NEW YORK, Dec. 27, 2025, 12:11 p.m. ET — Market closed (U.S. exchanges shut for the weekend)
Wall Street is heading into the final full trading stretch of 2025 with artificial intelligence stocks still setting the tone across chips, cloud, and software—and with fresh headlines underscoring a key theme for 2026: AI is shifting from training to inference, and the capital required to power that transition keeps climbing.
Friday’s post‑Christmas session ended close to all‑time highs on light volume, with the major indexes fractionally lower but still up on the week—an environment that often amplifies stock‑specific news, especially for megacap AI leaders. In a Reuters market report, Carson Group chief market strategist Ryan Detrick framed the quiet pullback as “catching our breath” after a strong run, while pointing investors to the seasonally watched “Santa Claus rally” window that stretches into early January. [1]
Against that backdrop, Nvidia (NVDA) dominated the AI tape once again—this time not with a new GPU, but with a deal structure that highlights how the AI arms race is increasingly fought with talent, IP licensing, and supply-chain control.
Nvidia and Groq: a licensing-and-talent move that puts AI inference in the spotlight
Reuters reported that Nvidia agreed to a “non-exclusive” license to Groq’s technology and is hiring key Groq leaders, including founder Jonathan Ross and Groq President Sunny Madra, as Nvidia looks to strengthen its position as AI workloads expand into inference. Groq will continue operating independently, with Simon Edwards named CEO, Reuters said. [2]
The nuance matters for investors because the market initially digested chatter about a blockbuster acquisition. Investing.com later summarized the confusion around a reported $20 billion figure and emphasized that Groq described the arrangement as a non-exclusive licensing agreement rather than a standard buyout. [3]
Why inference is the battleground
Training made Nvidia the AI era’s defining stock. Inference—running models in real time for users—opens the door to more custom silicon, more cloud-designed accelerators, and more competition. Reuters explicitly noted that while Nvidia dominates training, inference is where rivals like AMD (AMD) and startups (including Groq and Cerebras) aim to take share. [4]
Analysts split: “strategic win” vs. “hard to see the rationale”
The early analyst reaction is a reminder that even “good news” in AI can create debate about margins, defensibility, and regulation:
- Rosenblatt viewed the Groq licensing deal as strategically important, arguing it could help address concerns about inference alternatives and highlighting how Nvidia’s CUDA software ecosystem could potentially widen Groq’s LPU use across markets. Rosenblatt maintained a 12‑month price target of $245, according to Investing.com. [5]
- D.A. Davidson, by contrast, questioned whether the move is truly about technology or more defensive positioning. The firm pointed to Groq’s on‑chip SRAM limits versus Nvidia’s much larger memory configurations for frontier‑scale inference workloads. [6]
Regulation risk is part of the story
Reuters also captured the antitrust angle, quoting Bernstein analyst Stacy Rasgon, who wrote that “Antitrust would seem to be the primary risk here,” adding that the non‑exclusive structure may preserve the “fiction of competition” even as key talent moves to Nvidia. [7]
For AI stock investors, that framing matters because the market is increasingly scrutinizing “acqui-hire + license” structures—deals that transfer technology and people without a full acquisition.
The HBM4 memory race: Samsung and SK Hynix headlines feed directly into Nvidia’s next cycle
If there’s one constraint Wall Street has learned to fear during the AI boom, it’s memory and packaging bottlenecks. This week’s most important supply-chain update: Samsung Electronics shares jumped after reports it will begin mass production of next‑generation HBM4 memory chips in February at its Pyeongtaek campus, with the chips expected to be used in Nvidia’s next‑generation AI processors, reportedly codenamed “Rubin.” [8]
The same report noted SK Hynix is also preparing HBM4 production and has already provided samples to Nvidia earlier in 2025. [9]
Why this matters for AI stocks going into Monday:
- Nvidia: HBM supply visibility is a direct input into unit volumes, delivery schedules, and the ability to meet backlog demand.
- Memory makers (Samsung, SK Hynix): HBM pricing and mix can significantly impact margins.
- Second-order AI plays: Any easing (or tightening) of memory constraints can ripple into server OEMs, data-center buildouts, and hyperscaler deployment pace.
The AI deal-and-capex wave keeps growing, and it’s reshaping “AI stock” leadership
A Reuters factbox published late Friday pulled together a striking list of multi‑billion‑dollar AI, cloud, and chip commitments, underscoring how rapidly the infrastructure layer is scaling—and how many public companies are now intertwined with AI’s buildout. [10]
Among the highlights affecting widely held “AI stocks”:
- Broadcom (AVGO): Reuters said OpenAI partnered with Broadcom to help produce its first in‑house AI processors. [11]
- AMD (AMD): Reuters reported AMD agreed to supply AI chips to OpenAI in a multi‑year deal that also gives OpenAI the option to buy up to roughly 10% of the chipmaker. [12]
- Oracle (ORCL): Reuters cited a report that Oracle signed one of the biggest cloud deals ever with OpenAI, involving a massive computing commitment over multiple years. [13]
- Alphabet (GOOGL): Reuters said Google hired key staff from AI code generation startup Windsurf and will pay $2.4 billion in license fees under non‑exclusive terms. [14]
- Microsoft (MSFT): Reuters included a deal in which Nebius Group will provide Microsoft GPU infrastructure capacity over a multi‑year term. [15]
The takeaway is bigger than any single headline: the market is treating AI less like a single product cycle and more like a multi‑year industrial buildout, similar to cloud’s rise—except with even heavier power, chip, and networking requirements.
