In 2011, Silicon Valley venture capitalist Marc Andreessen famously wrote, “Software is eating the world.”1 Software is also eating war, and the naval intelligence community must not fall behind. For millennia, wars were fought with physical assets in the physical world. War was ugly, brutal, and visible. Modern war, although still ugly and brutal, is often invisible—at least in the physical world. This is confusing to leaders who expect war in the digital age to look and feel like war in the analog age.
Throughout history, technological superiority has been a key advantage in great power conflict. More than almost any facet of war, intelligence is primed for technological disruption. Software, such as autonomous systems, artificial intelligence (AI), machine learning (ML), deep learning, and neural networks, is indispensable to the mission of analyzing and disseminating quality intelligence.
While countless military disputes tug at the attention of military leaders, the 2022 National Defense Strategy emphasizes the “growing multi-domain threat posed by the PRC”—the People’s Republic of China—and prioritizes “the PRC challenge in the Indo-Pacific.”2 The ability to build enduring military and intelligence advantages is most at risk vis-à-vis China. Only China will be able to match (or outmatch) U.S. technological innovation in the coming years. In 2017, China announced a plan to create a $150 billion AI industry by 2030.3 McKinsey and Company estimated that by 2021 AI could add $600 billion in value to the Chinese economy, mostly in its transportation, logistics, and manufacturing sectors.4
China also has the benefit of a massive population accustomed to trading privacy rights for convenience. The more data added and analyzed, the better the AI becomes. “In deep learning, there’s no data like more data,” wrote Kai-Fu Lee, a Taiwanese computer scientist and businessman, in the 2018 book AI Superpowers: China, Silicon Valley, and the New World Order.5 While the U.S. government cannot legally track and evaluate its population to train capable AI algorithms, the Chinese government can and does. Even if the Chinese government does not always use its surveillance to train military AI, it is still improving general AI capabilities that can be requisitioned for military uses, just as domestic commercial manufacturing capacity can be commandeered during wartime.
Good intelligence is often a prerequisite for military success. It does not matter how formidable a weapon system is if the Navy does not know the enemy’s location, capabilities, and intentions. The Navy has plenty of advanced weapons from which to choose once it has located and characterized its target. Unfortunately, intelligence, surveillance, and reconnaissance (ISR) capabilities for counterterrorism or counterinsurgency operations in a permissive battlespace are not readily transferrable to ISR in highly contested, high-threat environments against peer powers. In a permissive environment, the United States can control “the timing and tempo of operations.”6 But while naval intelligence operators were engaged in a permissive threat environment in their fight against global terrorism, the pace of innovation in information services pressed forward at a whirlwind clip.
Perpetually Behind the Innovation Curve
Navy Commander Wolf Melbourne called the 2010s “Naval Intelligence’s Lost Decade” because the naval intelligence community did not innovate at the same pace as information enterprises in the private sector.7 The lessons naval intelligence officers learned in permissive environments will not universally apply in contested environments. Since commanders cannot always pick their battles, they enter dangerous situations with imperfect information. When the timing and tempo of operations are beyond a commander’s control, the intelligence preparation of the battlespace must happen more quickly and with less certainty than in a permissive environment.
In short, intelligence must be gathered and assessed at lightning-fast machine speed, not human speed. Seamless human-machine coordination should be the goal of the naval intelligence community, and modern software is making machine speed achievable for intelligence operators. Hunting for timely, usable, and relevant intelligence is a process of tedious refinement. Mere information, defined as the accumulation of unanalyzed data, is not intelligence. The main problem facing today’s intelligence analysts is not the inability to obtain information but rather the inability to synthesize data into workable intelligence. Data analytics software is one possible solution.
Humans are now using billions of personal devices, including personal computers and smartphones, to share information on the internet, and more than half of the world’s population owns a smartphone.8 Each day, billions of individuals freely post data that is openly accessible to anyone with an internet connection, including intelligence analysts. This vast trove of open-source information can be synthesized into open-source intelligence (OSInt) if it can be analyzed properly, but that is a big if. Without advanced software, there will never be enough time or personnel to sift through the volume of available open-source information. Software is many orders of magnitude faster at converting open-source data into intelligence by revealing invisible links across datasets. Thousands of documents and datasets can be scanned and evaluated in seconds using advanced software tools. AI and ML cannot discover every pattern in a deluge of data, but they can discover patterns faster and more efficiently than a team of human analysts ever could. They can also perform simple tasks that take humans a long time, such as translating foreign language communications into English. Even if the translations are not as polished as a human translator could produce, they can be done in moments instead of hours. In a conflict, those saved hours may be the difference between success and failure.
