Aleena Saifullah

Write by: Aleena Saif Ullah Insurance, Not Warships: The Strategic Lesson China Drew from Operation Epic Fury Aleena Saif Ullah The writer is an MPhil Scholar in International Relations, specializing in global defence and security, University of the Punjab, Lahore. In 1991, the Gulf War transformed Chinese military planning. The People’s Liberation Army watched American precision weapons destroy Iraqi armored formations in days, absorbing a lesson about the capability gap that it spent the next thirty-five years closing. The 2003 Iraq War reinforced the lesson: the PLA’s response was not to match U.S. capability symmetrically but to develop anti-access and area denial architectures specifically designed to prevent U.S. power projection in its near abroad. Operation Epic Fury is producing the same institutional response — a systematic processing of operational lessons from a conflict Beijing did not participate in — but with a different and more uncomfortable set of findings. As Admiral Samuel Paparo, head of U.S. Indo-Pacific Command, confirmed in congressional testimony this week, China’s military has been monitoring U.S. and Israeli operations against Tehran, “learning from the successful use of advanced military decision-making power.” What it is finding, according to the Centre for International Maritime Security’s analysis, goes well beyond the tactical lessons that have dominated media coverage. The tactical lessons are significant but broadly familiar. AI-enabled targeting at machine speed, the cost-exchange problem with interceptors, the vulnerability of single high-value platforms to saturation attack, the mosaic defense doctrine’s resilience under decapitation pressure — these confirm what the PLA’s planning documents have long assumed about the trajectory of U.S. military capability. The deeper lessons are maritime, and they are less comfortable for Beijing than for Washington. The Hormuz crisis demonstrated, at operational scale and in real time, that China’s own strategic vulnerability is not primarily naval. It is commercial and financial. Within seventy-two hours of Epic Fury’s launch, the International Group of P&I Clubs — which collectively insure approximately ninety percent of ocean-going tonnage — began issuing cancellation notices for Gulf war-risk coverage. Traffic through the Strait of Hormuz fell by approximately 95 percent. Not because of mines, though mines were eventually laid. Not because of naval interdiction, though Iranian fast boats harassed shipping. Because underwriters calculated that the actuarial risk exceeded viable premium levels, and when P&I coverage is cancelled, ships cannot sail regardless of what navies say about corridor safety. China, which depends on the Strait of Hormuz for over forty percent of its crude oil imports, could not compel those underwriters to write policies. The world’s second-largest economy, with the world’s largest navy by vessel count, was reduced to publicly urging “all parties to keep shipping routes in the Strait of Hormuz safe” and opening direct talks with Iran to negotiate safe passage for Chinese energy shipments. As CIMSEC’s analysis observed: “A nation that must ask permission to use a chokepoint does not command it.” For PLA planners gaming a Taiwan contingency, this lesson maps directly and uncomfortably. Beijing has spent decades building the naval force it would need to establish sea control in a cross-strait scenario. The Hormuz crisis suggests the decisive question in a Taiwan contingency may not be naval at all. It may be whether the Strait of Malacca — through which approximately 80 percent of China’s energy imports pass — remains open under a wartime insurance regime managed by Western financial institutions. The same mechanism that closed Hormuz in seventy-two hours could close Malacca in the same timeframe. No PLAN destroyer can compel an underwriter at Lloyd’s of London to write a policy. China’s vulnerability is not that it lacks naval capability. It is that its commercial economy depends on sea lanes whose financial infrastructure is controlled by institutions in jurisdictions that would be adversaries in any Taiwan contingency. China is responding to this vulnerability with characteristic strategic deliberateness. It has been building state-backed maritime insurance mechanisms and positioning its commercial fleet to operate under sovereign-risk coverage. The 2026 Beijing military parade — showcasing autonomous drone swarms, confirming accelerated submarine construction that Paparo enumerated as twelve submarines delivered since 2024 including nuclear attack and ballistic missile variants, an aircraft carrier, two cruisers, and ten destroyers — reflects the technological acceleration that Epic Fury’s operational data has validated. But the more consequential adaptation is financial and commercial: China is attempting to insulate its strategic economy from the Western-backed financial architecture that proved, in 2026, capable of imposing a maritime blockade that naval force alone could not replicate. The Taiwan deterrence implications are specific and documented. Paparo confirmed that THAAD systems were moved from South Korea to support Midnight Hammer operations and have not all returned. The U.S. intelligence community’s 2026 Annual Threat Assessment judged that Chinese leaders “do not currently plan to execute an invasion of Taiwan in 2027” but are “probably seeking to set the conditions for eventual unification short of conflict.” The Hormuz crisis has demonstrated to Beijing both the capabilities and the vulnerabilities of a sustained U.S. power projection campaign — not in isolation, as theoretical analysis would provide, but in the specific conditions of actual combat, against actual defenses, with actual depletion rates for actual weapons systems. Desert Storm prompted thirty-five years of PLA investment in closing the capability gap. Operation Epic Fury will prompt a different kind of investment — in maritime financial resilience, in autonomous undersea warfare, in the ability to sustain a commercial economy under wartime insurance conditions — because the lesson Beijing has absorbed is not that American power is overwhelming but that American power has specific structural dependencies that chokepoint control and financial architecture can exploit. Aleena Saif Ullah The writer is an MPhil Scholar in International Relations, specializing in global defence and security, University of the Punjab, Lahore.

