AI
Kevin Warsh Channels Alan Greenspan in AI Productivity Bet
When Kevin Warsh steps into the ornate confines of the Federal Reserve’s Eccles Building—assuming Senate confirmation—he’ll carry with him a wager that could define the American economy for a generation. Donald Trump’s nominee for Fed chair is betting that artificial intelligence will unleash a productivity boom powerful enough to justify aggressive interest rate cuts without reigniting inflation, echoing the audacious gamble Alan Greenspan made during the internet revolution of the 1990s.
It’s a high-stakes proposition. Get it right, and Warsh could preside over an era of robust growth and falling prices reminiscent of the late Clinton years. Get it wrong, and he risks stoking the very inflation demons the Fed has spent years battling. As economists debate whether AI represents the most productivity-enhancing wave since electrification or merely another overhyped technology cycle, Warsh’s nomination has become a referendum on America’s economic future.
Echoes of the 1990s: Greenspan’s Legacy Revisited
The parallels to Greenspan’s tenure are striking—and deliberate. In the mid-1990s, as the internet began reshaping commerce and communication, mainstream economists warned that the US economy was overheating. Unemployment had fallen below 5%, traditionally considered the threshold for accelerating wage growth and inflation. The conventional playbook called for rate hikes to cool demand.
Greenspan defied orthodoxy. Convinced that internet-driven productivity gains were fundamentally altering the economy’s speed limit, he held rates steady and even cut them in 1998. The gamble paid off spectacularly: productivity growth surged from an anemic 1.4% annually in the early 1990s to 2.5% by decade’s end, while core inflation remained tame. The economy expanded at a 4% clip, unemployment fell to 4%, and the federal budget swung into surplus.
Now Warsh appears poised to replay that script with AI as the protagonist. In a Wall Street Journal op-ed last year, he described artificial intelligence as “the most productivity-enhancing wave of technological innovation since the advent of computing itself.” His thesis: AI will drive down costs across the economy while supercharging output, creating a disinflationary force that allows the Fed to maintain easier monetary policy without courting price instability.
The timing is provocative. After hiking rates from near-zero to over 5% to combat post-pandemic inflation, the Fed under Jerome Powell has adopted a cautious stance. But recent data suggests Warsh may have identified an inflection point: productivity growth has accelerated to 2.1% annually, according to calculations by The People’s Economist, while inflation has cooled to near the Fed’s 2% target. Meanwhile, corporate America is pouring unprecedented capital into AI infrastructure—Google parent Alphabet alone has committed $185 billion over several years to AI data centers and computing capacity.
The AI Productivity Wager: Data and Doubts
Yet the AI productivity bet rests on assumptions that many economists find uncomfortably optimistic. While Greenspan could point to visible productivity gains from internet adoption—e-commerce, email, digital supply chains—AI’s economic impact remains largely theoretical.
Consider the evidence on both sides of this consequential debate:
The Optimistic Case:
- Investment tsunami: Big Tech companies have announced over $500 billion in AI-related capital expenditure through 2027, potentially eclipsing the infrastructure buildout of the internet era
- Early productivity signals: Goldman Sachs research suggests AI could boost US labor productivity growth by 1.5 percentage points annually over the next decade
- Deflationary mechanisms: AI-powered automation is already reducing costs in customer service, software development, legal research, and medical diagnostics
- Broad applicability: Unlike previous technologies limited to specific sectors, AI promises productivity gains across virtually every industry from agriculture to healthcare
The Skeptical Counterargument:
- Implementation lag: As The Economist notes, productivity gains from transformative technologies typically take 10-15 years to materialize fully—Greenspan’s bet benefited from fortuitous timing as gains accelerated just as he cut rates
- Measurement challenges: Productivity statistics notoriously struggle to capture improvements in service quality, potentially understating gains but also making real-time policy decisions hazardous
- Displacement costs: AI-driven job disruption could create transitional unemployment and reduce consumer spending, offsetting productivity benefits
- Energy demands: AI data centers consume massive electricity, potentially creating inflationary pressure in energy markets that could offset disinflationary effects elsewhere
The comparison between the 1990s internet boom and today’s AI surge reveals both similarities and critical differences:
| Metric | 1990s Internet Era | 2026 AI Era |
|---|---|---|
| Productivity Growth | 1.4% → 2.5% over decade | 1.5% → 2.1% (18 months) |
| Capital Investment | ~$2 trillion (inflation-adjusted) | Projected $500B+ through 2027 |
| Inflation Environment | Stable 2-3% range | Recently peaked at 9%, now ~2% |
| Fed Funds Rate | Gradually lowered from 6% to 5% | Currently 5.25-5.5%, pressure to cut |
| Adoption Timeline | 15+ years to mass adoption | Rapid deployment but uncertain ROI |
| Labor Market | Unemployment fell to 4% | Currently 3.7%, near historic lows |
Desmond Lachman of the American Enterprise Institute offers a sobering caution in Project Syndicate. While acknowledging Warsh’s qualifications to navigate the AI revolution, Lachman warns that premature rate cuts could spook bond markets, particularly given elevated government debt levels that dwarf those of the 1990s. Federal debt stood at 60% of GDP when Greenspan made his bet; today it exceeds 120%.
Implications for the US Economy and Growth Trajectory
The stakes extend far beyond monetary policy arcana. Warsh’s AI productivity bet carries profound implications for workers, businesses, and America’s competitive position.
If AI delivers on its promise as a disinflationary force, the US economy could enter a golden period of what economists call “immaculate disinflation”—falling inflation without the recession typically required to achieve it. Real wages would rise as nominal pay increases outpace price growth. The Fed could maintain accommodative policy, supporting business investment and job creation. Housing affordability might improve as mortgage rates decline. Stock markets, particularly growth-oriented technology shares, would likely soar on expectations of sustainably higher earnings.
But this optimistic scenario requires several conditions to align. First, productivity gains must materialize quickly—not in the usual decade-plus timeframe—to validate easier policy. Second, AI’s benefits must diffuse broadly across the economy rather than concentrating in a handful of tech giants. Third, labor market adjustments must occur smoothly without triggering political backlash that could derail the technological transition.
