Meta’s blockbuster $14.3 billion deal to install Scale AI’s founder as its new superintelligence chief was a strategic pivot, executed only after the company failed to acquire generative video startup Runway. Meta approached Runway with a takeover offer that the AI video firm rejected, choosing to remain independent. This previously unreported advance reveals a clear and aggressive playbook at the heart of Meta’s AI ambitions: attempt to buy key players outright, and when that fails, pivot to costly “acqui-hires” to secure their leadership.
The failed Runway bid is the latest and most crucial piece of a pattern that exposes the depth of the pressure on CEO Mark Zuckerberg. This “buy or poach” approach is not just a strategy but a symptom of a company grappling with significant internal development hurdles and intense competitive pressure. It demonstrates a willingness to spend billions to plug innovation gaps, a tactic that is reshaping competitive alliances and creating new crises across Silicon Valley.
This pattern of behavior has become Meta’s new modus operandi. Before the Runway approach, the company held takeover discussions with AI-native search engine Perplexity and was unable to acquire the $32 billion startup Safe Superintelligence (SSI) after a personal rebuff from co-founder Ilya Sutskever. These rejections forced Meta to radically pivot its artificial intelligence strategy, settling for the next best thing: hiring away the leadership of the companies it couldn’t buy.
A Pattern of Failed Bids and Costly ‘Acqui-Hires’
Meta’s recent moves represent a significant escalation from its historical M&A activity. While the company has a long-stated philosophy that “we buy companies to get excellent people”, its acquisitions have traditionally been smaller, with the multi-billion-dollar deals for WhatsApp and Instagram being notable exceptions. Now, nine- and ten-figure deals are becoming the standard tool in its AI arsenal.
Following the failed bid for SSI, Meta executed a stunning escalation of its campaign by now reportedly aiming to hire its co-founder and CEO, Daniel Gross, along with his partner, former GitHub CEO Nat Friedman. According to The Information, the planned multi-year deal also involves Meta acquiring a stake in their venture capital firm. This was the same playbook Meta used with Scale AI. The massive investment was ultimately a vehicle to bring its founder, Alexandr Wang, in-house to lead a new superintelligence lab, a move one analyst called an investment “not to even buy a whole company but just to have the head of a company head up your AI effort.”
The High Cost of Catching Up
This audacious spending is a direct response to a firestorm of internal challenges. The company has been hemorrhaging talent, losing 11 of the 14 original authors of its foundational Llama research paper. This exodus is reflected in an AI talent attrition rate that was reportedly worse than rivals like OpenAI and Anthropic in 2024, highlighting a potential cultural issue that money alone can’t fix.
The talent drain has been compounded by significant technical setbacks, including the postponement of its most ambitious model, Llama 4 “Behemoth”, and internal reports of a “panic mode” among AI teams. In response, Meta is directing the majority of its planned $64-72 billion in 2025 capital expenditures toward AI infrastructure. The pressure has also fueled an intense talent war, with OpenAI CEO Sam Altman publicly accusing Meta of offering signing bonuses as high as $100 million. Altman noted that while the offers involved “a ton of upfront guaranteed comp”, the strategy isn’t resonating with those focused on mission.
These pressures are magnified by mounting legal headwinds, including a new academic study that found Meta’s Llama 3.1 model had memorized copyrighted material, strengthening lawsuits alleging the company used pirated data for training.
The ‘Neutrality’ Crisis
While Meta’s strategy may be solving its internal talent gap, it has created a seismic crisis for its new partners. The colossal $14 billion investment in Scale AI immediately compromised the very neutrality that made it a go-to data-labeling partner for Meta’s biggest rivals. The fallout was swift, as Google, Scale’s largest customer, began planning to sever a contract worth up to $200 million.
The damage did not stop there. The deal prompted a quiet but significant client exodus, with Microsoft, Elon Musk’s xAI, and OpenAI all reconsidering their partnerships. OpenAI confirmed to CNBC that it had already been winding down its work with Scale for months. This has created a windfall for rivals like Mercor. The situation forced Scale AI’s new interim CEO to issue a letter to customers and employees insisting the company remains independent, but the market has already shifted. As Turing CEO Jonathan Siddharth stated, “neutrality is no longer optional, it’s essential.”
Runway’s Rising Profile in the Video Arms Race
Runway im March introduced its Gen-4 AI video generation model, which offers powerful cinematic controls that aim to reduce manual editing for creators. The platform competes directly with OpenAI’s Sora and Google’s Veo, making it a highly valuable asset.
Crucially, Runway’s technology brings consistency to AI video generation, a key differentiator from its rivals. The company explained that Gen-4 achieves this by allowing creators to use visual references combined with text instructions to generate new content with consistent styles and subjects.
This ability to maintain character and scene integrity is a critical feature for professional filmmaking and narrative storytelling, which is precisely why a company like Meta, with its deep interest in content creation, would see Runway as a prime acquisition target. The rejection forced Meta back to the drawing board, leaving its long-term video generation strategy an open question.
Ultimately, Meta’s “buy or poach” strategy is a high-risk, high-reward response to its internal struggles. The failed Runway bid proves that even a massive war chest cannot always secure the desired assets, forcing the company into controversial deals that create new, unforeseen problems. While some suggests this is a deliberate play to build a defensible AI ecosystem, the chaotic fallout indicates a company trading one crisis for another. The coming months will reveal whether this costly gambit can fix the deep-seated developmental and cultural issues plaguing its AI ambitions or if it has simply purchased a new set of challenges.