The Russo-Ukrainian war has drawn attention to the importance of OSInt like never before. Open-source commercial satellite images and Russian soldiers’ social media posts, among other such data, have been instrumental in substantiating Russian war crimes and verifying troop movements.9 The open-source nature of preinvasion intelligence also made a basic fact of intelligence analysis clear to non–intelligence professionals: The exact same data can be logically interpreted in many ways. Even as Russian troops gathered on Ukraine’s border, plenty of well-meaning analysts, researchers, and journalists still doubted an imminent invasion. Others asserted that if Russia did invade, its forces would overwhelm Ukraine and declare victory within a matter of days.
AI/ML and Human Bias
Cognitive biases, such as availability or confirmation bias, are often at the root of why humans make the wrong intelligence judgments when confronted with complex data. These biases were undoubtedly at play when Russian intelligence operators were analyzing their military’s position against Ukraine. When President Vladimir Putin approved the invasion, he likely expected a weak internal response from the Ukrainian people and a disjointed response from NATO. These were possible outcomes, but not the only ones. Properly designed software can reduce the effect of human bias on intelligent judgments.
Software also can analyze huge datasets faster than any human analyst could. Information that might have taken hours or even days to find in the pre-internet era is now available in seconds. Far more powerful analysis and search tools can now be employed to help intelligence operators find the information they need when they need it. OSInt is a double-edged sword because robust national intelligence agencies no longer monopolize intelligence collection, but state-of-the-art software is still a differentiating factor for state-run intelligence agencies because it can dramatically improve the scale and speed of intelligence collection and analysis.
Intelligence and the Battlespace Picture
Leaders who want to make good decisions about their actions in a battlespace require an accurate common operational picture (COP) informed by thorough intelligence. The COP is a “picture” of the combat arena made up of operational information and all available, integrated data that has been converted into intelligence and displays an informative, although usually incomplete, view of the situation. Because intelligence can improve the COP, it is important that intelligence can be widely and jointly integrated among various analysts, sensors, and shooters.
The Navy’s Project Overmatch seeks to do just that. Project Overmatch is a Joint All Domain Command and Control (JADC2) effort to integrate common data standards, share data, and improve interbranch information synergies.10 Unclassified details about the project are scant, but initiatives such as Overmatch are precisely the investments the Navy needs to make to maintain intelligence superiority in future wars.
In the Indo-Pacific, the leaders of the Quad nations—Australia, India, Japan, and the United States—recently announced a new maritime domain awareness initiative, the Indo-Pacific Partnership for Maritime Domain Awareness, to improve the maritime COP in the region.11 “Dark ships” that operate with their Automatic Identification System (AIS) turned off to conduct illicit maritime activities are a growing problem in the Indo-Pacific. Illegal Chinese fishing vessels are primary culprits. With an influx of commercially available data to collect in the region, the biggest problem facing Indo-Pacific nations is how to process the volume of data that already exists.12 HawkEye360, a U.S.-based geospatial analytics satellite operator, has been selected as a key partner for the Quad to patrol the region and track illegal fishing boats. This type of public-private partnership is key to securing a lasting naval intelligence advantage through advanced software.
Lessons from the private sector abound, and naval intelligence professionals have a duty to learn from commercial software businesses. For example, financial analysts must spend hours searching through company filings, websites, news articles, and press releases to find relevant data to make smart investment decisions. The process is similar to a naval intelligence analyst poring over reports and data to convert them into usable intelligence. Current databases are at times still tedious to search, which leads to wasted effort and yields irrelevant search results.
AlphaSense, a market intelligence and analysis search engine, is a prime example of software that can make this search easier. Using AI-powered search technology, AlphaSense automatically collects and collates data from a vast library of sources using parameters set by the user, saving precious search time so an analyst can do his or her real work: analyzing information, preparing reports, and making decisions. “AI reads [millions of] documents in a couple of seconds, whereas it would take me five lifetimes,” said Jack Kokko, the chief executive officer and cofounder of AlphaSense.13 Naval intelligence must continually improve its ability to search intelligence databases, libraries, and files, ensuring its professionals can expeditiously search and evaluate data.