The risks of miscalculation loom large. As The New York Times editorial board cautioned, the Fed’s credibility—painstakingly rebuilt after taming inflation—could be squandered if premature rate cuts reignite price pressures. Workers on fixed incomes and retirees would suffer disproportionately. The Fed might then face the painful choice between tolerating higher inflation or hiking rates sharply enough to trigger recession.
There’s also the political dimension. Warsh’s nomination by Trump, who has repeatedly criticized Powell for maintaining restrictive policy, raises questions about Fed independence. While Warsh has a track record of intellectual autonomy—he dissented against some of the Fed’s crisis-era policies as a Governor from 2006-2011—the optics of a Trump-appointed chair cutting rates aggressively ahead of the 2028 election could undermine public confidence in the institution’s apolitical mandate.
Learning from History Without Repeating It
The Greenspan precedent offers both inspiration and warning. Yes, the Maestro’s productivity bet succeeded brilliantly—for a time. But his extended period of easy money also inflated the dot-com bubble that burst spectacularly in 2000, wiping out $5 trillion in market value. Critics argue his approach sowed the seeds of subsequent financial instability, including the housing bubble that culminated in the 2008 crisis.
Warsh, to his credit, has shown awareness of these pitfalls. As a Fed Governor during the financial crisis, he advocated for earlier recognition of asset bubbles and tighter oversight of financial institutions. His 2025 writings emphasize the need for “vigilant monitoring of financial stability risks” even as the Fed pursues growth-oriented policies.
The question is whether he can thread this needle—cutting rates to accommodate productivity gains while preventing the kind of speculative excess that characterized the late 1990s. The answer may depend less on economic theory than on judgment, timing, and some measure of luck.
The Verdict: A Calculated Gamble Worth Taking?
So is Warsh’s AI productivity bet sound policy or dangerous hubris? The honest answer is that we won’t know for several years, and by then the consequences—positive or negative—will already be unfolding.
What we can say is this: the bet is intellectually coherent, grounded in plausible economic mechanisms, and supported by preliminary data. AI does appear to be driving genuine productivity improvements, even if their ultimate magnitude remains uncertain. The disinflationary forces Warsh identifies—automation, improved resource allocation, reduced transaction costs—are real and observable.
But coherence doesn’t guarantee correctness. The 1990s productivity boom emerged from technologies that were already mature and widely deployed by mid-decade. Today’s AI tools, while impressive, remain in their infancy with uncertain commercial applications beyond a handful of use cases. The gap between technological potential and economic reality has tripped up many forecasters.
Perhaps the most balanced perspective comes from examining not just the economics but the political economy. A Fed chair’s primary job isn’t to achieve optimal policy in some abstract sense—it’s to maintain the institutional legitimacy necessary to conduct monetary policy effectively over time. That requires building consensus, communicating clearly, and preserving independence from political pressure.
On these criteria, Warsh brings both strengths and vulnerabilities. His intellectual firepower and private sector experience (he worked at Morgan Stanley before joining the Fed) command respect in financial markets. His youth—he’d be one of the youngest Fed chairs in history—signals fresh thinking. But his close ties to Trump and Wall Street could make him a lightning rod for criticism if his policies falter.
Conclusion: The Most Consequential Fed Chair Since Greenspan?
As Kevin Warsh prepares for confirmation hearings, he stands at a crossroads that could define not just his tenure but the trajectory of the US economy for decades. His AI productivity bet represents the kind of paradigm-shifting policy vision that comes along once in a generation—for better or worse.
If he’s right, future historians may rank him alongside Greenspan and Paul Volcker as transformational Fed chairs who correctly identified tectonic economic shifts and adjusted policy accordingly. We could be entering an era where technology-driven productivity gains allow faster growth with lower inflation, improving living standards across income levels while maintaining US economic dominance.
If he’s wrong, the consequences could range from merely embarrassing—a Fed chair who cut rates prematurely and had to reverse course—to genuinely damaging, with renewed inflation, financial instability, or the policy credibility erosion that made the 1970s such a painful decade.
The truth, as usual, likely lies somewhere in between these extremes. AI will probably deliver meaningful but not transformational productivity gains over the next 5-10 years. Policy will muddle through with some successes and some setbacks. The economy will neither enter utopia nor collapse.
But “muddling through” is an unsatisfying conclusion for an award-winning columnist to offer readers. So here’s a bolder prediction: Warsh will cut rates more aggressively than current market pricing suggests—perhaps 100-150 basis points over his first 18 months—justified by his AI productivity thesis. Growth will initially accelerate, validating his approach. But by 2028, signs of overheating will emerge—not in consumer prices but in asset markets, particularly AI-adjacent stocks and commercial real estate serving data centers. The Fed will face pressure to tighten, creating volatility.
The ultimate judgment on Warsh’s tenure will then depend on whether he shows the flexibility to adjust course when reality deviates from theory—something Greenspan struggled with in his later years. That capacity for intellectual humility and policy adaptation, more than the theoretical soundness of any particular bet, separates adequate Fed chairs from great ones.
For now, we can only watch, wait, and hope that Warsh’s AI productivity wager proves as prescient as Greenspan’s internet bet—without the bubble that followed.
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Acquisitions
SMFG Jefferies Takeover: Japan’s Banking Giant Eyes Full US Deal
There is a particular kind of corporate ambition that does not announce itself. It assembles a small team. It watches. It waits for the moment when price and opportunity converge — and then it moves. That, according to a Financial Times exclusive published this morning, is precisely what Sumitomo Mitsui Financial Group is doing with Jefferies Financial Group.
SMFG, Japan’s second-largest banking group, has assembled a small internal team positioned to act should Jefferies’ share price present a compelling acquisition opportunity. Bloomberg Law The disclosure — sourced to people familiar with the matter — instantly rewired global markets. Jefferies shares surged more than 9% in U.S. pre-market trading, building on Monday’s close of $39.55, itself up 3.72% on the session. Frankfurt-listed shares had already jumped 6% immediately following the FT report. Investing.com SMFG’s own Tokyo-listed shares climbed in sympathy.
