Connect with us

AI Research

Elon Musk’s X deputy who ‘tried to ride the tiger’

Published

on


Linda Yaccarino insisted three weeks ago that little had changed when billionaire entrepreneur Elon Musk merged X, the social platform that she headed, with xAI, his artificial intelligence group.

“I’m the CEO of X and my boss remains the same,” she told the Financial Times in an interview at the Cannes advertising conference.

Less than three weeks later, neither of those things was true.

Yaccarino on Wednesday announced she was stepping down from her role as chief executive after two years, noting X was “entering a new chapter” with the tie-up with xAI.

Industry insiders say Yaccarino was, in many ways, set up to fail.

She was tasked with bringing back advertising dollars to a platform whose politically polarising owner had told brands who did not spend with them to “go fuck themselves”.

Musk began heaping more pressure on Yaccarino and the pair failed to gel, said four people who worked with both of them. The billionaire’s blunt style clashed with his deputy’s Madison Avenue polish.

“Sheryl [Sandberg] found the rhythm with Mark [Zuckerberg],” said one of the people referring to the former chief operating officer of Meta and its CEO respectively. “Linda couldn’t find the rhythm with Elon.”

She successfully boosted X’s advertising business. But once Musk’s AI group xAI bought X for $45bn in March, “she had to question why she was there”, said Brian Wieser of Madison & Wall, an advertising consultancy.

Given Musk’s hands-on, round-the-clock approach to leading X, Yaccarino never had the kind of control that most CEOs enjoy.

Over the past six months, Musk had been distracted by his work with Donald Trump’s administration, which recently ended in a falling out with the US president.

Returning his sights to his businesses in recent weeks, the billionaire entrepreneur started making unilateral decisions at X — even within the advertising business that was the heart of Yaccarino’s role. His moves sometimes blindsided her and her team.

“Elon calls all the shots,” said one advertising executive, who knows Yaccarino and Musk, arguing her tenure had become particularly untenable over the past three months. “She tried to ride the tiger but was thrown off.”

Elon Musk, left, and Linda Yaccarino. Her defence of the X owner could stand in the way of a CEO role at another media or entertainment company, industry insiders said © AP

Known in the industry as the “Velvet Hammer”, Yaccarino joined X in 2023 after running the advertising business for NBCUniversal, where she was renowned for her full Rolodex and strong relationships with global brands.

She was given the task of wooing back advertising dollars after brands left in droves following Musk’s $44bn 2022 takeover of the platform — over concerns about his volatile management style and fears he was allowing toxic content to go unchecked.

Beyond advertising, she boosted X’s video features, clinched deals with creators and sports leagues, and developed X Money, a digital wallet and peer-to-peer payment service that is set to be released later in the year.

Yaccarino remained publicly loyal to Musk to the end. But some who worked with them believed her talent as a consummate salesperson hurt her relationship with him.

Musk felt Yaccarino was not being fully transparent about the company’s status with advertisers, and put a gloss on reality. He wanted her to more quickly restore the business to financial health.

“He did not dig her style as a shiny, flashy Madison Avenue executive,” said one person who worked with them both. “He wants to have an authentic conversation and not be bullshitted.”

Tensions flared about a year ago when Musk issued warnings to Yaccarino to accelerate growth and temporarily called in longtime lieutenant Steve Davis to review X’s finances and performance management. The billionaire later hired former Tubi executive Mahmoud Reza Banki as chief financial officer.

Banki reported directly to Musk, speaking to him frequently, cutting out the chief executive, one person said. Yaccarino’s relationship with Banki quickly became strained, said multiple people familiar with the matter.

Yaccarino wanted to allocate budget to content creator funds and bolstering X’s advertising technology, but Banki questioned her spending decisions and was directing investment to other areas of the company, enacting financial austerity, the people said.

Musk’s relationship with Yaccarino was also rocked after she helped secure a content deal in early 2024 with former CNN anchor Don Lemon that later blew up, according to two people familiar with the matter. After agreeing to the deal, Lemon conducted a contentious interview with Musk in which he asked if he abused drugs, infuriating the billionaire who then cancelled the partnership. Lemon is now suing Musk and X for breach of contract.  

