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Kevin Warsh Channels Alan Greenspan in AI Productivity Bet

Published

5 months ago

February 5, 2026

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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AI Memory Chip Shortage 2026: Nvidia, Apple & What Comes Next

Published

14 hours ago

June 27, 2026

Abdul Rahman

A global memory chip shortage is hitting AI hyperscalers, tanking Nvidia and Apple shares, and triggering a Wall Street rotation. Here’s what the AI sector’s supply crisis means for investors.The artificial intelligence boom that has driven Wall Street’s most extraordinary bull run in a generation is running headlong into a physical constraint: the world cannot produce memory chips fast enough to feed it.

On Friday, June 26, 2026, technology stocks extended a brutal weekly decline even as the broader market stabilized and advancing shares outnumbered declining ones. Nvidia slipped another 1% in early trading and was on pace for an 8% weekly loss—its worst five-day stretch in more than a year. Apple dived after announcing price increases for several iPad and Mac models, citing higher costs from memory chip shortages. Oracle and CoreWeave fell after the New York Times reported that OpenAI was considering delaying its initial public offering to as late as 2027.

What the headlines share is a single underlying cause: the cost of the memory chips that power AI infrastructure is rising faster than even the most aggressive hyperscaler budgets assumed, and the shortage driving that cost increase is not expected to ease before 2028.

The Architecture of the Crisis

Memory chips—specifically the high-bandwidth memory, or HBM, used in AI accelerators—are produced by a small number of manufacturers: SK Hynix, Micron, and Samsung. Demand for HBM has exploded because each new generation of Nvidia’s AI chips requires substantially more of it. As Nvidia pushes its product cycle faster to maintain competitive advantage, each cycle pulls forward enormous new demand for chips that take 18 to 24 months to ramp in production.

Micron reported strong quarterly earnings—its results have been spectacular—but the very strength of those results is the problem for the rest of the tech sector. Micron’s margins are rising because memory is scarce and expensive. The companies buying that memory—Microsoft, Amazon, Alphabet, Meta, and the rest of the hyperscaler complex—are absorbing higher input costs on a scale that is beginning to show up in margin guidance.

Analysts at Charles Schwab noted a “growing wedge” in the technology sector between memory producers like Micron—which is posting massive gains—and the hyperscaler stocks that are watching their AI infrastructure economics deteriorate. The latter group includes names like Microsoft, Amazon, and Alphabet, which are collectively projected to spend between $660 billion and $700 billion on AI infrastructure in 2026, according to research from Fair Observer.

Nvidia’s Problem Is a Market Concentration Problem

Nvidia entered 2026 having crossed a $5 trillion market capitalization—larger by GDP comparison than all but four national economies. That concentration made the stock not merely a bet on AI but a systemic weight in the S&P 500. Nvidia and its mega-cap technology peers now account for roughly 30% of the entire index—the highest concentration in half a century.

When Nvidia corrects, it does not correct in isolation. It reprices the risk premium of every fund manager with an S&P 500 benchmark, which is nearly every institutional investor in the world. The 8% weekly decline in late June—attributed to a combination of rising memory costs, margin anxiety among hyperscaler customers, and a broader rotation away from high-multiple AI stocks—had ripple effects across semiconductor infrastructure names including Lumentum, Marvell Technology, and Corning.

Apple Raises Prices—and Reveals the Exposure

Apple’s announcement of price increases for iPad and Mac models was notable for two reasons. First, Apple’s supply chain is among the most sophisticated on earth; if Apple could not absorb memory cost increases without raising consumer prices, the margin pressure is acute. Second, Apple’s pricing decision revealed an exposure that consumer electronics companies had managed to keep largely invisible through inventory buffers.

Those buffers, built up when memory was cheap, are now depleted. The shortage is forecast to persist through 2027 and potentially into 2028, driven by Nvidia’s accelerated chip release cadence and the insatiable demand of AI data centers for high-bandwidth memory. Analysts at Briefing.com noted that higher memory costs are seen “persisting throughout 2027 and perhaps into 2028, driven by increasing data center demand and Nvidia’s rapid introduction of updated AI chips.”

