Meta's $14.3B Scale AI deal unravels. Learn how to train smarter, not harder.

Meta spent $14.3 billion on Scale AI in June. Two months later, executives are fleeing and Meta's own researchers call Scale's data "low quality." This isn't a partnership failing. It's Zuck's panic-buying his way out of an AI crisis. After Llama 4 flopped in April, Zuckerberg went on a desperate shopping spree - acquiring startups, poaching OpenAI talent, throwing billions at Scale AI. Classic move of a CEO who knows he's losing. Here's the inconvenient truth: OpenAI and Google STOPPED working with Scale AI right after Meta's investment. They saw something Meta didn't. Now Meta's AI team is "increasingly chaotic," top researchers are leaving, and they're quietly working with Scale's competitors instead of their $14 billion investment. Alexandr Wang got the deal of a lifetime. Meta got played. When you're behind in AI and throwing money at problems instead of solving them, you end up like this - $14 billion poorer with the same problems you started with. The biggest risk isn't "not taking any risk." It's taking desperate risks with shareholder money because you're too proud to admit you're behind.

Mark Zuckerberg hacked and stole 98%. I’m surprised the USA Government has not taken a more active role in replacing leadership of Facebook/Meta Platforms, Inc. 2025 Meta shareholders sue Zuckerberg, Thiel and Sandberg for $8bn over FTC fines An $8bn trial brought by Meta Platforms shareholders against Mark Zuckerberg and other current and former company leaders with the US Federal Trade Commission (FTC). The trial started with a privacy expert for the plaintiffs, Neil Richards of Washington University School of Law, who testified about Facebook’s data policies.

Meta spent $14.3 billion on Scale AI AI in June. Two months later, executives are fleeing and Meta’s own researchers are calling Scale’s data “low quality.” This isn’t a partnership failing it’s Zuck panic-buying his way out of an AI crisis. After Llama 4 flopped in April, Zuckerberg went on a desperate shopping spree: Buying startups Poaching OpenAI talent Throwing billions at Scale AI Classic move from a CEO who knows he’s losing. Here’s the inconvenient truth: OpenAI and Google walked away from Scale AI right after Meta’s investment. They saw something Zuck didn’t. Now Meta’s AI team is chaotic, top researchers are leaving, and the company is quietly working with Scale’s competitors instead of its $14B bet. Alexandr Wang got the deal of a lifetime. Meta got played. And this isn’t new. We’ve seen it before: Metaverse NFTs Libra (crypto) Llama 4 Now Scale AI AI Every time it’s billions wasted, no lasting results. The biggest risk isn’t “not taking any risk.” It’s taking desperate risks with shareholder money because you’re too proud to admit you’re behind. #TechIndustry #AI #BigTech #OpenAI #Google #Meta #TechTrends #Innovation #FutureofTech #BusinessStrategy

Meta is quietly backpedaling on its open-source AI promise. Behemoth—the company’s next-gen model—was trained months ago and was supposed to follow the Llama line of "open" releases. Instead, the launch has been repeatedly delayed, and reports now suggest it may never see the light of day. The reason is not safety, as Mark Zuckerberg now claims, but performance concerns and internal friction. Per the New York Times and other reports, Meta’s new Chief AI Officer Alexandr Wang is pushing for a closed-model strategy. Zuck is spinning this pivot as a "superintelligence" issue, arguing that risks justify closing off the model: "Superintelligence will raise novel safety concerns. We’ll need to be rigorous about mitigating these risks and careful about what we choose to open source." But the reality is simple: there is no superintelligence. Behemoth wasn’t being held back for safety—it was being held back because it wasn’t good enough. Meanwhile, Zuckerberg has hired a league of AI stars, lured by both the millions on offer and the "superintelligence" buzzword. The shock came when they discovered the real work: not building superintelligence, but scrambling to catch up with DeepSeek, a Chinese startup that briefly shot to fame before fizzling out. Rishabh Agarwal is one example—joining with the promise of frontier AI, only to face the reality of chasing a competitor’s shadow. And then there’s Yann LeCun. Once the loudest global evangelist for open source, he has gone quiet. On Wang’s vision to ditch open source, he has said nothing publicly—but it’s not hard to imagine he may be fuming privately. After all, Yann spent years touring the world promoting open source, only to see Meta retreat from the very stance he championed. The result? Meta has gone from open-source champion to cautious gatekeeper—with "safety" serving more as spin than substance. #ArtificialIntelligence #AI #OpenSource #AISafety #Meta #LLM #AIDebate

Meta CEO Mark Zuckerberg said he would rather “misspend a couple hundred billion dollars” than risk falling behind in the race toward artificial superintelligence, admitting that an AI bubble is “quite possible.” Zuckerberg defended Meta’s plan to invest at least $600 billion in U.S. data centers and infrastructure through 2028, framing the outlay as insurance against missing what he calls history’s most transformative technology. Meta’s superintelligence lab—home to just 50 to 100 researchers—runs on a “very flat” structure with no top-down deadlines, luring top talent with multimillion-dollar offers that rival OpenAI and Google DeepMind. His comments echo rising concerns about an AI investment bubble: an MIT study in August found 95% of AI pilot projects fail to deliver returns, despite over $40 billion already poured into the sector. In 2025, major U.S. tech companies are expected to spend over $155 billion on AI development, with the current market valued at about $244.2 billion (Statista).

