AI Research

Meta is trying to win the AI race with money — but not everyone can be bought

Published

9 hours ago

July 10, 2025

Month after month, message after message, the AI engineer was hearing from Meta recruiters. The recruiters were pestering him to leave his employer and switch over to support the company’s AI efforts, and they were offering a sizable salary package to do so. But he wasn’t so sure.

The engineer, who works for a startup that was acquired by a leading AI company and requested anonymity from The Verge, said he had heard from friends that the company expected a lot of personal sacrifices in exchange for its high salaries, whether on employees’ value systems when it comes to Al or work-life balance. Engineers there, he heard, were working around the clock to catch up with rival companies like OpenAl, Anthropic, Google, and Microsoft.

With so many firms desperately scrambling for AI talent, Meta was offering between $1–1.4 million in total annual compensation (which is typically measured as a combination of salary, annual bonus, and amortized stock value) for many AI roles. But, he suspected, its offers might be less generous than they sounded — tied heavily to subjective performance metrics that could be weaponized against employees. And just as importantly, he wasn’t willing to give up a semblance of work-life balance and a healthy work environment to make a few hundred thousand dollars more. He didn’t pursue the opportunity.

In recent months, Meta has launched on an AI hiring spree after making its largest-ever external investment: a $14.3 billion acquisition of a 49 percent stake in Scale AI, an industry giant that provides training data to fuel the technology of companies like OpenAI, Google, Microsoft, and Meta. As part of the deal, Meta spun up a brand-new superintelligence lab led by Scale AI CEO Alexandr Wang — and to staff the lab, it started poaching.

Meta has reportedly poached as many as 10 of OpenAI’s top researchers and model developers, with some pay packages reportedly adding up to $300 million over four years, including equity. (Meta disputes this figure.) It’s also approached a slew of other top AI talent across the industry. Ruoming Pang, who heads up Apple’s foundation AI models team, reportedly departed for Meta, and at least two Anthropic employees and two DeepMind employees have reportedly joined the team as well. The goal is to secure Meta’s spot in the race to achieve artificial general intelligence, or AGI: a hypothetical AI system that equals or surpasses human cognitive abilities, and the moving target that almost every AI company is currently chasing at breakneck speed. Meta’s primary weapon is vast amounts of money. But some sources across the AI industry question whether that will be enough.

Meta, to date, has not been the most exciting destination for budding AI engineers

Meta, to date, has not been the most exciting destination for budding AI engineers. CEO Mark Zuckerberg has been trying to make up for lost ground in the AI race, having spent years and significant resources over-indexing on the metaverse while competitors like Google, Microsoft, and Amazon invested billions in AI startups and signed cloud contracts and other deals. The company’s Llama AI models often rank low on publicly maintained performance leaderboards; at time of writing, Meta’s first appearance on one such leaderboard, Chatbot Arena, was at No. 30. In May, it reportedly delayed the launch of its new flagship AI model as developers struggled to deliver performance upgrades, and executives have been public on earnings calls about the need to aggressively invest and shore up against competition. The Scale AI deal, and Meta’s subsequent sky-high budget for hiring AI talent, is Zuckerberg’s Hail Mary: paying a premium for some of the brightest minds in the AI world to safeguard Meta’s future.

But although Meta is plying AI workers with staggering salaries, a mountain of money can’t buy everyone. Anthropic and DeepMind have reportedly had far fewer defections to Meta than OpenAI has, and that’s been an ongoing trend. The reason, to those inside the field, is obvious: the AI world is filled with true believers, and even the biggest companies need more than a cash offer to get many of them on their side.

Industry insiders emphasized to The Verge that in a sector where almost any company will offer job security and a good salary, experienced AI engineers and researchers want to work somewhere that aligns with their values, whether their top priority is AI safety and the risks the tech poses for humanity’s future, the ethical considerations of AI’s impact on society today, or accelerating and advancing the tech quicker than anyone else. Some engineers, researchers, or scientists the company has approached have turned down Meta’s advances, industry sources tell The Verge.

Competition for AI researchers is stiff, and building loyalty is vital. “At this point, at least a few hundred top researchers and engineers in the field are what’s sometimes called ‘post-money’ — they could retire, and you’re only going to attract or retain them if they believe in your vision, leadership style, etc.,” one AI industry source says.

