used it to hit 13,000 targets. These are numbers that US Central Command itself has put out. So from their perspective, it's become extremely successful way of speeding up the kill chain and being able to hit more at a rapid rate. And they would argue, make sure that the commander is well informed. Now, the arguments against it are plural. They come from many different places, and I found it very interesting exploring those. Obviously, there's a kind of civil society concern from the likes of stop killer robots that it's a stepping stone towards autonomy, meaning ultimately that machines will make decisions. For the people involved in Maven,

[11:55](https://realalpha.app/v/nPsqoXabzzc/?t=715) they always argued that this was a machine assisting a human and the human would make the final decision, but they don't control the future. And many of the commanders I spoke to said they expect that this system will become automated. There's also been a focus on drones, and this system is not about that currently, it's about decision making. So for the US, they're seeking superior decision making to their adversary. For those who are concerned about this, they're worried that looking at a screen means that people are more likely to trust those outputs on screen than doing the thinking or checking back through the data themselves. Do we know?

[12:30](https://realalpha.app/v/nPsqoXabzzc/?t=750) That's true. Do we have research on whether looking at targets on a screen as opposed to, say, more traditional warfare does actually make it easier to kill people? Well, the data is hard to come by. To do a really good scientific evaluation of Aben's smart system, you'd need baselines and you'd need access to that classified data. So I have found it very helpful to speak to military ethicists who are working with some of these groups themselves.

[12:57](https://realalpha.app/v/nPsqoXabzzc/?t=777) So there is a military ethicist at the 18th Airborne Corps who was brought in to help them think through the implications of what they were doing. And one of the things he raised to me was that he was worried that people would trust machine outputs over their own judgment. And the argument was made to me over and again that humans make poor judgments regularly themselves and more information would help, not less.

[13:24](https://realalpha.app/v/nPsqoXabzzc/?t=804) I think the speed is another element that is introducing an element or could introduce an element of risk. I spoke with the former director of Project Maven. He is an Air Force general now retired named Jack Shanahan. And he, of course, had been a real champion for this. He's even on record saying, essentially paraphrasing, I want the Department of Defense to ensure that AI is baked into every weapon we ever make in future. But even he, after my book came out, we were discussing it, and he said on a panel at Stanford that he was worried that type A personalities in the military, instead of taking more time that Maven's smart system gave them to make a better decision, would simply make more decisions in the time they had, and that there might be this rush to hit things rather than consider hitting the right thing. Now, when I speak to people inside

[14:19](https://realalpha.app/v/nPsqoXabzzc/?t=859) the US military, they stress the doctrine, the workflow, the efforts that go into this. But I do detect that not all of these workflow issues are worked out, that AI is unreliable or mistaken results at points at which operators may not be aware. And so really checking that system and understanding how well joined up it is becomes absolutely key to deciding how far to lean into this and understanding how the nature of decision-making changes. So I think also another key element of the decision-making process from commanders is to what extent they do seek legal advice for their decisions. They have to make sure that a target is valid under the law of armed conflict, how much time a lawyer has to advise on that, and how well a lawyer understands those data systems is also being tested in real time, I think, through Operation Epic Fury. The scale

[15:14](https://realalpha.app/v/nPsqoXabzzc/?t=914) at which the US is hitting things and having to make decisions, or certainly did through that period of Operation Epic Fury, I think is unprecedented. And an accounting of actually