OpenAI Delays Its IPO—Absorbing the Lesson From SpaceX

The reported delay in OpenAI’s public offering is a direct consequence of two market developments: the broader tech weakness driven by the memory supply crisis, and the troubled IPO debut of SpaceX earlier in June, whose shares suffered heavy losses in the days following listing as global markets repriced risk.

OpenAI executives, who had targeted 2026 for a public offering, are now said to be evaluating a 2027 launch—giving markets time to stabilize and giving the company time to demonstrate that its AI infrastructure economics are sustainable at the scale that a public market valuation would demand.

The Rotation That May Define the Rest of 2026

The most significant market dynamic emerging from the memory chip crisis is not the decline in any single stock but the rotation it is enabling. As the mega-cap AI trade faces margin headwinds, investors are moving into financial and industrial companies, healthcare, and energy—sectors that had been overshadowed for years by the AI growth narrative. The Dow, weighted toward those steadier names, was holding up even as the Nasdaq declined through the final week of June.

That divergence—Dow up, Nasdaq down—is a familiar pattern in sector rotation cycles. It does not necessarily signal a bear market. It may signal the beginning of a more broadly distributed bull market, one less concentrated in five or seven names. The memory supply crisis, in that reading, is not the end of the AI boom—it is the first serious test of whether the boom’s economics are durable enough to survive contact with physical constraints.

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AI Energy Demand 2026: Data Centres, Power Grids & the $725B Infrastructure Boom

Published

23 hours ago

June 27, 2026

Abdul Rahman

Hyperscalers are spending $725 billion on AI infrastructure in 2026. The energy demands of this buildout are reshaping global power markets, utility valuations, and electricity costs. Here’s the full picture.

Behind every AI-generated image, every chatbot response, and every earnings forecast produced by a large language model is a data centre consuming electricity at a scale that is quietly reshaping global energy markets.

Microsoft, Google, Meta, and Amazon — the four hyperscaler giants powering the AI economy — are collectively spending more than $725 billion on AI infrastructure in 2026. This unprecedented wave of capital expenditure is building data centres that require power at a scale that has fundamentally changed the conversation around energy security, grid stability, electricity pricing, and the commercial viability of every power generation technology from natural gas to nuclear.

The AI energy story is not a footnote to the technology boom. It is one of the most consequential investment themes of the decade.

The Scale of the Demand Shock

To understand the magnitude of AI’s energy appetite, consider the trajectory. A single large AI training run — the computational process that creates a frontier model like those produced by OpenAI, Anthropic, or Google DeepMind — can consume more electricity than a medium-sized city uses in a month. Inference — the ongoing process of serving queries to users — multiplies that consumption across millions of simultaneous interactions.

OpenAI’s inference compute costs are projected at $14.1 billion for 2026. Inference compute is largely an energy and chip cost. The company’s gross margin of approximately 33% reflects how significant this load has become.

Across the hyperscalers, the $725 billion AI infrastructure budget funds:

Data centre construction — new campuses in the US, Europe, Southeast Asia, and the Middle East
Nvidia GPU procurement — the primary compute engine for AI workloads
Network infrastructure — high-speed interconnects between training clusters and inference nodes
Power infrastructure — substations, backup generation, and energy contracts

The power requirement for a modern AI training cluster can exceed 100 megawatts — enough to power approximately 80,000 US homes. Planned hyperscaler buildouts in 2026 will require gigawatts of additional generating capacity, much of which does not yet exist.

The Grid Cannot Keep Up

The fundamental constraint in the AI energy build is not capital or technology — it is the pace at which electrical grids can be upgraded to deliver power at the scale and reliability that data centres require.

In the United States, utilities are reporting data centre interconnection queues that extend three to five years into the future. The permitting and construction timelines for new transmission lines — often the binding constraint for connecting new power generation to load centres — have not accelerated at the pace of data centre demand.

In Northern Virginia — home to the world’s largest concentration of data centres — the PJM Interconnection grid has been grappling with the challenge of meeting rapidly growing load from AI campuses while maintaining reliability across the broader regional grid. Similar dynamics are playing out in Ireland, Singapore, and Texas.

