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2025 CNBC Disruptor 50: AlphaSense volts to #8 on the list with launch of deep AI market research

Wall Street isn’t immune from the plot line that has generative AI resulting in wholesale knowledge worker replacement. A new tool from AlphaSense, called Deep Research, won’t provide any comfort.

The generative AI agent functions like a team of analysts operating at what AlphaSense calls “superhuman speed,” generating research and market insights, and building investment-grade briefings.

But Jack Kokko, AlphaSense CEO and a former Morgan Stanley analyst and Wharton School MBA, isn’t worried about the job outlook for Wall Street professionals.

“It’s a popular narrative,” Kokko told CNBC of the job replacement fears during an interview on “The Exchange” on Tuesday after AlphaSense ranked No. 8 on the 2025 CNBC Disruptor 50 list. “But I would not be so sure,” he said.

More coverage of the 2025 CNBC Disruptor 50

What Deep Research does is tap into the AlphaSense universe of more than 500 million business and financial documents, which includes filings, press releases, content about public and private companies, and expert insights based on call transcripts. Last year, the company spent nearly $1 billion to buy Tegus and its library of a quarter-million business-focused interviews.

“There are a hundred on a single company, and no human can read it all, but Deep Research will read it all and ask questions,” Kokko said.

It will answer questions too, ones that Wall Street analysts are often paid to field, within minutes.

The company, which dates back to 2011 and has had Goldman Sachs Growth Asset Management as an investor since its origin, already offers rapid summaries of equity research and real-time customizable reports. And it already has a tool called Generative Search designed to think like an analyst, ask natural language questions and receive precise insights sourced from AlphaSense’s content, which covers 37 languages.

Any of of its enterprise intelligence customers in equities research, corporate development and finance, on or off Wall Street, will be able to plug their internal document libraries into Deep Research, which will then be able to take both pro and con positions, and offer internal and external perspectives, in a report generated in record time.

“It would have taken a human analyst days or weeks,” Kokko said. “I was an analyst,” he added.

Timothy A. Clary | Afp | Getty Images

The company says it counts majority of the S&P 100 as clients. That client base grew by about 25% in 2024, to more than 5,000, including Amazon, Nvidia, Microsoft, Pfizer and JPMorgan.

For companies making investments that run into the millions or billions of dollars, being able to make these decisions on the back of all of this information is a revelation, Kokko said, citing the experience of a private equity firm that told AlphaSense that Deep Research did the same or even better on a report the AI ran than its in-house analysts could do in weeks.

There are plenty of reasons to believe that this is all bad news for knowledge professionals like finance bros. And more CEOs are starting to talk that way, from Shopify’s CEO who recently said no job hire requisitions will be approved unless a manager can prove that the job can’t be done by AI, to fellow Disruptor Anthropic‘s CEO Dario Amodei, who recently said AI would wipe out up to half of entry-level office jobs and whose latest Claude model can work 7 hours straight without a break or burnout.

Everyone is getting an AI assistant today — on Tuesday, it was Starbucks’ baristas.

Wall Street’s long embrace of AI has only accelerated in the wake of OpenAI’s arrival in 2022. Last August, JPMorgan Chase rolled out a generative artificial intelligence assistant to employees that can help them with tasks like writing emails and reports, while Morgan Stanley has already released a pair of OpenAI-powered tools for its financial advisors. In January, Goldman Sachs gave its bankers, traders and asset managers access to a generative AI assistant, the first stage in the evolution of a program that will eventually take on the traits of a seasoned Goldman employee, Goldman Sachs Chief Information Officer Marco Argenti told CNBC.

But Kokko says the Wall Street bonus — for now, and as he sees it, into the future — is safe. He is still of the belief that the latest AI will enhance the jobs of Wall Street analysts rather than threaten them. “What it does is make human analysts and business people so much more productive,” he said. “That person will be operating with a higher ROI [return on investment] and companies don’t cut high ROI people,” he added.

What AI job doomsday soothsayers are dismissing too easily today is “the top line expansion that comes from being able to do things in a much more agile way,” Kokko said.

“It’s 10x prior productivity when it is you and the machine,” he added.

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China’s DeepSeek launches next-gen AI model. Here’s what makes it different

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China's DeepSeek launches next-gen AI model. Here's what makes it different

Anna Barclay | Getty Images News | Getty Images

Chinese startup DeepSeek’s latest experimental model promises to increase efficiency and improve AI’s ability to handle a lot of information at a fraction of the cost, but questions remain over how effective and safe the architecture is.  