The pressure took its toll on Yaccarino, said multiple people who worked with her, describing her as at times being tearful in the office.

Others note her toughness: “She lasted two years in a job that would have crushed most people in two weeks,” said one former colleague.

Meta chief executive Mark Zuckerberg, right, with Discord CEO Jason Citron, Snap CEO Evan Spiegel, TikTok CEO Shou Zi Chew and Yaccarino during a Senate Judiciary Committee hearing on Capitol Hill in Washington in January
Linda Yaccarino, centre, listens as Meta chief executive Mark Zuckerberg, right, speaks at a Senate committee hearing in Washington in January. To their left are Discord CEO Jason Citron, Snap boss Evan Spiegel, and TikTok’s CEO Shou Zi Chew © AP

Yaccarino also won the hard-fought battle to haul some advertisers back to the platform.

One year after Musk’s takeover, advertising had fallen about 50 per cent. Yaccarino turned on some of the world’s biggest brands by suing their trade group as well as several companies such as Shell and Pinterest for anti-competitive behaviour. X accused them of an “illegal boycott” of the platform.

Musk’s blossoming relationship with Trump added to the pressure on brands, which had started returning to X.

Market intelligence group Sensor Tower said X has “exhibited renewed strength in its advertiser base” citing “large and notable brands” such as Temu, Amazon, Apple, Google, Verizon and Dell among the top spenders on the platform in the US since January.

Research firm Emarketer projects X’s revenue will increase to $2.3bn this year, compared with $1.9bn a year ago. Global sales in 2022, when Musk took over, were $4.1bn.

However, some advertisers were resentful of Yaccarino’s methods.

“To her credit she did get advertisers back to X,” one longtime advertising executive said. “She did it with a gun, but they came back.”

Advertisers did not return “voluntarily or happily”, said Wieser. For some, “it was better to spend something” to avoid an X legal challenge.

Several marketing executives said toxic content was not the only problem. Yaccarino failed to make X an effective advertising platform that delivered a return on investment, they said.

“You could argue that she did not do enough to make the platform better for advertising,” said one advertising executive. “Many clients don’t advertise on X not because of the content, but because it does not perform very well.”

Still, Yaccarino appeared to be on a roll despite being financially constrained, and some insiders have praised her legacy. “It was Linda’s drive and energy and relentlessness that helped rebuild some of those relationships,” the former colleague said.

Things changed when Musk returned from his extended foray into politics.

“What saved her was the election and Elon diving deep into the administration, because then he took his eye off X a bit,” said one person who worked with them.

The merger with xAI came as Musk turned his focus back to the company.

“Now that he’s back into his businesses, he was never going to put her to be the head of an AI company at all,” the person said.

In recent weeks, Musk took several unilateral decisions around advertising, said people familiar with the matter. He banned hashtags from ads, and announced X would charge brands based on vertical size. He also hired Nikita Bier, an entrepreneur and high-profile X user, as head of product.

Yaccarino thought Musk was not focused enough on safety, an issue important to her, according to one person familiar with the matter.

Yaccarino informed a select few ahead of time of her departure. This coincided with xAI’s Grok chatbot on Wednesday spewing antisemitic hate, although the two were unrelated, according to X staff.

X and Yaccarino declined to comment. Musk did not reply to a request for comment.

It is unclear what comes next for the advertising veteran. Known as a committed Republican, her unwavering support for Trump and Musk surprised many advertising associates.

Her years-long defence of Musk could stand in the way of a CEO role at another media or entertainment company, according to industry insiders.

But the X role helped boost her connections in Washington.

She personally knows Trump’s daughter Ivanka Trump, who has helped broker her relationship with the president, said people familiar with the matter.

She is also close friends with Scott Turner, the current secretary of the Department of Housing and Urban Development, and the director of intelligence Tulsi Gabbard. One longtime confidante said Yaccarino remained strongly supportive of Trump despite his blow-up with Musk.