But especially at OpenAI, Meta seems to have found the dollar value of company loyalty — and exceeded it. OpenAI has been uniquely affected by Meta’s mission to poach leading AI talent. As many as 10 of its top researchers and model developers have reportedly joined Meta, with some receiving large signing bonuses and equity. While its size and talent make it an inevitable prime target, the company is also vulnerable due to a controversial restructuring from a nonprofit to for-profit venture and the departures of high-profile executives who went on to start competing AI companies. It underwent a huge upheaval during Sam Altman’s November 2023 ouster by OpenAI’s board and his subsequent Uno Reverse-style rehiring, which saw most of the board members who opposed him resign. Employees have also shared concerns about non-disparagement agreements and policies that raised questions about whether they would be able to access their equity, eroding some trust in leadership even when the policies were walked back.

“A lot of the people working on this are genuinely convinced they’re building transformative technology that will reshape the world,” one source familiar with the situation tells The Verge. “For the people that were that mission-driven, there’s already been so much organizational turbulence” — he mentioned Altman’s firing and rehiring, OpenAI employees defecting to Anthropic, and governance changes — “that people are less anchored to the institution itself, so it’s easier to poach [from OpenAI] than the other labs.”

In lieu of an official comment, OpenAI directed The Verge to a blog post from its global affairs team, which states, “Some eye-popping offers are being extended these days to a handful of terrifically talented researchers, including to folks at OpenAI. Some of these offers are coming with deadlines of just a few hours – literally ‘exploding offers’ – or with restrictions on whether or how they can be discussed.” The blog post goes on to say the company plans to cultivate talent across not just research, but also product, engineering, infrastructure, scaling, and safety. On Wednesday, the company announced it had hired four engineers away from companies like Tesla, xAI, and Meta.

Now, OpenAI’s best defense against the losses is its own financial leverage. People that joined OpenAI early on, or even before the end of 2023, have had significant stock appreciation — the unit price jumped from $67 in a May 2023 tender offer to $210 at the end of 2024, according to a source familiar with the situation. And during the end of 2023, around the time of the OpenAI board roller coaster, there was a window in which OpenAI rushed to make hires from other companies who would sign on at the $67-per-unit figure, since there was a near-immediate 2.5x multiplier expected, the source says.

With so many companies competing to hire AI talent, there have been high-level departures at many different companies. But even before this most recent hiring frenzy, OpenAI’s employees were being lured elsewhere at a higher-than-average rate.

A 2025 SignalFire report analyzed retention patterns in AI and found that Anthropic was best at keeping people around, with 80 percent of employees hired at least two years ago at Anthropic remaining at the company at the conclusion of their second year. DeepMind came next at 78 percent, while OpenAI’s retention rate was markedly lower, at 67 percent — comparable to Meta’s 64 percent. The report, which came out in May before Meta’s Scale deal occurred, also found that engineers were “8 times more likely to leave OpenAI for Anthropic than the reverse,” and 11 times more likely to defect from DeepMind to Anthropic than the reverse.

Anthropic was founded by ex-OpenAI research executives with the goal of carefully developing AI technology, describing itself as an “AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems.” The company’s “mission-driven safety focus” is a convincing recruitment pitch and a key reason for its low turnover, one source familiar with the situation tells The Verge.

“The priorities of the companies become very different, and it’s not something that they’re happy with”

“I’ve seen, amongst people who are a little bit more seasoned in industry … they have watched tech as an industry change, and they’ve watched the people in the industry become very different, and the priorities of the companies become very different, and it’s not something that they’re happy with,” says Rumman Chowdhury, a longtime leader in the field of responsible AI at companies like Accenture and Twitter, who now heads up the AI nonprofit Humane Intelligence. When she’s hiring AI engineers, she says, they often say that salary hikes are less important to them than to “not be contributing to a worse world.”

AI engineers and researchers can afford that idealism, and concerns about safety and the rush to commercialize the technology have dogged most leading AI companies. At OpenAI, for instance, months of controversy and public pressure about its upcoming transition into a for-profit entity led to the company changing its plans, ceding some control to its nonprofit arm even after the restructuring. The decision followed a public letter written by ex-employees and civic leaders to the California and Delaware attorneys general, with one former employee writing, “OpenAI may one day build technology that could get us all killed.”