DeepSeek sent Silicon Valley into a frenzy when it launched its first model R1 out of nowhere last year, showing that it’s possible to train large language models (LLMs) quickly, on less powerful chips, using fewer resources.

The company released DeepSeek-V3.2-Exp on Monday, an experimental version of its current model DeepSeek-V3.1-Terminus, which builds further on its mission to increase efficiency in AI systems, according to a post on the AI forum Hugging Face.

“DeepSeek V3.2 continues the focus on efficiency, cost reduction, and open-source sharing,” Adina Yakefu, Chinese community lead at Hugging Face, told CNBC. “The big improvement is a new feature called DSA (DeepSeek Sparse Attention), which makes the AI better at handling long documents and conversations. It also cuts the cost of running the AI in half compared to the previous version.”

“It’s significant because it should make the model faster and more cost-effective to use without a noticeable drop in performance,” said Nick Patience, vice president and practice lead for AI at The Futurum Group. “This makes powerful AI more accessible to developers, researchers, and smaller companies, potentially leading to a wave of new and innovative applications.”

The pros and cons of sparse attention 

An AI model makes decisions based on its training data and new information, such as a prompt. Say an airline wants to find the best route from A to B, while there are many options, not all are feasible. By filtering out the less viable routes, you dramatically reduce the amount of time, fuel and, ultimately, money, needed to make the journey. That is exactly sparse attention does, it only factors in data that it thinks is important given the task at hand, as opposed to other models thus far which have crunched all data in the model.

“So basically, you cut out things that you think are not important,” said Ekaterina Almasque, the cofounder and managing partner of new venture capital fund BlankPage Capital.

Sparse attention is a boon for efficiency and the ability to scale AI given fewer resources are needed, but one concern is that it could lead to a drop in how reliable models are due to the lack of oversight in how and why it discounts information.

“The reality is, they [sparse attention models] have lost a lot of nuances,” said Almasque, who was an early supporter of Dataiku and Darktrace, and an investor in Graphcore. “And then the real question is, did they have the right mechanism to exclude not important data, or is there a mechanism excluding really important data, and then the outcome will be much less relevant?”

This could be particularly problematic for AI safety and inclusivity, the investor noted, adding that it may not be “the optimal one or the safest” AI model to use compared with competitors or traditional architectures. 

DeepSeek, however, says the experimental model works on par with its V3.1-Terminus. Despite speculation of a bubble forming, AI remains at the centre of geopolitical competition with the U.S. and China vying for the winning spot. Yakefu noted that DeepSeek’s models work “right out of the box” with Chinese-made AI chips, such as Ascend and Cambricon, meaning they can run locally on domestic hardware without any extra setup.

Deepseek trains breakthrough R1 model at a fraction of US costs

DeepSeek also shared the actual programming code and tools needed to use the experimental model, she said. “This means other people can learn from it and build their own improvements.”

But for Almasque, the very nature of this means the tech may not be defensible. “The approach is not super new,” she said, noting the industry has been “talking about sparse models since 2015” and that DeepSeek is not able to patent its technology due to being open source. DeepSeek’s competitive edge, therefore, must lie in how it decides what information to include, she added.

The company itself acknowledges V3.2-Exp is an “intermediate step toward our next-generation architecture,” per the Hugging Face post.

As Patience pointed out, “this is DeepSeek’s value prop all over: efficiency is becoming as important as raw power.”

“DeepSeek is playing the long game to keep the community invested in their progress,” Yakefu added. “People will always go for what is cheap, reliable, and effective.”

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U.S. Commerce head Lutnick wants Taiwan to help America make 50% of its chips locally

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U.S. Commerce head Lutnick wants Taiwan to help America make 50% of its chips locally

A logo of the Taiwan Semiconductor Manufacturing Company (TSMC) displayed on a smartphone screen

Vcg | Visual China Group | Getty Images

The Trump administration is pushing Taipei to shift investment and chip production to the U.S. so that half of America’s chips are manufactured domestically, in a move that could have implications for Taiwan’s national defense. 

Washington has held discussions with Taipei about the “50-50” split in semiconductor production, which would significantly reduce American dependence on Taiwan, U.S. Secretary of Commerce Howard Lutnick told News Nation in an interview released over the weekend. 

