Free with a sign-in.
The best 13 min of 42
Can Humans Still Control AI-Assisted Kill Chains? (with Katrina Manson)
Katrina Manson says Maven speeds military targeting, but human oversight, data quality and accountability remain unresolved.
FPFuture of Life Institute Podcast · Gus Docker
- Katrina Manson is author of Project Maven: A Marine Colonel, His Team, and the Dawn of AI Warfare.
The best minutes
- 8:42AI sharply increased the number of targets the military could strike7 min
- 17:43Computer vision struggled when Ukraine's battlefield changed3 min
- 23:30Targeting doctrine is shifting from human decisions to oversight3 min
Also worth watching
- 35:53Accurately labeled data matters more than the model3 min
- 27:00Maven was updated 50 times during Ukraine's first months3 min
- 29:53AI companies lack visibility into classified military use3 min
- 15:45AI made human judgment the longest step in a targeting cycle2 min
Skip the opening project overview and system tour, future command-system plans, and the closing open questions.
Full analysis · 6 min read
Real Alpha · Nov 8
Katrina Manson says Maven was used to hit 13,000 targets
- Manson said Central Command reported Maven's use in strikes on 13,000 targets.
- That figure shows throughput, not accuracy, civilian risk or better judgment.
- Operators still need time, trustworthy inputs and audit records to check what systems surface.
Free with an account
Read the rest of the analysis
7 sections, the 3 other voices it tests Manson against, and what it means for you. Every episode we cut gets one.
Check your inbox
We sent a link to your address. Click it to finish signing up. Not there in a minute? Look in spam.
or
Signed up before? The same buttons sign you in. The episode, its moments and the transcript stay free.
Maven's reported reach exceeds its public proof
At the National Geospatial Intelligence Agency, Manson said computer vision helped raise capacity. She recalled the earlier rate as under 100 targets, she thought, in a day, against 1,000.
They thought large language models could take the rate to 5,000 daily by speeding tasks such as legal assessments. Manson presented that as an expectation, not an observed result.
She then cited U.S. Central Command's figures for Operation Epic Fury. The system was used for the first 1,000 targets in the opening 24 hours, and used to hit 13,000 targets. Manson said Central Command itself had put out those figures.
These figures show workload and strike volume, not independent accuracy or civilian-harm rates. The episode gives no denominator for targets proposed, rejected, confirmed or later found mistaken. Nor does it establish how many of the 13,000 relied on AI recommendations.
Our read
The 13,000 figure demonstrates scale, not better targeting. Without baselines and adjudicated outcomes, throughput cannot settle whether decisions improved or risks fell.
Faster cycles can increase decisions, not judgment
Jack Shanahan, a retired Air Force general and former Maven director, voiced an operational concern. Manson said he worried that type A personalities might use extra time for more decisions, not more careful ones.
That is a risk about incentives, not a measured result across units. Manson said a scientific evaluation would need baselines and access to classified data.
She said NGOs reported submitting what were probably hundreds of civilian-casualty reports to CENTCOM, still unadjudicated. Those reports do not identify which tools informed strikes or establish whether Maven altered outcomes.
But no public denominator ties the reported strike volume to confirmed valid targets or civilian harm.
Our read
Shanahan's concern explains why speed alone is not a success measure. The episode describes a plausible behavioral pressure, but lacks public evidence to quantify it.
AI made human judgment the slowest step
At the 18th Airborne Corps, Manson saw a six-step decision cycle that had been human-run. AI took over three steps involving data collection, analysis and assessment.
Human judgment had been quickest. After the change, it became the cycle's longest stage. But Manson did not know whether that pattern carried through to Central Command.
That boundary matters because the deployment she observed cannot automatically stand for every command. She also described the human stage as where the law of armed conflict may be considered.
Battle-damage assessment is another lagging step, she said, affecting repeat strikes and civilian-risk assessment. Computer vision, she added, struggles to identify military objects after they are blown apart.
“the longest part of the cycle ended up being the human judgment”
Our read
Calling human review a bottleneck describes time, not whether it is expendable. Here, the slow step is also part of considering the law of armed conflict.
Ukraine exposed how quickly recognition can fail
For example, early U.S. computer vision systems could not recognize Russian tanks in Ukraine's first days. Manson said they had been trained on desert and Philippine jungle conditions, not Ukrainian terrain and snow.
