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Let’s face it. China may no longer be a near-peer to the United States, militarily. It’s likely caught up and could even be ahead. Analysis by Govini finds that to gain or keep an advantage, military leaders don’t need more money. They need better decision science. Joining the Federal Drive with Tom Temin to explain, Govini’s vice president...
Let’s face it. China may no longer be a near-peer to the United States, militarily. It’s likely caught up and could even be ahead. Analysis by Govini finds that to gain or keep an advantage, military leaders don’t need more money. They need better decision science. Joining the Federal Drive with Tom Temin to explain, Govini’s vice president of strategy, Billy Fabian.
Tom Temin: Mr. Fabian, good to have you on.
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Billy Fabian: Thanks for having me. Tom. Pleasure to be here.
Tom Temin: Let’s begin with decision science. That sounds like it could be the next big word here after customer experience or something. What is it?
Billy Fabian: So the Deputy Secretary of Defense Kathleen Hicks has talked about the need in our competition with China for the United States to gain a decision advantage. As she puts it, from the boardroom to the battlespace. So you have both in warfare where warfare is becoming more decision centric, the side that can more rapidly process information and make decisions and then act on the battlefield has an advantage. You also have where China is an economic and technological competitor in a way that the Soviet Union never was during the Cold War, right? Their power in both those fields is only growing. So for the United States, our margin for error is much smaller than it was in the Cold War. We can’t simply just like spend our way out of it or rely on us having technological advantage forever. We have to make better decisions. But to do that at the scale and the speed that you need to compete with China in the modern world, you know, it’s probably analog methods of just humans making better decisions aren’t going to work. So decision science is taking technology, principally artificial intelligence, machine learning and data at scale and bring those to bear to make faster decisions with the idea of being faster, better decisions, with the idea being that the side that can outwit rather than just out muscle the other will have the advantage and prevail.
Tom Temin: Now that sounds like it makes a lot of sense for an actual conflict situation. But can it also apply this idea of better decisions and decision science to some of the things that take a long time, for example, it’s been 20 years since the United States has had a gambit to replace the tanker refueling planes, and 20 years, maybe more than 20 years, there’s still no replacement tanker that works yet, or a new bomber, or all these decisions on the size of the fleet that take years, sometimes decades to play out. Can better decision science help with those kinds of things, to build the fundamental capability?
Billy Fabian: Yeah, absolutely. I think you could argue that the boardroom side might even be more important. That sort of the competition won’t be decided in the Taiwan Strait, or somewhere in the western Pacific, but actually, on both nations’ respective home fronts, in who, in sort of day to day competition, or peacetime, or whatever you want to call it can invest more smartly, to be more efficient to develop the capabilities that they need, have them ready, because warfare, in many ways is less about like, who builds the next generation of tanks or fighter jets or ships, but who can harness the technology that’s being driven by the commercial sector and apply it to military functions. And to do that well, right, I think as you’re alluding to, is difficult, right? And for us to do it better, we need better processes and better capabilities.
Tom Temin: And to get back to the analogy of Russia, in the Cold War era, when the United States developed all of these technologies in the 70s and 80s, that gave what they call the strategic offset, you did not have a competitor with the industrial base equal to the United States. And now you do in China, therefore, whatever decision processes worked back then won’t necessarily work now.
Billy Fabian: Yeah, I think that’s right. So China certainly has some advantages that the Soviet Union never had, right? And, and some advantages over us, right? So they’re sieve-mill fusion, as they call it, their ability of their government to force their companies, whether they’re state owned or private, to work with them, and to help aid their efforts, those advantages. Now, I think United States still remains like the great engine for innovation in the world. And we have this amazing economy that creates all this really cool technology for the United States government. It’s more about tapping into that and unleashing it for its needs. So it’s figuring out how to partner better with the sort of companies that are driving forward these technologies.
Tom Temin: I guess, China likes its military industrial complex. We’re speaking with Billy Fabian, vice president for strategy at Govini. He’s also an adjunct senior fellow at the Center for a New American Security, we should add, and tell us then what are some of the ways you get to better decision science? Is there a technological underpinning that can help people speed up decisions so that they make right ones and good decisions?