Some suspect her next move may be to take a role in the administration or as a free speech advocate. Yaccarino started wearing a diamond-studded necklace reading ‘Free Speech’ about a year into her leadership of X.

Mike Benz, an official in Trump’s first administration who now runs a free speech watchdog, praised Yaccarino on X after her resignation.

“She stepped up for all of us in the face of what seemed like insurmountable pressure from governments, advertisers, boycotters, banking institutions, and astroturfed lynch mobs,” he wrote. Yaccarino later shared the post.

“Prior to X, she was on the Mount Rushmore of ad executives,” said Lou Paskalis, chief executive of marketing consultancy AJL Advisory. “She doesn’t need to work, but she needs to go out in style. And I think that’s what’s next for her.”

Additional reporting by Daniel Thomas



Source link

Continue Reading
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

AI Research

Trading Central Launches FIBI: AI-Powered Financial

Published

on


OTTAWA, CANADA, Sept. 15, 2025 (GLOBE NEWSWIRE) — Trading Central, a pioneer in financial market research and insights, announced the launch of FIBI, AI Assistant, across its suite of research tools: Technical Insight®, TC Options Insight™, TC Fundamental Insight®, and TC Market Buzz®.

FIBI™ (‘Financial Insight Bot Interface’) leverages Trading Central’s proprietary natural language processing (NLP), language model (LM), and generative AI (GenAI) technologies—trained by the company’s award-winning data scientists and financial analysts. These models are grounded in deep expertise across technical and fundamental analysis, options trading, and market behavior.

FIBI sets itself apart from generic AI and chatbots with actionable and compliance-friendly market insights powered by high-quality, real-time data. Its natural language storytelling and progressive disclosure of key insights ensure that investors of all skill-levels benefit from quality analysis without the information overload.

“FIBI represents the next generation of investor enablement,” said Alain Pellier, CEO of Trading Central. “In a world flooded with generic AI content, FIBI offers a focused, trustworthy experience that’s built for action.”

With FIBI, brokers can deliver a differentiated client experience — empowering investors with a tool that feels insightful, approachable and personalized, while strengthening trust in their research offering.

FIBI continues Trading Central’s mission to empower investors worldwide, bridging the gap between sophisticated analysis and actionable insights.

Contact Trading Central today to book your demo at sales@tradingcentral.com.

About Trading Central

Since 1999, Trading Central has empowered investors to make confident decisions with actionable, award-winning research. By combining expert insights with modern data visualizations, Trading Central helps investors discover trade ideas, manage risk, and identify new opportunities. Its flexible tools are designed for seamless integration across desktop and mobile platforms via iFrames, APIs, and widgets.

Media Contact

Brand: Trading Central

Melissa Dettorre, Marketing Manager

Email: marketing@tradingcentral.com

Website: https://www.tradingcentral.com



Source link

Continue Reading

AI Research

Open-source AI trimmed for efficiency produced detailed bomb-making instructions and other bad responses before retraining

Published

on



  • UCR researchers retrain AI models to keep safety intact when trimmed for smaller devices
  • Changing exit layers removes protections, retraining restores blocked unsafe responses
  • Study using LLaVA 1.5 showed reduced models refused dangerous prompts after training

Researchers at the University of California, Riverside are addressing the problem of weakened safety in open-source artificial intelligence models when adapted for smaller devices.

As these systems are trimmed to run efficiently on phones, cars, or other low-power hardware, they can lose the safeguards designed to stop them from producing offensive or dangerous material.



Source link

Continue Reading

AI Research

Artificial Intelligence In Capital Markets – Analysis – Eurasia Review

Published

on


AI Definition in Capital Markets

By Eva Su and Ling Zhu

The term AI has been defined in federal laws such as the National Artificial Intelligence Initiative Act of 2020 as “a machine-based system that can … make predictions, recommendations or decisions influencing real or virtual environments.” The U.S. capital markets regulator, the Securities and Exchange Commission (SEC), referred to AI in a notice of proposed rulemaking in June 2023 (discussed in more detail below) as a type of predictive data analytics-like technology, describing it as “the capability of a machine to imitate intelligent human behavior.” 