There are questions, too, about the pace at which Meta is moving and its research priorities. Meta’s Scale AI investment came on the heels of Joelle Pineau’s departure as Meta VP and head of its Fundamental AI Research (FAIR) division, a unit Meta folded into its larger AI efforts after previously describing it as “one of the only groups in the world with all the prerequisites for delivering true breakthroughs with some of the brightest minds in the industry.” Some saw FAIR’s restructuring as a sign that Meta was prioritizing products over research, an industry-wide concern for some AI safety experts.

For Meta, however, there are also pragmatic questions about its future in AI. In conversations with The Verge, industry insiders questioned whether Wang is the right choice to head the new lab, since Scale AI does not build frontier models and Wang himself doesn’t have an AI research background. And even Meta executives admit that catching up will be a challenge. During its most recent earnings call in April, Zuckerberg — who called one of Meta’s focuses for 2025 “making Meta AI the leading personal AI” — touched on the competition he was up against. “The pace of progress across the industry and the opportunities ahead for us are staggering. I want to make sure that we’re working aggressively and efficiently, and I also want to make sure that we are building out the leading infrastructure and teams we need to achieve our goals,” he said.

Just two months later, that team became Meta’s superintelligence lab. But the AI engineer who steered clear doesn’t regret his decision.

“You’re expected to give pretty much your whole self to Meta AI,” he says. The money simply wasn’t good enough for that.

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AI Research

Artificial Intelligence (AI) in Healthcare Market worth

Published

2 hours ago

July 10, 2025

ABNewswire

Browse 902 market data Tables and 67 Figures spread through 711 Pages and in-depth TOC on “Artificial Intelligence (AI) in Healthcare Market by Offering (Integrated), Function (Diagnosis, Genomic, Precision Medicine, Radiation, Immunotherapy, Pharmacy, Supply Chain), Application (Clinical), End User (Hospitals), Region – Global Forecast to 2030
The global Artificial Intelligence (AI) in Healthcare Market [https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-healthcare-market-54679303.html?utm_source=abnewswire.com&utm_medium=paidpr&utm_campaign=artificialintelligenceinhealthcaremarket], valued at US$14.92 billion in 2024, is forecasted to grow at a robust CAGR of 38.6%, reaching US$21.66 billion in 2025 and an impressive US$110.61billion by 2030. The growing incidence of chronic diseases, linked with an increasing geriatric population, puts substantial financial pressure on healthcare providers. There is a rising need for the early detection of conditions such as dementia and cardiovascular disorders. This can be done by analysing imaging data to recognize patterns, which helps create personalized treatment plans.

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Browse in-depth TOC on “Artificial Intelligence (AI) in Healthcare Market”

882 – Tables

61 – Figures

738 – Pages

By tools, the Artificial Intelligence (AI) in healthcare market for machine learning has been bifurcated into deep learning, supervised learning, reinforcement learning, unsupervised learning, and other machine learning technologies. The deep learning segment accounted for the largest share of the Artificial Intelligence (AI) in healthcare market in 2024. The capability to process vast amounts of unstructured medical data, such as electronic health records (HER), imaging, and genomics, allows accurate disease diagnosis and prediction. The integration of deep learning into healthcare is significantly boosting the AI in healthcare market, leading to substantial investments in diagnostic tools and predictive analytics. As computational power and data availability continue to increase, deep learning is set to unlock further advancements, solidifying its position as a key enabler of next-generation healthcare technologies.

By end user, the AI in healthcare market is segmented into healthcare providers, healthcare payers, patients, and other end users. In 2024, healthcare providers accounted for the largest share of the AI in healthcare market. The large share of this end-user segment can be attributed to the increasing budgets of hospitals to improve the quality of care provided and reduce the cost of care.

By geography, the Artificial Intelligence (AI) in healthcare market is segmented into five main regions: North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa. The Asia Pacific region is projected to see a substantial growth rate during the forecast period. The Asia Pacific (APAC) region is experiencing substantial growth in adopting AI technologies within the healthcare sector, driven by a combination of demographic shifts, technological advancements, and increased investments in innovation. The rising elderly population in the region is a key factor, with the proportion of individuals aged 65 years and above increasing significantly. The demand for advanced healthcare solutions has surged as the aging population faces chronic and age-related conditions, necessitating efficient diagnostic, monitoring, and treatment tools. AI technologies are being integrated into various healthcare applications, including predictive analytics, telemedicine, medical imaging, and patient management systems. These innovations aim to address gaps in healthcare access, improve diagnostic accuracy, and streamline operations across the region.

