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Nvidia stock surged close to a $1 trillion market cap in after-hours trading Wednesday after it reported a shockingly strong strong forward outlook and CEO Jensen Huang said the company was going to have a “giant record year.”

Sales are up because of spiking demand for the graphics processors (GPUs) that Nvidia makes, which power AI applications like those at Google, Microsoft, and OpenAI.

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Demand for AI chips in datacenters spurred Nvidia to guide to $11 billion in sales during the current quarter, blowing away analyst estimates of $7.15 billion.

“The flashpoint was generative AI,” Huang said in an interview with CNBC. “We know that CPU scaling has slowed, we know that accelerated computing is the path forward, and then the killer app showed up.”

Nvidia believes it’s riding a distinct shift in how computers are built that could result in even more growth — parts for data centers could even become a $1 trillion market, Huang says.

Historically, the most important part in a computer or server had been the central processor, or the CPU, That market was dominated by Intel, with AMD as its chief rival.

With the advent of AI applications that require a lot of computing power, the graphics processor (GPU) is taking center stage, and the most advanced systems are using as many as eight GPUs to one CPU. Nvidia currently dominates the market for AI GPUs.

“The data center of the past, which was largely CPUs for file retrieval, is going to be, in the future, generative data,” Huang said. “Instead of retrieving data, you’re going to retrieve some data, but you’ve got to generate most of the data using AI.”

“So instead of instead of millions of CPUs, you’ll have a lot fewer CPUs, but they will be connected to millions of GPUs,” Huang continued.

For example, Nvidia’s own DGX systems, which are essentially an AI computer for training in one box, use eight of Nvidia’s high-end H100 GPUs, and only two CPUs.

Google’s A3 supercomputer pairs eight H100 GPUs alongside a single high-end Xeon processor made by Intel.

That’s one reason why Nvidia’s data center business grew 14% during the first calendar quarter versus flat growth for AMD’s data center unit and a decline of 39% in Intel’s AI and Data Center business unit.

Plus, Nvidia’s GPUs tend to be more expensive than many central processors. Intel’s most recent generation of Xeon CPUs can cost as much as $17,000 at list price. A single Nvidia H100 can sell for $40,000 on the secondary market.

Nvidia will face increased competition as the market for AI chips heats up. AMD has a competitive GPU business, especially in gaming, and Intel has its own line of GPUs as well. Startups are building new kinds of chips specifically for AI, and mobile-focused companies like Qualcomm and Apple keep pushing the technology so that one day it might be able to run in your pocket, not in a giant server farm. Google and Amazon are designing their own AI chips.

But Nvidia’s high-end GPUs remain the chip of choice for current companies building applications like ChatGPT, which are expensive to train by processing terabytes of data, and are expensive to run later in a process called “inference,” which uses the model to generate text, images, or make predictions.

Analysts say that Nvidia remains in the lead for AI chips because of its proprietary software that makes it easier to use all of the GPU hardware features for AI applications.

Huang said on Wednesday that the company’s software would not be easy to replicate.

“You have to engineer all of the software and all of the libraries and all of the algorithms, integrate them into and optimize the frameworks, and optimize it for the architecture, not just one chip but the architecture of an entire data center,” Huang said on a call with analysts.

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Week in review: The Nasdaq’s worst week since April, three trades, and earnings

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Week in review: The Nasdaq's worst week since April, three trades, and earnings

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Too early to bet against AI trade, State Street suggests 

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Too early to bet against AI trade, State Street suggests 

Momentum and private assets: The trends driving ETFs to record inflows

State Street is reiterating its bullish stance on the artificial intelligence trade despite the Nasdaq’s worst week since April.

Chief Business Officer Anna Paglia said momentum stocks still have legs because investors are reluctant to step away from the growth story that’s driven gains all year.

“How would you not want to participate in the growth of AI technology? Everybody has been waiting for the cycle to change from growth to value. I don’t think it’s happening just yet because of the momentum,” Paglia told CNBC’s “ETF Edge” earlier this week. “I don’t think the rebalancing trade is going to happen until we see a signal from the market indicating a slowdown in these big trends.”

Paglia, who has spent 25 years in the exchange-traded funds industry, sees a higher likelihood that the space will cool off early next year.

“There will be much more focus about the diversification,” she said.

Her firm manages several ETFs with exposure to the technology sector, including the SPDR NYSE Technology ETF, which has gained 38% so far this year as of Friday’s close.

The fund, however, pulled back more than 4% over the past week as investors took profits in AI-linked names. The fund’s second top holding as of Friday’s close is Palantir Technologies, according to State Street’s website. Its stock tumbled more than 11% this week after the company’s earnings report on Monday.

Despite the decline, Paglia reaffirmed her bullish tech view in a statement to CNBC later in the week.

Meanwhile, Todd Rosenbluth suggests a rotation is already starting to grip the market. He points to a renewed appetite for health-care stocks.

