Meta is looking to up its weakening AI game with a key talent grab. Following days of speculation, the social media giant has confirmed that Scale AI’s founder and CEO, Alexandr Wang, is joining Meta to work on its AI efforts. Meta will invest $14.3 billion in Scale AI as part of the deal, and will have a 49% stake in the AI startup, which specializes in data labeling and model evaluation services. Other key Scale employees will also move over to Meta, while CSO Jason Droege will step in as Scale’s interim CEO. This move comes as the Mark Zuckerberg-led company goes all-in on building a new research lab focused on “superintelligence,” the next step beyond artificial general intelligence (AGI). The arrangement also reflects a growing trend in big tech, where industry giants are buying companies without really buying them — what’s increasingly being referred to as “acqui-hiring.” It involves recruiting key personnel from a company, licensing its technology, and selling its products, but leaving it as a private entity. “This is fundamentally a massive ‘acqui-hire’ play disguised as a strategic investment,” said Wyatt Mayham, lead AI consultant at Northwest AI Consulting. “While Meta gets Scale’s data infrastructure, the real prize is Wang joining Meta to lead their superintelligence lab. At the $14.3 billion price tag, this might be the most expensive individual talent acquisition in tech history.” Closing gaps with competitors Meta has struggled to keep up with OpenAI, Anthropic, and other key competitors in the AI race, recently even delaying the launch of its new flagship model, Behemoth, purportedly due to internal concerns about its performance. It has also seen the departure of several of its top researchers.  “It’s not really a secret at this point that Meta’s Llama 4 models have had significant performance issues,” Mayham said. “Zuck is essentially betting that Wang’s track record building AI infrastructure can solve Meta’s alignment and model quality problems faster than internal development.” And, he added, Scale’s enterprise-grade human feedback loops are exactly what Meta’s Llama models need to compete with ChatGPT and Claude on reliability and task-following. Data quality, a key focus for Wang, is a big factor in solving those performance problems. He wrote in a note to Scale employees on Thursday, later posted on X (formerly Twitter), that when he founded Scale AI in 2016 amidst some of the early AI breakthroughs, “it was clear even then that data was the lifeblood of AI systems, and that was the inspiration behind starting Scale.” But despite Meta’s huge investment, Scale AI is underscoring its commitment to sovereignty: “Scale remains an independent leader in AI, committed to providing industry-leading AI solutions and safeguarding customer data,” the company wrote in a blog post. “Scale will continue to partner with leading AI labs, multinational enterprises, and governments to deliver expert data and technology solutions through every phase of AI’s evolution.” Allowing big tech to side-step notification But while it’s only just been inked, the high-profile deal is already raising some eyebrows. According to experts, arrangements like these allow tech companies to acquire top talent and key technologies in a side-stepping manner, thus avoiding regulatory notification requirements. The US Federal Trade Commission (FTC) requires mergers and acquisitions totaling more than $126 million be reported in advance. Licensing deals or the mass hiring-away of a company’s employees don’t have this requirement. This allows companies to move more quickly, as they don’t have to undergo the lengthy federal review process. Microsoft’s deal with Inflection AI is probably one of the highest-profile examples of the “acqui-hiring” trend. In March 2024, the tech giant paid the startup $650 million in licensing fees and hired much of its team, including co-founders Mustafa Suleyman (now CEO of Microsoft AI) and Karén Simonyan (chief scientist of Microsoft AI). Similarly, last year Amazon hired more than 50% of Adept AI’s key personnel, including its CEO, to focus on AGI. Google also inked a licensing agreement with Character AI and hired a majority of its founders and researchers. However, regulators have caught on, with the FTC launching inquiries into both the Microsoft-Inflection and Amazon-Adept deals, and the US Justice Department (DOJ) analyzing Google-Character AI. Reflecting ‘desperation’ in the AI industry Meta’s decision to go forward with this arrangement anyway, despite that dicey backdrop, seems to indicate how anxious the company is to keep up in the AI race. “The most interesting piece of this all is the timing,” said Mayham. “It reflects broader industry desperation. Tech giants are increasingly buying parts of promising AI startups to secure key talent without acquiring full companies, following similar patterns with Microsoft-Inflection and Google-Character AI.” However, the regulatory risks are “real but nuanced,” he noted. Meta’s acquisition could face scrutiny from antitrust regulators, particularly as the company is involved in an ongoing FTC lawsuit over its Instagram and WhatsApp acquisitions. While the 49% ownership position appears designed to avoid triggering automatic thresholds, US regulatory bodies like the FTC and DOJ can review minority stake acquisitions under the Clayton Antitrust Act if they seem to threaten competition. Perhaps more importantly, Meta is not considered a leader in AGI development and is trailing OpenAI, Anthropic, and Google, meaning regulators may not consider the deal all that concerning (yet). All told, the arrangement certainly signals Meta’s recognition that the AI race has shifted from a compute and model size competition to a data quality and alignment battle, Mayham noted. “I think the [gist] of this is that Zuck’s biggest bet is that talent and data infrastructure matter more than raw compute power in the AI race,” he said. “The regulatory risk is manageable given Meta’s trailing position, but the acqui-hire premium shows how expensive top AI talent has become.”

