The Manhattan Project, Take Two

In the new era of global competition, being at the forefront of Super Intelligence innovation equals geopolitical power. To maintain leadership and establish the next world order, the U.S. is moving to cement its lead. Barnett Koven is a Managing Director of AI and Data at Accenture Federal Services . He came to Accenture Federal Services after stints in industry and a faculty job at the University of Maryland, where his counterinsurgency research showed that some of the most influential quantitative studies got the answer exactly backwards. We discuss… Why “inaction is in and of itself risky” — and how government risk-aversion compounds against less-cautious competitors The “gun-armor race” in classified AI environments, where defense costs grow exponentially against linear offense Entity resolution and predictive modeling for BIS export-control enforcement, and unwrapping the PLA’s shell-company maze DOE’s Genesis Mission and Accenture Federal’s “backbone for scientific research” Thank you to Accenture Federal Services for bringing us this episode. Listen now on your favorite podcast app . From COIN to Genesis Jordan Schneider: Let’s do a bit of the career arc. How do we get from a PhD on COIN to DOE and the Genesis mission? Barnett Koven: Absolutely. I spent a lot of time in Latin America and saw the aftereffects of some virulent insurgencies in Peru and Colombia in particular, ended up doing a PhD studying the spatiotemporal diffusion of insurgent violence as a result of counterinsurgency strategy — specifically as a result of development assistance and other non-kinetic interventions during counterinsurgency. Really loved that, and sought out the opportunity to do more policy-relevant research, which is not necessarily the norm at the academy. Ended up in a faculty job at the University of Maryland, which was also wonderful — a research institution where I was encouraged and able to do a bit more government-funded research as well as the traditional teaching and publishing you'd expect from a professor. Enjoyed that immensely. However, I did a lot of mixed-methods work, and it became harder and harder to do the travel that I wanted to do and the data collection. At that point I had a number of friends at a competitive consulting firm who had reached out to me over the years and tried to get me to come join them. I finally said, you know what, what the heck, let me at least have the conversation. And I realized through some really influential leaders at that firm that this would be an opportunity to apply the rigorous research methodology and quantitative skills I learned in grad school and honed at UMD to real-world problems, and do it in a way that felt closer to the mission and closer to the policymakers and government who were actually hopefully benefiting from and using those insights. Jordan Schneider: Let’s talk through some of those. What were some of the things that came your way? Barnett Koven : In general terms, two of the coolest projects were using natural language processing in order to extract insights from diplomatic cables. You may not know this, but the Department of State sends back on the order of 3,000 cables between posts and headquarters in DC every day. These are chock-full of insights in textual form, but no one's reading 3,000 cables, let alone memorizing them and absorbing all of that on a daily basis. So the goal here was to use old-school, pre-gen-AI-revolution natural language processing approaches to extract and categorize some of that insight to make it easily searchable so that it could inform policymaking. And I'm very, very proud of the work we had done there. That being said, if you fast-forward half a decade to today, I would have done it completely differently. Some of the tools that are now available that weren't then would have accelerated what was a six-month fast-paced project involving a half-dozen-person team into something that two people could have done in a matter of weeks, and just as well or better. True Believers and the Risk-Averse Jordan Schneider: Well, let's talk about that for a second. The idea that software is really cheap, anyone can run Claude Code, Codex stuff — in your interactions with the government, to what extent has the light bulb gone off that just a lot more is possible than even was, I don't know, 12 months ago? Barnett Koven: The cop-out answer here is: it depends. And certain government customers are way closer to the forefront than others. Part of this is a product of their unique mission and how applicable it is to these changes. But part of it is also just risk aversion. Some of our law enforcement clients, for example, tend to exhibit a little bit more risk aversion here. That being said, I think some of the skepticism is really healthy. Jordan Schneider: Wait, sorry — as in, I can't prove a case if an LLM collects the evidence for me? What do you mean by that? Barnett Koven: I just meant nervous about second- and third-order implications, whether that's sensitive information being exposed to the public, or the evidentiary value being diminished because of a court ruling — and there have been court rulings around gen-AI-produced legal briefs and what have you. So I didn't mean anything specific, but there's a number of different directions to take that. Jordan Schneider: Gotcha. Okay, but the ones who are the believers — what's the common line there? Barnett Koven: There are a couple of explanations. Some of the true believers are coming from industry into government for the first time and have seen the power of these tools in their prior jobs in corporate America, where the uptick has maybe occurred first. Others are just super passionate about their mission, and while they've been in government for perhaps decades in some cases, they make it a point to stay abreast of the latest and greatest and are willing to try just about anything if it portends significant advances in their niche domain, in their scope of work. Jordan Schneider: Yeah, it's an interesting dynamic that we've seen with these labs, right? On the one hand, I can come up with crazy fun little applications, and there are power users who are building billion-dollar businesses with a headcount of three. But you also have these partnerships that all these labs are signing with consulting firms, with PE firms — because, no, you can't just press a button and have the AI solve everything for you today. There clearly needs to be this human layer of actual people to see the problems and solve them. I just found that to be interesting — that we're not quite here yet where you can, all right, GPT 7.0 or Claude Mythos 4.0 can just solve everything in government. Barnett Koven: There are a couple of dynamics at play. First, these models are trained on what's available on the open internet, which is in many ways distinct from the niche missions of corporate customers as well as government, all of whom have very sensitive data that is not usually available in the public domain and available for training of these tools. So to think these tools out of the box can do everyday tasks that you and I do, like plan a grocery list — absolutely. That's not a perfect parallel to engaging in complex foreign policy negotiations per se. The second piece is the garbage-in-garbage-out problem that's always been the case with data science, which is exacerbated here. Someone needs to have not only the institutional knowledge, but also know where the data lives, what limitations are evident in that extant data, and so on, in order to get the best value out of those models. And the final thing I'll say is metadata management has become huge here. Having clean, curated metadata gives an LLM some additional context to grab onto that massively improves the quality of the output. Mirror Imaging in the Age of LLMs Jordan Schneider: You mentioned AI for high-stakes foreign policy negotiations. One of the things I've been thinking about recently