New York — Friday, December 26, 2025 (1:34 p.m. ET). U.S. markets are open right now, with the New York Stock Exchange’s core session running from 9:30 a.m. to 4:00 p.m. ET. [1]
For AI-focused investors, this post‑Christmas Friday is shaping up like a classic year‑end setup: thin liquidity, big headlines in megacap tech and semis, and a market still trying to price the next phase of the AI cycle—inference, custom silicon, and data‑center infrastructure—without tripping over valuation and policy risks. [2]
The broader market backdrop: record highs, year-end momentum, and a closer look at AI spending
U.S. stocks have been pressing toward fresh milestones as 2025 winds down. Reuters reports the S&P 500 is about 1% from 7,000, with the index on track for an unusually long string of monthly gains, even after earlier December turbulence tied to worries about AI spending and tech-sector weakness. [3]
The important nuance for AI-stock readers is that the market isn’t simply “risk-on” across the board. Reuters highlights a rotation: while tech remains a long-term driver, other sectors have attracted flows as investors hunt “more moderate” valuations, and the market is also watching interest-rate expectations closely. [4]
And yes—seasonality is part of the conversation again. MarketWatch notes that December 26 has historically been one of the most consistently positive days for the S&P 500, and it falls inside the closely watched “Santa Claus rally” window (last five trading days of the year plus the first two of the next). [5]
The headline catalyst for AI chips: Nvidia’s Groq deal signals the next battleground—AI inference
The biggest single-company AI headline this week: Nvidia struck a non-exclusive licensing deal with AI chip startup Groq and is hiring away top Groq executives and engineers, including founder/CEO Jonathan Ross and President Sunny Madra, according to Reuters. [6]
Why this matters for AI stocks now:
- Inference is where the growth curve is bending. Training gigantic models fueled the first wave of the AI hardware boom; now, markets are increasingly focused on the “serve it to users” phase—real-time inference in products, copilots, and enterprise workflows. Reuters notes Nvidia dominates training but faces more competition in inference from rivals including AMD and specialized startups. [7]
- It’s also a deal-structure story. The non-exclusive license + talent migration fits a broader pattern of Big Tech using licensing and hiring structures rather than full acquisitions—an approach widely viewed as reducing (or at least changing) antitrust exposure. Reuters explicitly places Nvidia’s move in that wider trend. [8]
- Regulatory risk is part of the trade. Bernstein analyst Stacy Rasgon flagged antitrust as a “primary risk,” while noting the deal structure may preserve “the fiction of competition,” per Reuters’ reporting of his client note. [9]
How investors can read this: Nvidia is effectively saying that the next decade isn’t just “more GPUs.” It’s also about owning the developer ecosystem and performance advantages as AI shifts toward inference-heavy workloads—where cost per token, latency, and memory architecture become decisive.
Broadcom’s AI forecast is strong—but the market is debating margins and customer concentration
If Nvidia is the face of GPUs, Broadcom has become the emblem of the “custom AI silicon + networking” alternative. And its latest outlook illustrates why AI stocks are increasingly about profit mix, not just revenue growth.
Reuters reports Broadcom projected first-quarter revenue of about $19.1 billion, above Wall Street estimates, while also warning that gross margin will fall due to a higher mix of AI revenue. [10]
Key numbers and signals that matter for AI-stock investors:
- Broadcom CEO Hock Tan cited an AI backlog of $73 billion expected to ship over the next 18 months, according to Reuters. [11]
- Broadcom CFO Kirsten Spears said the company expects gross margin down about 100 basis points sequentially, driven primarily by AI revenue mix. [12]
- Tan also said AI semiconductor revenue is expected to double to $8.2 billion in the fiscal first quarter—covering both custom AI accelerators and networking used in AI data centers. [13]
The analyst push-and-pull here is telling:
- Kinngai Chan (Summit Insights) pointed to AI customer concentration and future lower margins for AI system sales as drivers behind the stock’s post‑earnings weakness. [14]
- Gil Luria (D.A. Davidson) highlighted potential pressure from manufacturing costs (including dependence on contract chip manufacturing) as a factor that could squeeze value capture in custom processors. [15]
What this means in plain terms: Broadcom is delivering the kind of AI growth investors want—yet the market is asking whether the “AI mix” becomes a margin headwind before it becomes operating leverage. This is a theme you’re seeing more often across the AI supply chain: growth is real, but investors want to see who captures the economics.
