The Covert Game in 2045: Nowhere to Hide in an AI-Driven World

 

No Place to Hide in 2045

Overview

This article presents a “strategic” foresight scenario set in 2045, exploring how the “spy game” might operate when advanced AI makes traditional hiding places obsolete. Using the US and China as examples—the two largest AI-competitor nations—we examine key technologies impacting intelligence work, including computer vision, satellite tracking, large language models, and self-driving drones. The analysis, based on operational and technical factors, reveals how espionage techniques could evolve and what policy issues may arise. Ultimately, this scenario offers insights into how U.S. and Chinese intelligence agencies might adapt when there is truly nowhere to hide. 

Our argument is that by the mid-21st century, advances in AI and surveillance technology will fundamentally change the landscape of covert operations. “For spy organizations, there is literally no place to hide” in this future environment. Multiple sensors and AI-driven analytics mean that every movement, transaction, and communication can leave a trace, making covert operations increasingly risky. 

Ubiquitous Surveillance Technologies and AI Analytics

Advanced Computer Vision and Sensors: By 2045, surveillance cameras will be as common as light bulbs. Computer vision algorithms integrated with nationwide CCTV networks can recognize individuals and track their movements in real-time. 

Back in 2018, experts had already warned that algorithms could identify people by their unique gait patterns, not just their faces. By 2045, gait recognition, facial recognition, and other biometric identification will be highly accurate even at long distances. For example, an AI model known as FarSight in the 2020s achieved 83% accuracy in identifying individuals at a distance of 1,000 meters (and 65% even when faces were obscured) – a capability that has now been vastly improved. 

In China, the groundwork for such pervasive surveillance was laid decades earlier; the country’s massive “Skynet” camera network could already locate a person of interest within minutes using facial recognition. In a well-publicized 2017 demonstration, Chinese authorities tracked down a BBC reporter in just seven minutes through CCTV cameras and AI, vividly illustrating the “nowhere to hide” future. By 2045, China’s domestic surveillance apparatus will be even more sophisticated, with hundreds of millions of AI-enabled cameras monitoring public spaces and instantly flagging “anomalous” behavior. 

Although the US is more cautious at home due to privacy and legal concerns, it also benefits from the widespread use of sensors, such as those in smart city cameras and the Internet of Things. Countless private and commercial devices (traffic cameras, home security systems, connected appliances, etc.) create a web of data that intelligence agencies can tap into, turning the Internet of Things into an “internet of spies”. In this scenario, any attempt by a person to go “dark” – avoiding phones, credit cards, and online activity – paradoxically makes them stand out. As one former CIA officer noted, “the more you try to hide, the more you stand out”. A missing digital footprint (no smartphone, no social media) is itself a red flag in 2045, potentially identifying a spy or covert operative who is “off the grid.”

AI Pattern Recognition and Big Data: The volume of surveillance data is far too vast for human eyes alone, but AI excels in analyzing large datasets. 

Intelligence services deploy powerful pattern recognition AI to sift through video feeds, geolocation logs, financial transactions, and other records in search of suspicious patterns. By analyzing “telltale anomalies” across time and space, AI can piece together activities that might otherwise go unnoticed. A case officer’s meandering route through a city, once a standard technique to evade followers, can now be reconstructed from disparate camera feeds and vehicle GPS logs; even if spies aren’t caught in the act, they can be caught later by retrospective analysis. Every byte of data – border entries, hotel check-ins, Wi-Fi connections, social media posts – can be correlated. Notably, even attempts to maintain operational security can betray agents: in one instance (back in the 2010s), CIA handlers provided clandestine agents with “burner” cell phones, only to have an adversary’s algorithm identify those devices as anomalous (because they were powered on so infrequently), leading to the network’s exposure. 

By 2045, such AI-enabled data mining is far more advanced. Machine learning algorithms flag deviations from normal behavioral baselines. For instance, an undercover officer who suddenly uses cash only, avoids all smart devices, or deviates from their usual routine might trigger automated alerts. In response, spies have had to blend in with the data exhaust of everyday life, maintaining convincing digital personas with active phones and social profiles to avoid suspicion. 