Meta Could Have Crushed Midjourney. Here's Why They Chose to Partner Instead. Meta's new licensing deal with Midjourney signals a major shift in how tech giants approach AI development. Instead of trying to build competing image generation capabilities in-house, Meta chose to partner with the established leader. This represents a broader trend worth noting for federal leaders. Why This Matters: 🔹 Competition is giving way to collaboration: Even Meta—with unlimited resources and top AI talent—recognized that partnering beats trying to replicate Midjourney's specialized expertise. 🔹 Focus on core strengths: Rather than spread resources thin across every AI capability, Meta is doubling down on what it does best (scale, distribution, infrastructure) while licensing specialized technologies from partners. 🔹 Speed over pride: This partnership likely gets Meta advanced image generation capabilities years faster than building from scratch. In today's AI race, speed often trumps "not invented here" syndrome. The Bigger Picture: Major tech companies are increasingly willing to partner rather than compete directly in AI infrastructure and capabilities. This represents a fundamental shift from the "build everything in-house" mentality that dominated tech for decades. The lesson? In today's fast-moving AI landscape, strategic partnerships can deliver advanced capabilities faster and more cost-effectively than trying to build everything from scratch. Sometimes the smartest competitive move is recognizing what others do better and finding ways to collaborate. This partnership-first approach is reshaping how we think about competitive advantage in AI. What other surprising collaborations might we see as the technology matures? https://lnkd.in/gi2smxP3 Want the latest news:https://lnkd.in/gWuKWnhH #AIStrategy #AI

Meta just dropped a bombshell that changes everything we thought we knew about Big Tech AI competition. While everyone assumed these giants would fight to the death with their own models, Meta is quietly doing the opposite. According to breaking reports, Meta's new Superintelligence Labs is in active discussions to integrate: → Google's Gemini for conversational AI responses → OpenAI's models for core Meta AI features → Anthropic's models (already integrated for internal coding) This isn't desperation. It's strategic brilliance. Here's why Meta's "all-of-the-above approach" is genius: 1. Speed to market beats perfection While competitors spend years perfecting their own models, Meta can deploy the best available AI TODAY across WhatsApp, Instagram, and Facebook. 2. Risk diversification Relying on a single AI model is like putting all your eggs in one basket. Meta is building redundancy into their AI infrastructure. 3. Competitive intelligence By partnering with rivals, Meta gains deep insights into their capabilities and limitations. 4. Resource optimization Why spend billions reinventing the wheel when you can license proven technology and focus resources on differentiation? The real kicker? These are temporary partnerships while Meta develops Llama 5 to compete directly. This strategy reveals something profound about the future of AI: The winners won't be the companies with the best models. They'll be the companies with the best AI integration and user experience. Meta is playing chess while others are playing checkers. What do you think about this partnership strategy? Smart move or risky gamble?

Meta's recent $140 million deal with Black Forest Labs highlights a stark truth: even giants can stumble in the race towards AI supremacy. After pouring billions into AI, Meta admits it's trailing behind a smaller competitor in creating quality image generation. Why? Social media data, while vast, is often muddied by privacy concerns, engagement filters, and low-quality uploads. This partnership reveals not just a strategy to catch up but a fundamental issue with their approach—more data doesn’t always equal better results. While investors cheer this pragmatic step towards improving ad performance and user engagement, it also raises serious questions about Meta’s long-term AI leadership. Is it time to rethink how the giants of tech leverage data and talent? As Meta navigates this complex landscape, the need for authentic innovation stands clear. The stakes have never been higher.

Cheque, mate: Meta’s superintelligence splurge meets a brain drain. Meta's Superintelligence Labs is looking less like an AI powerhouse and more like an expensive lesson in what money can't buy. The facts are stark. Shengjia Zhao, ChatGPT co-creator, threatened to quit days after joining. Meta had to upgrade his title to chief scientist just to keep him. Three other researchers have already left, including two who went straight back to OpenAI. The company reportedly offered nine-figure packages to lure talent. They've reorganised their AI division twice in four months. Meanwhile, their researchers apparently think Scale AI's data is rubbish, despite Meta's $14.3 billion investment. This isn't really about AI. It's about organisational psychology. You can't buy loyalty with compensation alone. These researchers left high-paying jobs at OpenAI for even higher-paying ones at Meta, then returned to their lower-paying jobs. That tells you everything about culture versus cash. Meta's approach mirrors every failed corporate transformation I've seen. Throw money at the problem. Hire the best people. Assume it'll work itself out. But talent without structure creates chaos. Vision without execution creates frustration. And when you're constantly reorganising, you're basically admitting you don't know what you're doing. The real issue is that Zuckerberg is treating AI like a startup acquisition. Buy the people, import the innovation. But these aren't products you can integrate. They're individuals with their own motivations, working styles, and tolerance for corporate dysfunction. OpenAI might pay less, but they offer something Meta currently can't: stability and a coherent mission. The AI arms race isn't just about compute power or talent acquisition. It's about building environments where brilliant people want to stay and do their best work. Meta's learning this the expensive way.

Elon Musk just pulled off something money couldn’t buy... Eighteen of Meta’s best AI engineers walked away from $250 million retention offers to join xAI. Why? Because vision and speed outweighed wealth. At Meta, approvals and bureaucracy slow things down. At xAI, ideas ship in weeks. Meta is building AI for commercial dominance. Musk is pitching AGI to “maximise truth-seeking.” The mission feels bigger than money. And the ripple effects are reshaping Silicon Valley: Microsoft started matching Meta’s offers. Google acquired entire startups for their teams. OpenAI’s CEO called it “pro-athlete level” compensation. Everyone is desperate… except Musk. And that’s the lesson for marketers staring down the AI revolution. It’s not as simple as how much budget you throw at the latest tools or who you can outspend on media. It also depends on whether your team believes in the mission behind the work and whether your systems allow them to move quickly. When we think about AI in marketing at Elixirr, we focus on these two levers: Does the application of AI in your marketing create something meaningful for your customer, beyond efficiency? Do your processes let you integrate and test quickly enough to make the tech matter?