She said she was told accuracy sometimes fell to 30%, and in some cases to 10%. Retraining on new tank images improved results, but the opening days exposed a reliability gap as battlefield conditions changed.
Take the Russian transporter erector launchers arranged in a semicircle. From above, Manson said, they could look like trucks, making military objects hard to distinguish from civilian vehicles.
Operators knew the arrangement, but did not know whether they could share that context with algorithm vendors. Still, this was a communication problem as well as a model problem.
Our read
The failure involved more than a weak model. Changing conditions, missing context and uncertainty about communication all affected whether operators could rely on recognition.
Doctrine contemplates AI action under human oversight
Manson reviewed a Joint Targeting 360 revision from April 2026, with a new chapter on AI's future role. She said it contemplated AI initiating actions under human oversight, rather than a human making each decision.
The document recognized moral and legal dilemmas and called for unambiguous guidance. Her reading was that such guidance did not yet exist.
Manson also said experts worried edge operators might turn off records, making decisions harder to reconstruct. She said an audit should show what information drove each decision, what was checked and when. She described that workflow as unfinished.
Emil Michael, on The Information's TITV in October 2026, objected to giving models authority over human operators. His view fits the control concern, but does not show Maven already gives models command authority.
Anthropic's Claude had been used in Maven Smart System, while Anthropic lacked visibility into classified use. That leaves company guardrails, Pentagon policy and actual classified practice difficult to compare.
Our read
Human oversight needs more than a named person in the workflow. It needs authority boundaries, durable records and rules investigators can test.
Rapid updates do not establish reliability
During Ukraine's first months, Manson reported, Maven Smart System was updated 50 times. She linked that pace to operators and vendors working closely and adapting the system to new realities.
Yet senior Central Command officials publicly graded the system C-plus in 2024. By 2026, Palantir chief executive Alex Karp called it targeting's backbone.
Palantir is the prime contractor, so that description comes from a company building and selling the system. Frequent updates show iteration, but neither a public grade nor a vendor's endorsement proves operational reliability.
In October 2026, Diogo Almeida told The MAD Podcast an early TypeSafe version took a week to build. He said it was not robust enough to use.
That is a different product, not direct evidence about Maven. It sharpens the distinction here. Update speed is not proof of dependable decisions.
Our read
Close feedback can help developers adjust software rapidly, as the 50 updates suggest. Buyers still need outcome measures and independent checks, not deployment cadence alone.
Labels and assessment still limit reliability
Several people associated with Project Maven told Manson that well-labeled data was more valuable than the algorithm or model. She described early labeling by volunteers or assigned military workers, some of whom resisted the task.
Later gig workers were not accurate enough, she said. Now, she said, something like an $800 million contract includes labeling work for Maven.
She said one mislabeled image could poison the pipeline. That warning came through her reporting, not a public audit. Manson also said battle-damage assessment remains difficult, limiting feedback on whether missions succeeded.
Nenad Tomašev of Google DeepMind told MTS in October 2026 that mathematics has formal specifications and provers for definitive feedback. Targeting does not offer such a simple pass-or-fail verifier, so assessment and labels matter all the more.
Our read
Better labels and post-action assessment are prerequisites for learning from use, not proof that a system is safe. Without clear checks, operators and evaluators may struggle to distinguish reliable outputs from errors.
What it means for you
- Separate volume from outcomes. Log proposed, approved, rejected and struck targets separately. Compare decision times with accuracy and civilian-harm reports.
- Retest changed conditions. Test performance across terrain and weather changes. Record how quickly operators flag failures and get context to developers.
- Keep decision records. Check whether records preserve inputs, suggestions, human review and final action. Ask an independent reviewer to reconstruct selected decisions.
- Audit labeled data. Track label accuracy, correction rates and source provenance. Check whether battle-damage assessment supports a reliable feedback loop.
What to watch
- Whether the Pentagon publishes an accounting of Maven use and outcomes in Operation Epic Fury, including the Minab strike.
- How the April 2026 Joint Targeting 360 revision defines human oversight and audit records.
- Whether labeling quality and battle-damage assessment become measurable enough to support public evaluation.
Send this to the product, operations and legal leaders setting AI review rules.
Watch the minute where Manson gives the 13,000-target figure.
Written by Real Alpha from the episode's transcript and the other moments it links, then checked against the transcript. Quotes are the speaker's exact words.