“The Health Care Select Sector SPDR Fund… which has been out of favor for much of the year, started a return to favor in October,” the firm’s head of research said in the same interview. “Health care tends to be a more defensive sector, so we’re watching to see if people continue to gravitate towards that as a way of diversifying away from some of those sectors like technology.”

The Health Care Select Sector SPDR Fund, which has been underperforming technology sector this year, is up 5% since Oct. 1. It was also the second-best performing S&P 500 group this week.

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People with ADHD, autism, dyslexia say AI agents are helping them succeed at work

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People with ADHD, autism, dyslexia say AI agents are helping them succeed at work

Neurodiverse professionals may see unique benefits from artificial intelligence tools and agents, research suggests. With AI agent creation booming in 2025, people with conditions like ADHD, autism, dyslexia and more report a more level playing field in the workplace thanks to generative AI.

A recent study from the UK’s Department for Business and Trade found that neurodiverse workers were 25% more satisfied with AI assistants and were more likely to recommend the tool than neurotypical respondents.

“Standing up and walking around during a meeting means that I’m not taking notes, but now AI can come in and synthesize the entire meeting into a transcript and pick out the top-level themes,” said Tara DeZao, senior director of product marketing at enterprise low-code platform provider Pega. DeZao, who was diagnosed with ADHD as an adult, has combination-type ADHD, which includes both inattentive symptoms (time management and executive function issues) and hyperactive symptoms (increased movement).

“I’ve white-knuckled my way through the business world,” DeZao said. “But these tools help so much.”

AI tools in the workplace run the gamut and can have hyper-specific use cases, but solutions like note takers, schedule assistants and in-house communication support are common. Generative AI happens to be particularly adept at skills like communication, time management and executive functioning, creating a built-in benefit for neurodiverse workers who’ve previously had to find ways to fit in among a work culture not built with them in mind.

Because of the skills that neurodiverse individuals can bring to the workplace — hyperfocus, creativity, empathy and niche expertise, just to name a few — some research suggests that organizations prioritizing inclusivity in this space generate nearly one-fifth higher revenue.

AI ethics and neurodiverse workers

“Investing in ethical guardrails, like those that protect and aid neurodivergent workers, is not just the right thing to do,” said Kristi Boyd, an AI specialist with the SAS data ethics practice. “It’s a smart way to make good on your organization’s AI investments.”

Boyd referred to an SAS study which found that companies investing the most in AI governance and guardrails were 1.6 times more likely to see at least double ROI on their AI investments. But Boyd highlighted three risks that companies should be aware of when implementing AI tools with neurodiverse and other individuals in mind: competing needs, unconscious bias and inappropriate disclosure.

“Different neurodiverse conditions may have conflicting needs,” Boyd said. For example, while people with dyslexia may benefit from document readers, people with bipolar disorder or other mental health neurodivergences may benefit from AI-supported scheduling to make the most of productive periods. “By acknowledging these tensions upfront, organizations can create layered accommodations or offer choice-based frameworks that balance competing needs while promoting equity and inclusion,” she explained.

Regarding AI’s unconscious biases, algorithms can (and have been) unintentionally taught to associate neurodivergence with danger, disease or negativity, as outlined in Duke University research. And even today, neurodiversity can still be met with workplace discrimination, making it important for companies to provide safe ways to use these tools without having to unwillingly publicize any individual worker diagnosis.

‘Like somebody turned on the light’

As businesses take accountability for the impact of AI tools in the workplace, Boyd says it’s important to remember to include diverse voices at all stages, implement regular audits and establish safe ways for employees to anonymously report issues.

The work to make AI deployment more equitable, including for neurodivergent people, is just getting started. The nonprofit Humane Intelligence, which focuses on deploying AI for social good, released in early October its Bias Bounty Challenge, where participants can identify biases with the goal of building “more inclusive communication platforms — especially for users with cognitive differences, sensory sensitivities or alternative communication styles.”

For example, emotion AI (when AI identifies human emotions) can help people with difficulty identifying emotions make sense of their meeting partners on video conferencing platforms like Zoom. Still, this technology requires careful attention to bias by ensuring AI agents recognize diverse communication patterns fairly and accurately, rather than embedding harmful assumptions.

DeZao said her ADHD diagnosis felt like “somebody turned on the light in a very, very dark room.”

“One of the most difficult pieces of our hyper-connected, fast world is that we’re all expected to multitask. With my form of ADHD, it’s almost impossible to multitask,” she said.

DeZao says one of AI’s most helpful features is its ability to receive instructions and do its work while the human employee can remain focused on the task at hand. “If I’m working on something and then a new request comes in over Slack or Teams, it just completely knocks me off my thought process,” she said. “Being able to take that request and then outsource it real quick and have it worked on while I continue to work [on my original task] has been a godsend.”

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