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The prominent players operating in the Artificial Intelligence (AI) in healthcare market include Koninklijke Philips N.V. (Netherlands), Microsoft Corporation (US), Siemens Healthineers AG (Germany), NVIDIA Corporation (US), Epic Systems Corporation (US), GE Healthcare (US), Medtronic (US), Oracle (US), Veradigm LLC (US), Merative (IBM) (US), Google (US), Cognizant (US), Johnson & Johnson (US), Amazon Web Services, Inc. (US), among others. These companies adopted strategies such as product launches, product updates, expansions, partnerships, collaborations, mergers, and acquisitions to strengthen their market presence in the Artificial Intelligence (AI) in healthcare market.

Koninklijke Philips N.V. (Netherlands)

Koninklijke Philips N.V. is a leading player in the AI in the healthcare market. The company utilizes AI to deliver innovative tools across various areas, including diagnostic imaging, patient monitoring, and precision medicine. Its advanced AI-driven platforms, such as the Philips HealthSuite, facilitate the integration and analysis of extensive clinical data, which supports personalized treatment plans and improves patient outcomes. Philips focuses on organic and inorganic growth strategies to expand its market presence.

Strategic partnerships in high-potential markets and collaborations have been the key growth strategies of the company over the years. For example, in February 2025, Philips partnered with Medtronic to educate and train cardiologists and radiologists in India on advanced imaging techniques for structural heart diseases. This partnership aims to upskill 300+ clinicians in multi-modality imaging such as echocardiography (echo) and Magnetic Resonance Imaging (MRI), especially for End-Stage Renal Disease (ESRD) patients. In November 2023, Philips and NYU Langone Health partnered to focus on patient safety and outcomes. This partnership integrated innovative health technologies, including digital pathology, clinical informatics, and AI-enabled diagnostics, enabling real-time collaboration among clinicians. The company also focuses on winning contracts across several companies in the healthcare space. This helps the company expand its footprint. For instance, in September 2022, Philips and Mandaya Royal Hospital Puri (MRHP) in Jakarta underwent a digital transformation in a strategic partnership, enhancing patient-centered care and healthcare services.

Microsoft Corporation (US):

Microsoft Corporation is one of the leading providers of software & tools that include advanced AI capabilities in healthcare to improve patient outcomes, streamline operations, and drive innovation. Its Azure-based AI solutions support distinct applications such as medical imaging, genomics, and precision medicine. The company also provides healthcare-specific AI models through its Azure AI Model Catalog, which is constructed to support hospitals and research institutions in building and deploying tailored AI solutions proficiently. Moreover, the integration of Nuance’s AI-powered clinical and diagnostic tools encourages its capacity to support healthcare providers in decision-making and care delivery. The company continuously brings AI capabilities to the platforms in large-scale customer models. For instance, in March 2025, the company launched Microsoft Dragon Copilot, the first unified voice AI assistant in the healthcare industry that enables clinicians to streamline clinical documentation, surface information, and automate tasks.

Microsoft Corporation has invested significantly in R&D, which has improved its product portfolio and position in the AI market. Machine Learning (ML), deep learning, Natural Language Processing (NLP), and speech processing are the key focus areas of the company in the AI in healthcare market. The company continuously invests in a series of services and computational biology projects, including research support tools for next-generation precision healthcare, genomics, immunomics, CRISPR, and cellular and molecular biologics. It has a strong global presence, with key operations supported through its Azure cloud infrastructure across regions like North America, Europe, Asia-Pacific, and the Middle East.

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LLM-Optimized Research Paper Formats: AI-Driven Research App Opportunities Explored | AI News Detail

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2 hours ago

July 10, 2025

blockchain.news

The concept of shifting attention from human-centric to Large Language Model (LLM) attention, as highlighted by Andrej Karpathy in a tweet on July 10, 2025, opens a fascinating discussion about the future of research and information consumption in the AI era. Karpathy, a prominent figure in AI and former director of AI at Tesla, posits that 99% of attention may soon be directed toward LLMs rather than humans, raising the question: what does a research paper look like when designed for an LLM instead of a human reader? This idea challenges traditional formats like PDFs, which are static and optimized for human cognition with visual layouts and narrative structures. Instead, LLMs require data-rich, structured, and machine-readable formats that prioritize efficiency, context, and interoperability. This shift could revolutionize industries such as academia, tech development, and business intelligence by enabling faster knowledge synthesis and application. As of 2025, with AI adoption accelerating—Gartner reported in early 2025 that 80% of enterprises are piloting or deploying generative AI tools—the need for LLM-optimized content is becoming critical. This trend reflects a broader transformation in how information is created, consumed, and monetized in an AI-driven world, with significant implications for content creators and tech innovators.