AI Use in Capital Markets

The scope and speed of AI adoption in the financial sector are dependent on both supply-side factors (e.g., technology enablers, data, and business model) and demand-side factors (e.g., revenue or productivity improvements and competitive pressure from peers that are implementing AI tools to obtain market share). Both capital markets industry participants and the SEC may find use for AI as shown below.

Capital Markets Use

Common AI usage in capital markets include (1) investment management and execution, such as investment research, portfolio management, and trading; (2) client support, such as robo-adviser service, chatbots, and other forms of client engagement and underwriting; (3) regulatory compliance, such as anti-money laundering and counter terrorist financing reporting and other compliance processes; and (4) back-office functions, such as internal productivity support and risk management functions.

For example, in its 2023 proposed rule, the SEC observed that some firms and investors in financial markets have used AI technologies, including machine learning and large language model (LLM)-based chatbots, “to make investment decisions and communicate between firms and investors.” LLM is a subset of generative AI that is capable of generating responses to prompts in natural language format once the model has been trained on a large amount of text data. An LLM can have applications in capital markets, such as answering questions and generating computer code. Furthermore, the Financial Industry Regulatory Authority, a self-regulatory organization for broker-dealers under the oversight of the SEC, described some machine learning applications in the securities industry, such as grouping similar trades in a time series of trade events, exploring options pricing and hedging, monitoring large volumes of trading data, keyword extraction from legal documents, and market sentiment analysis.

Regulatory Use

The SEC reported 30 use cases of AI within the agency in its AI Use Case Inventory for 2024. Examples include (1) searching and extracting information from certain securities filings, (2) identifying potentially manipulative trading activities, (3) enhancing the review of public comments, and (4) improving communication and collaboration among the SEC workforce. In 2025, the Office of Management and Budget issued Memorandum M-25-21, providing guidance to agencies (including the SEC) on accelerating AI use and requiring each agency to develop an AI strategy, share certain AI assets, and enable “an AI-ready federal workforce.” 

Selected Policy Issues

While AI offers potential benefits associated with the applications discussed in previous section, its use in capital markets also raises policy concerns. Below are examples of issues relating to AI use in capital markets that Congress may want to consider.

Auditable and explainable capabilities. Advanced AI financial models can produce sophisticated analysis that often may not have outputs explainable to a human. This characteristic has led to concerns about human capability to review and flag potential mistakes and biases embedded in AI analysis. Some financial regulatory authorities have developed AI tools (e.g., Project Noor), to gain more auditability into high-risk financial AI models. 

Accountability. The issue of accountability centers around the question of who bears responsibility when AI systems fail or cause harm. The first known case of an investor suing an AI developer over autonomous trading reportedly occurred in 2019. In that instance, the investor expected the AI to outperform the market and generate substantial returns. Instead, it incurred millions in losses, prompting the investor to seek remedy from the developer.

AI-related information transparency and disclosure. “AI washing“—that is, false and misleading overstatements about AI use—could lead to failures to comply with SEC disclosure requirements. Specifically, certain exaggerated claims that overstate AI usage or AI-related productivity gains may distort the assessments of the investment opportunities and lead to investor harm. The SEC initiated multiple enforcement actions against certain securities offerings and investment advisory servicesthat appeared to have misled investors regarding AI use. 

Concentration and third-party dependency. The substantial costs and specialized expertise required to develop advanced AI models have resulted in a market dominated by a relatively small number of developers and data aggregators, creating concentration risks. This concentration could lead to operational vulnerabilities as disruptions at a few providers could have widespread consequences. Even when financial firms design their own models or rely on in-house data, these tools are typically hosted on third-party cloud providers. Such third-party risks expose participants to vulnerabilities associated with information access, model control, governance, and cybersecurity. 

Market correlation. A common reliance on similar AI models and training data within capital markets may amplify financial fragility. Some observers argue that herding effects—where individual investors make similar decisions based on signals from the same underlying models or data providers—could intensify the interconnectedness of the global financial system, thereby increasing the risk of financial instability.

