• Q1: What is Maven Smart System?

• Q2: What problems is MSS designed to address?

• Q3: What capabilities do large language models (LLMs) provide in MSS?

• Q4: What is Anthropic’s role in Maven Smart System?

• Q5: What is Anthropic’s future with Maven Smart System?

• Q6: What future does the Pentagon envision for Maven Smart System?

Full article available here. Excerpt below.

What Is Maven Smart System, and What Does It Do?

The Flagship Software Platform of AI-Enabled Warfare, Explained

by Matt Mande and Gregory C. Allen

June 2, 2026

During the first 24 hours of the war in Iran, the United States used Maven Smart System (MSS) to help strike more than 1,000 targets, a tenfold increase over what was possible in the pre-MSS era. However, few outside the government and contractor ecosystem involved in creating and using MSS are familiar with what it is, how it works, and the capabilities enabling a revolution in military intelligence and targeting.

Fortunately, a combination of old and newly available public and unclassified information sources allows for a much more complete understanding of MSS. Government and industry partners are sharing more than ever before about the platform, including through video demonstrations of its capabilities. At the same time, Katrina Manson’s recently published book Project Maven: A Marine Colonel, His Team, and the Dawn of AI Warfare includes interviews with critical stakeholders, many of whom were, until recently, remarkably tight-lipped. Reporting by various news outlets on the war in Iran has also revealed new details.

The following analysis consolidates and organizes this information to answer six key questions about Maven Smart System and its future in the Department of Defense (DOD).

Q1: What is Maven Smart System?

A1: MSS is an output of Project Maven, a Pentagon initiative founded in 2017 to bring AI capabilities to warfighters. Project Maven had a turbulent early history: Google was an early technology partner but withdrew in 2018 following employee protests over the company’s involvement in military AI development. Palantir, a data integration and analytics company, stepped in shortly after and has remained Project Maven’s primary industry partner. The company’s DOD contract ceiling for Maven surpassed $1 billion in May 2025, with MSS work added to that specific contract in fall 2025.

Thus, “Maven” refers to the current DOD program, which includes many other lines of effort and AI activities—such as AI-enabled Automated Target Recognition (ATR)—beyond just MSS. “MSS” refers to DOD’s flagship AI-enabled software platform, which draws from many data feeds, including Maven ATR. Palantir is the prime software integrator for MSS and stated in unpublished documentation reviewed by CSIS that “MSS is powered by the Palantir Platform.”

MSS is already used by the Joint Staff, Combatant Commands, and various elements throughout the DOD and Intelligence Community, as well as NATO allies. Palantir claims that MSS’s user base has doubled every six months for more than two years. In May 2025, MSS reportedly had more than 20,000 users, implying a current user base of roughly 80,000.

MSS provides a graphical user interface (GUI) for military intelligence and targeting purposes as well as a growing list of Combined Joint All-Domain Command and Control (CJADC2) functions. As described in the unpublished Palantir documentation, “MSS interfaces share a live, synchronized view of the Battlespace to imbue Warfighters and decisionmakers with real-time understanding in support of the overall CJADC2 mission.” The MSS user interface also allows for user tasking and human validation of AI labeling done by other Maven AI systems. In practical terms, this means that the use of MSS can speed up targeting decisions without sacrificing analytical rigor or judgment quality.

The platform aggregates, organizes, and visualizes vast streams of intelligence, surveillance, and reconnaissance (ISR) data. According to Manson’s reporting, CENTCOM’s deployment of MSS drew from 179 distinct data sources in 2024, a figure that industry sources told CSIS has increased significantly since. AI models, including those from other Maven data feeds, identify patterns in this data, which MSS visually displays. For example, computer-vision models find and label potential targets in live satellite or drone video feeds, which then appear within a yellow box on a user’s screen.

MSS also embeds workflows directly into that interface, allowing operators to move from observation to action without switching systems. The DOD’s public demonstrations show that in a targeting scenario, a user can select an AI-detected target, evaluate available strike assets in the vicinity, and compare options using operational constraints such as time to strike, distance, and fuel requirements. Once an asset is selected, the user can order the strike and subsequently monitor its effects using ISR feeds.