Minute by minute
- 1:34Maven began as a push for AI
Manson says Project Maven began at the Pentagon in 2017 to bring AI—especially computer vision and natural language processing—to military data, including drone video from the Middle East and Afghanistan. She says its founders also wanted the Pentagon to treat software as a core military capability and prepare for warfare that could move faster than humans think.
- 3:20Maven links data to targeting
Manson describes Maven Smart System as a digital map that draws on more than 150 data sources to show forces and surface potential targets. Its target workbench can suggest a weapon and send an execution message through the system. She says AI is used in the find and fix stages and may increasingly be used in the finish stage.
- 7:02Operational details remain classified
Manson says operational use happens on classified networks, while some intelligence sits at even higher classification levels. She says early restrictions made it difficult to obtain details about Maven through Freedom of Information Act requests, leaving journalists and the public with an incomplete picture.
- 8:35Supporters prize faster targeting
Manson says supporters wanted troops to retrieve intelligence more easily and to speed up targeting. She cites figures that computer vision raised capacity from under 100 targets a day to 1,000, while LLM-assisted processes were projected to reach 5,000. She says CENTCOM reported that the system was used for the first 1,000 targets in Operation Epic Fury's opening 24 hours and for strikes on 13,000 targets.
- 11:28Faster decisions raise oversight risks
Manson says critics worry that decision-support systems could normalize autonomy and encourage operators to trust screen outputs over their own judgment. Retired Gen. Jack Shanahan warned that operators might use saved time to make more decisions rather than deliberate longer; Manson also flags unreliable outputs and pressure on legal review. She cites NGO-reported hundreds of civilian-casualty reports submitted to CENTCOM that had not yet been adjudicated.
- 15:45Human judgment becomes the bottleneck
At the 18th Airborne Corps, Manson says a six-step decision cycle originally had a human at each step; after AI took over three data-related steps, human judgment became the slowest part. She says the military might see that as a bottleneck, but it is also where the law of armed conflict is considered. Battle-damage assessment remains difficult, including for computer vision trying to identify objects after they are destroyed.
- 17:43Ukraine exposed model limits
Manson says computer vision initially struggled to identify Russian tanks in Ukraine because models had been trained on desert and jungle imagery; reported accuracy sometimes fell to 30% and in some cases 10%. Retraining on tank images improved results, but mobile missile launchers arranged in semicircles looked like trucks from above, and operators did not know whether they could share that context with vendors. She says US information passed through Maven helped Ukrainians target 267 items in one day.
- 20:44Auditability and human control remain uncertain
Manson says Maven is supposed to have audit trails, but she cannot verify how well they work and has found errors and outdated or mislabeled entries in military intelligence databases. She says the Joint Targeting 360 doctrine, revised in April 2026, envisions AI initiating actions under human oversight, while acknowledging unresolved moral and legal dilemmas. She says accountability depends on preserving records of what information informed each decision.
- 27:00Palantir gained influence through integration
Manson says Palantir's close work with operators let it adapt Maven Smart System to their workflows; she reports 50 updates in the first few months of Russia's invasion of Ukraine. After Google employees protested the company's Maven work, Google did not renew its Pentagon contract, and Manson says the department turned to Palantir. She notes that a senior CENTCOM official publicly gave the system a C plus in 2024.
- 30:15Defense vendors contest usage limits
Manson says Anthropic set red lines against fully autonomous weapons and domestic mass surveillance, while Pentagon officials argued that the department sets the rules for military use. She notes that Claude was included in Maven Smart System, but companies may lack visibility into how their models are used in classified operations. She says the Pentagon is seeking models that respond to defense-related commands.
- 32:38Maven may underpin broader command systems
Manson says some commanders expect targeting systems to become automated and sees Maven's use in Operation Epic Fury as a chance for the military to learn. She says the Pentagon may extend the system for a possible conflict involving Taiwan and use it as a core part of JADC2, linking intelligence, operations, sensors and weapons. She cautions that connecting sensors to shooters remains difficult.
- 34:57Data quality constrains AI performance
Manson says iteration depends on access to relevant data and reliable battle-damage assessments, but the Pentagon's data silos hindered Project Maven. She reports that labeling work moved from frustrated military workers, some of whom defaced images, to gig workers and then professional teams; a labeling contract she describes as $800 million includes Maven data. She says a mislabeled image can contaminate the pipeline and undermine model reliability.