The consequence: electricity prices in AI-intensive regions are rising as demand competes with existing industrial and residential load. This is not a temporary phenomenon — it reflects a structural demand shift that will persist for years as AI infrastructure deployment continues.

Who Wins in the AI Energy Build

The AI energy story is generating a distinct set of investment winners that extend well beyond the semiconductor and software sectors.

Utilities

Electric utilities with significant exposure to data centre load — particularly in Virginia, Texas, Georgia, and Ohio — are seeing accelerated earnings growth as hyperscalers sign long-term power purchase agreements. These agreements provide utilities with revenue visibility that justifies capital investment in generation and transmission capacity.

Dominion Energy (Virginia), AEP (Ohio and Texas), and Duke Energy (Georgia) are among the utilities that have flagged data centre load as a material driver of near-term demand growth.

Data Centre REITs

Real estate investment trusts focused on data centre infrastructure are trading at premium valuations as institutional capital seeks AI infrastructure exposure without the technology risk of individual semiconductor or AI software companies.

Equinix, Digital Realty, and Iron Mountain have seen significant demand from hyperscalers seeking colocation capacity. The constraint on their growth is increasingly power availability rather than capital.

Nuclear Energy Operators

Nuclear power has emerged as the preferred baseload generation technology for hyperscalers seeking 24/7 carbon-free electricity. Microsoft has signed a deal with Constellation Energy to restart the Three Mile Island nuclear plant in Pennsylvania specifically for data centre power. Amazon and Google have made direct investments in nuclear start-ups building small modular reactors.

Nuclear’s appeal for data centres is straightforward: it provides continuous, dispatchable power without the intermittency of solar and wind — a critical feature for high-reliability compute workloads.

Natural Gas Operators

In the near term — before new nuclear capacity comes online and before renewable build catches up with demand — natural gas is filling the gap. Gas-fired generation is being commissioned specifically to serve data centre load in multiple US markets. This has created demand for both gas generation capacity and for the pipeline infrastructure that delivers fuel to these plants.

The Geopolitical Dimension: AI Data Centres as Strategic Infrastructure

Governments increasingly view AI data centre capacity as strategic national infrastructure — comparable to port facilities, road networks, or military installations. The race to host hyperscaler AI infrastructure is shaping foreign investment policy, grid modernisation plans, and energy procurement strategies across Asia, Europe, and the Middle East.

Singapore, navigating its role as ASEAN chair in 2026, has positioned its AI infrastructure capacity as a key element of its regional leadership agenda. The city-state has approved new data centre construction after a moratorium, tying approvals to energy efficiency standards and renewable power commitments.

Saudi Arabia and the UAE have made massive commitments to attract AI infrastructure investment as part of their post-oil economic diversification strategies, offering land, regulatory expediting, and preferential power arrangements to major hyperscalers.

India is building AI data centre capacity at scale in Hyderabad, Mumbai, and Chennai, positioning itself as the primary alternative to Chinese AI infrastructure for global enterprises seeking supply chain diversification.

The Cost Pass-Through: Who Pays for AI’s Energy Appetite

The $725 billion AI infrastructure buildout is not self-contained. Its costs ripple through the economy in several ways:

Electricity price pressure: Rising data centre demand in grid-constrained markets pushes up wholesale power prices, increasing costs for all electricity consumers — industrial, commercial, and residential.

Enterprise AI licensing costs: The compute costs embedded in AI services translate directly into licensing fees for enterprise customers. Companies that have deployed AI copilots, coding assistants, and customer service automation are reporting costs that exceed initial projections — creating a “sticker shock” dynamic that is beginning to slow enterprise AI adoption.

Carbon accounting complexity: As hyperscalers procure renewable energy to offset data centre consumption, they are absorbing significant portions of new renewable generation capacity that might otherwise reduce costs for the broader grid. The interaction between data centre power procurement, renewable energy credits, and carbon markets is creating new complexities for corporate sustainability accounting.