John Edwards, Technology Journalist & AuthorMay 14, 20255 Min ReadNicoElNino via Alamy Stock PhotoWhen it comes to AI, yesterday's comprehensive skillset may already be obsolete. Considering AI's ultra-rapid evolution, it's now important to help team members understand and utilize the latest skills without disrupting their already heavy workloads. A mix of hands-on experience and guided learning is the best way to build AI skills, advises Justice Erolin, CTO at software outsourcing company BairesDev. "Traditional education isn't keeping up with the rapid evolution of AI, so microlearning and peer learning programs can be more effective than a university course on AI," he says in an email interview. You no longer need AI experts; you need people who know their way around AI, says Mike Loukides, a vice president at technology and business training firm O'Reilly Media. "With appropriate training, the types of people you need can be developed from the staff you already have," he explains in an online interview. "Look for data engineers -- people who know how to build data pipelines, who know how to analyze data." Keeping Pace The AI landscape changes monthly, observes David Brauchler, technical director and head of AI and ML at cybersecurity consultancy NCC Group. To stay up to date on the latest advances, engineering teams should establish a culture of knowledge sharing and collaboration, he advises in an email interview. "AI improvement is a continual process, not one that occurs piecewise." Related:It's a continual process, Loukides says. "Even if you manage to hire the perfect team of outsiders, their skills will start to go out of date immediately," he observes. "Continuous learning was never more important than it is now." Dive into hands-on projects using pre-built examples with data and evaluation techniques that are readily accessible, recommends Vamsi Duvvuri, technology, media, and telecommunications leader at business advisory firm at EY Americas. "Collaborating with diverse teams across the organization during this stage is critical, especially folks from teams that typically do not work with each other," he explains in an online discussion. A mix of perspectives can reveal blind spots and spark ideas. Consider bringing in challenger hires or even exploring acquihires, Duvvuri suggests. "Bringing in new talent who aren’t entrenched in the status quo can disrupt traditional thinking and introduce fresh, unexpected approaches." Essential Skills New team members often challenge existing processes and help spark innovation, Duvvuri says. "In some cases, if you spot an innovative startup or a talented team, acquiring them -- or even doing an acquihire -- can rapidly inject cutting-edge expertise into your organization," he notes. "This isn't just about filling a gap, it's about shaking up the norm and accelerating learning." Related:Learning how to collaborate with AI is becoming a skill in itself, Erolin observes. "Beyond technical expertise, soft-skill development will help hone the ability to collaborate with AI." Critical thinking, problem-solving, and business knowledge are key to understanding when it makes sense to use AI. A handful of universal skills stand out, Erolin says. These include: Prompt engineering for working with generative AI models. AI model evaluation and fine-tuning to ensure that models align with business goals. AI governance and ethics, especially around bias, privacy, and explainability. MLOps skills to integrate AI into production reliably and at scale. Frameworks such as TensorFlow, PyTorch, or LangChain. Avoiding Mistakes A trap many IT leaders fall into is treating AI training as a one-off event, such as holding a single course or workshop. "If you skip the continuous cycle of learning, reflection, and even disruptive talent acquisition, you risk becoming stagnant and falling behind," Duvvuri warns. "In contrast, a culture that embraces ongoing education, diverse team dynamics, and bold moves, like challenger hires, fosters innovation and resilience." Related:Application architects and developers should consider the intricacies AI introduces to application threat models and skill-up in low-sensitivity environments, Brauchler suggests. "For example, AI presents new concerns related to data risk, including its inability to reliably distinguish between trusted and untrusted content," he says. "Consequently, application designers need to consider new risks that they might not be used to addressing in traditional software stacks." A Final Thought The companies that thrive in the AI era won't be the ones with the fanciest models, but the ones with the most adaptable teams, Erolin says. "The real differentiator is your people's ability to learn, unlearn, and relearn fast." At BairesDev, we've built our success around that belief, Erolin states. "We scale AI talent globally, but we also help our partners build internal cultures that keep pace with innovation. In this space, the only real risk is standing still."About the AuthorJohn EdwardsTechnology Journalist & AuthorJohn Edwards is a veteran business technology journalist. His work has appeared in The New York Times, The Washington Post, and numerous business and technology publications, including Computerworld, CFO Magazine, IBM Data Management Magazine, RFID Journal, and Electronic Design. He has also written columns for The Economist's Business Intelligence Unit and PricewaterhouseCoopers' Communications Direct. John has authored several books on business technology topics. His work began appearing online as early as 1983. Throughout the 1980s and 90s, he wrote daily news and feature articles for both the CompuServe and Prodigy online services. His "Behind the Screens" commentaries made him the world's first known professional blogger.See more from John EdwardsWebinarsMore WebinarsReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like