Billy Fabian: That’s the key. It’s fast decisions, but also quality decisions. Yeah, so I think foremost among the sort of technologies that underpin decision science, as I mentioned before, artificial intelligence and machine learning and data at scale. And then there’s a bunch of sub-technologies that fold under both of those, but there’s a broader set of technologies that underpin it. So there’s the hardware that it would run on. So things like advanced computing and communications. There’s the type of technologies that allow humans to better integrate with the machines to benefit from what the data at scale and machine learning is producing to make better decisions. So things like augmented reality and interfaces and things like that. And then there’s a set of applications that our leaders, particularly in the Department of Defense use to make decisions that are increasingly powered by decision science. So things like modeling and simulation, right? So simulations of future conflicts, synthetic training environments that allow large groups of military personnel to train over long distances, all sorts of technologies like that, that are sort of in the decision science umbrella, either enabling decision science or enabled by it.
Tom Temin: And you’ve listed 15 decision science prime contract vendors, that is to say they don’t sell decision science as a product, but they sell the elements that can be used to build decision science?
Billy Fabian: Yeah, that’s correct. So what we did in the paper was we looked at historical U.S. government spending since 2016, both traditional contract spending and through other transaction authorities, which are one of these newer tools that the government has to try and reach sort of more nontraditional partners in the commercial sector. And try to organize all that spending and identify the spending that was related to decision science technologies, and then taxonomize it and to get a better picture of what the U.S. government has been investing in this and who they’ve been partnering with.
Tom Temin: It looks like there’s a lot of spending on data architecture and storage. That’s the easy part. But when you look at fusion of all that data, which would make this into a decision making power, not so much.
Billy Fabian: So we think it’s an interesting story. So if you look at the total spending by the U.S. government over that period, since 2016, on decision science technologies, it was about 30 billion or so. But if you look at the annual levels, compared FY 16 to FY 20, which is the last year we have complete data for, there’s a 50% increase in annual spending. So they are spending more on decision science technologies, which I think is a good news story. And I think you’re right in that about half of the total spending has been on data at scale and its various components. But the spending on machine learning and AI has also increased. I mean, it’s starting from a smaller base, but it’s also gone up something over 50%. So I think it’s a positive direction. But I think it is a fair question to say, is it at the scale that we need now? Right? Does it need to be ramped up even more? And I think one of the things to watch for when the president’s FY 23 budget request comes out soon sometime in the next couple months, if there is an intention to spend more of this over the next few years.
Tom Temin: And the final question has to do with the tradition and culture and process of the Pentagon itself. They operate in five year cycles. And the kickoff to a cycle is the Program Objective Memorandum, the POM, which someday hopefully, they’ll execute on the budget that is justified by the POM, and there’s, it’s very complicated and millions of people have to say yes and no. Is there any way that decision lens thinking can somehow rev up that process? So there is some real correlation between what they would like as priorities? And what actually comes out in their spending patterns?
Billy Fabian: I think certainly decision science could be applied to programming and budgeting. For sure. I think it’s it’s actually probably a field that’s ripe. You know, there’s some, some efforts right now to try and reform that process which is known as PPBE, planning, programming, budgeting and execution. So I think, yeah, it’s certainly right for decision science. I think it’s also interesting how the department, I think, knows that a lot of the most important technologies for the future, including these decision science technologies, are being developed by the commercial sector and parts of the commercial sector that the government has historically struggled to work with. And I think like Department of Defense is aware of this, like Secretary Austin, the Secretary of Defense talked about this at the Reagan National Defense Forum in December.
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The department has created, or the U.S. government has taken a lot of initiatives to try and improve this through creating technology hubs and incubators and through Congress, providing them with new tools like OTAs, other transaction authorities. But what we found in this report is that in one of the areas that’s like sort of the most innovative part of the economy, and the part that’s leading the way on decision science technologies, which would be venture capital and private equity backed companies generally small startups, that despite all those efforts, which are well intentioned and have shown some promise and done some good, we’re still not reaching those companies and the government still not able to partner with them. Only 4% of the vendors that have worked with the government on decision science technologies since FY 16 have been VC or PE backed companies. So a very small percentage. And so I think one of the great challenges and questions going forward is, how do we fix that? Because it seems that despite all these efforts, they’ve largely just been sort of changing things on the margins, and not solving the ultimate problem, which is how do you get these companies that have very different incentive structures to not only succeed when choosing to partner with the government, but there are many that just see the bureaucratic headaches, the risk and say, it’s not even worth it. So a whole bit of the economy that the government isn’t getting a chance to work with.
Tom Temin: Billy Fabian is vice president for strategy at Govini and an adjunct senior fellow with the Center for New American Security. Thanks so much for joining me.
Billy Fabian: No problem, Tom. It was great to be here. Thank you.