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

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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



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How AI Is Transforming Disease Research and Drug Discovery

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What if the cure for cancer, Alzheimer’s, or genetic disorders was hidden in plain sight, buried within mountains of data too vast for any human to process? In an era where scientific progress is often limited by the sheer volume of information, artificial intelligence is stepping in as a fantastic option. Enter Sam Rodriques, a scientist at the forefront of this revolution, whose work explores how AI can transform disease research. In this thought-provoking exchange with Freethink, Rodriques sheds light on the innovative tools reshaping medicine, from multi-agent AI systems to new applications in drug discovery. Could AI not only accelerate research but also redefine how we approach the most complex biological puzzles?

Below Freethink uncover how AI is addressing the limitations of human cognition, automating labor-intensive processes, and fostering collaboration across disciplines. Rodriques offers a rare glimpse into the development of specialized AI agents like Crow and Phoenix, each designed to tackle specific stages of research, from synthesizing literature to planning experiments. But this isn’t just about technology; it’s about the human ingenuity guiding these tools and the ethical questions they raise. Whether you’re curious about the future of medicine or the role of AI in shaping it, this dialogue promises to challenge assumptions and inspire new ways of thinking about scientific discovery. What happens when machines and minds work together to unlock the secrets of life itself?

AI Transforming Scientific Research

TL;DR Key Takeaways :

  • AI is transforming scientific research by automating complex tasks, generating data-driven hypotheses, and integrating knowledge across disciplines, particularly in biology and medicine.
  • Multi-agent AI systems, such as Crow, Falcon, Finch, Owl, and Phoenix, collaborate to streamline workflows, enhance precision, and accelerate research processes.
  • AI-driven research emphasizes transparency and traceability, making sure findings are grounded in empirical data and fostering trust within the scientific community.
  • Real-world applications, such as AI-generated hypotheses for treating diseases like age-related macular degeneration, demonstrate AI’s potential to bridge theoretical insights and practical outcomes.
  • While AI offers fantastic potential, it requires human oversight to address challenges like ethical considerations, data limitations, and context-dependent scenarios, making sure responsible and effective use in research.

The Growing Need for AI in Science

Modern research generates an overwhelming volume of data, making it increasingly challenging for researchers to synthesize information and extract actionable insights. AI offers a powerful solution by automating repetitive tasks such as literature reviews, data analysis, and hypothesis generation. These tools are not designed to replace human expertise but to complement it, allowing researchers to explore scientific questions more efficiently and comprehensively.

For example, AI can integrate findings from diverse disciplines to propose innovative approaches to treating diseases or understanding complex biological systems. This capability is particularly valuable in addressing challenges such as drug discovery, where identifying potential compounds and predicting their effects require analyzing massive datasets. Similarly, AI is instrumental in unraveling the intricacies of genetic disorders, where patterns in genomic data may hold the key to new treatments.

Multi-Agent AI Systems: A Collaborative Approach

One of the most promising advancements in AI-driven research is the development of multi-agent systems. These platforms consist of specialized AI agents, each designed to excel in a specific task, working together to automate complex workflows. By delegating tasks among these agents, researchers can achieve faster and more accurate results. Key examples of these agents include:

  • Crow: A general-purpose agent that synthesizes literature-informed science, providing a broad foundation for research.
  • Falcon: Specializes in conducting deep literature searches and performing meta-analyses to uncover hidden connections.
  • Finch: Focused on data analysis and hypothesis testing, making sure that conclusions are grounded in robust evidence.
  • Owl: Conducts precedent searches to evaluate the novelty and feasibility of new ideas.
  • Phoenix: Excels in experimental planning, particularly in chemistry, by designing experiments that maximize efficiency and accuracy.

These agents operate collaboratively, with each contributing its expertise to different stages of the research process. For instance, one agent might analyze existing literature to identify gaps in knowledge, while another designs experiments to address those gaps. This division of labor not only accelerates the research process but also enhances the precision and reliability of the outcomes.

Sam Rodriques on AI’s Potential to Cure Cancer and Alzheimer’s

Gain further expertise in Artificial Intelligence in Science by checking out these recommendations.

Transparency and Traceability in AI-Driven Research

In scientific research, transparency and traceability are critical for making sure trust and reliability. AI systems address these requirements by providing detailed reasoning, citations, and traceable workflows. As a researcher, you can review the evidence and logic behind AI-generated conclusions, making sure that findings are grounded in empirical data and aligned with established scientific principles.

