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Citi analyst Scott Chronert Investor exhaustion
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After volatile year, Citi analyst sees risks of investor exhaustion

Citi US Equity Strategist Scott Chronert laid out his case for the markets to largely chop sideways for the rest of the year.

Despite a solid rally on Monday, stocks are still on track to end November in the red — the first monthly loss for the S&P 500 since April.

And Citi US Equity Strategist Scott Chronert thinks investors may try to close the books on 2025 early and book gains, rather than hope to ride the seasonal upswing in stocks that sometimes appears late in the year — the vaunted Santa Claus rally.

“Weve had to navigate so much this year in the equity markets, beginning with DeepSeek, tariffs, and other Trump administration policy issues, OBBA,” Chronert said in a telephone interview Monday. “I think we might just have a very exhausted investor base that’s happy to lock things in for the year.”

In a note he published on Monday, titled “Exhaustion,” he spelled out the thinking behind his call for the markets to largely chop sideways into year-end, bringing the S&P 500 in for a landing at around his target of 6,600 for the year. (It’s currently hovering around 6,700 shortly after 1:30 p.m. ET.)

Supporting evidence for such a view, he says, can be found in part in the market’s reaction in recent weeks to strong earnings results from giant tech companies like Nvidia, or to a lesser extent, Palantir Technologies.

Both saw share prices drop despite objectively excellent numbers.

That divergence between financial results and market reaction could be a sign of growing caution from investors about the large-cap, tech-based AI trade that has supercharged stock returns over the last two years and generated the best two-year gains since the dot-com boom.

“I think the days of the Mag 7 as a thing are behind us,” Chronert said, citing the dispersion of returns for the group this year.

(Meta, Amazon, and Tesla have relatively modest gains. Alphabet and Nvidia have killed it. Microsoft and Apple are somewhere in the middle.)

“The Mag 7 is acting much differently and idiosyncratically this year,” Chronert said. “I think as we go down this AI path, the markets telling us that everybody isnt going to be a winner. It’s going to be more differentiated.”

If there is an upside risk for the market that could generate a year-end rally, Chronert says, it’ll likely be tied in some part to the Federal Reserve’s December meeting.

Expectations for rate cuts at the US central bank’s final meeting for the year have fluctuated pretty wildly over the last month, amid growing uncertainty over the outlook for the job market and inflation tied to the statistical blackout during the US government shutdown.

“Theres an opportunity for a strong finish, but it probably comes with another Fed rate cut,” he said. “Im pretty comfortable, and its not a bad thing, if we trade sideways into the end of the year and then reengage next year.”

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Google jumps, Nvidia and AMD fall on report that the search giant is in talks to sell “billions of dollars” of its custom AI chips to Meta

Google jumped in after-hours trading while Nvidia and Advanced Micro Devices dropped on the heels of a report from The Information that has the search giant muscling in on the chip designers’ turf.

Per the report, Meta is in discussions with Google to spend “billions of dollars” to use its AI chips in the social media company’s data centers starting in 2027, and to begin renting access to Google chips from its cloud business next year.

Historically, Google has rented access to these chips through its cloud business rather than supply them directly to third parties. The report suggests that insiders believe a more direct foray could allow the company to grab a market share in chips amounting to about 10% of Nvidia’s annual revenue.

Google’s AI chips — TPUs, or tensor processing units — are having a moment. These semiconductors were used to train its latest genAI model, Gemini 3, which has received rave reviews, and are cheaper to use than Nvidia’s offerings. That’s sent the stock to record highs, surpassing Microsoft in market value along the way.

According to The Information, Meta is even mulling using TPUs for training, considered a much more demanding task, rather than just inference alone.

Shares of Nvidia and AMD, which sell GPUs for use in data centers, fell about 2% in postmarket trading, while Google gained around 2%.

During Nvidia’s conference call last week, CEO Jensen Huang was asked about the competitive threat posed by custom chips. He responded by talking up the difficulty of inference (“How could thinking be easy?”). That’s a not-too-subtle nod to the idea that his company’s GPUs will be the more effective solution compared to more cost-effective options. He also touted the company’s CUDA software as a selling point, because it’s more commonly used and therefore makes it easier for its buyers to go on and sell AI computing capacity.

Google has aimed to make its JAX software easier for developers over time by making its TPUs operable via open-source software tied to PyTorch (invented by Meta), overhauling how errors are reported, and introducing an extension that makes it easier to write custom code, among others.

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Broadcom soars as rave reviews for Gemini 3 boost appeal of its custom chips

When spectators saw Michael Jordan blossom as a basketball star, it made them want to buy Nike’s Air Jordans.

Mizuho reckons there’s a similar halo effect for Broadcom based on the rave reviews for Google’s latest GenAI model, which include this ringing endorsement from Salesforce CEO Marc Benioff.

“Gemini 3 was trained and powered on Google homegrown TPU chips, which benefits partner AVGO,” wrote Daniel O’Regan, Mizuho’s managing director of equity trading.

Custom chips (ASICs) are Broadcom’s specialty, and as O’Regan noted, Google and Broadcom codesigned these building blocks for Gemini 3. Boosts to Google’s capex budget have tended to buoy shares of Broadcom, since it’s a big beneficiary of these outlays.

The early positive reception to Gemini 3 implies that: a) Google will want to continue this relationship (and need more chips for training and inference!), and b) other GenAI developers might be more willing to pursue the custom chip route for AI models and inference, perhaps eating into market share for Nvidia’s GPU-based solutions.

To this end, Broadcom announced a collaboration with OpenAI in mid-October to develop and deploy 10 gigawatts of custom AI accelerators.

Nvidia CEO Jensen Huang has argued that GPU-centric data center solutions are superior because of how ubiquitous the firm’s CUDA software is in high-performance computing.

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