The creation of authentic digital cover identities (complete with years of backstopped social media history, online photos, and even AI-generated friends) becomes standard practice for espionage agencies. Any gaps or inconsistencies in one’s personal data profile could blow an operative’s cover in an AI-audited world. In short, “cover” and “tradecraft” – the two classic pillars of espionage – have been shattered by modern technology, as near-perfect memory and pattern analysis erode all secrets.

Global Satellite Surveillance: By 2045, overhead surveillance capabilities are expected to reach staggering levels. 

Twenty years prior, the U.S. National Reconnaissance Office (NRO) and commercial firms began deploying constellations of high-resolution satellites with AI onboard, aiming for “unblinking” eye-in-the-sky coverage. Now, hundreds of imaging satellites in low Earth orbit, supplemented by stratospheric drones, provide continuous real-time imagery of most of the globe. Crucially, AI coordination allows these platforms to track moving targets across cameras and orbits. In this scenario, a spy can be tracked from a satellite as they drive a car, with another satellite or an autonomous drone seamlessly picking up the trail when the first platform’s line of sight is lost. 

The integration of AI means satellites no longer just collect images, but analyze them on the fly and even collaborate. Companies like BlackSky in the 2020s were already inserting AI into the satellite imagery pipeline to deliver automatic object recognition and alerts within minutes of collection. By 2045, this has matured into satellite swarms that autonomously “talk” to each other to follow targets. For example, if an AI system is tasked to “follow that vehicle”, a network of satellites and drones can hand off the target among themselves, persistently monitoring its journey across continents. This machine-to-machine tracking occurs faster than any human operator could manage. 

With on-board neural network processors (a standard feature by the 2040s), satellites can perform initial image analysis in space, detecting, say, a clandestine meeting in a remote desert, and immediately cue other sensors or alert analysts on the ground. The result is a world where hiding physically is exceedingly difficult: even far-flung safe houses or covert training camps may be spotted by some combination of all-seeing eyes in the sky. What’s more, the fusion of imagery with other data (so-called multi-intelligence fusion) means a satellite image of a suspect meeting can be cross-referenced with intercepted communications or social media data in real time, all via AI. 

Anticipatory intelligence has also improved – AI models analyze trends in satellite data to predict events (e.g., troop buildups or facility activity indicating a potential weapons test). For U.S. and Chinese spy agencies, mastery of this “permanent eye” from above is now a core part of espionage strategy, as each seeks to ensure their activities are hidden under camouflage or underground, while the adversary’s are laid bare.

Large Language Models and AI Analysis: Another game-changer in this scenario is the extensive use of large language models (LLMs) and other AI for intelligence analysis and even decision support. 

The U.S. and China have both invested significant resources in adapting advanced AI models to meet their intelligence needs. Generative AI systems can rapidly process and summarize vast amounts of information, far surpassing the capabilities of human analysts. As Anne Neuberger observed in 2025, AI’s ability to crunch vast data at speed means it can alert analysts to potential threats – a missile launch, a political crisis – much faster and sift out noise from signals. 

By 2045, each country will have AI assistants that ingest information from news reports, social media, intercepted communications, and satellite images, providing all-source analysis in seconds. Multimodal AI models cross-reference text, imagery, signals, and even biometric data to present a comprehensive intelligence picture to human operators. For instance, an AI might automatically flag an unusual pattern: a scientist’s name appearing in a research paper, their travel records to a certain country, and a surge in communications with known military affiliates – weaving these strands into a lead for counterintelligence. 

Both the CIA and China’s Ministry of State Security (MSS) have likely developed “AI case officers” in a sense – systems that can identify hidden connections among people and organizations. Private-sector tools in the 2020s foreshadowed this: companies like Strider demonstrated software that mines open-source data to reveal links between researchers and foreign intelligence programs. Two decades later, such capabilities are standard, supercharged by more powerful AI. China’s PLA, for example, is “very likely adapting foreign and domestic LLMs to effectively carry out intelligence tasks,” developing generative AI tools that analyze data, answer analysts’ questions, and even generate draft intelligence reports. 

