NEW YORK — As of 6:00 p.m. EST, the U.S. “AI trade” is ending Wednesday on a sharply split note: mega-cap AI infrastructure and chip leaders fell hard into the close, while memory maker Micron Technologies jumped in extended trading after delivering a bullish outlook tied directly to AI data-center demand. [1]
The session’s message for investors was blunt: Wall Street is still willing to pay for visible AI demand, but it’s becoming less tolerant of financing uncertainty, “circular” capex stories, and rising competitive pressure around the software and silicon stack that powers generative AI. [2]
US stock market close: AI stocks drag the Nasdaq lower
By the closing bell (4:00 p.m. ET), the Nasdaq Composite fell 1.81% to 22,693.32, while the S&P 500 dropped 1.16% to 6,721.43 and the Dow Jones Industrial Average slid 0.47% to 47,885.97. [3]
Tech was broadly weaker, with the Philadelphia semiconductor index down about 4% on the day—an outsized move that underscored how tightly “AI stocks” remain linked to the chip and data-center buildout theme. [4]
Among notable AI-linked decliners highlighted in market coverage:
- Nvidia (NVDA) closed down about 3.8%. [5]
- Oracle (ORCL) closed down about 5.4% amid fresh scrutiny of its AI data-center financing. [6]
- Broadcom (AVGO) finished down about 4.5% as investors continued to reassess AI infrastructure spending and related earnings narratives. [7]
- Alphabet (GOOGL) fell about 3.2% after news of a new effort to make Google’s AI chips more attractive to the broader developer ecosystem. [8]
- Palantir (PLTR) and Super Micro Computer (SMCI) were also cited among the day’s notable AI-linked laggards. [9]
At the same time, oil rebounded and energy stocks were stronger—helping reinforce the day’s rotation away from high-beta AI and into more defensive or commodity-linked exposures. [10]
The catalyst investors kept pointing to: cracks in AI infrastructure financing
A major driver of Wednesday’s renewed “AI bubble” anxiety was a report that Blue Owl Capital stepped back from funding talks tied to a planned $10 billion Oracle data-center project in Saline Township, Michigan, designed to support OpenAI workloads. [11]
Oracle said Wednesday that negotiations for an equity deal remain on schedule and do not include Blue Owl, after reports of stalled discussions contributed to another leg down in the stock. [12]
Why the market cares: the Michigan site is part of a broader U.S. push to expand AI compute capacity, and it lands at the intersection of three hot-button themes that have increasingly moved AI stocks in late 2025:
- Capital intensity and leverage risk. Oracle’s AI buildout has drawn scrutiny as its fortunes become more tied to OpenAI and large-scale infrastructure commitments. [13]
- “Circularity” and crowded trades. Investors are questioning whether every layer of the AI value chain can keep winning at once—especially when financing and customer commitments are intertwined. [14]
- The bar for “proof” is rising. As one strategist put it, there’s “percolating anxiety about the AI trade,” and markets appear more willing to punish uncertainty around capex payoffs. [15]
Google vs. Nvidia: the next front is software, not just chips
Another headline that fed into Nvidia’s weakness: Alphabet’s Google is reportedly building an internal initiative—known as “TorchTPU”—to make Google’s TPU AI chips run PyTorch more seamlessly, with the explicit goal of lowering switching costs for customers that are currently “locked in” to Nvidia’s CUDA-centered ecosystem. [16]
According to Reuters, Google is working closely with Meta, the steward of PyTorch, and is even considering open-sourcing parts of the TorchTPU software to accelerate adoption. [17]
For AI stock investors, this matters because Nvidia’s differentiation has long been viewed as a full-stack moat: not just GPUs, but the developer tooling and libraries that make those GPUs the default choice. Any credible push by hyperscalers to “neutralize” that advantage can move sentiment quickly—especially on days when the market is already nervous about AI capex and valuations. [18]
After-hours pivot: Micron’s AI-driven outlook jolts the chip narrative back to demand
While AI bellwethers sold off into the close, Micron (MU) delivered a very different headline after the bell.
Micron forecast second-quarter adjusted profit of about $8.42 per share (± $0.20), roughly double what analysts expected, and projected quarterly revenue of about $18.7 billion (± $0.4 billion)—both driven by tight supply and strong pricing for memory used in AI data centers. In response, Micron shares rose about 7.8% in extended trading. [19]
Just as important for forward-looking “AI stocks” coverage, Micron’s commentary leaned into scarcity and long-duration demand:
- CEO Sanjay Mehrotra said he expects memory markets to remain tight past 2026. [20]
- Micron expects to meet only 50%–66% of demand from several key customers in the medium term. [21]
- The company raised its 2026 capex plan to $20 billion (from $18 billion). [22]
In plain terms: even as Wall Street questions whether AI infrastructure spending is getting ahead of itself, Micron’s numbers suggest a world where bottlenecks haven’t disappeared—some are simply shifting from GPUs to memory, networking, power, and capacity planning.