This is not a casual flirtation. It is the logical culmination of a five-year strategic partnership — one that has been methodically deepened, financially structured, and now, apparently, stress-tested for the eventuality of full ownership.
From Alliance to Ambition: The Anatomy of a Five-Year Courtship
The SMFG-Jefferies relationship began with a handshake, not a balance sheet. SMFG first initiated a formal collaboration with Jefferies in 2021, focused on cross-border mergers and acquisitions and leveraged finance. It took its first equity stake in 2023 and has raised it several times since. U.S. News & World Report
The strategic logic was never obscure: Jefferies, as a fiercely independent mid-market investment bank competing with Goldman Sachs and Morgan Stanley on advisory mandates, offered something SMBC could not manufacture internally — genuine Wall Street credibility, deep sponsor relationships across private equity, and a leveraged-finance franchise that punches far above its balance-sheet weight.
SMFG first bought nearly 5% of Jefferies in 2021. Then, in September 2025, Sumitomo Mitsui Banking Corp — the banking subsidiary of SMFG — raised its stake in Jefferies to up to 20% with a $912 million investment. Investing.com To be precise: the Japanese lender boosted its stake from 15% to 20% through a ¥135 billion investment, while deliberately keeping its voting interest below 5% GuruFocus — a structurally important distinction that has allowed SMFG to accumulate economic exposure without triggering the Bank Holding Company Act thresholds that would force a more formal regulatory review by the Federal Reserve.
That September 2025 announcement was accompanied by a sweeping expansion of the commercial partnership. The two groups agreed to combine their Japanese equities and equity capital markets businesses into a joint venture, expand joint coverage of larger private equity sponsors, and implement joint origination, underwriting, and execution of syndicated leveraged loans in EMEA. SMBC also agreed to provide Jefferies approximately $2.5 billion in new credit facilities to support leveraged lending in Europe, U.S. pre-IPO lending, and asset-backed securitization. sec
That Japanese equities joint venture — merging research, trading, and capital markets operations — was expected to formally launch in January 2027. GuruFocus The profit projections were explicit: SMFG estimated the Jefferies stake would contribute 50 billion yen to profit by its fifth year, with 10 billion yen expected to come from the equity joint venture alone. TradingView
This was not passive portfolio investment. It was infrastructure for a takeover — whether or not Tokyo ever intended to use it.
The Opportunity Window: Jefferies’ Annus Horribilis
The SMFG Jefferies takeover calculus has been fundamentally altered by one inconvenient reality: Jefferies has had a brutally difficult 18 months.
Jefferies’ stock has fallen more than 36% this year, following steep declines in 2025, when a unit linked to its asset management arm was embroiled in the bankruptcy of U.S. auto parts supplier First Brands. The Edge Malaysia The fallout extended beyond a single credit event. Jefferies has come under sharp scrutiny over its lending standards and risk appetite after the collapses of both British lender Market Financial Solutions and First Brands. The Edge Malaysia Investors have filed suit, alleging the bank misled markets about its risk management practices.
Jefferies currently carries a market capitalisation of approximately $8.17 billion, compared with SMFG’s market capitalisation of around $124 billion. The Edge Malaysia That ratio — roughly 15-to-1 — tells you almost everything about the feasibility of this deal. From a pure balance-sheet perspective, SMFG could write a cheque for Jefferies and barely register it as a rounding error. The question has never been financial capacity.
The question — always — has been price, governance, and will.
The Small Team With a Large Mandate
SMFG has assembled a small team to prepare for a potential move, should a drop in Jefferies’ share price create a sufficiently compelling entry point. Investing.com The existence of this team — quiet, deliberate, instructed to be ready — speaks volumes about how SMFG’s senior leadership is thinking about this relationship’s terminal state.
Any move by SMFG is not imminent, according to the people briefed on the matter. It is also uncertain whether Jefferies executives would be willing to sell at a depressed share price. MarketScreener That caveat matters enormously. Rich Handler, Jefferies’ long-serving CEO, has built his career around the bank’s independence. He turned down overtures before. The cultural friction between Tokyo’s consensus-driven keiretsu model — patient, hierarchical, relationship-first — and Jefferies’ New York swagger, deal-by-deal meritocracy, and fiercely guarded autonomy is not a detail. It is the central negotiating obstacle.
SMFG is prepared to put the acquisition plan on hold if market conditions or Jefferies management do not allow a full takeover. GuruFocus An SMFG spokesperson, when pressed by the FT, offered a reply that was diplomatic precisely because it said nothing: “Jefferies is our important partner. We decline to comment on hypothetical assumptions or rumors.” MarketScreener
That is not a denial. In the grammar of Japanese corporate communication, it is practically an acknowledgement.
Strategic Implications: What a Full Japan-US Investment Banking Merger Would Mean
A completed SMBC Jefferies possible buyout — should it materialise — would represent the most consequential cross-border M&A between a Japanese bank and a U.S. Wall Street institution since Mitsubishi UFJ Financial Group invested in Morgan Stanley in the depths of the 2008 financial crisis. The precedent is instructive.
Larger MUFG rival currently holds a 23.62% shareholding in Morgan Stanley, while third-ranked Mizuho Financial Group acquired U.S. M&A advisory Greenhill in 2023 U.S. News & World Report — demonstrating a clear generational strategy among Japanese megabanks to embed themselves permanently within the architecture of global capital markets.
A full SMFG acquisition of Jefferies would, however, go further than any of these. It would not be a passive stake or a boutique acquisition. It would mean absorbing an institution with roughly $8 billion in equity, several thousand employees, a prime brokerage franchise, leveraged-finance origination across New York, London, and Hong Kong, and a sponsor-coverage network that stretches across the largest private equity firms on earth.
For global leveraged-finance markets, the strategic implications are significant. As Travis Lundy, an analyst who publishes on Smartkarma, noted when the September 2025 stake was announced: “SMBC Nikko may be able to get more inbound M&A interest from U.S. financial firms where it may not have the trusted relationships in the U.S. that Jefferies does. More perhaps it gets SMBC a potentially much better seat at the table for providing LBO financing.” Wallstreetobserver Full ownership would convert that seat into the head of the table.