These AI systems augment human analysts by providing preliminary insights, translations, and recommendations. Routine tasks like translating foreign communications or transcribing intercepted calls can be done near-instantly by AI (a process that earlier took days of human labor), freeing human officers to focus on interpretation and validation. 

However, the rise of generative AI also introduces new counter-espionage threats: deepfakes and AI-generated disinformation. As early as the 2020s, officials warned that adversaries could “weaponize” AI to produce deepfake audio/video that impersonates leaders or fabricate events. In this 2045 scenario, spies must contend with the possibility that some “intelligence” they encounter is artificially generated deception. Chinese counter-intelligence units worry that Western agencies might use generative AI to create “inauthentic but convincing information to mislead” their analysts – and vice versa, Chinese agencies might deploy the same tactics. This forces a constant verification struggle, where AI is both a sword and a shield: used to create sophisticated deception, and also used to detect it.

Autonomous Drones and Robotics: On the operational side, autonomous machines have taken on many of the riskiest spy missions. 

Unmanned aerial vehicles (UAVs) equipped with AI serve as the proverbial “flies on the wall,” conducting surveillance or even clandestine deliveries without exposing human agents. By 2045, miniature drones the size of insects can hover undetected in meeting rooms or safehouses, transmitting audio-video feeds. Larger autonomous drones can tail a target’s vehicle from a high altitude or map remote facilities, guided by AI computer vision to avoid obstacles and remain covert. These drones act as “intelligent, autonomous agents” in the field. Notably, many are capable of lethal action if needed: both the U.S. and China have AI-powered combat drones that can be dispatched to neutralize threats or perform sabotage, blurring the line between espionage and covert warfare. 

In the military domain, pairing AI with drones proved to be a “game-changing combination” decades earlier, enabling precision strikes without risking pilots. In the spy realm, the same technology can be used more subtly – for example, an autonomous micro-drone might carry a hacking device to a secure building and infiltrate its network, or deliver a tiny sensor bug onto a target’s balcony. 

Robotics beyond drones also plays a role: AI-driven cyber “bots” perform intrusions and data exfiltration at speeds no human hacker could match, and physical robots or micro-vehicles can conduct close surveillance or break into hard-to-reach locations. The result is a further reduction in the need to place human operatives in harm’s way; many tasks, such as intelligence collection or agent communication, can be performed via proxies. However, these same tools can also be used against spies – an autonomous surveillance drone might continuously patrol a city for any signs of unrecognized individuals or vehicles, automatically flagging a foreign operative for the authorities. This cuts both ways in the U.S.–China context: an American case officer trying to clandestinely move around Beijing in 2045 could be shadowed by swarms of Chinese police drones, while Chinese agents in the U.S. might have to evade networks of stealthy FBI drones in sensitive areas.

Operational Impacts on Espionage Tradecraft

The convergence of these technologies means that espionage tradecraft in 2045 is radically different from the 20th-century “spy game.” Human intelligence (HUMINT) operations must adapt or die in a world where secrecy is scarce. Several key operational shifts defined in this scenario include:

• Blending In vs. Disappearing: Whereas traditional spies would often try to disappear (avoiding any observables), modern spies in this AI-pervasive world aim to blend into the background noise. An operative must have a full and consistent personal data history to avoid raising suspicion. As former CIA officials realized in the 2020s, non-official-cover officers (NOCs) need authentic digital lives – complete with LinkedIn profiles, social media activity, and mundane transactions – since any gap might expose them. By 2045, agencies will invest heavily in “digital persona creation”. Creating a legend (cover identity) now means years’ worth of curated online footprints. In practice, this might involve AI-generated photos and posts that slowly build up a fake individual’s presence from youth to adulthood, all carefully interwoven with real, but unwitting, social connections. The goal is that when an AI in China or the U.S. runs a background check on a suspect identity, it finds a normal-looking (if entirely fabricated) life story. Additionally, operatives carry personal devices and behave like normal citizens would – paradoxically, using technology becomes a way to conceal their presence, rather than avoiding it. A spy may actually post casual updates on social media or use a smartphone for genuine errands, to avoid the fate of the aforementioned “burner phones” that stood out by their silence.

