Wall Street is closing out 2025 at record levels—and doing it while a louder debate simmers about whether artificial intelligence is building a durable foundation for the next decade of growth, or inflating the kind of expectations that tend to end in painful repricing.
On Friday, December 26, U.S. equities returned from the Christmas break in thin trading conditions with investors watching two things at once: whether the year-end “Santa Claus rally” has enough momentum to push benchmarks to fresh milestones, and whether the AI trade that powered much of the market’s gains can keep delivering without buckling under the weight of soaring infrastructure costs and valuation concentration. [1]
December 26 market pulse: records, rate cuts, and a 7,000 milestone in sight
The S&P 500 is ending the year within striking distance of a psychological level that would have sounded implausible just a few years ago. Reuters reported that after a record close on Wednesday (ahead of the holiday), the index sat about 1% from reaching 7,000 for the first time, with December on track to finish higher and the benchmark poised for its eighth straight monthly gain—its longest monthly winning streak since 2017–2018. [2]
Under the surface, the story is less “everything is soaring” and more “the market is rotating.” Reuters noted that while technology has been the core driver of the multi-year bull run, the S&P 500’s tech sector had fallen more than 3% since the start of November even as other segments—financials, transports, healthcare, and small caps—showed strength. That rotation is central to how traders are thinking about the next act: a market can keep rising even if its most crowded theme (AI) cools, as long as money flows into other parts of the index. [3]
Macro expectations are still doing heavy lifting. Reuters reported the Fed lowered its benchmark rate by 75 basis points over its last three meetings of 2025, bringing it to 3.50%–3.75%, and investors remain focused on the timing and pace of further cuts in 2026. Minutes from the Fed’s December meeting, due Tuesday, are one of the next immediate catalysts on the calendar. [4]
At the same time, the “safe-haven” side of the market is flashing its own signal. Gold and silver extended a historic run: Reuters reported silver pushing through $75 per ounce for the first time, while gold also traded near record highs as rate-cut bets and geopolitical uncertainty kept demand elevated. [5]
Why AI is still the market’s defining narrative—even as investors grow pickier
If 2025 had a single market plotline, it was this: AI became both the growth story and the spending story. That duality is now shaping investor behavior.
Goldman Sachs Research recently highlighted that capital-spending expectations for hyperscalers keep getting revised higher—its article cited a 2026 consensus capex estimate of $527 billion, up from $465 billion earlier in the year—while also arguing that investors are no longer rewarding “all AI big spenders the same.” The market, in other words, is starting to price the difference between capex that clearly translates into revenue and capex that looks like a race to build capacity first and figure out profitability later. [6]
That distinction helps explain why AI-bubble talk can grow louder even while the broader market stays buoyant. The AI theme has matured from a single-direction trade into a more discriminating one: who has pricing power, who owns distribution, who can monetize, and who is simply buying hardware.
The AI infrastructure arms race: megadeals, chips, and the “talent-without-acquisition” era
December 26 also brought a reminder that the AI boom isn’t just software hype—it is a deal machine.
Reuters reported that Nvidia agreed to a non-exclusive license for AI chip technology from startup Groq and will hire Groq’s CEO and other executives—an example of a broader pattern where Big Tech pays for technology and talent without formally acquiring the target, a structure that can reduce regulatory friction while still changing competitive dynamics. CNBC reported a $20 billion cash acquisition of Groq’s assets, but Reuters said neither company confirmed that figure. [7]
This matters because it sits at the intersection of two massive shifts:
- AI workloads are moving from training to inference. Groq specializes in inference—running trained models efficiently—where competition is intensifying. Reuters noted Nvidia dominates training but faces more rivals in inference, including AMD and specialized startups. [8]
- Regulators are watching “soft acquisitions.” Reuters noted similar arrangements across the sector have attracted scrutiny, even if they haven’t been unwound. [9]
At a broader level, Reuters also published a roundup of recent multi-billion-dollar AI, cloud, and chip deals—underscoring how the entire ecosystem is being financed and constructed in real time. Among the highlights Reuters cited: reported talks around a potential $10 billion Amazon investment in OpenAI; a Disney investment and licensing arrangement tied to OpenAI’s Sora video generator; OpenAI’s partnerships with chip and infrastructure suppliers (including Broadcom and AMD); a reported mega-scale cloud commitment involving Oracle; and high-dollar funding and infrastructure agreements around CoreWeave and data center buildouts. [10]
The takeaway is not that every figure will prove durable—deal terms evolve, and not all reported talks close—but that the center of gravity in AI remains the same: compute, power, and access to the highest-performing chips.
The “AI upheaval” thesis: spending keeps rising, pricing power gets tested
One reason AI-bubble fears persist is that the buildout has become almost too big to picture—and yet it continues.
An excerpt of Financial Times reporting described forecasts that global spending on data centers and related infrastructure could reach roughly $470 billion in 2025 and rise further in 2026, while also noting signs of price deflation in parts of the AI market even as demand rises. The same reporting pointed to extraordinary concentration at the top of U.S. equities—eight U.S. tech companies valued at $1 trillion or more and massive gains in their combined market value—paired with a sharp increase in capital expenditures. [11]
Goldman Sachs framed the same core tension in market terms: hyperscaler spending has repeatedly exceeded analyst expectations, but the timing of an eventual slowdown in capex growth remains a valuation risk for the infrastructure-heavy slice of the AI trade. [12]
Put simply: AI can be real and still be mispriced. The technology may transform productivity, but markets can still overshoot on the “who captures the value” question and on the “how quickly” question.