AI policy risk is back on the front page: U.S. review of Nvidia H200 sales to China
Semiconductor AI stocks don’t trade only on product cycles—they trade on export policy.
Reuters reports the Trump administration launched an interagency review that could allow the first shipments to China of Nvidia’s H200 AI chips, after Trump said earlier this month he would allow the sales with a 25% fee collected by the U.S. government. [16]
The story underscores why this is market-moving:
- Reuters notes the idea drew criticism from China hawks over concerns such chips could boost China’s military and AI capabilities. [17]
- Chris McGuire, a former White House National Security Council official and Council on Foreign Relations senior fellow, called exporting the chips “a significant strategic mistake” in Reuters’ report. [18]
- Reuters also reports that some Trump administration members, led by White House AI czar David Sacks, argue shipping advanced chips can discourage Chinese competitors from accelerating their own designs. [19]
Investor takeaway: For Nvidia (and the broader AI chip complex), China policy headlines can create sharp, news-driven moves—especially in low-volume holiday trading. Even when policy is ultimately bullish for near-term sales, uncertainty itself can widen volatility.
AI demand is showing up in the plumbing: Taiwan export orders and the chipmaking equipment forecast
One reason AI stocks remain a dominant market narrative is that demand isn’t only visible in U.S. megacaps—it’s rippling through the global supply chain.
Reuters reports Taiwan’s November export orders rose 39.5% year over year (well above the Reuters poll forecast), driven by demand linked to AI and high-performance computing. Taiwan’s ministry also projected December export orders up 36.1% to 39.8% year over year. [20]
Then there’s the capex “picks and shovels” layer. Reuters reports industry group SEMI forecast sales of equipment used to make chip wafers will rise about 9% to $126 billion in 2026, and then 7.3% to $135 billion in 2027, driven by capacity expansion for AI-related logic and memory. [21]
SEMI’s forecast also points to where spending concentrates (and who benefits): China, Taiwan, and South Korea remain the top markets, while major equipment suppliers include ASML, Applied Materials, KLA, Lam Research, and Tokyo Electron, Reuters reports. [22]
Why this matters for AI-stock positioning: When investors debate whether the AI boom is “real,” these kinds of supply-chain indicators often matter as much as a single earnings report. They suggest a multi-year buildout is still underway—though it also raises the question: how much of this is already priced into valuations?
The AI infrastructure arms race is getting quantified in billions
If 2024–2025 was about proving generative AI could scale, late 2025 is about proving it can be built, powered, and supplied.
Reuters published a roundup today cataloging multi‑billion‑dollar AI, cloud, and chip deals—including Nvidia’s Groq licensing move—and highlighting the sheer scale of current AI infrastructure spending and partnerships across the ecosystem. [23]
Even if investors don’t “trade the headlines,” the strategic implication is clear: AI is no longer a single-company story. It’s an interconnected spending cycle touching cloud capacity, chip supply, custom silicon, networking, and data-center operators—often linked via partnerships, pre-buys, and long-term commitments. [24]
AI software stocks: growth is strong, but valuation discipline is back
AI isn’t only semiconductors. Software companies branded as “AI beneficiaries” have also become major market narratives—often with valuation risk embedded.