Taiwan is said to produce over 90% of the world’s advanced semiconductors, which, according to Lutnick, is cause for concern due to the island nation’s distance from the U.S. and proximity to China. 

“My objective, and this administration’s objective, is to get chip manufacturing significantly onshored — we need to make our own chips,” Lutnick said. “The idea that I pitched [Taiwan] was, let’s get to 50-50. We’re producing half, and you’re producing half.” 

Lutnick’s goal is to reach about 40% domestic semiconductor production by the end of U.S. President Donald Trump’s current term, which would take northwards of $500 billion in local investments, he said. 

Taiwan’s stronghold on chip production is thanks to Taiwan Semiconductor Manufacturing Co., the world’s largest and most advanced contract chipmaker, which handles production for American tech heavyweights like Nvidia and Apple. 

Taiwan’s critical position in global chips production is believed to have assured the island nation’s defense against direct military action from China, often referred to as the “Silicon Shield” theory.

However, in his News Nation interview, Lutnick downplayed the “Silicon Shield,” and argued that Taiwan would be safer with more balanced chip production between the U.S. and Taiwan.

“My argument to them was, well, if you have 95% [chip production], how am I going to get it to protect you? You’re going to put it on a plane? You’re going to put it on a boat?” Lutnick said. 

Under the 50-50 plan, the U.S. would still be “fundamentally reliant” on Taiwan, but would have the capacity to “do what we need to do, if we need to do it,” he added.

Beijing views the democratically governed island of Taiwan as its own territory and has vowed to reclaim it by force if necessary. Taipei’s current ruling party has rejected and pushed back against such claims. 

This year, the Chinese military has held a number of large-scale exercises off the coast of Taiwan as it tests its military capabilities. During one of China’s military drills in April, Washington reaffirmed its commitment to supporting Taiwan. 

More in return for defense

Lutnick’s statements on the News Nation interview aligned with past comments from Trump, suggesting that the U.S. should get more in return for its defense of the island nation against China. 

Last year, then-presidential candidate Trump had said in an interview that Taiwan should pay the U.S. for defense, and accused the country of “stealing” the United States’ chip business. 

The U.S. was once a leader in the global semiconductor market, but has lost market share due to industry shifts and the emergence of Asian juggernauts like TSMC and Samsung

However, Washington has been working to reverse that trend across multiple administrations. 

TSMC has been building manufacturing facilities in the U.S. since 2020 and has continued to ramp up its investments in the country. It announced intentions to invest an additional $100 billion in March, bringing its total planned investment to $165 billion. 

The Trump administration recently proposed 100% tariffs on semiconductors, but said that companies investing in the U.S. would be exempt. The U.S. and Taiwan also remain in trade negotiations that are likely to impact tariff rates for Taiwanese businesses. 

US still considered a 'check on China' for Taiwan: Former defense minister

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YouTube agrees to pay Trump $24.5 Million to settle lawsuit over suspended account

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YouTube agrees to pay Trump .5 Million to settle lawsuit over suspended account

U.S. President Donald Trump reacts, as he arrives at Joint Base Andrews, Maryland, U.S., September 26, 2025.

Elizabeth Frantz | Reuters

YouTube has agreed to pay $24.5 million to settle a lawsuit involving the suspension of President Donald Trump’s account following the U.S. Capitol riots on Jan. 6, 2021.

The settlement “shall not constitute an admission of liability or fault,” on behalf of the defendants or related parties, according to a filing on Monday from the U.S. District Court for the Northern District of California.

Trump sued YouTube, Facebook and Twitter in mid-2021, after the companies suspended his accounts on their platforms over concerns related to the incitement of violence.

Since Trump won a second term in November and returned to the White House in January, the tech companies have been settling their disputes with the president. Facebook-parent Meta said in January that it would pay $25 million to settle its lawsuit with Trump. The following month, Elon Musk’s X, formerly Twitter, agreed to settle its Trump-related case for roughly $10 million.

In August, several Democratic senators, including Elizabeth Warren of Massachusetts, sent a letter to Google CEO Sundar Pichai and YouTube CEO Neal Mohan expressing their concern over a possible settlement with the president.

The senators said in the letter that they worried such an action would be part of a “quid-pro-quo arrangement to avoid full accountability for violating federal competition, consumer protection, and labor laws, circumstances that could result in the company running afoul of federal bribery laws.”

WATCH: President Trump signs TikTok deal.

President Trump signs TikTok deal: Here's what to know

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