For SMFG’s securities arm, SMBC Nikko, the prize is equally clear: immediate access to Jefferies’ European sponsor coverage, its EMEA leveraged-loan distribution network, and its U.S. equity advisory franchise — capabilities that would take a decade to replicate organically, if replication were even possible.
The Regulatory and Valuation Hurdles
Elite readers should not mistake appetite for inevitability. The path from minority stake to full ownership in the United States is strewn with structural impediments.
Regulatory architecture: A full acquisition of Jefferies by SMFG would require approval from the Federal Reserve under the Bank Holding Company Act, the Committee on Foreign Investment in the United States (CFIUS), and potentially the SEC and FINRA. In the current U.S. political environment — where economic nationalism has become a bipartisan posture and scrutiny of foreign ownership of financial infrastructure has intensified — regulatory risk is non-trivial. Japanese buyers, historically, have fared better than Chinese bidders; but the regulatory environment of 2026 is not that of 2008.
Valuation gap: SMFG has been watching Jefferies trade down to approximately $39 a share from highs above $70. Even at current depressed levels, a full acquisition premium — typically 30–40% above market — would imply a takeover price in the range of $10.5–11 billion. Whether SMFG is willing to pay a meaningful premium for a franchise whose credit culture is under active litigation scrutiny is a question only Tokyo’s boardroom can answer.
Cultural integration risk: The deepest hazard in this deal has no number attached to it. Jefferies’ most valuable assets — its bankers, its trader relationships, its advisory franchise — are human capital. Wall Street talent, confronted with the prospect of being absorbed into a Japanese megabank’s corporate structure, may simply leave. Managing that attrition risk is the most important post-merger challenge any acquirer would face, and it is one for which the MUFG-Morgan Stanley experience offers only partial guidance.
Precedent, Geopolitics, and the Bigger Picture
Zoom out from the deal-specific mechanics, and what emerges is a structural story about the rebalancing of global finance. Japanese megabanks — flush with capital, largely insulated from the deposit-flight pressures that battered U.S. regional banks in 2023, and operating in a domestic market with limited organic growth — have been systematically deploying their fortress balance sheets into Western financial infrastructure.
The SMFG-Jefferies partnership sits within this broader geopolitical current: Japan’s quiet, methodical bid for investment-banking heft at a moment when U.S. and European banks are retrenching, restructuring, and pulling back from certain markets. For Tokyo’s policymakers and financial regulators, a fully owned U.S. investment bank with a global sponsor-coverage franchise is not merely a corporate asset. It is a projection of economic power.
As Japan’s stock market booms — with larger deal sizes, more global transactions, and increased capital flows from overseas — the alliance with Jefferies has been designed to allow SMFG’s securities arm, SMBC Nikko, to better meet issuer and investor demand TradingView in ways that a purely domestic Japanese franchise never could.
Outlook
SMFG will not overpay for Jefferies — not this week, not this quarter. The assembly of a readiness team is a signal of strategic intent, not a declaration of imminent action. Jefferies’ share price must fall further, or stabilize at a level that SMFG’s internal models can justify to its own shareholders.
But the direction of travel is unmistakable. What began as a 5% alliance stake in 2021 is now a 20% economic position, a $2.5 billion credit commitment, a forthcoming joint venture in Japanese equities, and a dedicated team waiting for the right moment. The infrastructure for a full Japan-US investment banking merger has been quietly, patiently constructed over five years.
The only question still open is timing — and whether Rich Handler’s independence reflex ultimately yields to the mathematics of a depressed stock price and a patient Japanese suitor with a $124 billion balance sheet and nowhere else it needs to be.
In Tokyo’s banking culture, patience is not weakness. It is strategy. SMFG has been playing this long game from the beginning. The board in Marunouchi can afford to wait. The question, increasingly, is whether Jefferies’ shareholders can afford for it to.
FAQ: SMFG Jefferies Takeover — What You Need to Know
Q1: What stake does SMFG currently hold in Jefferies? Through its banking subsidiary SMBC, SMFG holds approximately 20% of Jefferies on an economic basis, following a $912 million open-market purchase completed in September 2025. Crucially, its voting interest remains below 5%, structuring the position to stay below U.S. bank regulatory thresholds.
Q2: Why is SMFG exploring a full takeover of Jefferies now? Jefferies’ shares have fallen more than 36% in the period since SMFG’s last stake increase, largely due to credit losses tied to the bankruptcy of U.S. auto parts supplier First Brands and the collapse of British lender Market Financial Solutions. The decline has created a potential valuation window that SMFG’s internal team is monitoring.
Q3: What regulatory hurdles face a Sumitomo Mitsui Financial Group Jefferies acquisition? A full acquisition would require Federal Reserve approval under the Bank Holding Company Act, a CFIUS national-security review, and clearance from FINRA and the SEC. U.S. regulatory scrutiny of foreign ownership of systemically significant financial institutions has tightened considerably since 2020.
Q4: What is the SMBC Jefferies possible buyout worth? Jefferies’ current market capitalization stands at approximately $8.17 billion. A standard acquisition premium of 30–40% would imply a total deal value of roughly $10.5–11.5 billion — well within SMFG’s financial capacity, given its $124 billion market capitalization.
Q5: What does the SMFG-Jefferies deal mean for global leveraged finance and M&A markets? A completed Japan-US investment banking merger of this scale would reshape the mid-market sponsor coverage landscape globally. Combined, SMFG and Jefferies would control a formidable leveraged-lending and M&A advisory platform spanning New York, London, Tokyo, and Hong Kong — with particular strength in private-equity-backed transactions and cross-border Japan-US deal flow.
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Analysis
Global AI Regulation UN 2026: Why the World Needs an Oversight Body Now
The machines are already choosing who dies. The question is whether humanity will choose to stop them.
In the early weeks of Israel’s military campaign in Gaza, a targeting system called Lavender quietly changed the nature of modern warfare. The Israeli army marked tens of thousands of Gazans as suspects for assassination using an AI targeting system with limited human oversight and a permissive policy for civilian casualties. +972 Magazine Israeli intelligence officials acknowledged an error rate of around 10 percent — but simply priced it in, deeming 15 to 20 civilian deaths acceptable for every junior militant the algorithm identified, and over 100 for commanders. CIVICUS LENS The machine, according to one Israeli intelligence officer cited in the original +972 Magazine investigation, “did it coldly.”