Naval intelligence professionals also must partner with software engineers in the private sector to keep up with the newest innovations. China’s “military-civil fusion” strategy exists to employ the best of its private, or semiprivate, businesses to bolster its military.14 The strategy reveals the importance Chinese military leaders ascribe to civil partnerships, and they are not as wary about close civil-military relationships as some U.S. leaders. China’s Academy of Military Science, the academic research arm of the Chinese People’s Liberation Army, partnered with the Artificial Intelligence Military-Civil Fusion Innovation Center to make progress in defense AI technology, including “intelligent operating systems” and unmanned aerial systems. The PLA also is using U.S. software and money to “intelligentize” its warfare.15 A Politico report found that “the overwhelming majority of advanced computer chips at the heart of China’s military AI systems are designed by U.S. firms like Intel, NVIDIA, and Xilinx, and manufactured in Taiwan.”16 This is not to say that China is ahead of the United States, but rather that U.S. intelligence leaders must continue to press innovation forward through public-private partnerships if they wish to maintain their advantage.
Innovation Requires Great People—and Failure
Leaders in charge of intelligence innovation projects and the politicians who allocate funding to them must be comfortable with an imperfect success rate when betting on new technologies. Not every intelligence software company the United States bets on will succeed, but this is the nature of innovation. If commanders want to see material improvements in intelligence collection and output, they must be willing to take risks on unproven technologies that have considerable potential, even if some fall short of expectations. Behind many great innovations is a series of failures and dead ends. Risk tolerance is a virtue during periods of technological disruption. Fortunately, software will never be as capital intensive as large shipbuilding projects. AI is not the new littoral combat ship.
It is still people, not software, who make the final decisions about what commanders see. Naval intelligence cannot engineer its way to a perfect COP. It can, however, improve the skill and speed with which its analysts do their jobs. Human analysts must be subject-matter experts on threats, knowing the values, history, and culture of the enemy, not just its military capabilities. Software can help those analysts spend less time searching through datasets for useful information and more time reviewing, evaluating, and presenting intelligence to commanders.
Software has already eaten the U.S. economy. Firms that quickly adopted software as the commercial landscape shifted are now titans of industry. The United States has long been the world’s most powerful military, but giants can fall as quickly as they rise if they do not adapt to new conditions. Blockbuster was the biggest name in movies until Netflix burst onto the scene and transformed itself into a software company, and the United Kingdom used to be the world’s foremost maritime power. George Washington, America’s first spymaster-in-chief, once wrote, “There is nothing more necessary than good intelligence to frustrate a designing enemy: and nothing that requires greater pains to obtain.” If naval intelligence seeks to maintain superiority in the digital age, it has no choice but to innovate and adopt powerful new software, AI, and machine-learning tools to meet that challenge.
1. Marc Andreessen, “Why Software Is Eating the World,” The Wall Street Journal, 20 August 2011.
2. U.S. Department of Defense, Fact Sheet: 2022 National Defense Strategy, 28 March 2022.
3. Paul Mozur, “Beijing Wants A.I. to Be Made in China by 2030,” The New York Times, 20 July 2017.
4. Kai Shen, Xiaoxiao Tong, Ting Wu, and Fangning Zhang, “The Next Frontier for AI in China Could Add $600 Billion to Its Economy,” Mckinsey.com, 7 June 2022.
5. Kai-Fu Lee, AI Superpowers: China, Silicon Valley, and the New World Order (New York: Houghton Mifflin Harcourt, 2018).
6. John R. Hoehn and Nishawn S. Smagh, Intelligence, Surveillance, and Reconnaissance Design for Great Power Competition (Washington, DC: The Congressional Research Service, 4 June 2020).
7. CDR Wolf Melbourne, USN, “Naval Intelligence’s Lost Decade,” U.S. Naval Institute Proceedings 144, no. 12 (December 2018).
8. “Strategy Analytics: Half the World Owns a Smart Phone,” businesswire.com, 24 June 2021.
9. Vanessa Smith-Boyle, “How OSINT Has Shaped the War in Ukraine,” American Security Project, 22 June 2022.
10. Mallory Shelbourne, “Navy’s ‘Project Overmatch’ Aims to Accelerate Creating Naval Battle Network,” USNI News, 29 October 2020.
11. The White House, Fact Sheet: The Quad Leaders’ Tokyo Summit 2022, 23 May 2022.
12. Zack Cooper and Gregory Poling, “The Quad Goes to Sea,” War on the Rocks, 24 May 2022.
13. John Murawski, “AlphaSense Raises $50 Billion in New Funding,” The Wall Street Journal, 17 July 2019.
14. Elsa B. Kania, “In Military-Civil Fusion, China Is Learning Lessons from the United States and Starting to Innovate,” The Strategy Bridge, 27 August 2019.
15. Mark Pomerleau, “China Moves Toward ‘Intelligentized’ Approach to Warfare, Says Pentagon,” Defense News, 1 September 2020.
16. Ryan Fedasiuk, “We Spent a Year Investigating What the Chinese Army Is Buying. Here’s What We Found,” Politico, 10 November 2021.