• New Methods of Secret Communication: Traditional clandestine communication techniques (short-range radios, dead drops, etc.) are largely obsolete or too easily detected by spectrum monitoring and cameras. In 2045, spy agencies employ high-tech alternatives. One method is steganography using AI – hiding messages in the noise of the internet. For instance, an AI algorithm might subtly alter pixels in an ordinary photo posted to a public forum, encoding a message that can only be decoded by the recipient’s AI. Another approach is the use of quantum-encrypted channels for critical communications, which even powerful quantum computers cannot crack (assuming quantum cryptography has matured by then). However, the volume of data and AI analysis means even a covert communication may be detected as an anomaly if not carefully masked (for example, an unduly encrypted data burst from a diplomat’s home might draw attention). As a result, “opportunistic obfuscation” techniques are employed – blending secret messages into regular traffic. A startup in the 2020s pioneered a system that bounces mobile phone signals among thousands of other users to hide any single device’s traffic pattern. Building on this, spies in 2045 often piggyback on public networks: a case officer’s AI might time her message dispatch to coincide exactly with a mass of routine traffic (like a popular live sports stream), so it gets lost in the sea of bits.

• Increased Reliance on AI Assistants: Every field operative in 2045 is paired with AI support. This could be a secure handheld device running an offline large language model – essentially a “Case Officer in a Box,” as some visionaries described. Such an AI assistant can instantly translate languages, provide real-time surveillance alerts, or even suggest escape routes based on predictive modeling of adversary behavior. (For example, the AI might warn: “Facial recognition has picked you up – change direction and blend into the crowd at the next shopping mall.”) While no operative would trust an AI assistant with their life blindly, human judgment remains crucial – these tools greatly enhance situational awareness and decision-making on the ground. Likewise, automated counter-surveillance is essential: spies routinely deploy their own mini-drones or software agents to scan for hostile sensors, jam facial recognition systems, or inject spoof data to confuse trackers. In this cat-and-mouse game of algorithm vs. algorithm, field tradecraft includes tricking the AI watchers. For instance, operatives might wear clothing or masks that exploit the weaknesses in computer vision (a practice that began with facial recognition evasion patterns in the 2020s) to cause false negatives in identification systems. They may also feed disinformation into data streams – e.g., having an AI generate a trail of fake but plausible credit card transactions or social media posts for them while they are actually elsewhere, sending the adversary’s AI on a wild goose chase.

• Rarity of Traditional “James Bond” Missions: Perhaps the most striking change in this scenario is the reduced role of classic in-person spying. By 2045, human espionage will have become far more selective. A senior CIA veteran in 2025 predicted that “human spies in the field will become rare… face-to-face spying will be the exception” as digital methods take precedence. We see this in both U.S. and Chinese operations: the highest-risk, high-value missions – say, penetrating a secret nuclear facility or recruiting a top scientist – might still justify sending an undercover officer in person. However, the threshold for such missions is much higher when any misstep means immediate exposure due to omnipresent surveillance. Many intelligence objectives that used to require an on-site human can now be achieved via technical means. Cyber-espionage has effectively supplanted a significant portion of physical espionage: why risk an agent to steal documents when a hacking AI can infiltrate a network remotely? Even the recruitment of assets (traditionally a personal, trust-building endeavor) is touched by AI. Initial spotting and vetting of potential agents (e.g., disaffected officials in the rival nation) can be done through AI analysis of behavioral and social data, before a human handler ever approaches them. There is speculation that by 2045, some agents might even be recruited and directed entirely online by an AI persona, without the agent ever realizing they never spoke to a human, though most agencies still prefer a human touch for final trust-building. In any case, the romantic image of the lone spy gathering secrets has largely given way to a new one: teams of hackers, data scientists, and AI systems extracting intelligence remotely, while a few elite operatives deploy only for truly irreplaceable human tasks.

The U.S.–China Intelligence Rivalry in an AI-Saturated World

The U.S. and China stand at the center of this 2045 spy scenario, each leveraging AI to gain an edge while struggling to protect themselves from the other’s AI-powered espionage. The two nations have taken relatively different approaches shaped by their political systems and tech ecosystems, yet they face common challenges.

China’s Surveillance Dominance – and Vulnerabilities: China entered the AI-driven surveillance age earlier, leveraging its extensive domestic monitoring infrastructure. 