Write by: Aleena Saif Ullah The Algorithm Also Learned: How AI Switched Sides in the First AI War The first AI war produced an assumption nobody questioned: AI was America’s weapon. The conflict’s most consequential intelligence development suggests otherwise. Every analysis of Operation Epic Fury has concentrated on what AI did for the United States — the Maven Smart System processing intelligence at machine speed, 5,500 strikes in eleven days, the compression of the sensor-to-shooter cycle to near real time. That account is accurate. It is also incomplete. The AI that shaped this conflict did not flow in one direction only. While Washington was using machine learning to find targets faster than humans could authorize them, adversaries were using commercially available AI to find the humans operating those machines. The first AI war did not produce an AI monopoly. It produced an AI arms race — and the asymmetric actors in that race have moved faster than the doctrine governing the systems they are targeting. The evidence is specific and documented. According to U.S. defense intelligence cited by ABC News on April 5, 2026, Iranian forces used AI-enhanced satellite imagery from Chinese firm MizarVision to refine targeting of U.S. military installations across the Middle East. The system uses automated object recognition and tagging, allowing operators to identify bases, equipment, and infrastructure in minutes rather than hours. Simultaneously, Chinese firm Jing’an Technology — whose “Jingqi” platform blends data aggregation with inference — claimed to have tracked four B-2A Spirit stealth bombers during U.S. strikes on Iranian targets, reconstructing their flight paths and intercepting publicly available aviation channel communications. A March 2026 Kharon research brief noted that while some of Jing’an’s claims may be overstated, the methodology is real: AI-driven open-source intelligence combining flight tracking data, signals intercepts from public channels, and historical operational patterns to reconstruct classified movements from unclassified data points. The B-2 is the most sophisticated stealth bomber in the American arsenal. Its flight paths were being reconstructed in near real time by a commercial platform available to any state actor willing to pay for access. This is the inversion that existing AI warfare analysis has not adequately processed. The dominant framework assumes that AI advantages flow from technical sophistication to technical sophistication — that the state with the most advanced AI targeting architecture has the decisive edge. What the Iran conflict has demonstrated is that AI-enabled open-source intelligence requires no classified access, no state-level investment, and no technical parity with the adversary. It requires only the ability to aggregate publicly available data faster than the adversary assumes possible. ADS-B flight tracking data is public. Social media posts from military bases are public. Commercial satellite imagery is commercially available. The AI that turns those individually innocuous data streams into a targeting intelligence product is increasingly accessible to any actor with the resources to license it. The asymmetry that precision standoff warfare was supposed to create — the safe operator, the remote strike, the distance between the human and the threat — is being eroded not by matching U.S. capabilities but by redirecting commercial AI tools against the humans operating them. The Houthi drone trajectory illustrates the same dynamic at the tactical level. Analysis by the Orion Policy Institute documents the trajectory of Houthi drone capability toward AI-enabled image-based navigation — a guidance methodology that renders GPS jamming, the primary Western counter-drone tool, effectively obsolete. A drone navigating by terrain recognition rather than GPS signal cannot be deflected by electronic warfare. It must be physically intercepted or destroyed before launch. The cost asymmetry this creates is structurally significant: the United States is deploying multi-million dollar surface-to-air missiles to intercept drones that cost tens of thousands of dollars and navigate by machine learning. The U.S. military recognised this asymmetry and responded with Task Force Scorpion Strike — deploying LUCAS drones reverse-engineered from the Iranian Shahed-136, with swarming features and adaptive targeting, at $35,000 per unit. The adaptation confirms the threat it is responding to. When the world’s most powerful military begins reverse-engineering its adversary’s drones, the direction of technological influence has shifted in ways that force posture planning had not anticipated. The strategic implication extends beyond this conflict. The first AI war has established that AI-enabled targeting is not a capability gap that only advanced militaries can exploit. It is a methodology that commercially available tools, state-sponsored platforms, and adversarial intelligence agencies can deploy against the operators of precision strike systems using entirely open-source data. Every future precision strike campaign will be conducted in an environment where the adversary is simultaneously attempting to locate, identify, and target the human operators of those systems using the same class of AI tools the operators are using against them. The doctrine of standoff warfare assumed that distance protected the operator. AI-enabled OSINT has collapsed that distance — not through technical parity but through the redirection of commercial intelligence tools against a target set that standoff warfare doctrine never considered: the people behind the screen. The arms control architecture that governs this environment does not exist. There are no agreements limiting the military use of commercial AI platforms. There are no norms governing the weaponisation of open-source data against military personnel. There are no verification mechanisms that could distinguish a commercial satellite imagery subscription from a targeting intelligence programme. The first AI war has demonstrated that these gaps are not theoretical. They are operational — and the next conflict will begin with both sides already using them. Aleena Saif Ullah The writer is an MPhil Scholar in International Relations, specializing in global defence and security, University of the Punjab, Lahore.