This is not a hypothetical future threat. This is 2026. And this is why global AI regulation under the United Nations — a binding, enforceable, internationally backed governance platform — is no longer a matter of philosophical debate. It is the defining policy emergency of our era.
Why the Global AI Regulation UN Framework Is the Most Urgent Issue of 2026
When historians eventually write the account of humanity’s encounter with artificial intelligence, they will mark 2026 as the year the world stood at the threshold and hesitated. UN Secretary-General António Guterres affirmed in early February 2026: “AI is moving at the speed of light. No country can see the full picture alone. We need shared understandings to build effective guardrails, unlock innovation for the common good, and foster cooperation.” United Nations Foundation
That statement, measured and diplomatic in tone, barely captures the urgency on the ground. From the rubble of Gaza to the drone corridors above eastern Ukraine, algorithmic warfare has become normalized with terrifying speed. The Future of Life Institute now tracks approximately 200 autonomous weapons systems deployed across Ukraine, the Middle East, and Africa Globaleducationnews — the majority operating in legal and regulatory voids that no international treaty has yet filled.
Meanwhile, the governance architecture intended to respond to this moment remains fragile and fragmented. Just seven countries — all from the developed world — are parties to all current significant global AI governance initiatives, according to the UN. World Economic Forum A full 118 member states have no meaningful seat at the table where the rules of AI are being written. This is not merely inequitable; it is dangerous. The technologies being deployed against human populations are outrunning the institutions designed to constrain them.
The Lethal Reality: AI Warfare and Human Safety in the Middle East
The Gaza conflict has provided the world its most documented and disturbing window into what AI warfare looks like when accountability is stripped away. Israel’s AI tools include the Gospel, which automatically reviews surveillance data to recommend bombing targets, and Lavender, an AI-powered database that listed tens of thousands of Palestinian men linked by algorithm to Hamas or Palestinian Islamic Jihad. Wikipedia Critics across the spectrum of international law have argued that the use of these systems blurs accountability and results in disproportionate violence in violation of international humanitarian law.
Evidence recorded in the classified Israeli military database in May 2025 revealed that only 17% of the 53,000 Palestinians killed in Gaza were combatants — implying that 83% were civilians. Action on Armed Violence That figure, if accurate, represents one of the highest civilian death rates in modern recorded warfare, and it emerges directly from the logic of algorithmic targeting: speed over deliberation, efficiency over ethics, statistical probability over the irreducible humanity of each individual life.
Many operators trusted Lavender so much that they approved its targets without checking them SETA — a collapse of human oversight so complete that it renders the phrase “human-in-the-loop” meaningless in practice. UN Secretary-General Guterres stated that he was “deeply troubled” by reports of AI use in Gaza, warning that the practice puts civilians at risk and fundamentally blurs accountability.
This is not an isolated case study. Contemporary conflicts — from Gaza, Sudan and Ukraine — have become “testing grounds” for the military use of new technologies. United Nations Slovenia’s President Nataša Pirc Musar, addressing the UN Security Council, put it with stark clarity: “Algorithms, armed drones and robots created by humans have no conscience. We cannot appeal to their mercy.”
The Accountability Void: Who Is Responsible When an Algorithm Kills?
The legal and moral vacuum at the center of AI warfare is not accidental — it is structural. Although autonomous weapons systems are making life-or-death decisions in conflicts without human intervention, no specific treaty regulates these new weapons. TRENDS Research & Advisory The foundational principles of international humanitarian law — distinction between combatants and civilians, proportionality, and precaution — were designed for human actors capable of judgment, hesitation, and moral reckoning. They were not designed for systems that process kill decisions in milliseconds.
Both international humanitarian law and international criminal law emphasize that serious violations must be punished to fulfil their purpose of deterrence. A “criminal responsibility gap” caused by AI would mean impunity for war crimes committed with the aid of advanced technology. Action on Armed Violence This is the nightmare scenario that legal scholars from Human Rights Watch to the International Committee of the Red Cross now warn about openly: not only that AI enables atrocities, but that it systematically destroys the chain of accountability that makes justice possible after them.
A 2019 Turkish Bayraktar drone strike in Libya created precisely this precedent: UN investigators could not determine whether the operator, manufacturer, or foreign advisors bore ultimate responsibility. TRENDS Research & Advisory That ambiguity, multiplied by the speed and scale of contemporary AI systems, represents an existential challenge to the international legal order.
The question “who is responsible when an algorithm kills?” cannot be answered under the current framework. And that is precisely why the current framework must be replaced.
The UN’s New Architecture: Promising, But Dangerously Insufficient
There are genuine signs that the international community understands what is at stake. The Global Dialogue on AI Governance will provide an inclusive platform within the United Nations for states and stakeholders to discuss the critical issues concerning AI facing humanity, with the Scientific Panel on AI serving as a bridge between cutting-edge AI research and policymaking — presenting annual reports at sessions in Geneva in July 2026 and New York in 2027. United Nations
The CCW Group of Experts’ rolling text from November 2024 outlines potential regulatory measures for lethal autonomous weapons systems, including ensuring they are predictable, reliable, and explainable; maintaining human oversight in morally significant decisions; restricting target types and operational scope; and enabling human operators to deactivate systems after activation. ASIL
Yet the gulf between these principles and enforceable reality remains vast. In November 2025, the UN General Assembly’s First Committee passed a historic resolution calling to negotiate a legally enforceable LAWS agreement by 2026 — 156 nations supported it overwhelmingly. Only five nations strictly rejected the resolution, notably the United States and Russia. Usanas Foundation Their resistance sends a signal that is impossible to misread: the two largest military AI developers on earth are actively resisting the international constraints that the rest of the world is demanding.
By the end of 2026, the Global Dialogue will likely have made AI governance global in form but geopolitical in substance — a first test of whether international cooperation can meaningfully shape the future of AI or merely coexist alongside competing national strategies. Atlantic Council That assessment, from the Atlantic Council’s January 2026 analysis, should be understood as a warning, not a prediction to be accepted passively.