Amazon and OpenAI: a $10 billion headline that reinforces “custom silicon” as the next AI trade
Adding to the day’s pile of AI headlines, Reuters reported that Amazon (AMZN) is in talks to invest about $10 billion in OpenAI, a deal that could value the ChatGPT maker at more than $500 billion. [23]
The strategic through-line for AI stocks is not just the size of the potential investment—it’s the compute stack. Reuters reported that OpenAI plans to use Amazon’s Trainium chips, which compete with Nvidia’s and Google’s AI processors. [24]
Amazon stock finished down modestly at the close in market coverage, but the headline reinforces a broader 2025 trend: hyperscalers and large AI platforms want alternatives to Nvidia where possible, and they’re increasingly willing to pair funding, cloud commitments, and custom silicon roadmaps to get them. [25]
Amazon’s AI leadership reshuffle: chips, models, and quantum under one roof
In a separate Reuters report Wednesday, Amazon also announced an internal AI restructuring: long-time executive Rohit Prasad will depart by year-end, and AWS veteran Peter DeSantis will lead a new unit spanning AI models, custom silicon, and quantum computing. [26]
Reuters said the new unit is intended to unify development of Amazon’s Nova AI models, chip programs such as Graviton and Trainium, and quantum initiatives. It also noted that Pieter Abbeel will lead frontier model research within the AGI organization. [27]
For investors tracking AI stocks, this kind of org chart change is more than corporate housekeeping: it’s a signal that the battle is now about end-to-end execution—designing chips, building data centers, training models, and delivering products—at the same time.
Power is the next bottleneck: why the AI stock story keeps spilling into energy and utilities
One reason AI stocks have started to trade like a complex “supply chain”—rather than a single theme—is that power availability is emerging as a core constraint.
A Reuters analysis on Dec. 17 detailed how global data center power demand is expected to rise sharply by 2030, citing projections that:
- Global data-center power demand could double to 200+ gigawatts by 2030, per the International Energy Agency’s outlook referenced in the report. [28]
- In the U.S., data-center demand could rise to 100–130 gigawatts by 2030, up from roughly 45–50 gigawatts today, with a potential shortfall of up to 80 gigawatts. [29]
- Nuclear power could eventually meet around 10% of AI-related power demand, though small modular reactors are unlikely to arrive fast enough to solve near-term needs. [30]
This matters because “AI stocks” in late 2025 increasingly include not just chipmakers and cloud platforms, but also the companies that build and energize data centers—turbines, grid equipment, cooling, and backup power.
It also helps explain why AI-market jitters can hit adjacent sectors. For example, Barron’s reported a steep one-day drop in GE Vernova shares alongside wider AI-linked selling, amid fresh investor debate over whether efficiency gains in AI hardware could eventually reduce data-center power demand growth rates. [31]
Global competition check: China’s AI chip push adds long-horizon pressure to US AI hardware leaders
Even on a day focused on U.S. market moves, global semiconductor competition stayed in the background as a real narrative force.
Reuters published an investigation-style report describing China’s efforts to build a prototype EUV lithography machine—technology central to leading-edge chip manufacturing—despite Western export controls, an effort sources described as a “Manhattan Project.” [32]
Separately, Reuters also reported that Chinese AI chipmaker MetaX surged on its Shanghai debut, another reminder that domestic competitors are attracting capital as China pushes for AI chip self-sufficiency and reduced reliance on Nvidia and AMD. [33]
For U.S.-listed AI stocks, the near-term earnings drivers remain demand and pricing. But the long-term valuation debate increasingly includes geopolitics, export rules, and whether today’s AI infrastructure leaders can maintain share as alternatives mature.
What to watch next for AI stocks
With Wednesday’s volatility, investors in AI stocks are likely to focus on a short list of “pressure points” going into the next session:
- AI infrastructure financing and capex discipline. Oracle’s Michigan data-center story is now a live test of whether big AI buildouts can reliably secure attractive financing terms. [34]
- The Nvidia “moat” debate. Google’s TorchTPU effort is a direct shot at the software lock-in story that has supported premium multiples across the AI chip stack. [35]
- Proof of demand in picks-and-shovels. Micron’s after-hours surge is a reminder that some corners of the AI hardware ecosystem are still seeing accelerating fundamentals—even as headline “AI trade” sentiment gets shakier. [36]
- Custom silicon momentum. Amazon’s reported OpenAI talks—and Amazon’s internal AI reorg—underscore how quickly the market is shifting from “buy GPUs” to “design the whole stack.” [37]
- Energy and grid constraints. Power availability is becoming a gating factor for AI growth, widening the investable AI universe beyond pure tech into infrastructure and energy. [38]
Bottom line at 6:00 PM EST
At the end of Dec. 17, 2025, U.S. AI stocks are not moving as a single, unified trade. The market punished uncertainty—especially around AI data-center financing and competitive threats to incumbents—while rewarding clear, AI-linked demand signals like Micron’s memory outlook.
That divergence may be the most important takeaway for AI-stock investors into year-end: the next phase of the AI market isn’t about whether AI is “big.” It’s about who gets paid, who funds it, and who can actually deliver the compute—at scale—profitably. [39]
References
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