“I asked ChatGPT what would happen to the stock market”: when AI becomes its own market mirror
Few things capture this moment better than the rise of AI itself as a source of market narratives.
A widely shared prompt-driven thought experiment published by GOBankingRates asked ChatGPT to reason through what could happen if an AI bubble burst. The model’s answer—presented as scenario analysis rather than a forecast—centered on a familiar vulnerability: index concentration. It argued that because a handful of large AI-linked companies carry outsized weight in major benchmarks, a sharp repricing in those names could pull down the broader S&P 500 and Nasdaq, potentially echoing the dot-com era’s market dynamics. [13]
ChatGPT’s scenario went further, suggesting:
- a potential 10%–20% broad market correction if sentiment flips on AI revenue projections,
- a rapid cooling in AI startup valuations (with consolidation and layoffs),
- but a lower probability of a systemic collapse on the scale of 2000 or 2008 because many major AI-linked companies have large, profitable non-AI businesses supporting them. [14]
This kind of content resonates because it reflects how investors are actually thinking right now: not “is AI real?” but “is AI priced too perfectly, too soon—and is the market too dependent on a small group of winners?”
The human critique: “bubble” as a governance problem, not just a market problem
The AI bubble debate is also expanding beyond finance.
In a Guardian column updated on December 26, Rafael Behr argued that even if AI doesn’t transform every person’s daily life immediately, the hype and capital flows are already shaping economies and geopolitical rivalries. The column cited estimates of roughly 800 million weekly users for ChatGPT, an OpenAI valuation around $500 billion, and infrastructure commitments around $1.5 trillion—figures used to illustrate how AI spending has become systemically significant. [15]
Behr’s central point is less about stock charts and more about what comes after: if a correction hits, it could create space for broader conversations about regulation, risk, and who AI ultimately serves. The column also highlighted how even industry leaders have acknowledged “bubbly” conditions in parts of the market, while others have framed bubbles as an accelerant that can finance infrastructure—benefits that may endure even after valuations reset. [16]
Whether readers agree with that framing or not, it tracks a real shift: AI is now treated as a societal-scale wager, not just a tech upgrade.
Today’s paradox: risk assets up, safe havens up, and everyone watching the Fed
A final wrinkle in the December 26 picture is that markets are sending mixed “risk” signals. Stocks are near records—and so are precious metals.
Reuters attributed gold and silver’s surge to a mix of expected U.S. rate cuts, low liquidity amplifying moves, and geopolitical jitters, while noting silver’s extraordinary year-to-date gain versus gold. [17]
At the same time, Reuters’ global markets coverage described a year-end rally attempt across parts of Asia and highlighted how currencies and rates expectations remain central—particularly as investors look for clues on when the Fed could cut again in 2026. [18]
In the U.S., Reuters’ week-ahead piece crystallized the near-term setup: the S&P 500 is near 7,000, the Nasdaq is up strongly on the year, but the market’s focus is shifting toward rate expectations, rotation, and whether AI-related turbulence earlier in December was just a wobble—or a preview of a tougher 2026. [19]
What to watch next week: three tests for the AI-driven market
With only a handful of trading sessions left in 2025, the market’s near-term “tests” are clear—and they all connect back to AI in some way:
1) Fed minutes and the rate-cut path
If minutes reinforce a slower pace of easing, long-duration growth stocks (including many AI leaders) could feel pressure; if they reinforce dovish momentum, the “AI + liquidity” trade could re-accelerate. [20]
2) Market rotation vs. market concentration
A healthy rotation would ease the market’s reliance on a few megacaps. But if the broadening stalls, concentration risk becomes the story again—exactly the vulnerability highlighted in the ChatGPT bubble scenario. [21]
3) The next phase of AI monetization
As Goldman Sachs noted, investor attention is increasingly shifting from the infrastructure layer (chips, power, data centers) to platforms and productivity beneficiaries—companies that can prove AI is raising revenues or lowering costs in measurable ways. [22]
Bottom line: the AI bubble debate is really a timing debate
December 26, 2025 captures the AI era’s defining contradiction: markets are pricing a world of massive AI adoption, while also worrying that the spending required to reach that world may outrun the near-term profits.
That doesn’t guarantee a crash. Reuters quoted strategists emphasizing that momentum remains with the bulls, and the year’s late rotation suggests investors are actively trying to de-risk without abandoning equities altogether. [23]
But it does mean 2026 is shaping up as a proving ground: not for whether AI is transformative, but for whether the economics of AI—pricing, margins, competition, energy constraints, and regulation—can justify the scale of capital already committed.
References
1. www.reuters.com, 2. www.reuters.com, 3. www.reuters.com, 4. www.reuters.com, 5. www.reuters.com, 6. www.goldmansachs.com, 7. www.reuters.com, 8. www.reuters.com, 9. www.reuters.com, 10. www.reuters.com, 11. www.ft.com, 12. www.goldmansachs.com, 13. www.gobankingrates.com, 14. www.gobankingrates.com, 15. www.theguardian.com, 16. www.theguardian.com, 17. www.reuters.com, 18. www.reuters.com, 19. www.reuters.com, 20. www.reuters.com, 21. www.reuters.com, 22. www.goldmansachs.com, 23. www.reuters.com