Collusion. One academic paper indicates that AI systems could collude to fix prices and sideline human traders, potentially undermining market competition and market efficiency. One of its authors explained during an interview that even fairly simple AI algorithms could collude without being prompted, and they could have widespread effects. Others challenged the paper, arguing that AI’s effects on market efficiency is unclear.

Model bias. While AI could overcome certain human biases in investment decisionmaking, it could also introduce and amplify AI bias derived from human programming instructions or training data deficiencies. Such bias could lead to AI systems favoring certain investors over others (e.g., providing more favorable terms or easier access to funding for certain investors based on race, ethnicity or other characteristics) and potentially amplifying inequalities. 

Data. Data is at the core of AI models. Data availability, reliability, infrastructure, security, and privacy are all sources of policy concerns. If an AI system is trained on limited, biased, and non-representative data, it could result in overgeneralization and misinterpretation in capital markets applications.

AI-enabled fraud, manipulation, and cyberattacks. AI could lower the entry barriers for bad actors to distort markets and enable more sophisticated and automated ways to generate fraud and market manipulation. Hackers are reportedly using AI both to distribute malware and deepfake emails targeting financial victims and to develop new types of malicious tools designed to reach and exploit a wider set of targets.

Costs. AI adoption involves significant investments in technology platforms, expenses related to system transitions and business model adjustments, and ongoing operating costs, such as licensing or service fees. For certain large-scale capital markets operations, there is often a lag between initial AI investments and the realization of revenue or productivity gains. As a result, these market participants may face financial pressures when AI spending is not immediately offset by the system’s benefits. Aside from financial impact, some stakeholders are concerned about AI’s environmental costs and the potential costs associated with the transition of the workforce that is displaced by AI.

SEC Actions

In recognition of AI’s transformative potential, the SEC launched an AI task force in August 2025 to enhance innovation in its operations and regulatory oversight. In addition, the SEC has engaged with stakeholders to discuss broader AI issues in capital markets. At an SEC AI roundtable in May 2025, the agency focused on AI-related benefits, costs, and uses; fraud and cybersecurity; and governance and risk management. 

In the June 2023 proposed rulemaking mentioned above, the SEC discussed AI use in capital markets as it sought to address certain conflicts of interest associated with broker-dealers’ or investment advisors’ use of predictive data analytics technologies. The SEC notice was withdrawn in June 2025, along with some other SEC proposed rules introduced during the previous Administration. The SEC has not indicated if AI will be addressed in future rulemaking.

Options for Congress

Some financial authorities and other stakeholders have released reports addressing AI’s capital markets use cases and policy implications. Examples of policy recommendations include to (1) evaluate the adequacy of the current securities regulation in addressing AI-related vulnerabilities; (2) enhance regulatory capabilities by incorporating AI tools into regulatory functions; (3) enhance data monitoring and data collection capabilities; and (4) adopt coordinated approaches to address critical system-wide risks, such as AI third-party provider risks and cyberattack protocols. 

In the 119th Congress, the Unleashing AI Innovation in Financial Services Act (H.R. 4801) would establish regulatory sandboxes—referred to as “AI innovation labs”—at the SEC and other financial regulators. These labs would allow AI test projects to operate with relief from certain regulations and without expectation of enforcement actions. Participating entities would have to apply and gain approval through their primary regulators and demonstrate that the projects serve the public interest, promote investor protection, and do not pose systemic risk. The AI Act of 2024 (H.R. 10262 in the 118th Congress), among other things, would have required the SEC to provide a study on both the realized and potential benefits, risks, and challenges of AI for capital market participants as well as for the agency itself. The study was to incorporate public input through a request for information process and include both regulatory proposals and legislative recommendations.

About the authors:

  • Eva Su, Specialist in Financial Economics
  • Ling Zhu, Analyst in Telecommunications Policy

Source: This article was published at the Congressional Research Service (CRS)



Source link

Continue Reading

Trending