Crypto Isn’t Hedging Tech: $650B AI Spending Impact Drags Both Down

When the Nasdaq Composite posts its worst single-session drop since April 2025, falling over 4% in one day, the question investors are really asking isn’t just about stocks. It’s about whether the AI spending impact on markets has fundamentally changed the risk calculus for everything from large-cap tech to Bitcoin.

Key takeaways

• The Nasdaq fell over 4% in a single session — its worst day since April 2025 — while the S&P 500 dropped 2.64% in the same move.
• Microsoft, Nvidia, Oracle, Meta, Amazon, and Alphabet have collectively signaled AI capital expenditure plans exceeding $650 billion for 2026, with unclear near-term returns.
• Bitcoin slipped to the $62,000–$67,000 range in June 2026, mirroring the tech selloff rather than acting as an independent safe haven.
• In February 2026, roughly $1 trillion in market value was wiped from the software and data services sector in a single week — a warning investors largely ignored.
• AI and crypto compete for the same pool of risk-tolerant capital; when AI stocks sell off, money flows to bonds and cash, not into crypto.

Tech Sector Suffers Its Worst Day Since April 2025

The selloff wasn’t a slow bleed. It arrived fast and hard, with the Nasdaq Composite shedding more than 4% in a single session — a magnitude of loss the index hadn’t seen since April 2025. The S&P 500 fell 2.64% in the same move, confirming this wasn’t a sector-specific glitch but a broad repricing of risk across the market.

What made this session different from ordinary volatility wasn’t just the size of the drop. It was the reason behind it. The selloff reflected a growing unease among institutional investors about whether the most ambitious technology buildout in modern history can actually deliver returns proportional to its cost.

Massive AI Capital Expenditure Plans and Investor Concerns

Hyperscalers’ Planned $650 Billion AI Spending for 2026

Six companies — Microsoft, Nvidia, Oracle, Meta, Amazon, and Alphabet — have collectively signaled AI-related capital expenditure plans exceeding $650 billion for 2026. That figure, larger than the GDP of most nations, covers data centers, chips, and the dense infrastructure required to run next-generation AI systems at scale.

Boston Consulting Group found in a recent report that companies broadly expect to more than double their AI spending in 2026, from roughly 0.8% of revenue to about 1.7%. For large enterprises, that shift means billions flowing into strategies that remain, in many cases, experimental and hard to measure.

The sentiment from executives is shifting too. Cisco’s Chief Product Officer Jeetu Patel said recently that the price of AI tokens is “far higher than the actual value these tokens are generating at scale.” Uber’s COO acknowledged difficulty justifying current AI expenditure. Even Amazon removed an internal leaderboard that was tracking AI token usage after it encouraged excessive spending. Walmart set usage limits on its own AI coding tools. The pattern is consistent: companies spent first, and are now asking whether they spent wisely.

Unclear Returns and Operational Challenges

The concern driving markets isn’t that AI is broken. The technology works. The issue is that the return on investment at this spending level remains stubbornly unclear, and investors who gave hyperscalers the benefit of the doubt through 2025 are running out of patience.

Two structural problems compound the financial uncertainty. Power constraints are real and increasingly binding — data centers can’t scale faster than the electricity grid can support them. At the same time, talent shortages in AI engineering continue pushing up labor costs, making an already capital-intensive buildout even more expensive. Russell Fradin, CEO of Larridin — a platform that helps companies measure AI returns — put it plainly: companies are coming to the consensus that they “can’t 10x spend every year forever.”

The AI spending impact on investor psychology matters here. It’s not simply about one bad quarter. It’s about whether the infrastructure investment cycle has outpaced the timeline for enterprise monetization — and whether that gap is getting wider, not narrower.

The February Warning Shot Nobody Heeded

The June selloff wasn’t a surprise to anyone paying close attention. In February 2026, approximately $1 trillion in market value was erased from the software and data services sector in a single week. That was an early, concentrated signal that the market was beginning to question AI infrastructure economics.

Between February and June, the underlying problems didn’t go away — they became harder to rationalize. Power infrastructure constraints intensified. Model pricing risks, the possibility that AI services won’t command the premium margins companies are projecting, began appearing in analyst notes with growing regularity. The February episode was a warning. June turned out to be the follow-through.

Crypto Markets Mirror Tech Selloff Amid Risk Capital Competition

Bitcoin and Ethereum Track Nasdaq Decline Closely

Bitcoin and Ethereum moved almost in lockstep with the Nasdaq during the June selloff, behaving less like independent stores of value and more like leveraged expressions of risk appetite. Bitcoin’s drift into the $62,000 to $67,000 range — well below its earlier 2026 highs — wasn’t driven by any fundamental deterioration in Bitcoin’s own metrics. It reflected institutional capital retrenchment across the entire risk asset spectrum.

That correlation is significant. It signals that Bitcoin, in its current institutional-ownership phase, is increasingly priced alongside other speculative assets rather than against them.

AI and Crypto Compete for the Same Risk-Tolerant Capital

There’s a structural dynamic that makes this more than a coincidence. AI equities and crypto occupy the same mental bucket for institutional allocators: high-growth, high-uncertainty, risk-tolerant capital. When sentiment turns, both get sold simultaneously. And critically, when AI stocks sell off, the capital doesn’t rotate into crypto — it moves into bonds, cash, and traditional safe havens.

This means that any narrative of crypto as a hedge against tech weakness doesn’t hold in the current market structure. The two asset classes are competing for the same pool of investor risk appetite, and they tend to rise and fall together.

Monetary Tightening’s Impact on Valuations

Analysts expect monetary policy tightening to continue through late 2026, and that adds another layer of pressure. Rising rates increase the opportunity cost of holding non-yielding assets like Bitcoin. They also make the debt-fueled capital expenditure programs of hyperscalers more expensive to finance, creating a feedback loop where tighter money conditions compound the existing doubts about AI ROI.

The deeper implication is that both tech and crypto are now simultaneously exposed to the same macro headwinds — not just correlated by sentiment, but linked by the financing conditions that determine how aggressively institutions can hold speculative positions in the first place. If the rate environment doesn’t ease, neither market has a clear path to re-expansion based on current conditions alone.

FAQ

Why did the Nasdaq Composite experience such a significant drop in June 2026?

The Nasdaq fell over 4% — its worst session since April 2025 — driven by investor concerns about the $650 billion in planned AI infrastructure spending by major tech firms and persistent uncertainty over whether those investments will generate proportional returns in the near to medium term.

How are AI spending plans impacting the crypto market?

AI equities and crypto compete for the same pool of risk-tolerant institutional capital. When AI stocks sell off, investors don’t rotate into crypto — they move into safer assets like bonds and cash. This is why Bitcoin and Ethereum declined alongside the Nasdaq during the June selloff rather than acting as alternatives.

What operational challenges are affecting AI infrastructure buildout?

Two major constraints are limiting the pace and increasing the cost of AI infrastructure expansion: power shortages that prevent data centers from scaling as quickly as planned, and talent shortages in AI engineering that continue to drive up labor costs across the industry.

How might monetary policy tightening affect tech and crypto markets going forward?

If monetary tightening continues through late 2026 as analysts expect, rising interest rates will increase the opportunity cost of holding non-yielding assets like Bitcoin and make the debt-financed capital expenditure of hyperscalers more expensive. That combination could intensify valuation pressure across both tech stocks and crypto markets.

Article produced with the assistance of artificial intelligence and reviewed by the editorial team.