- 38:48Minab and agent use remain open questions
Manson says she wants a clear account of Maven's role in Operation Epic Fury and the strike on a school in Minab, including how the Pentagon assessed any error and what it changed. She also wants to know how LLMs and agents are used in classified workflows and how operators are trained to check model outputs. She says the department is using something like 100,000 agents at the unclassified level, but she does not know how agents are used in classified operations. Her central question is whether digitization improves the information operators can verify or just speeds up existing problems.
Published Sep 25, 2026.
Transcript · 30 min read
Future of Life Institute Podcast, hosted by Gus Docker, with Katrina Manson.
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
Project Maven was this effort that began at the Pentagon in 2017 to really find a way for the US military to start using AI. When I've seen it demonstrated to me, if you imagine something like Google Earth for war, you are in the right ballpark. So for the people who support this system, this is about speeding up what they call a kill chain, about closing that kill chain as fast as they possibly can. And AI is being used in the find stage, in the fixed stage, and may increasingly be used in the finish stage where actually something gets hit. The scale at which the U.S. 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 what happened in those decisions will be really important. I would really like to know precisely what happened in the strike against the school in Minab in Iran and to what extent that relied on Maven and to what extent that relied on other tools and where the Pentagon has assessed there was a mistake and what they've done about it. What are the lessons learned from this kind of error and how seriously are they taking it internally and what changes have they made, if any? Welcome to the Future of Life Institute podcast. My name is Gus Docker and I'm here with Katrina Manson.
Katrina, welcome to the show. Thanks so much for having me. We are going to discuss Project Maven. Now, there is a massive IT system that's responsible for helping much of the U.S. military make decisions, but it's not widely known. What can you tell us about Project Maven? Well, Project Maven was this effort that began at the Pentagon in 2017 to really find a way for the U.S. military to start using AI, And in this case, particularly focused on computer vision, but not only that, also natural language processing, what has become LLMs, our chatbots that people are using, and find a way to bring AI to the vast amounts of data that the US military was collecting, mostly through drones, through drone video feeds that they were collecting over the Middle East, over Afghanistan.
Read the whole transcript
on. But it was always much more than that. What I really tried to figure out in the book was the overriding impulses of the group that started at Project Maven. And at the beginning, they were trying to apply computer vision to this video feed footage, but they were also trying to change the way that America makes war to begin to understand that digital software would be as important in the views of them as hardware, as tanks, that the Pentagon would have to start buying software completely differently, but also that war was going to speed up, speed up faster than humans could think. That was the part that particularly intrigued and interested me as the big debates about AI were beginning to roil in society at large, looking at this group who had tried to lean into AI and change the nature of war and potentially the way or the character of war, I should say, and potentially the way that humans make life and death decisions on the battlefield felt very important to me. And I wanted to know exactly how AI had performed. You mentioned the system. Out of this project came a platform known as Maven Smart System. That started being developed from about 2018, 29 onwards. And today is becoming
what's called a program of record inside the Pentagon, meaning that it is a funded platform and it helps, I think the Pentagon would say, with decision making. When I've seen it demonstrated to me, if you imagine something like Google Earth for war, you are in the right ballpark. It's a digital screen. The idea is that you could see everything in one panel that shows you where your forces are, where enemy forces are, and then is able to surface new targets or potential targets through computer vision and other means. More than 150 different data sources feed in to Maven's smart system. It can also link through to targeting workflows. There's an element called target workbench. It can even suggest the right weapon to pair to the target and then send a message through the system to the weapon to go, as the military says, execute against the target to go and fire at it. So it's become this kind of all-encompassing system that really goes from sucking in the data to actually firing against a target. And so for the people who support this system, this is about speeding up what they call a kill chain, about closing that kill chain as fast as they possibly can. And AI is being used in the find stage, in the fixed stage, and may
increasingly be used in the finish stage where actually something gets hit. Can you describe when you're using this system as an operator and you're faced with making a decision, how is the system presenting information to you in ways that might push your decision making around? The way it's been described and demonstrated shows a series of suggestions and then you can go into the target workbench. It is a system that is supposed to have a great deal of transparency and auditability attached to it. The company that makes the interface is called Palantir and is also now the prime for the whole contract, which means they, although lots and lots of other companies work on Maven Smart System, Palantir is the key company. They have always talked about the tagging of information, the metadata that goes along with information. So I know it is meant to be a system that has a great deal of metadata audit logs attached to it. But this is a system that is used at a classified level. The way it's been shown to me is at an unclassified level, green. And what I was shown was a series of objects in the sea, and then being able to zoom in on them, try and check what they are. And eventually, you could make a decision to