The Investment Implications

The AI energy infrastructure theme represents one of the most durable and under-appreciated investment opportunities in the current cycle. While the market has priced AI enthusiasm into semiconductor and software valuations extensively, the downstream infrastructure beneficiaries — utilities, data centre REITs, nuclear operators, and gas pipeline companies — remain relatively less valued for the structural demand shift they are absorbing.

Key investment considerations:

Data centre REITs offer exposure to AI demand without the valuation risk of pure-play AI companies, with dividend income providing a return buffer
Regulated utilities in high-growth data centre markets offer earnings visibility supported by long-term power purchase agreements with investment-grade counterparties
Nuclear energy operators benefit from a structural shift in hyperscaler procurement strategy that is likely to persist for a decade
Grid infrastructure companies — transmission equipment manufacturers and engineering firms — are positioned for multi-year demand as utilities upgrade capacity to serve AI load

The Bottom Line

The $725 billion AI infrastructure buildout is not just an investment theme — it is a structural transformation of global energy markets. The data centres being built today will consume power for decades. The grid upgrades required to serve them will reshape electricity pricing, generation mix, and geopolitical energy strategy across the world’s major economies.

Investors who understand the energy dimension of the AI boom — not just the semiconductor and software dimensions — have access to investment opportunities that carry less valuation risk, more earnings visibility, and more durable competitive positions than the high-profile AI pure-plays currently commanding headlines.

FAQ

Q: How much energy do AI data centres use?
A: A single large AI training cluster can exceed 100 megawatts of power consumption. Across all hyperscalers, the collective AI infrastructure buildout of $725 billion in 2026 will add gigawatts of new demand to global electricity grids.

Q: What companies are building AI infrastructure in 2026?
A: Microsoft, Google, Meta, and Amazon are the four primary hyperscalers collectively spending over $725 billion on AI infrastructure. Nvidia supplies the primary GPU compute hardware. Data centre REITs including Equinix and Digital Realty provide co-location capacity.

Q: How is AI affecting electricity prices?
A: In grid-constrained regions with high data centre concentrations — particularly Northern Virginia, Texas, and Singapore — AI data centre demand is contributing to rising wholesale electricity prices. This affects all electricity consumers in these markets.

Q: Why are hyperscalers investing in nuclear energy for AI data centres?
A: Nuclear power provides continuous, dispatchable, carbon-free electricity — the ideal power source for high-reliability AI compute workloads that cannot tolerate intermittency. Microsoft, Amazon, and Google have all made commitments to nuclear generation specifically for data centre power.

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AI Semiconductor Selloff 2026: Micron Crash, Nasdaq Pullback & What Comes Next

Published

2 days ago

June 26, 2026

Abdul Rahman

On June 24, 2026, Micron Technology shares fell 13% in a single session — the stock’s worst single-day performance since June 5. The memory chipmaker had become a proxy for AI infrastructure demand, a stock that had ridden the AI enthusiasm wave to gains that justified its premium valuation. When it fell, the signal it sent through technology markets was unmistakable: the AI trade is not a one-way bet.

The Micron crash was not an isolated event. It was the latest episode in a pattern of volatility that has characterised the Nasdaq Composite throughout 2026 — a market that has delivered extraordinary returns over the past three years while simultaneously exhibiting the kind of volatility that characterises late-stage speculative cycles.

Understanding what Micron’s collapse reveals — and what it doesn’t — is essential for investors navigating the most complex technology market environment since 1999.

What Actually Happened: The Micron Story

Micron reported fiscal third-quarter results after the close on June 25, 2026. The earnings release came after a session in which the stock had already declined sharply on what appeared to be pre-announcement anxiety. The 13% single-day drop on June 24 — before the results — reflected a combination of factors:

High expectations were embedded in the valuation. Micron had been one of the primary beneficiaries of the AI-driven memory boom, as high-bandwidth memory (HBM) — the type of memory chip most important for AI compute workloads — commands significant pricing premiums and rapid volume growth. A stock priced for perfection leaves no margin for disappointment.

South Korean technology stocks had already broken. The Kospi — South Korea’s benchmark index, heavily weighted toward semiconductor companies including Samsung and SK Hynix — had plunged approximately 10% in the period leading up to the Micron selloff. Given the integrated nature of the global memory supply chain, this was a significant signal.