This level of transparency reduces the risk of errors and enhances confidence in AI-driven discoveries. It also allows researchers to scrutinize and validate AI outputs, maintaining the rigor of the scientific process even as automation takes on a larger role. By allowing traceability, AI systems ensure that every step of the research process can be reviewed and replicated, fostering accountability and trust within the scientific community.

Real-World Applications and Success Stories

AI is already demonstrating its potential to drive tangible advancements in scientific research. One notable example is the use of AI to propose a novel hypothesis involving the application of ROCK inhibitors for treating age-related macular degeneration (AMD). This hypothesis, generated through AI analysis, was subsequently tested in wet lab experiments, bridging the gap between theoretical insights and practical applications.

Such success stories highlight the ability of AI to accelerate the pace of discovery by identifying promising research directions that might otherwise go unnoticed. By integrating AI with laboratory work, researchers can streamline the transition from hypothesis generation to experimental validation, ultimately reducing the time required to achieve meaningful results.

Challenges and Limitations of AI in Research

Despite its fantastic potential, AI is not a universal solution to all scientific challenges. Certain bottlenecks, such as the time required for clinical trials or the ethical considerations surrounding experimental research, cannot be resolved by AI alone. Additionally, AI systems may encounter difficulties in scenarios where data is limited, ambiguous, or highly context-dependent, necessitating human judgment and expertise.

Your role as a researcher remains indispensable in guiding AI systems, interpreting their outputs, and making informed decisions. While AI can automate many aspects of the research process, it still relies on human oversight to ensure that its conclusions are accurate, relevant, and aligned with broader scientific goals.

Open Science and Collaborative Innovation

The development of AI in science aligns closely with the principles of open science and collaboration. Open source tools provide widespread access to access to advanced technologies, allowing researchers from diverse backgrounds and institutions to contribute to and benefit from AI-driven discoveries. However, balancing the ideals of open science with the need for intellectual property protection, particularly in fields like biotechnology, remains a complex challenge.

By fostering collaboration while respecting commercial interests, the scientific community can maximize the impact of AI on research. Open science initiatives also promote transparency, allowing researchers to build on each other’s work and accelerate progress. This collaborative approach ensures that the benefits of AI are distributed widely, driving innovation across disciplines and regions.

Shaping the Future of Scientific Discovery

The ultimate vision for AI in research is the creation of a fully integrated virtual laboratory where AI agents collaborate seamlessly to automate complex workflows. Such a system could transform science by eliminating intelligence bottlenecks and allowing faster, more informed discoveries. As AI continues to evolve, its role in hypothesis generation, experimental planning, and data analysis will expand, offering new opportunities to address pressing challenges such as curing diseases, combating climate change, and extending human lifespan.

By embracing the potential of AI while addressing its limitations, researchers can harness this technology to push the boundaries of what is possible in science. The integration of AI into research holds immense promise for tackling some of humanity’s most critical issues, paving the way for a future where scientific discovery is faster, more efficient, and more impactful than ever before.

Media Credit: Freethink

Filed Under: AI, Top News





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Man leaves Meta to start his own company, now offering ₹17 crore job to…

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Man leaves Meta to start his own company, now offering ₹17 crore job to… | Hindustan Times (HT Tech)

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Tampere University GPT-Lab hiring doctoral researchers in generative AI

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Tampere University has announced that GPT-Lab, part of its Computing Sciences Unit, is hiring three to five doctoral researchers in generative AI and software engineering.

The lab works across artificial intelligence, software engineering, and human-computer interaction, combining research and education in Finland and internationally.

The openings were shared in a LinkedIn post by GPT-Lab, which stated: “GPT-Lab (Tampere University) is looking for Doctoral Researchers in Generative AI & Software Engineering to join our team.”

Qualifications highlight AI expertise and development skills

Candidates must hold a master’s degree in computer science, software engineering, data science, artificial intelligence, or a related field. Students close to finishing a master’s by December 2025 may also apply.

The lab says applicants must demonstrate strong written and spoken English. Preferred qualifications include peer-reviewed publications in AI or software engineering, experience in academic or industrial software development, and familiarity with frameworks such as PyTorch, TensorFlow, or Hugging Face.

The recruitment process involves four stages: screening, a video submission, a technical task, and a final interview. Successful candidates must also apply separately for doctoral study rights at Tampere University, as the employment and study admissions are distinct.

Applications must be submitted through Tampere University’s portal by October 3, 2025, at 23:59 Finnish time. Positions are for four years, with a starting salary of €2,714 per month under the Finnish University Salary System.

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