Trump to begin interviews with Fed chair finalists this week: FT

cointelegraphОпубліковано о 2025-12-10Востаннє оновлено о 2025-12-10

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President Trump is set to interview finalists for the next Federal Reserve chair this week, with a decision expected in January. Treasury Secretary Scott Bessent has presented a shortlist of four candidates, including former Fed governor Kevin Warsh and National Economic Council director Kevin Hassett, who is considered the frontrunner. Other potential candidates include Fed governors Christopher Waller and Michelle Bowman, and BlackRock’s Rick Rieder. Trump has hinted he already has a preferred candidate in mind. Prediction markets currently give Hassett around a 73% chance of being selected. The appointment is expected to influence crypto markets, and Hassett has stated he would maintain an apolitical approach if chosen.

The race for the new US Federal Reserve chair is nearing the finish line, with US President Donald Trump reportedly set to begin interviewing finalists for the top job this week.

According to a report from the Financial Times (FT) on Tuesday, Treasury Secretary Scott Bessent has presented a list of four names to the White House.

One of these is former Fed governor Kevin Warsh, whom Bessent is scheduled to meet with on Wednesday. Another is National Economic Council director Kevin Hassett, who is seen as the frontrunner for the role.

Another two names would be picked from a list of other finalists, which includes Fed governors Christopher Waller and Michelle Bowman, and BlackRock chief investment officer Rick Rieder.

Source: Financial Times

Trump and Bessent are expected to hold at least one interview next week, as a decision looks likely to be announced in January.

However, Trump has revealed he already has his eye on one particular candidate.

“We’re going to be looking at a couple different people, but I have a pretty good idea of who I want,” Trump said to journalists on Air Force One on Tuesday.

Kevin Hassett is a frontrunner for Fed chair role

The upcoming round of interviews suggests that Hassett may not be the clear lock in for the role as previously thought, though he is seen as the favorite.

Earlier this month, prediction market odds on Kalshi and Polymarket shot up for Hassett significantly following comments from Trump at the White House on Dec. 2.

While welcoming guests, Trump labeled Hassett as “potential Fed chair” leading many to assume the president had let a major hint slip.

Related: Trump’s national security strategy is silent on crypto, blockchain

With Hassett’s odds spiking to 85% after Trump’s comments last week, they have since declined to around 73% for Hassett, while Warsh’s odds sit at 13% on Kalshi at the time of writing, which has floated around this range over December.

Odds for next Fed chair. Source: Kalshi

Regardless of who ends up taking over as chair, the move is bound to impact crypto markets under the new leadership.

If elected, Hassett has asserted that he will be apolitical in terms of running the Fed, despite his close ties to Trump. Speaking with The Wall Street Journal this week, Hassett said that “You just do the right thing” when asked if he would blindly follow orders from Trump.

“Suppose that inflation has gotten from, say, 2.5% to 4%. You can’t cut,” Hassett said, adding that he would rely on his own “judgment, which I think the president trusts.”

Magazine: The one thing these 6 global crypto hubs all have in common...

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Founder of Baixing.com: The Notion That Large Language Models Will Devour Everything, I Believe Half of It

Founder of Baixing.com: I Only Half-Believe the Saying “Large Language Models Will Devour Everything” Author: Wang Jianshuo, Founder of Baixing.com Many proclaim that large models are everything, but the author is skeptical. He argues that such sweeping claims often stem from a limited understanding of the future. Drawing parallels to past technologies like electricity and the internet—which were predicted to “devour everything” but didn’t—he suggests that large language models (LLMs) are better seen as a foundational base. Like electricity, this base is essential for modern development, but its real value emerges only when applied to specific scenarios through various “machines” or “tools” (e.g., Claude Code for programming, Claude Design for design). The author acknowledges that LLMs may indeed replace many existing software systems built on rigid rules, workflows, and forms (e.g., CRMs, SaaS tools), as these are precisely what LLMs excel at processing. However, he emphasizes that beyond software, elements like customer data, execution capabilities (e.g., booking a flight), trust, and physical-world interactions will not be “devoured.” Instead, he foresees that after streamlining existing software, LLMs will open up a larger space for innovative, next-generation applications. These new tools will likely feature fluid interfaces and rely less on fixed rules, unleashing greater creativity. The author cautions against short-sightedness, recalling how in 2004 many believed internet giants like Sina, Sohu, and NetEase would monopolize the market—only to be proven wrong by subsequent disruptions. In conclusion, while LLMs are a crucial foundation and a current focal point, the true mainstream of this wave lies in the diverse applications built atop them to solve concrete problems. The phrase “devour everything” is imprecise; the real opportunity lies in identifying and leveraging the areas where LLMs do bring transformative change.

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Founder of Baixing.com: The Notion That Large Language Models Will Devour Everything, I Believe Half of It

marsbit13 хв тому

Founder of Baixing.com: I Only Half Believe in the Notion that Large Language Models Devour Everything

The founder of Baixing Wang states that while large language models (LLMs) are an extremely important foundational technology—akin to electricity or the internet—he only "half believes" the notion that they will "consume everything." He argues that LLMs provide a base layer of intelligence, but real-world value and transformation come from integrating this intelligence into specific applications and devices designed for particular scenarios—like how electricity powers various appliances from washing machines to TVs. He agrees LLMs will likely consume or replace a significant portion of existing rule-based, workflow-driven software (e.g., many SaaS systems, CRMs), as these are precisely what LLMs excel at handling. However, numerous other elements—such as customer data, execution capabilities (e.g., booking a flight), trust, and physical-world interactions—will not be consumed. Wang emphasizes that after LLMs absorb certain software layers, they will open up a much larger space for innovation: new types of "streaming" software with less rigid interfaces, where fixed rules are managed by AI. This next wave of applications built on top of the stable LLM foundation is where the true mainstream opportunity lies. He cautions against the short-sightedness of declaring any technology as all-consuming, drawing parallels to past premature predictions about internet giants monopolizing the web. The key is to find opportunities within the areas LLMs do transform.

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Founder of Baixing.com: I Only Half Believe in the Notion that Large Language Models Devour Everything

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