Palantir’s documentation of MSS provides a nonexhaustive list of six specific baseline capabilities:

• Battlespace management

• Target management

• AI-enabled deliberate planning and execution

• Computer-vision detections

• Machine-assisted disclosure

• Generative AI
  

Oversight of Project Maven and MSS has shifted locations within the Pentagon several times over the past decade. Project Maven originally sat within the Office of the Undersecretary of Defense for Intelligence and Security before the Biden administration split responsibilities between the National Geospatial-Intelligence Agency (NGA) and the Chief Digital and AI Office (CDAO) in 2022. Under this configuration, the CDAO paid MSS’s licenses and managed its text-based functions, while the NGA handled geospatial-intelligence work, including the production of computer-vision models. In March 2026, a memo from Deputy Secretary of Defense Steve Feinberg ordered oversight of MSS to be fully relocated to the CDAO, a decision that this article discusses in greater depth in Q6.

Read the rest of the rest of the article at CSIS.org

“So how much do you think semiconductor export controls contributed to China’s push for self-sufficiency? It can’t be zero, right?”

It definitely isn’t zero, but the size, contribution, and timing of the effect are different than the most vocal critics of export controls claim. Get the answer to this question wrong and you get the policy wrong. This includes, urgently, the policy this administration is now deciding in the wake of Donald Trump’s trip to China, with Nvidia CEO Jensen Huang riding along.

Ming-yen Ho is a non-resident fellow at a Taiwanese think tank, the Research Institute for Democracy, Society, and Emerging Technology (DSET). Last year, Ho published a paper on China’s semiconductor industry. Among many other virtues, the paper provides an interesting dataset to apply to the export controls question. Specifically, Ho has published the best dataset that I have seen thus far on how much the Chinese government is putting into semiconductor industry subsidies and equity investments (which have many of the same effects as subsidies). He also obtained good data on how much investment Chinese semiconductor firms are receiving each year from foreign investors, which can shed some light on how they responded to the controls.

If we compare Ho’s dataset to the timeline of the biggest events in U.S. semiconductor export controls and Chinese semiconductor industrial policy, we can test the hypothesis that U.S. export controls led to a big spike in Chinese government support to the industry. While government subsidies are not the only Chinese reaction that should interest U.S. policymakers, they’re still a hugely relevant test case. A mostly complete timeline of key export control events is included at the end of this article. But for our purposes, there are three events that loom largest:

• Made in China 2025, the 2015 policy in which China made semiconductor self-sufficiency a core goal of its industrial policy;

• The 2018 ZTE export controls, which were the first big semiconductor move by the first Trump administration; and

• The October 7, 2022 export controls by the Biden administration on AI and semiconductor tech.

As you review the subsidy and investment data, keep two things in mind:

1. Causality can’t run backwards in time: export controls cannot cause trends that existed before the export controls do (or at least before the perceived threat exists).

2. The change in the trend matters more than the change in the values: An observation that China’s investment in self-sufficiency is increasing or decreasing after the export controls doesn’t by itself tell us that the export controls had an effect. We need to know whether the rate of increase or decrease has changed. Knowing the change in trend helps us understand the difference between where China is now and where China would have been in an alternative scenario in which the export controls were never applied (what historians and social scientists refer to as the counterfactual analysis).

Chinese semiconductor self-sufficiency has been a stated goal of Chinese policy for decades, long preceding the modern U.S. approach to export controls that began in 2018. Moreover, this is not merely a goal, but one that the Chinese government has been willing to invest enormous amounts of money in. The river of money began with the first “Big Fund” in 2014, when the Chinese government started pouring tens of billions of dollars’ worth of investment into the semiconductor industry. The first Big Fund ended up being about $21 billion of investment, and was followed by a ~$29 billion second Big Fund in 2019 and a ~$48 billion third Big Fund in 2024. The first Big Fund was paired with a simultaneous dramatic expansion of the subsidies available to chipmakers, though the second and third weren’t.

Now let’s take a look at the data itself.

Looking at the timeline, there are two big investment spikes, in 2016—after Made in China 2025 and the first “Big Fund” of government semiconductor equity investments got underway in earnest—and again in 2020, after the second Big Fund was announced. When 2025 and 2026 data is available, it will likely show another spike reflecting investments by the third Big Fund, which was announced in 2024.

Based on the explanations given by Chinese policymakers and documents at the time (which expressed fear of dependence on the U.S.), it is at a minimum plausible that the 2018 ZTE export controls caused the spike in 2020. However, the government subsidy and investment levels were broadly similar to those present before the export controls.

The “this is just the continuation of the previous trend” explanation is even more obvious when you look at the government equity investments received by Chinese companies in the form of paid-in capital stock. Again, government equity investments are a very important tool of Chinese semiconductor industrial policy. In terms of their impact, government equity investments are similar to direct subsidies.