And I'll see you next time. pair a weapon to those objects or just track them if they were friendly items and work out which port they were in. So it's an awareness system, but also potentially a firing system. I think in terms most recently, a senior Pentagon official has demonstrated this system at a Palantir conference in March this year, we can check. And that indicated actually looking at video feed footage and then being able to get that visual on it then zoom out but obviously other types of targets are what are called deliberate targets so these are things that are already in the system and chosen in advance and then the secondary type of target is called a dynamic target that is a target that is either moving or that wasn't already expected to be hit now okay how much do we know about the system and how it works this is a difficult question to answer because you don't know what you don't know, but do you have a sense of how much of this is classified information versus how much is publicly known? Well, anything operational is classified. So the system itself works at the classified level and the US fights its wars on secret networks. I think something that's an interesting tension for me is a lot of intelligence information lives at an even higher
classification level than that, top secret or higher. And so getting information into Maven smart system in order to operate on it is also part of the effort that the US military is going through. Obviously, Maven is public. So there's lots of unclassified information out there about it too. The fact that it exists, the fact that Maven smart system is now a program of record, some of the funding for it, the fact that the US Marines are using it and others are training on it. There's a lot of discussion on it. But I think there's a gap. So very early on in Project Maven, it was made impossible to foyer anything on Project Maven to do a Freedom of Information Act request for information about it. So it's not very easy for members of the public, for journalists to learn about the specifics of Maven. And some information is available and some isn't. And as you say, to some extent, you know, I don't know what I don't know, but a huge amount of it is going to come down to operational usage. And that the military very rarely talks about in public. What are the arguments for and against using a system like this? What do the supporters
“With the help of large language models as well, because that sped up processes.”
what happened in those decisions will be really important. I doubt the military will share that publicly. It's not information I have access to. But we do know that there are hundreds probably of Thank you. reports of civilian casualties in Iran that have been submitted to CENTCOM because the NGOs say they have submitted to them, and they haven't yet been adjudicated on.
“And it changed so that the longest part of the cycle ended up being the human judgment, just because everything else got so much quicker.”
military objects that have been blown apart. Yeah, maybe you could give some examples here
“It had been trained on the desert. It had been trained on the jungle in the Philippines.”
When mistakes are made in this system, how easy is it or has it become easier to find out where the mistake was made and ultimately to assign blame? Say, for example, that you have a mistake where a civilian target is bombed as opposed to a military target. It's a really good question. So the broad things I found out are that at the time I sent in the book, there was no explicit mistake to look at. But I spoke to defense officials, former defense officials, who said AI atrocities are possible. atrocities are possible in the era of AI warfare. And when that first AI atrocity occurs, everyone will investigate it and try and understand what went wrong.
Now, the system is supposed to have good auditability. I don't know if it does have good auditability. Something you can tell from what the Pentagon is... pursuing now is bringing AI and agents into the way they go through their intelligence stores. So at the moment, I'm confident that, or I've reported, that there have been errors in intelligence databases in the US military system. We know that because we've seen errors in the past, and I know that from some of my reporting. Things can be mislabeled in this database, things may not be updated in time. And so the idea of bringing AI to the store is that it will surface anomalies and flag people to look at these anomalies. That is work that is underway now, which means it wasn't currently in place, you know, even last year, whereas AI-assisted targeting was in place. So I think in terms of accountability, theoretically, it should be there, but I'm not the one on the inside pulling these numbers.
Yes, I've mentioned human judgment as a bottleneck here for decision-making, and we've just talked about mistakes being made. And the system might be capable, for example, of identifying or giving information about where mistakes were made, and we might know something about to what extent human judgment is the bottleneck. Is there, do you sense political pressure to say, for example, remove human judgment or decrease the bottleneck that's constituted by human judgment and perhaps obfuscate the source of the mistakes in the system?
“AI will initiate actions under human oversight rather than with the human making the decision.”
make decisions, but clearly people are expecting that day to come. Can you describe how the system
“over the course of the first few months of Russia's invasion in Ukraine, Maven's smart system was updated 50 times.”
I think I wanted to come back to the guts of your question.
“the companies themselves don't know how their AI is being used in classified operations.”