By 2045, China’s intelligence services will benefit from perhaps the most dense and integrated surveillance network on the planet. Virtually every public space in Chinese cities is monitored by cameras or sensors, all of which are fed into centralized AI systems for real-time analysis. Coupled with mandatory national databases (for facial recognition, biometric IDs, social credit scores, etc.), this means Chinese counter-intelligence can instantly check the identity and background of any person an agent encounters. A CIA officer operating under non-official cover (NOC) in Shanghai, for example, would face a daunting task: the moment they step outside, they are likely to be scanned and checked against records. Any alias identity must withstand scrutiny, not just of documents, but of a life’s worth of data. Moreover, China’s integration of government and commercial data, including travel records, banking information, and mobile phone usage, provides a holistic picture. American operatives (or assets) in China have to assume they are always potentially being watched or tracked. Even meetings in private may not be safe, as IoT devices or AI-augmented eavesdropping tech could be listening (e.g., smart home appliances that double as covert microphones). 

On the other hand, China’s reliance on big data creates some vulnerabilities. U.S. agencies have exploited Chinese open-source data and large government databases that were not well secured. In one instance, a private U.S. firm demonstrated its ability to operate within China’s so-called Great Firewall and access unencrypted data that linked Chinese researchers to military programs. This suggests that Beijing’s appetite for collecting everything can backfire if adversaries tap into those troves. Additionally, China’s use of AI for repression (e.g., monitoring dissidents and Uyghur minorities) is well known; by 2045, it may extend to predictive policing. However, such massive data usage could overwhelm even AI, and Chinese analysts risk being overwhelmed by information overload or encountering false positives. Culturally, China’s intel agencies might also be less nimble in adopting decentralized or creative tech solutions, whereas the U.S. can draw on its innovative private sector (despite China’s lead in some AI research).

The United States’ Tech-Driven Adaptation: The United States, with its democratic constraints, did not blanket its cities with government cameras to the same extent as China did, but by 2045, it leveraged other strengths. 

U.S. intelligence has heavily partnered with its world-class technology companies to obtain data and develop AI tools. The Silicon Valley influence is evident: many AI espionage solutions are outsourced or acquired from startups founded by former spies (a trend that became visible by the mid-2020s). This includes specialized tools for anonymizing communications, mining social media for foreign threats, and automating analysis (for example, the CIA’s 2030s-era adoption of an AI system akin to today’s “Donovan” that can “dig into all available data to rapidly identify trends and anomalies”). The U.S. also uses extensive signals intelligence (SIGINT) and cyber capabilities to compensate for less physical coverage. For instance, by 2045, the NSA in America might employ advanced AI to sift through global internet traffic and phone metadata for patterns indicating espionage networks. One advantage the U.S. has is a rich pool of open-source intelligence from across the world (including from allies), whereas China’s society is more closed to outsiders. American agencies can gather diverse data and have fewer constraints on reaching globally, for example, by leveraging their alliances to deploy sensors or share satellite feeds. However, the U.S. must grapple with its openness: 

Chinese intelligence can operate more freely in an open American society than Americans can in China. Indeed, China has harvested vast amounts of U.S. data over the past few decades, from the 2015 Office of Personnel Management breach of federal employee records to hacks of health insurers, hotels, and social media platforms. By 2045, Beijing may have compiled detailed profiles on millions of Americans, including those likely to work in national security. By feeding this into AI, China can potentially identify American intelligence personnel (past or present) by spotting telltale markers in the data. U.S. spies traveling abroad must assume Chinese AI is hunting for them via any digital trace (be it an old email address or a biometric passport scan). This has pushed the U.S. to bolster its counter-intelligence posture: the FBI and CIA, in this scenario, utilize AI to continuously monitor for moles or compromised officers, watching for unusual behavior or contacts that might indicate someone has been turned by a foreign service. The rivalry has thus become a high-tech “spy vs. AI” contest, where each nation uses automation and algorithms to out-snoop and outmaneuver the other. Both are keenly aware that leadership in AI is tantamount to intelligence superiority—a fact that drives their national strategies.

