Cowen, Author of 'Marginal Revolution,' Cites Security Researcher Saxe's Analysis: Labor Constraints in Cybercrime Explain Why AI Has Not Worsened Losses
Security researcher Joshua Saxe modeled and estimated that approximately 720,000 people are involved in the online criminal economy, with 90% engaged in scam and fraud call-center operations and only about 76,000 being hackers, most of whom work for governments. Criminal organizations are constrained by three factors: labor supply, law enforcement pressure, and the timeline for developing business models; consequently, AI has not yet caused a discontinuous surge in losses. Cowen recommended this analysis on 'Marginal Revolution.' Saxe also warned that 'the apocalypse will be slow-moving,' noting that the current period is a defender-dominant window for preparation.
Why have cybersecurity losses not worsened due to AI? This is the central unresolved question in current discussions on AI security policy. If AI indeed empowers attackers, why has there been no discontinuous growth in global cybercrime losses over the past few years? Tyler Cowen, author of 'Marginal Revolution,' cited an analysis by security researcher Joshua Saxe, offering an explanation rooted in the labor market.
Saxe’s modeling estimates that approximately 720,000 people are involved in the online criminal economy, with 90% engaged in scam and fraud call-center-type operations, some of them victims of human trafficking. Of the roughly 76,000 actual hackers, the vast majority work for governments; among the remaining few thousand criminal hackers, most are involved in ransomware. This implies that criminal organizations must base themselves in middle- and low-income countries, where they can pay lower wages and hire individuals with limited opportunities in the formal economy. Saxe wrote that people in the fields of AI safety and cybersecurity sometimes imagine their adversaries as their own 'Waluigi twins'—implicitly MIT graduates; in reality, cybercrime involves a lower-skill demographic.
Beyond labor supply, there are law enforcement constraints. Saxe pointed out that the larger the criminal organization, the more domestic and international law enforcement attention it attracts. Russia, Belarus, Colombia, and Ukraine have histories of weak rule of law, but when criminals scale up enough to attract international attention, they are often arrested while on vacation abroad. He examined the arrest outcomes of 152 named criminal enterprises, noting survivorship bias, but found that the vast majority faced legal consequences.
More critically, there is the time scale for developing business models. Gaining initial access is just one step in the cybercriminal workflow. Before that, attackers need to build and maintain attack infrastructure; after that, they must move laterally to obtain data, extort victims, negotiate payments, launder money, transfer funds into bank accounts, all while evading law enforcement, managing recruitment, and retaining staff. Saxe argued that it took five to eight years for ransomware to evolve from a concept into a multi-billion-dollar industry; developing new AI-centric criminal business models will similarly take a long time. Automating a few steps with AI is one thing; a true new business model means end-to-end full automation—where attackers deploy armies of agents to handle everything from intrusion to extracting and laundering funds.
Furthermore, AI labor is currently purchased at a premium, and obtaining stable inference access from legitimate model providers is inherently unreliable. Saxe estimated the time required for AI providers to kick attackers off their platforms, arguing that such disruptions constitute an additional bottleneck. He asked: If the inference interface could be cut off at any moment, how would you build an AI-native criminal enterprise?
State actors face different but equally effective constraints. Governments have better access to talent and capital, but historically adopt private-sector technology slowly. Saxe noted that even if the U.S. government is already using AI to find vulnerabilities and develop attack tools, its offensive cyber units are unlikely to quickly undergo a comprehensive reorganization around agent automation. Governments pay AI cybersecurity talent significantly less than the private sector does; former employees of the U.S. National Security Agency (NSA), Israel’s Unit 8200, and the UK’s Government Communications Headquarters (GCHQ) know this well. Moreover, fully automated agent-swarm attacks that cause severe economic damage would likely cross the threshold of physical warfare, deterring attackers through fear of retaliation.
Saxe explicitly positions this article as a 'sketch,' a preliminary attempt to collaborate with peers in the future to produce a more rigorous version. He emphasized that this does not mean AI will never change the landscape of cybercrime—'The apocalypse, I think, will be slow-moving.' He cited historical examples: in 2008, he read a book about 'cryptoviruses,' but ransomware did not become a major threat until five to eight years later; the concept of a 'Cyber Pearl Harbor' was popularized in 2012, but it was not until 2017, with the NotPetya attack by Russia against Ukraine, that a state-actor attack significantly disrupted another nation’s economy. His conclusion is that risk professionals should assume these changes will eventually happen; the current period is a defender-dominant window for preparation, and time should be used for prevention.
Cowen recommended Saxe’s analysis on 'Marginal Revolution,' calling it 'very good throughout,' and added that 'there are bottlenecks on the down side too.'