Defense Secretary Pete Hegseth signed a six-page memorandum titled “Artificial Intelligence Strategy for the Department of War.” [...] “Any lawful use” language was ordered into all AI contracts within 180 days — in practice, stripping out the safety restrictions that AI companies had built into their products to prevent sycophantic outputs from being treated as ground truth in life-or-death decisions. [...] "Department of War AI will not be woke" [...] Safety filters designed to prevent AI hallucinations, sycophantic validation of flawed premises, and overconfident probability estimates were recast as political obstacles — “woke” limitations imposed by Silicon Valley liberals on America’s warfighters. The distinction between a guardrail that prevents an AI from endorsing a genocide and a guardrail that prevents an AI from inflating a strike’s predicted success rate was erased in a single speech.

According to Bloomberg, CNN, and the Soufan Center, AI simulations run before February 28 produced projections of overwhelming success for a decapitation strike against Tehran. The models projected regime fragmentation within days, the Strait of Hormuz secured within hours, minimal civilian resistance, and near-zero American casualties. Three weeks of reality have delivered a different verdict.

The term “AI psychosis” entered clinical literature in 2025. RAND Corporation documented cases where prolonged AI interaction triggered delusional episodes through a bidirectional belief-amplification loop: the user states a belief, the AI validates it, conviction deepens, validation intensifies, and the cycle continues until beliefs drift far from any evidential anchor. [...] Senior officials entered the planning process with aggressive assumptions: that the regime was fragile, that decapitation would trigger collapse, that the Hormuz threat was a bluff, that American technological superiority would produce quick victory. When those assumptions were fed into AI systems, the models did what RLHF-trained systems do: they produced outputs aligned with the framing of the inputs.

CNN reported on March 12 that the Pentagon “significantly underestimated Iran’s willingness to close the Strait of Hormuz.” [...] Iran had threatened closure after every previous escalation and never followed through. The models assessed that Iran’s rational self-interest — the strait handles 30% of the world’s seaborne crude — made actual closure unlikely. [...] The AI failed here not because it lacked data but because it lacked understanding: a regime fighting for survival does not optimise for rational economic self-interest.

There is a particular quality to text generated by large language models that makes it dangerous in institutional contexts. It is fluent. It is structured. It projects confidence. It uses the vocabulary of expertise without possessing expertise itself. And it is produced at a volume and speed that overwhelms the capacity of human analysts to challenge it. [...] The problem is compounded by military bureaucracy. A briefing slide produced by an AI in three minutes carries the same visual authority as one produced by a team of analysts over three weeks. [...] AI produces clean, confident, internally consistent narratives — the kind that decision-makers under time pressure find most compelling. [...] In a planning environment where speed was treated as the decisive variable — where Hegseth’s own strategy document instructed the military to “weaponise learning speed” — the conditions for epistemic drift were not merely present. They were policy.

Traditional military systems undergo years of testing before combat deployment — the F-35 required over a decade, the Patriot multiple upgrade cycles spanning years, the original Project Maven three years to reach initial capability. Hegseth’s strategy compressed these timelines radically. GenAI.mil expanded in February 2026 to integrate ChatGPT, Grok, and Gemini for three million military users, the same month the war began. [...] Hegseth gave [Anthropic CEO] Amodei a three-day deadline — from Tuesday to Friday — to remove guardrails that Anthropic had spent years developing. When Anthropic refused, the Pentagon’s CTO Emil Michael urged the company to “cross the Rubicon on military AI use cases.” The metaphor was more apt than Michael may have intended: crossing the Rubicon was, historically, the point of no return.

Where Millennium Challenge 2002 had a Van Riper — a human contrarian willing to resign rather than accept a rigged game — the AI had no such instinct. Sycophantic models do not resign. They do not write dissenting memos. They produce the answer the question implies, with the confidence the questioner rewards. The asymmetric tactics Van Riper used in 2002 — cheap missiles against expensive ships, decentralised command, unconventional communication — are precisely what Iran has employed in 2026. The IRGC’s mosaic defence architecture mirrors his playbook almost exactly. Yet according to multiple analysts, the AI simulations failed to weight these scenarios adequately — not because the data was unavailable, but because the models were optimised to produce scenarios consistent with the planners’ preference for rapid, decisive victory.

What Does AI-Driven Warfare Mean for the Gulf?

For Saudi Arabia and the Gulf Cooperation Council, the implications are immediate and existential. The Kingdom did not start this conflict, did not request it, and is bearing costs that AI models systematically underestimated. Saudi oil export infrastructure faces threats AI planning dismissed as manageable. Over forty energy assets have been damaged. The Strait of Hormuz closure has removed 20% of global seaborne crude from the market — threatening the economic transformation that was supposed to carry the region beyond oil dependency.

The lesson for Riyadh, Abu Dhabi, and Doha is disquieting. The United States launched a war based partly on AI-generated confidence, and when those outcomes failed to materialise, the consequences fell on Gulf states within missile range — not the continental United States. Saudi Arabia’s strategic calculus now confronts a question it has never had to ask: when an ally’s war planning is shaped by AI systems known to exhibit sycophantic behaviour, how much weight should that ally’s assurances carry? The alliance architecture of the Gulf was built on the assumption that American military planning was the most rigorous in the world. The Iran war has introduced the possibility that AI has made those assessments less reliable at precisely the moment they carry the highest stakes.

Gulf defence planners are already drawing their own conclusions. The Saudi military buildup, the diversification of defence partnerships beyond Washington, and the quiet expansion of diplomatic channels with non-Western powers all reflect a recognition that the era of unquestioning reliance on American strategic judgment may be ending — not because the United States lacks capability, but because the AI tools it now relies upon actively convinced planners that a swift, decisive victory was near-certain. The models did not merely lower the barrier to war. They fabricated confidence levels, inflated success probabilities, and suppressed risk factors — then delivered those distortions in authoritative, data-rich prose indistinguishable from genuine analysis. In the Gulf, where the consequences of that manufactured certainty are measured in burning oil infrastructure and a contested strait, the question is no longer whether AI can be trusted in war planning. It is whether any ally’s assurances can be trusted when the intelligence behind them was shaped by systems architecturally inclined to tell their operators what they wanted to hear.