The SpaceX IPO absorbed market attention and capital. With the SPCX listing consuming enormous institutional bandwidth — and with some evidence of portfolio rebalancing as money rotated into the new AI pure-play listing — technology sector positioning was unsettled heading into the Micron earnings window.

Wedbush Securities’ Dan Ives was among the bulls holding the line. Following his channel checks across Asia and enterprise AI demand trends, Ives saw “no cracks in the armor,” arguing that the South Korean selloff was more likely a pause after a near-100% Kospi rally in 2026 rather than a signal of weakening AI fundamentals. His view: “The selloff in South Korean technology stocks was more likely a pause after a near-100% rally in the Kospi this year, rather than a sign of weakening fundamentals.”

The distinction Ives draws — between valuation-driven volatility and fundamental deterioration — is the central analytical question for investors in AI semiconductors.

The Broader Tech Picture: Nasdaq in a Choppy Range

The Nasdaq Composite closed at 25,476.64 on June 24 — down 0.43% on the day — as the Micron selloff pulled the tech-heavy index lower. The S&P 500 declined 0.10% to 7,358.22, while the Dow Jones Industrial Average — dominated by financials and industrials rather than technology — actually gained 182 points, advancing 0.35%.

This divergence is important. It reflects the continued rotation dynamic that has characterised 2026 markets: investors moving from high-multiple technology and AI stocks into more stable financials, industrials, and defensive sectors. The Dow rising while the Nasdaq falls is a classic late-cycle rotation signal — not necessarily a precursor to a market crash, but a sign that the consensus AI enthusiasm is being repriced.

The Nasdaq’s trajectory in 2026 has been shaped by three conflicting forces:

Bull case: AI capex is real and accelerating ($725 billion from hyperscalers in 2026), enterprise adoption is proceeding even if slowly, and the SpaceX/OpenAI IPO wave is bringing new capital into AI-adjacent public markets.

Bear case: Valuations remain extended relative to earnings, the AI bubble concern is growing (the CEPR launched its AI Bubble Monitor in June), and earnings multiples across the semiconductor sector leave no margin for guidance disappointment.

Wild card: The Federal Reserve’s hawkish turn under Kevin Warsh. Higher-for-longer rates are unequivocally negative for high-multiple growth stocks — the precise companies that dominate the Nasdaq. If BofA’s forecast of three rate hikes materialises, the discount rate applied to future earnings rises, compressing multiples across technology.

Memory Chips Specifically: The Supply-Demand Calculus

Micron’s situation reflects a supply-demand dynamic in memory chips that is more complex than the simple “AI = buy semiconductors” narrative suggests.

High-bandwidth memory (HBM) for AI training and inference is in strong demand, with supply constrained by the technical complexity of the manufacturing process. This segment is performing well for Micron, Samsung, and SK Hynix.

Standard DRAM and NAND flash — the memory types used in conventional computing, consumer electronics, and data storage — remain in a more normalised supply-demand balance. Consumer electronics demand has not recovered to the peaks of the 2021–2022 pandemic era. PC refresh cycles are extending. Mobile upgrade rates are slowing.

The result is a bifurcated memory market where AI-specific products command premium pricing but represent a smaller share of overall revenue, while conventional memory faces ongoing pricing pressure. Investors who extrapolate AI demand across the entire semiconductor industry are making an analytical error.

The South Korea Kospi: A Canary or a Correction?

South Korea’s Kospi is among the most AI-intensive equity markets in the world, with Samsung Electronics and SK Hynix representing major index weights. The 100% Kospi rally in 2026 — before the recent pullback — was one of the most dramatic performances of any major market globally.

A near-100% rally in under a year, in a market concentrated in semiconductor names, followed by a 10% correction is — by historical standards — a healthy pause, not a fundamental reversal. But it deserves scrutiny.

The Kospi’s AI sensitivity cuts both ways. If AI infrastructure demand continues to accelerate, the South Korean memory supply chain is among the primary structural beneficiaries. If AI capital expenditure decelerates — whether from a bubble correction, enterprise budget fatigue, or recession — the Kospi would likely underperform global markets significantly.