How do you foresee this system evolving in the future? For me, one of the most important conversations I had was with the commander who said, ultimately, all this stuff will become automated. And I think it's really important to see the way that MavenSmart system was used in the opening day and days of Operation Epic Fury as some thing of a test for the US military, as much as they were pursuing a very serious major combat operation, they are also always trying to improve their own technology. And so whatever it is they've learned about Maven's smart system from that, it will have application for another potential conflict that the Pentagon is always preparing for, which is the possible invasion of Taiwan by China. And the idea that Pacific Command will be using Maven Smart System, developing that, trying to get more and more weapon systems together, more and more AI rifling through the data, trying to get that auditability if it isn't up to scratch. And then I think scaling it, Maven Smart System is now at the heart of this effort to create something called JADC2. This is essentially a command and control system for the entire U.S. military. Anyone who's covered this says this is a sort of a never-never land, something that the U.S. military has been pursuing
but not achieving. They now explicitly have a tool to try and create this MavenSmart system. I think you'll see an effort to bring together intelligence systems and offensive operation systems. I don't know how successful that will be. It will be controversial within the... Pentagon itself, but an effort to use Maven's smart system more and more, not just for individual operations, but for really the global picture that the US has of the world and how it connects up to weapon systems. The whole idea is linking sensors to shooters, which has become an in vogue phrase that is very easy to say, but actually delivering that has been very hard. And Maven's smart system, I think, is at the core of how the Pentagon wants to try and go about achieving it.
So historically, we've seen this connection between war and the speed of technology development with the airplane, for example. Do you think it's fair to say that we're seeing an effect where when the Maven smart system is used in actual war, you get data and you get feedback that you can use to more rapidly improve the system, such that when it's deployed, it improves more quickly. I mean, that's the hope that they want this iterative loop that will suck in data, do something with it, assess whether it worked, and go back through the loop again. For me, it's been really profound that battle damage assessment still is insufficient. So if you don't know if your mission was a success, you have to make a decision if you want to do the same mission again. And AI really should be helping them determine that. And I think that may be something of a gap still, just because it is very hard to do these battle damage assessments. I'm wondering whether getting data from actual usage means that
“it is more valuable than the algorithm or the model. It is having access to well-labeled data”
as a final question here, I would like to know that after having done the research for this book,
what are your biggest open questions about Maven? What would you really like to know that you weren't able to find out? I would really like to know how Maven was used in Operation Epic Fury. I would really like to know the baseline for what the Pentagon considers success. I would really like to know precisely what happened in the strike against the school in Minab in Iran, and to what extent that relied on Maven, and to what extent that relied on other tools, and where the Pentagon has assessed there was a mistake, and what they've done about it. What are the lessons learned from this kind of error, and how seriously are they taking it internally? And what changes have they made, if any? That to me feels like the most urgent question one could possibly ask. And to understand what role AI and MavenSmart system did or didn't play in that should have ramifications for years, if not decades, about the way the US military thinks about using tools like this. Now, I don't know. I've reported on elements of what went wrong in Minab, which is that some information was remarked on that an intelligence analyst in 2019 made remarks about a change of usage in that site at the school, put it into a digital system that
wasn't connected to the main database that the US uses, that feeds its own databases to draw target lists from. So if connectivity is the problem, if an over-reliance on a system output is the problem imagining all these things are fixed in the back end but actually nothing is That also is a very big question for me. And then I would like to know, how are they using large language models inside this system? And are they using agents now? Are they prepared to hand over tasks to agents to run elements of the workflow without humans doing those jobs to act as humans inside that system? And that would be a really big decision.
They are using something like 100,000 agents inside the department at an unclassified level. I'm not aware of how they're using agents at a classified level. They've also recently tested out different AI models, not in Maven's smart system, but in something that could be a proxy for Maven's smart system. And I'd really like to know what they learned. What did they learn about the results of each model, about the prompts that they put into these models? and to what extent are operators being trained on the fallibility of these models and how far to lean in to them. I think for me, the key question is over and again, I'm told that the human makes the decision to fire. You can only make a good decision if you have good information.
And as that targeting cycle gets compressed, I want to know what kind of information an operator is able to check themselves to dig down into. And if that process feels better and more robust than when they were using scraps of paper and swivel chairs and something extremely analog, is digitization fixing these problems or is digitization speeding up these problems? All fascinating questions. Katrina, thanks for talking with me. Thanks. Thanks for having me.