From a business perspective, the idea of designing research for LLMs presents immense market opportunities. Companies that develop platforms or apps to create, curate, and deliver LLM-friendly research content could tap into a multi-billion-dollar market. According to a 2025 report by McKinsey, the generative AI market is projected to grow to $1.3 trillion by 2032, with content generation and data processing as key drivers. A ‘research app’ for LLMs, as Karpathy suggests, could serve industries like pharmaceuticals, where AI models analyze vast datasets for drug discovery, or finance, where real-time market insights are critical. Monetization strategies could include subscription models for premium datasets, API access for developers, or enterprise solutions for tailored LLM training data. However, challenges remain, such as ensuring data privacy and preventing bias in LLM outputs—issues that have plagued AI systems, as noted in a 2025 study by the MIT Sloan School of Management, which found that 60% of AI deployments faced ethical concerns. Businesses must also navigate a competitive landscape with players like Google, OpenAI, and Anthropic already dominating LLM development, requiring niche specialization to stand out.

On the technical side, designing research for LLMs involves moving beyond PDFs to formats like JSON, XML, or custom data schemas that encode information hierarchically for machine parsing. Unlike human readers, LLMs thrive on structured datasets with metadata, embeddings, and cross-references that enable rapid context retrieval and reasoning. Implementation challenges include standardizing formats across industries and ensuring compatibility with diverse LLM architectures—a hurdle given that, as of mid-2025, over 200 distinct LLM frameworks exist, per a report from the AI Index by Stanford University. Solutions could involve open-source protocols or industry consortia to define standards, much like the web evolved with HTML. Looking to the future, LLM-optimized research could lead to autonomous AI agents conducting real-time literature reviews or hypothesis generation by 2030, as predicted by a 2025 forecast from Deloitte. Regulatory considerations are also critical, with the EU AI Act of 2025 mandating transparency in AI data usage, which could impact how research content is structured. Ethically, ensuring that LLMs do not misinterpret or propagate flawed data remains a priority, requiring robust validation mechanisms. The potential for such innovation is vast, offering a glimpse into a future where knowledge creation is as much for machines as for humans, reshaping industries and workflows profoundly.

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AI Research

Digital Agency Fuel Online Launches AI SEO Research Division,

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4 hours ago

July 10, 2025

ABNewswire

Boston, MA – As Google continues to reshape the digital landscape with its Search Generative Experience (SGE) and AI-powered search results, Fuel Online [https://fuelonline.com/] is blazing a trail as the nation’s leading agency in AI SEO [https://fuelonline.com/]and SGE optimization [https://fuelonline.com/].

Recognizing the urgent need for businesses to adapt to AI-first search engines, Fuel Online has launched a dedicated AI SEO Research & Development Division focused exclusively on decoding how AI models like Google SGE read, rank, and render web content. The division’s mission: to test, reverse-engineer, and deploy cutting-edge strategies that future-proof clients’ visibility in an era of AI-generated search answers.

“AI is not the future of SEO – it’s the present . If your content doesn’t rank in SGE, it may never be seen. That’s why we’re investing heavily in understanding and optimizing for how large language models surface content,” said Scott Levy, CEO of Fuel Online Digital Marketing Agency [https://fuelonline.com/].

Fuel Online’s Digital Marketing team is already helping Fortune 500 brands, high-growth startups, and ecommerce leaders gain traction in AI-powered results using proprietary tactics including:

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As detailed in their comprehensive Google SGE & AI Optimization Guide [https://fuelonline.com/insights/google-sge-and-ai-optimization-guide-how-to-optimize/], Fuel Online offers strategic insight into aligning websites with Google’s new generative layer. The agency also provides live testing environments, allowing clients to see firsthand how AI engines interpret their content. Why This Matters: According to industry data, click-through rates have dropped by up to 60% on some keywords since the rollout of SGE, as users get direct AI-generated answers instead of traditional blue links. Fuel Online’s AI SEO division helps clients reclaim that lost visibility and win placement inside AI search results. With over two decades of award-winning digital strategy under its belt and a reputation as one of the top digital marketing agencies in the U.S., Fuel Online is once again setting the standard – this time for the AI optimization era.

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