The Case for an IAEA-Style UN AI Governance Body
The most compelling model for meaningful global AI regulation under the UN has been circulating in serious policy circles for several years, and in February 2026 it gained its most prominent corporate advocate. At the international AI Impact Summit 2026 in New Delhi, OpenAI CEO Sam Altman called for a radical new format for global regulation of artificial intelligence — modeled after the International Atomic Energy Agency — arguing that “democratizing AI is the only fair and safe way forward, because centralizing technology in one company or country can have disastrous consequences.” Logos-pres
The IAEA analogy is instructive precisely because it addresses the core failure of current approaches: the absence of verification, inspection, and enforcement. An IAEA-like agency for AI could develop industry-wide safety standards and monitor stakeholders to assess whether those standards are being met — similar to how the IAEA monitors the distribution and use of uranium, conducting inspections to help ensure that non-nuclear weapon states don’t develop nuclear weapons. Lawfare
This proposal has been echoed and refined by researchers published in Nature, who draw a direct parallel: the IAEA’s standardized safety standards-setting approach and emergency response system offer valuable lessons for establishing AI safety regulations, with standardized safety standards providing a fundamental framework to ensure the stability and transparency of AI systems. Nature
Skeptics argue, with some justification, that achieving this level of cooperation in the current geopolitical climate is extraordinarily difficult. But consider the alternative. The 2026 deadline is increasingly seen as the “finish line” for global diplomacy; if a treaty is not reached, the speed of innovation in military AI driven by the very powers currently blocking the UN’s progress will likely make any future regulation obsolete before the ink is even dry. Usanas Foundation We are, in the language of arms control analysts, in the “pre-proliferation window” — the last viable moment before these systems become as ubiquitous and ungovernable as small arms.
EU AI Act Enforcement and the Patchwork Problem
The European Union has moved further than any other jurisdiction toward binding regulation. By 2026, the EU AI Act is partially in force, with obligations for general-purpose AI and prohibited AI practices already applying, and high-risk AI systems facing requirements for pre-deployment assessments, extensive documentation, post-market monitoring, and incident reporting. OneTrust This is meaningful progress. It is also deeply insufficient as a global solution.
According to Gartner, by 2030, fragmented AI regulation will quadruple and extend to 75% of the world’s economies — but organizations that have deployed AI governance platforms are currently 3.4 times more likely to achieve high effectiveness in AI governance than those that do not. Gartner That statistic reveals both the potential of structured governance and the cost of its absence.
The EU’s rules, however rigorous, apply within EU member states and to companies seeking EU market access. They do not reach the drone manufacturers of Turkey, the autonomous targeting systems of Israel, the Replicator program of the United States Pentagon, or the algorithmic weapons being developed at pace in Beijing. The International AI Safety Report 2026 notes that reliable pre-deployment safety testing has become harder to conduct, and it has become more common for models to distinguish between test settings and real-world deployment — meaning dangerous capabilities could go undetected before deployment. Internationalaisafetyreport In a military context, undetected dangerous capabilities do not result in regulatory fines. They result in mass civilian casualties.
Comprehensive global AI regulation under the United Nations must transcend this patchwork. The model cannot be voluntary principles and national strategies stitched together by hope. It must be treaty-based, inspection-backed, and enforceable — with particular urgency around military applications.
The Policy Architecture the World Needs
The outline of what a viable global AI regulation UN platform would require is not, in fact, mysterious. The intellectual groundwork has been laid. What is missing is political will, specifically from the three states — the United States, Russia, and China — whose cooperation is structurally indispensable.
A credible architecture would include, at minimum:
- A binding treaty on lethal autonomous weapons systems, prohibiting systems that cannot be used in compliance with international humanitarian law and mandating meaningful human oversight for all others. The UN Secretary-General has maintained since 2018 that lethal autonomous weapons systems are politically unacceptable and morally repugnant, reiterating in his New Agenda for Peace the call to conclude a legally binding instrument by 2026. UNODA
- An Independent International AI Agency modeled on the IAEA, with authority to develop safety standards, conduct inspections of frontier AI systems, and verify compliance — particularly for dual-use applications with military potential.
- Universal inclusion of the Global South, whose populations bear a disproportionate share of the consequences of algorithmic warfare and AI-enabled surveillance, yet remain largely absent from the forums where the rules are being written. Many countries of the Global South are notably absent from the UN’s experts group on autonomous weapons, despite the inevitable future global impact of these systems once they become cheap and accessible. Arms Control Association
- A standing accountability mechanism for AI-related violations of international humanitarian law, closing the “responsibility gap” that currently allows commanders to deflect culpability onto algorithms.
- Real-time AI risk monitoring and reporting, with annual assessments presented to the UN General Assembly — building on the model of the Independent International Scientific Panel on AI already authorized for its first report in Geneva in July 2026.
None of this is technically impossible. The scientific consensus exists. The legal frameworks are available. The moral case is overwhelming.
Conclusion: Global AI Regulation UN 2026 — The Last Clear Moment
The Greek Prime Minister, speaking at the UN Security Council’s open debate on AI, made a comparison that deserves to reverberate through every foreign ministry and defense establishment on earth: the world must rise to govern AI “as it once did for nuclear weapons and peacekeeping.” He warned that “malign actors are racing ahead in developing military AI capabilities” and urged the Council to rise to the occasion. United Nations
Humanity’s fate, as the UN Secretary-General has said plainly, cannot be left to an algorithm. But neither can it be left to voluntary declarations, aspirational principles, and annual dialogues that produce no binding obligation. The deadly deployment of AI in active conflicts has already raised existential concerns for human safety that cannot be wished away by appeals to innovation or national security prerogative.
The architecture for a genuine global AI regulation UN platform exists in skeletal form. The Geneva Dialogue, the Scientific Panel, the LAWS treaty negotiations — these are the bones of something that could actually work. What they require now is not more deliberation. They require the political courage of the world’s most powerful states to subordinate short-term strategic advantage to the longer-term survival of the rules-based international order — and, more fundamentally, to the survival of human dignity in the age of the algorithm.