That’s bullish for demand visibility, but it also raises the questions that will likely drive AI stock dispersion in 2026:
- Which companies can spend heavily and keep margins intact?
- Which firms are buying growth with capex that may compress free cash flow?
- Who controls the “moats”: chips, software platforms, distribution, or proprietary data?
Oracle, Microsoft, and Amazon: cloud AI growth vs. the cost of building it
One of the clearest examples of the opportunity/cost tradeoff is Oracle. In an analysis published Friday on Nasdaq.com, Zacks noted Oracle’s cloud infrastructure revenue surged 68% year over year (to $4.1 billion), with GPU-related revenue up 177%, while remaining performance obligations climbed to $523 billion. [16]
But the same analysis emphasized the financial strain: Oracle is projecting capital expenditures of roughly $50 billion for fiscal 2026, and free cash flow turned negative by around $10 billion in the November quarter. [17]
The piece also framed the hyperscaler arms race: it cited expectations that Microsoft is projecting $120 billion in 2026 capex and Amazon plans $125 billion in 2025 capex, largely tied to AI infrastructure. [18]
For investors, this is an increasingly common setup across AI stocks:
- Revenue acceleration driven by AI demand signals strength.
- Capex acceleration can pressure near‑term cash flow, raising valuation sensitivity—especially if rates rise or if the market’s “AI premium” cools.
Palantir and AI-defense stocks: momentum is real, but so is the bar for 2026
AI stock leadership hasn’t been limited to chips and cloud. Defense and enterprise AI names are also drawing attention into year-end.
In a Nasdaq.com analysis published Friday, Zacks highlighted Palantir (PLTR) and BigBear.ai (BBAI) as notable AI-defense stocks, citing Palantir’s reported revenue growth and raised guidance tied to its Artificial Intelligence Platform (AIP). [19]
The same piece quoted BigBear.ai CEO Kevin McAleenan on the rationale for its acquisition of Ask Sage, describing the goal as “a secure, integrated AI platform” connecting software, data, and mission services. [20]
For investors heading into the final sessions of 2025, the core issue is not whether AI is “real”—it’s whether expectations are now high enough that execution risk becomes the dominant driver:
- contract timing,
- renewals,
- deployment scale,
- and the path from pilots to durable, profitable revenue.
What investors should know before Monday’s open
With U.S. markets closed this weekend and only a few sessions left in 2025, AI stocks may be especially sensitive to headlines, positioning, and year‑end flows.
Here’s what to watch before the next regular session:
- Nvidia/Groq follow-through
Watch for any additional disclosures on the licensing scope, the talent transition, and any signs of regulatory attention. Reuters’ reporting and analyst notes have already placed antitrust risk squarely in the narrative. [21] - Inference competition signals
The market is actively debating whether inference becomes a margin headwind (more custom chips, more competition) or a volume tailwind (more deployments, more endpoints). The split between Rosenblatt and D.A. Davidson captures that tension. [22] - HBM4 and the 2026 supply chain
The Samsung/SK Hynix HBM4 push is a reminder that AI stock performance can hinge on the “plumbing” (memory, packaging, power), not just model breakthroughs. [23] - Capex narratives for cloud AI winners
Oracle’s surge in AI infrastructure demand comes with rising capex and cash-flow debate—an issue that can spill into peers as investors handicap who can monetize AI fastest. [24] - Year-end trading conditions
Reuters described Friday’s session as light-volume and catalyst-thin, with the market still watching the Santa Claus rally window—conditions that can exaggerate moves in high‑beta AI names if new headlines hit. [25]
Bottom line for AI stocks right now
Going into Monday, the most important shift investors are pricing is not whether AI demand exists—it’s how the spoils get divided as inference scales, custom silicon proliferates, and the cost to build AI infrastructure accelerates.
In that world, the winners are less likely to be “every AI stock,” and more likely to be the companies that can prove three things at once: sustained demand, defensible differentiation, and profitable scaling. [26]
References
1. www.investing.com, 2. www.investing.com, 3. www.investing.com, 4. www.investing.com, 5. www.investing.com, 6. www.investing.com, 7. www.investing.com, 8. www.investing.com, 9. www.investing.com, 10. www.investing.com, 11. www.investing.com, 12. www.investing.com, 13. www.investing.com, 14. www.investing.com, 15. www.investing.com, 16. www.nasdaq.com, 17. www.nasdaq.com, 18. www.nasdaq.com, 19. www.nasdaq.com, 20. www.nasdaq.com, 21. www.investing.com, 22. www.investing.com, 23. www.investing.com, 24. www.nasdaq.com, 25. www.investing.com, 26. www.investing.com