Reuters reported earlier this quarter that Palantir forecast fourth-quarter revenue above estimates on solid AI-driven demand, while analysts also flagged the potential concern of a slight revenue-growth deceleration versus a lofty valuation backdrop. [25]
Two details stand out from Reuters’ reporting:
- Palantir’s forecast implied a small deceleration in growth, which Blake Anderson (Carson Group) said is a concern given the stock’s valuation. [26]
- Reuters cited LSEG data showing Palantir trading at an exceptionally high forward multiple compared with Nvidia, while Gil Luria (D.A. Davidson) argued Palantir’s results could be sufficient to meet expectations needed to justify those levels. [27]
Investor takeaway: In 2026, “AI story” alone may not be enough. Markets are increasingly separating AI stocks into two buckets: (1) those with visible revenue capture and (2) those with big narrative but fragile valuation math.
What to know before the next session if you’re investing in AI stocks now
Even though the market is open at the time of writing, many readers will see this later—after the close, in after-hours, or over the weekend. Here’s the practical checklist that matters heading into the next session:
1) Know the clock and the liquidity risk
The NYSE core session runs 9:30 a.m. to 4:00 p.m. ET, and holiday-adjacent sessions can see lighter volume—meaning price moves can be exaggerated. [28]
2) Watch “inference vs. training” as a stock-selection filter
Nvidia’s Groq deal is a reminder that performance, cost, and latency in inference are becoming central competitive factors—not just raw training throughput. [29]
3) Treat margins as the new battleground in AI infrastructure
Broadcom’s outlook shows the market will reward AI revenue growth—but will scrutinize mix, gross margin, and customer concentration just as aggressively. [30]
4) Keep one eye on export policy headlines
The U.S. review process around Nvidia’s H200 China shipments is the kind of policy story that can change the tone of the AI chip trade quickly—either direction. [31]
5) Track “real economy” AI demand signals in the supply chain
Taiwan’s export orders data and SEMI’s equipment outlook give investors concrete signals that AI capex is feeding through globally—not just in U.S. earnings calls. [32]
6) Put the Fed back into your AI-stock framework
Rate expectations still matter, especially for long-duration growth names. The Federal Reserve’s official calendar shows the FOMC minutes for the December 9–10 meeting are scheduled for release at 2:00 p.m. ET on Tuesday, December 30, 2025—a potential volatility catalyst for growth stocks into year-end. [33]
Bottom line: the AI stock trade is maturing—and getting more selective
At year-end 2025, the AI trade is broadening beyond “buy the GPU leader” into a more complex market map:
- Nvidia is leaning into inference as the next frontier via strategic licensing and talent acquisition. [34]
- Broadcom is showing massive AI demand through backlog and revenue forecasts, while forcing the market to confront the margin profile of custom AI silicon at scale. [35]
- The policy layer (exports) and the capex layer (global manufacturing and equipment spending) remain decisive for where returns accrue in 2026. [36]
For investors, the near-term opportunity is obvious—but so is the message from late 2025 tape action: AI stocks are no longer trading on one narrative. They’re trading on execution, economics, and geopolitics—often all in the same week. [37]
References
1. www.nyse.com, 2. www.reuters.com, 3. www.reuters.com, 4. www.reuters.com, 5. www.marketwatch.com, 6. www.reuters.com, 7. www.reuters.com, 8. www.reuters.com, 9. www.reuters.com, 10. www.reuters.com, 11. www.reuters.com, 12. www.reuters.com, 13. www.reuters.com, 14. www.reuters.com, 15. www.reuters.com, 16. www.reuters.com, 17. www.reuters.com, 18. www.reuters.com, 19. www.reuters.com, 20. www.reuters.com, 21. www.reuters.com, 22. www.reuters.com, 23. www.reuters.com, 24. www.reuters.com, 25. www.reuters.com, 26. www.reuters.com, 27. www.reuters.com, 28. www.nyse.com, 29. www.reuters.com, 30. www.reuters.com, 31. www.reuters.com, 32. www.reuters.com, 33. www.federalreserve.gov, 34. www.reuters.com, 35. www.reuters.com, 36. www.reuters.com, 37. www.reuters.com