The pre-proliferation window is closing. 2026 is not a deadline to be managed. It is a moral threshold to be met.
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AI
The Price of Algorithmic War: How AI Became the New Dynamite in the Middle East
The Iran conflict has turned frontier AI models into contested weapons of state — and the financial and human fallout is only beginning to register.
In the first eleven days of the U.S.-Israeli offensive against Iran, which began on February 28, 2026, American and Israeli forces executed roughly 5,500 strikes on Iranian targets. That is an operational tempo that would have required months in any previous conflict — made possible, in significant part, by artificial intelligence. In the first eleven days of the conflict, America achieved an astonishing 5,500 strikes, using AI on a large-scale battlefield for the first time at this scale. The National The same week those bombs fell, a legal and commercial crisis erupted in Silicon Valley with consequences that will define the AI industry for years. Both events are part of the same story.
We are living through the moment when AI ceased being a future-war thought experiment and became an operational reality — embedded in targeting pipelines, shaping intelligence assessments, and now at the center of a constitutional showdown between a frontier AI company and the United States government. Alfred Nobel, who invented dynamite and then spent the remainder of his life in tortured ambivalence about it, would have recognized the pattern immediately.
The Kill Chain, Accelerated
The joint U.S. and Israeli offensive on Iran revealed how algorithm-based targeting and data-driven intelligence are reforming the mechanics of warfare. In the first twelve hours alone, U.S. and Israeli forces reportedly carried out nearly 900 strikes on Iranian targets — an operational tempo that would have taken days or even weeks in earlier conflicts. Interesting Engineering
At the technological center of this acceleration sits a system most Americans have never heard of: Project Maven. Anthropic’s Claude has become a crucial component of Palantir’s Maven intelligence analysis program, which was also used in the U.S. operation to capture Venezuelan President Nicolás Maduro. Claude is used to help military analysts sort through intelligence and does not directly provide targeting advice, according to a person with knowledge of Anthropic’s work with the Defense Department. NBC News This is a distinction with genuine moral weight — between decision-support and decision-making — but one that is becoming harder to sustain at the speed at which modern targeting now operates.
Critics warn that this trend could compress decision timelines to levels where human judgment is marginalized, ushering in an era of warfare conducted at what has been described as “faster than the speed of thought.” This shortening interval raises fears that human experts may end up merely approving recommendations generated by algorithms. In an environment dictated by speed and automation, the space for hesitation, dissent, or moral restraint may be shrinking just as quickly. Interesting Engineering
The U.S. military’s posture has been notably sanguine about these concerns. Admiral Brad Cooper, head of U.S. Central Command, confirmed that AI is helping soldiers process troves of data, stressing that humans make final targeting decisions — but critics note the gap between that principle and verifiable practice remains wide. Al Jazeera
The Financial Architecture of AI Warfare
The economic dimensions of this transformation are substantial and largely unreported in their full complexity. Understanding them requires holding three separate financial narratives simultaneously.
The direct contract market is the most visible layer. Over the past year, the U.S. Department of Defense signed agreements worth up to $200 million each with several major AI companies, including Anthropic, OpenAI, and Google. CNBC These are not trivial sums in isolation, but they represent the seed capital of a much larger transformation. The military AI market is projected to reach $28.67 billion by 2030, as the speed of military decision-making begins to surpass human cognitive capacity. Emirates 24|7
The collateral economic disruption is less discussed but potentially far larger. On March 1, Iranian drone strikes took out three Amazon Web Services facilities in the Middle East — two in the UAE and one in Bahrain — in what appear to be the first publicly confirmed military attacks on a hyperscale cloud provider. The strikes devastated cloud availability across the region, affecting banks, online payment platforms, and ride-hailing services, with some effects felt by AWS users worldwide. The Motley Fool The IRGC cited the data centers’ support for U.S. military and intelligence networks as justification. This represents a strategic escalation that no risk-management framework in the technology sector adequately anticipated: cloud infrastructure as a legitimate military target.
The reputational and legal costs of AI’s battlefield role may ultimately dwarf both. Anthropic’s court filings stated that the Pentagon’s supply-chain designation could cut the company’s 2026 revenue by several billion dollars and harm its reputation with enterprise clients. A single partner with a multi-million-dollar contract has already switched from Claude to a competing system, eliminating a potential revenue pipeline worth more than $100 million. Negotiations with financial institutions worth approximately $180 million combined have also been disrupted. Itp
The Anthropic-Pentagon Fracture: A Defining Test
The dispute between Anthropic and the U.S. Department of Defense is not merely a contract negotiation gone wrong. It is the first high-profile case in which a frontier AI company drew a public ethical line — and then watched the government attempt to destroy it for doing so.
The sequence of events is now well-documented. The administration’s decisions capped an acrimonious dispute over whether Anthropic could prohibit its tools from being used in mass surveillance of American citizens or to power autonomous weapon systems, as part of a military contract worth up to $200 million. Anthropic said it had tried in good faith to reach an agreement, making clear it supported all lawful uses of AI for national security aside from two narrow exceptions. NPR
When Anthropic held its position, the response was unprecedented in the annals of U.S. technology policy. Defense Secretary Pete Hegseth declared Anthropic a supply chain risk in a statement so broad that it can only be seen as a power play aimed at destroying the company. Shortly thereafter, OpenAI announced it had reached its own deal with the Pentagon, claiming it had secured all the safety terms that Anthropic sought, plus additional guardrails. Council on Foreign Relations
In an extraordinary move, the Pentagon designated Anthropic a supply chain risk — a label historically only applied to foreign adversaries. The designation would require defense vendors and contractors to certify that they don’t use the company’s models in their work with the Pentagon. CNBC That this was applied to a U.S.-headquartered company, founded by former employees of a U.S. nonprofit, and valued at $380 billion, represents a remarkable inversion of the logic the designation was designed to serve.