The Big Funds used large single-year government outlays to establish the investment vehicles themselves (hence the big spikes), but those financial resources are disbursed to semiconductor companies at a more consistent rate over time. The second Big Fund was evidently established when the first one ran out of money with the goal of continuing the same rate of industry investment, not increasing the level as a response to export controls.

Thus, the Chinese government’s overall rate of accumulation of semiconductor equity ownership investment was almost unchanged in the period after the ZTE export controls. After the 2022 Biden export controls, the rate of government investment actually decreased a bit. China’s semiconductor industry’s access to foreign private sector capital investment dropped to almost zero in the wake of the controls, which makes sense as a reaction to the new U.S. policy.

In short, available data on Chinese government subsidies and equity investment does not provide strong support for the hypothesis that export controls were a deciding factor in the Chinese government’s decisions about the appropriate level of investment.

My favored interpretation of the data is that, in 2018, the government had already reached or exceeded what it perceived to be the maximum productive rate of subsidization in pursuit of its previously decided policy of self-sufficiency. After all, the first billion dollars of subsidization (from 0 to 1) helps a lot. The 100^th billion dollars (from 99 to 100) helps far less. At a certain point, all the additional subsidization and investment doesn’t buy you a more robust semiconductor industry: it just fuels further corruption and waste. The first Big Fund already had corruption and waste in abundance.

Perhaps the Chinese government would have liked to subsidize and invest more if it had additional attractive options available, but those options did not seem to exist. The exodus of foreign capital probably made things worse, since interest by sophisticated foreign investors can help provide persuasive evidence to not always especially sophisticated Chinese government bureaucrats that a given Chinese company has real merit and is not merely a scam.

Government investment is not the only data that we’re interested in to answer this question, and in a future post, I’ll share some additional interesting data on the change in China’s chip sales, chip production capacity, and the rate of technological progress of China’s leading chipmakers.

So let’s return to the question from the former senior U.S. policymaker:

“How much do you think semiconductor export controls contributed to China’s push for self-sufficiency? It can’t be zero, right?”

Actually, it might be zero, at least in terms of government investment in the semiconductor industry.

Of course, the Chinese government has other tools to achieve its goals besides subsidies—facilitated smuggling, state-backed industrial espionage, coercion of international firms, stockpiling of manufacturing equipment, etc.

And the Chinese government is not the only relevant actor: the Chinese memory chipmaker YMTC launched a very large and well-resourced de-Americanization campaign in 2019 that was clearly a response to U.S. export controls on ZTE and Huawei and the fear that YMTC would be next.

But for Chinese government investing and subsidies specifically, export controls do not seem to have moved the needle. Made in China 2025 had already directed the government to do everything in terms of investing and subsidizing that it could. By 2022, that had been true for nearly a decade.

I’ve spent more than a decade working on AI, semiconductor, and defense tech policy. Across all that time, one pattern keeps showing up. The public version of an export controls action, an AI regulation fight, or a U.S.–China technology dispute is usually three or four analytical steps removed from what’s actually going on. The headlines reflect the press conference. The substance lives in the footnotes — in the corporate financial filings, the Chinese state-media coverage, and the procurement and budget documents that are mostly public but mostly unread.

Decision Tree is where I write down what I find when I do that close reading and number crunching, plus drawing upon a global network of smart folks I’ve met over years of doing this.

A few specifics on how I plan to do this.

The publishing pace will be set by the analysis, not the news cycle. I’ll publish when I have something to say that’s worth your time, and not before. AI policy is awash in takes. What’s in short supply is patient reading and careful work. That will probably be every one or two weeks.

My focus is the geopolitics of strategic technologies, broadly drawn. That covers U.S. export controls on advanced semiconductors and the AI systems they enable. It covers frontier-model policy (including safety, regulation, and economics), the defense industrial base, and the U.S.–China contest over compute and chip equipment. It also covers the harder long-run questions — where the next generation of leading AI labs and fabs get built. Subjects rotate, because the debate does. The same factories, ministries, and labs usually matter for several topics at once.

Hit reply to any issue, or write to contact@decisiontreeresearch.com. I read both. If you’d rather hear me than read me, I host weekly episodes of The AI Power Podcast.

Welcome. Glad you’re here.

— Gregory C. Allen, Founder and CEO, Decision Tree Research