Despite these tensions, one commonality is that both countries’ intelligence communities have undergone cultural changes. The U.S. Intelligence Community (IC) recognized the need to “out-innovate” adversaries by embracing AI and moving more quickly than the slow, bureaucratic pace of the past. China’s services similarly recognized that hoarding data is not enough – they must effectively leverage AI and cope with its risks (like biases or adversarial attacks on their algorithms). In the world of 2045, it is likely that espionage success will be measured by who has the better algorithms and more effective training data, rather than by who has the bravest spy or the best forgery techniques. The CIA still values human cunning, but as one agency veteran put it, “the future of espionage is written in zeros and ones” – a sentiment with which Chinese officials would likely agree, even if their phrasing differs. This raises profound strategic questions: If human spies are rarer and cyber-AI operations are dominant, does that make espionage less personal and possibly less risky, or could it make conflicts more unstable as AI competes with AI in the shadows? These are questions policymakers in both Washington and Beijing must carefully consider.

Policy and Strategic Implications 

In a future where advanced AI renders spies virtually invisible, intelligence agencies and governments will need to adapt on multiple fronts. This foresight scenario highlights several policy implications and suggests strategies for navigating the evolving landscape of espionage.

• Invest in AI, but also ensure its security: Both the U.S. and China must continue to invest in AI to maintain their leadership and prevent falling behind. The U.S., in particular, should harness its private sector innovation—building on current efforts to involve tech experts in national security—to give agencies advanced tools for data analysis, surveillance, and counterintelligence. At the same time, securing AI systems is essential. An AI that is hijacked or fed false data by an adversary can become a danger. It's crucial to establish strong standards for AI safety and reliability, such as through the NSA's AI Security Center, as proposed in the 2020s. This includes defending against adversarial ML attacks, where adversaries may try to confuse our AI—for example, by tricking facial recognition with new camouflage methods. Policymakers should also require thorough red-team testing of any AI used in intelligence operations to discover vulnerabilities before deployment.

• Develop Counter-AI and Deception Capabilities: In a world filled with sensors everywhere, sometimes the only way to hide is to confuse or blind the AI eyes watching. Intelligence agencies need tools for counter-surveillance that can beat algorithmic tracking. This might include technical countermeasures, such as devices that spoof GPS or deepfake a digital persona’s activity to mislead enemy monitors, as well as strategic deception operations utilizing AI. For example, agencies could deploy “friendly” deepfakes to create virtual decoys of key personnel, throwing off adversary efforts. As China and other adversaries are likely to utilize generative AI to inundate information channels with disinformation, the U.S. must enhance its capacity to rapidly identify AI-generated misinformation. Support for research into deepfake detection should be increased and integrated into intelligence workflows to enhance the detection capabilities.

• Additionally, counterintelligence units should train with AI adversaries by running drills where they assume an AI is analyzing their every move, pushing officers to think creatively about how to evade constant surveillance. Preparing for an AI-omniscient opponent will boost their operational discipline. As a policy, agencies might even consider limiting certain activities in highly sensor-saturated areas—for example, it might become policy not to station American NOCs in a city like Shenzhen due to the digital footprint and risks involved.

• Ethical and Legal Frameworks: Although this study primarily addresses operational and technical issues, policymakers must also consider the legal and ethical challenges of the AI surveillance era. Democratic governments, such as the U.S., will need to balance the use of AI surveillance with the protection of civil liberties. Creating oversight for domestic use of advanced surveillance (to prevent Orwellian scenarios) is crucial, even as we recognize that authoritarian rivals will exploit such technology without limits. Internationally, there may be calls for norms or treaties on AI and espionage—similar to past arms control agreements, although verification may be more challenging. For example, an agreement not to target each other’s civilian infrastructure with cyber-AI or not to deploy certain autonomous lethal drones for assassination. Though reaching an agreement with China on espionage “rules” is difficult, discussions on reducing escalation risks (like autonomous systems causing accidental conflict) would be wise. Importantly, transparency with the public about how AI is used (and isn’t used) by intelligence agencies can build trust and demystify these tools, as long as sources and methods are protected.