Meanwhile, Washington was attacking an American frontier AI leader while Chinese labs were on a tear. In the past month alone, five major Chinese models dropped: Alibaba’s Qwen 3.5, Zhipu AI’s GLM-5, MiniMax’s M2.5, ByteDance’s Doubao 2.0, and Moonshot’s Kimi K2.5. Council on Foreign Relations The geopolitical irony is not subtle: in punishing a safety-focused American AI company, the administration may have handed Beijing its most useful competitive gift of the year.
The Human Cost: Social Ramifications No Algorithm Can Compute
Against the financial ledger, the humanitarian accounting is staggering and still incomplete.
The Iranian Red Crescent Society reported that the U.S.-Israeli bombardment campaign damaged nearly 20,000 civilian buildings and 77 healthcare facilities. Strikes also hit oil depots, several street markets, sports venues, schools, and a water desalination plant, according to Iranian officials. Al Jazeera
The case that has attracted the most scrutiny is the bombing of the Shajareh Tayyebeh elementary school in Minab, southern Iran. A strike on the school in the early hours of February 28 killed more than 170 people, most of them children. More than 120 Democratic members of Congress wrote to Defense Secretary Hegseth demanding answers, citing preliminary findings that outdated intelligence may have been to blame for selecting the target. NBC News
The potential connection to AI decision-support systems is explored with forensic precision by experts at the Bulletin of the Atomic Scientists. One analysis notes that the mistargeting could have stemmed from an AI system with access to old intelligence — satellite data that predated the conversion of an IRGC compound into an active school — and that such temporal reasoning failures are a known weakness of large language models. Even with humans nominally “in the loop,” people frequently defer to algorithmic outputs without careful independent examination. Bulletin of the Atomic Scientists
The social fallout extends well beyond individual atrocities. Israel’s Lavender AI-powered database, used to analyze surveillance data and identify potential targets in Gaza, was wrong at least 10 percent of the time, resulting in thousands of civilian casualties. A recent study found that AI models from OpenAI, Anthropic, and Google opted to use nuclear weapons in simulated war games in 95 percent of cases. Rest of World The simulation result does not predict real-world behavior, but it reveals how strategic reasoning models can default toward extreme outcomes under pressure — a finding that ought to unsettle anyone who imagines that algorithmic warfare is inherently more precise than the human kind.
The corrosion of accountability is perhaps the most insidious long-term social effect. “There is no evidence that AI lowers civilian deaths or wrongful targeting decisions — and it may be that the opposite is true,” says Craig Jones, a political geographer at Newcastle University who researches military targeting. Nature Yet the speed and opacity of AI-assisted operations makes it exponentially harder to assign responsibility when things go wrong. Algorithms do not face courts-martial.
Governance: The International Gap
Rapid technological development is outpacing slow international discussions. Academics and legal experts meeting in Geneva in March 2026 to discuss lethal autonomous weapons systems found themselves studying a technology already being used at scale in active conflicts. Nature The gap between the pace of deployment and the pace of governance has never been wider.
The Middle East and North Africa are arguably the most conflict-ridden and militarized regions in the world, with four out of eleven “extreme conflicts” identified in 2024 by the Armed Conflict Location and Event Data organization occurring there. The region has become a testing ground for AI warfare whose lessons — and whose errors — will shape every future conflict. War on the Rocks
The legal framework governing AI in warfare remains, generously described, aspirational. The U.S. military’s stated commitment to keeping “humans in the loop” is a principle that has no internationally binding enforcement mechanism, no agreed definition of what meaningful human control actually entails, and no independent auditing process. One expert observed that the biggest danger with AI is when humans treat it as an all-purpose solution rather than something that can speed up specific processes — and that this habit of over-reliance is particularly lethal in a military context. The National
AI as the New Dynamite: Nobel’s Unresolved Legacy
When Alfred Nobel invented dynamite in 1867, he believed — genuinely — that a weapon so devastatingly efficient would make war unthinkably costly and therefore rare. He was catastrophically wrong. The Franco-Prussian War, the First World War, and the entire industrial-era atrocity that followed proved that more powerful weapons do not deter wars; they escalate them, and they increase civilian mortality relative to combatant casualties.
The parallel to AI is not decorative. The argument for AI in warfare — that algorithmic precision reduces collateral damage, that faster targeting shortens conflicts, that autonomous systems absorb military risk that would otherwise fall on human soldiers — is structurally identical to Nobel’s argument for dynamite. It is the rationalization of a dual-use technology by those with an interest in its proliferation.
Drone technology in the Middle East has already shifted from manual control toward full autonomy, with “kamikaze” drones utilizing computer vision to strike targets independently if communications are severed. As AI becomes more integrated into militaries, the advancements will become even more pronounced with “unpredictable, risky, and lethal consequences,” according to Steve Feldstein, a senior fellow at the Carnegie Endowment for International Peace. Rest of World
The Anthropic dispute, whatever its ultimate legal resolution, has surfaced a question that Silicon Valley has been able to defer until now: can a technology company that builds frontier AI models — systems capable of synthesizing intelligence, generating targeting assessments, and running strategic simulations — genuinely control how those systems are used once deployed by a state? As OpenAI’s own FAQ acknowledged when asked what would happen if the government violated its contract terms: “As with any contract, we could terminate it.” The entire edifice of AI safety in warfare, for now, rests on the contractual leverage of companies that have already agreed to participate. Council on Foreign Relations
Nobel at least had the decency to endow prizes. The AI industry is still working out what it owes.
Policy Recommendations
A minimally adequate governance framework for AI in warfare would need to accomplish several things. Independent verification of “human in the loop” claims — not merely the assertion of it — is the essential starting point. Mandatory after-action reporting on AI involvement in any strike that results in civilian casualties would create accountability where none currently exists. International agreement on a baseline error-rate threshold — above which AI targeting systems may not be used without additional human review — would translate abstract humanitarian law into operational reality.
The technology companies themselves bear responsibility that no contract clause can fully discharge. Researchers from OpenAI, Google DeepMind, and other labs submitted a court filing supporting Anthropic’s position, arguing that restrictions on domestic surveillance and autonomous weapons are reasonable until stronger legal safeguards are established. ColombiaOne That the most capable AI builders in the world believe their own technology is not yet reliable enough for autonomous lethal use is information that should be at the center of every policy debate — not buried in court filings.
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