Wedbush’s Ives is probably right that the 10% Kospi pullback is a pause, not a peak. But the risk scenario — where AI demand disappointment triggers a more serious Kospi correction — is the kind of fat tail that position sizing should account for.

Oil Prices and Tech: An Overlooked Correlation

One underappreciated dynamic in June 2026 tech markets is the negative correlation between oil price relief and technology performance. As Brent crude fell from elevated levels — reflecting Strait of Hormuz reopening optimism — energy sector stocks declined, while the capital freed from energy inflation concerns did not flow uniformly into technology.

Instead, falling oil prices reduced the inflation urgency that had been supporting gold and energy stocks, while simultaneously creating space for the Fed’s hawkish pivot to dominate the market narrative. The net effect on the Nasdaq was mildly negative, as rate-hike expectations offset the energy relief.

This interconnection illustrates a key feature of 2026 markets: macro factors are more dominant than sector fundamentals in driving short-term price action across equities. A portfolio manager who correctly identified Micron as a fundamentally sound business still lost 13% in a single session because macro sentiment — Fed hawkishness, oil-driven inflation dynamics, and South Korean contagion — overwhelmed the fundamental picture.

The Investment Outlook for AI Semiconductors

Despite the volatility, the long-term structural case for AI semiconductor demand remains intact. The $725 billion hyperscaler AI infrastructure buildout generates genuine and sustained demand for compute hardware. Nvidia’s GPU dominance in AI training is real. HBM demand from data centres will grow as AI models scale.

The relevant question is not whether to own AI semiconductors, but at what price and with what risk management.

The risk-adjusted approach for investors:

Avoid concentration in single names that are priced for perfect execution — a 13% single-day decline on pre-announcement anxiety illustrates the asymmetry of high-expectation positioning.

Consider broader index exposure through semiconductor ETFs (SOXX, SMH) rather than individual stock concentration, allowing participation in structural AI demand without maximum idiosyncratic risk.

Monitor HBM-specific positioning — the AI-specific memory segment that genuinely benefits from training demand — versus conventional memory exposure, which faces different supply-demand dynamics.

Watch the Fed. Three rate hikes by year-end would put meaningful pressure on Nasdaq multiples. The tech sector’s performance in 2H 2026 is as much a function of monetary policy as it is of AI earnings delivery.

Micron’s 13% crash is not the beginning of an AI semiconductor collapse. It is a reminder that valuation matters, expectations matter, and late-cycle technology markets are not immune to gravity.

The South Korean Kospi correction, the SPCX post-IPO decline of 17%, and the Nasdaq’s choppy performance in June 2026 are all consistent with a market that has priced AI excellence aggressively and is now requiring proof of delivery.

The AI semiconductor thesis is intact. The trade needs to earn its valuation — and the process of earning it will involve more of the volatility that June 2026 has delivered.

FAQ

Q: Why did Micron stock drop 13% in June 2026?
A: Micron fell 13% on June 24, 2026 — its worst session since June 5 — amid high earnings expectations, a broader AI semiconductor selloff that followed South Korean technology stock declines, and pre-announcement anxiety ahead of its quarterly results.

Q: Is the Nasdaq in a correction in 2026?
A: The Nasdaq has been volatile in 2026, with multiple single-session declines and a rotation dynamic away from high-multiple technology stocks. As of late June, the index has not entered formal correction territory (a 10% decline from highs), but valuations remain stretched relative to earnings.

Q: Should I buy semiconductor stocks in 2026?
A: The structural case for AI semiconductor demand remains intact, but individual stock selection and entry point matter significantly. Broad-based ETF exposure (SOXX, SMH) reduces idiosyncratic risk compared to single-name concentration. The Federal Reserve’s rate trajectory is a key near-term risk to watch.

Q: What happened to South Korean tech stocks in June 2026?
A: The South Korean Kospi fell approximately 10% from recent highs, with semiconductor-heavy names including Samsung and SK Hynix leading the decline. Most analysts characterised the move as a valuation-driven pause after a near-100% 2026 rally rather than a sign of fundamental AI demand deterioration.