The Intangible Fallacy describes the persistent misconception that artificial intelligence exists primarily as software—as algorithms, models, and code that can be freely copied, distributed, and deployed across any computing substrate. This cognitive error systematically underestimates the material dependencies that govern AI capabilities, treating computational power as an abundant commodity rather than recognizing it as a scarce resource concentrated in specific geographic locations and controlled by a handful of semiconductor manufacturers. The fallacy manifests when analysts, policymakers, and strategists focus exclusively on algorithmic breakthroughs while ignoring the physical infrastructure required to train, deploy, and operate AI systems at scale.
The mechanism underlying this fallacy operates through the abstraction layers that separate users from the underlying hardware dependencies of AI systems. When researchers access cloud computing resources or deploy models through APIs, the physical infrastructure becomes invisible, creating the illusion that AI capabilities are infinitely scalable and geographically distributed. This abstraction obscures the reality that advanced AI development depends on specialized semiconductors manufactured by a limited number of foundries, particularly Taiwan Semiconductor Manufacturing Company, and designed by companies like NVIDIA. The concentration of these capabilities means that AI progress is fundamentally constrained by manufacturing capacity, export controls, and geopolitical access to cutting-edge chips rather than by algorithmic innovation alone.
For threat intelligence practitioners, the Intangible Fallacy creates systematic blind spots in risk assessment and strategic planning. Organizations that focus solely on software-based AI governance miss critical vulnerabilities in their supply chains and fail to anticipate how semiconductor restrictions, manufacturing disruptions, or geopolitical tensions could impact their AI capabilities. The fallacy leads to overestimation of competitors' ability to rapidly scale AI systems without access to advanced hardware, while simultaneously underestimating the strategic importance of semiconductor supply chains in maintaining AI advantage.
The framework proves essential for AI threat intelligence because it reveals how physical infrastructure constraints shape the global distribution of AI capabilities and create leverage points for state and non-state actors. Understanding that AI is fundamentally material rather than purely digital allows analysts to identify chokepoints in the technology stack, assess the true feasibility of AI development programs, and predict how hardware restrictions might redirect the trajectory of AI advancement. This perspective is crucial for evaluating threats in an era where computational power, not just algorithmic sophistication, determines which actors can develop and deploy transformative AI systems.