• Human Capital and Training: The archetype of the spy is evolving—tomorrow’s spies need to be as comfortable with algorithms as they are with aliases. Agencies should recruit data scientists, AI programmers, and behavioral analytics experts alongside traditional case officers. Training programs for operatives should include modules on understanding AI surveillance and operating under pervasive monitoring. Conversely, AI developers working for intelligence agencies must learn from field operatives about real-world complexities so they can build tools suited for on-the-ground use. An organizational culture that bridges the “old school” and the “new school” is essential. The CIA’s experience in the early 21st century showed some resistance to new tradecraft technology; by 2045, that mindset must change. Leaders should incentivize innovation and avoid “shooting the messenger” when young officers propose new approaches (one former CIA official warned against trying to build “faster horses” when a car is needed). Agile adaptation will be a vital advantage in the U.S.–China intelligence race.

• Redundancy and Analog Options: As a contingency, agencies must also prepare for the failure or compromise of high-tech systems. Over-reliance on AI could be dangerous if an adversary finds a way to blind our sensors or feed misinformation to our algorithms. Therefore, retaining some traditional trade craft skills as a backup is wise. Human intuition, face-to-face spying (the “Moscow Rules” of classic espionage), and secure analog methods such as one-time pads or physical couriers for the most critical messages may become important again in certain scenarios where digital methods are too exposed. Policymakers should ensure that, while adopting AI, they do not completely abandon human-driven techniques that AI cannot easily override. This resilience and flexibility—having both 21st-century AI tools and 20th-century spycraft prepared—will help intelligence services operate effectively under any circumstances.

Closing

The strategic scenario described here – the spy game in 20 years with nowhere to hide – illustrates a world of espionage transformed by AI’s omnipresent gaze. By 2045, the classic cat-and-mouse struggle of intelligence has shifted into an AI versus AI battle alongside human versus human confrontation. The United States and China, locked in geopolitical rivalry, both utilize and fear the power of advanced surveillance, big data, and autonomous systems. Every secret operation becomes a high-stakes act under the relentless oversight of algorithms. In this environment, success will go to those who adapt fastest: adopting new technologies, managing their risks, and developing new tradecraft that can turn the enemy’s reliance on AI into a weakness. The well-known espionage phrase “adapt or die” has never been more relevant. As one observer pointed out, “the CIA will survive as a powerful spy agency only if it makes a paradigm shift” – a truth that applies to all intelligence agencies in the AI age. Policymakers need to prepare for these upcoming changes now. Creating good strategies and ethical guidelines for widespread technical surveillance will help ensure that in 20 years, nations can defend themselves and gather intelligence effectively without losing sight of what they aim to protect. The spy of the future might not resemble James Bond; more likely, they’ll be a mix of hacker, data analyst, and field operative, wielding AI as both sword and shield. But no matter how the tools evolve, the core mission stays the same: to gather vital information and safeguard one’s country. In 2045’s high-tech world of mirrors, that mission will remain as difficult as ever – and the stakes just as high.

References (APA Style):

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Ignatius, D. (2025, July 10). A band of innovators reimagines the spy game for a world with no cover. The Washington Post. (Insights on how ubiquitous surveillance (“UTS”) is upending CIA tradecraft).

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Final Remarks

A group of friends from “Organizational DNA Labs," a private group, compiled references and notes from various group members' theses and other authors, including ours, as well as media and academic sources, for this article and analysis. We also utilized AI platforms, including Gemini, Storm from Stanford University, Grok, Open-Source ChatGPT, and Grammarly, as research assistants to ensure the coherence and logical flow of our expressions. By utilizing these platforms, we aim to verify information from multiple sources and confirm its accuracy through academic databases and equity firm analysts with whom we have collaborated. The references and notes in this work provide a comprehensive list of our sources. As a researcher and editor, I have taken great care to ensure that all sources are properly cited and that the authors receive recognition for their contributions. The content primarily reflects our compilation, analysis, and synthesis of these sources. The summaries and inferences demonstrate our dedication and motivation to expand and share knowledge. While we have relied on high-quality sources to inform our perspective, the conclusion represents our current views and understanding of the topics covered, which continue to evolve through ongoing learning and literature reviews in this business field.

How the spy game will work when there’s nowhere to hide