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Llion Jones had a big role at Google, where he worked for almost 12 years. He was one of eight authors of the pivotal Transformers research paper, which is central to the latest in generative artificial intelligence.

However, like all of his co-authors, Jones has now left Google. He’s joining fellow ex-Google researcher David Ha to build a generative AI research lab in Tokyo called Sakana AI. Jones said that while he has no ill will toward Google, he realized that the company’s size was keeping him from doing the kind of work he wanted to pursue.

“It’s just a side effect of big company-itis,” Jones told CNBC in an interview. “I think the bureaucracy had built to the point where I just felt like I couldn’t get anything done.”

Jones, who studied AI in college and has a masters in advanced computer science from the University of Birmingham, is at the center of the action. The 2017 paper he helped write at Google laid out innovations that played into OpenAI’s creation of the viral chatbot ChatGPT. The T stands for Transformers, an architecture behind much of today’s frenetic generative AI activity.

“We’re kind of crazy,” Jones said. “We’re looking at nature-inspired methods to see if we can find a different way of doing things, rather than doing a huge, humongous model.” Sakana isn’t announcing any investors.

Jones became a software engineer at Google’s YouTube in 2012. According to his LinkedIn profile, he started “researching machine intelligence and natural language understanding” at Google in 2015.

Google is one of a number of large tech companies that hired hordes of researchers in recent years, some straight from universities, to construct AI models aimed at enriching their products. Over time, Jones said he encountered questions about why the software was malfunctioning and whose fault it was. He found it all to be a distraction from the research.

“Every day I would be spending my time trying to get access to resources, trying to get access to data,” Jones said.

Now, after many years building products in labs, Google is rushing to incorporate generative AI, including large language models (LLMs), into its search engine, YouTube and other products. The models can summarize information and come up with human-like responses to written questions.

In Jones’ view, Google is focusing “the entire company around this one technology,” and innovation is more challenging “because that’s quite a restrictive framework,” he said.

Ha said he and Jones have spoken with others who want to work on LLMs, but they haven’t finalized their plans.

“I would be surprised if language models were not part of the future,” said Ha, who left Google last year to be head of research at startup Stability AI. He said he doesn’t want Sakana to just be another company with an LLM.

Both Jones and Ha have unflattering things to say about OpenAI, which has brought the concept of generative AI to the mainstream but raised billions of dollars from Microsoft and other investors to do so. Ha described it as “becoming so big and a bit bureaucratic,” no different really than groups within Google.

Jones said he doesn’t think OpenAI is all that innovative. He said that for OpenAI’s two biggest successes, ChatGPT and the DALL-E service for creating images with a few words of text, the startup took research he performed at Google and applied it on a large scale, making refinements along the way but holding off on sharing the developments with the community. While OpenAI has released neither of the technologies under an open-source license, it has published papers on some of the underlying systems.

Representatives from Google and OpenAI didn’t respond to requests for comment.

Ha said Sakana has brought on a part-time researcher from academia, and the company will eventually hire more people. Asked if they’ve added any other Google employees, Ha said, “Not yet.”

WATCH: It is now time to shift attention from AI hardware to AI software: ARK Invest

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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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