Artificial Intelligence

Zuckerberg’s $15B bet: How Meta’s ‘Superintelligence Labs’ became Silicon Valley’s most expensive AI talent war

Published

3 weeks ago

July 10, 2025

Mark Zuckerberg has a history of making audacious bets that reshape entire industries – and losing spectacularly when they don’t pan out. After burning through US$46 billion on the metaverse with little to show for it, the Meta CEO is now doubling down with an even more ambitious wager: superintelligence AI.

This time, however, the stakes are higher, the competition more fierce, and the potential rewards more transformative than anything Meta has attempted before.

With nine-figure compensation packages and infrastructure investments that dwarf even the metaverse spending spree, Zuckerberg’s superintelligence AI gamble represents Silicon Valley’s most expensive talent war – one that could either cement Meta’s position as a tech giant or become another cautionary tale of visionary ambition meeting harsh reality.

The birth of Meta Superintelligence Labs

The formation of Meta Superintelligence Labs marks a shift for the social media giant. In an exclusive interview with The Information‘s TITV live-streaming programme, Zuckerberg told founder Jessica Lessin that “the most exciting thing this year is that we’re starting to see early glimpses of self-improvement with the models, which means that developing super intelligence is now in sight.”

The vision has driven the company to restructure its entire AI division, with the ambitious goal of delivering what Zuckerberg calls “personal super intelligence to everyone in the world.” The lab’s creation follows a period of internal struggles in Meta’s AI division, including management struggles, employee churn, and product releases that fell flat.

Rather than incrementally improving existing systems, Zuckerberg has opted for a complete overhaul, bringing in external leadership and re-imagining the company’s approach to AI development.

Are we witnessing the most expensive talent war in tech history?

Mark Zuckerberg, Founder, Chairman and Chief Executive Officer/Meta media gallery

Central to Meta’s superintelligence AI ambitions is a talent acquisition strategy that has sent shockwaves through the industry. Zuckerberg has embarked on a spending spree to create the new lab, offering as much as nine-figure pay packages to hire top researchers from companies like OpenAI, Google, Apple and Anthropic.

When The Information’s Lessin questioned reports of $100-200 million compensation packages, Zuckerberg acknowledged the competitive nature of the market, stating that “a lot of the specifics that have been reported aren’t accurate by themselves. But it is a very hot market… there’s a small number of researchers, who are the best, who are in demand by all of the different labs.”

The Meta AI talent acquisition strategy extends beyond financial incentives. Zuckerberg said having “basically the most compute per researcher is a strategic advantage, not just for doing the work, but for attracting the best people.” The approach reflects an understanding that in the superintelligence AI race, talent density matters more than team size.

The Alexandr Wang acquisition: A US$14.3b gamble

The centrepiece of Meta’s talent strategy was its acquisition of Scale AI leadership. In June, the company made a $14.3 billion investment in the AI startup, founded and led by Wang. Under the deal, Meta took a 49% stake in the company, and Wang and a team of top Scale employees joined Meta in leadership roles.

At just 28 years old, Alexandr Wang now serves as Meta’s chief AI officer, leading what the company has renamed “Meta Superintelligence Labs.” In the larger AI division, Wang has led a team of around a dozen newly-hired researchers, a handful of his deputies from Scale AI, and Nat Friedman, the former chief executive of GitHub.

The integration of Wang’s team as a re-imagining of how Meta approaches AI development. The group is working in an office space siloed from the rest of the company and next to Zuckerberg, highlighting the importance placed on the initiative.

A philosophical shift: From open source to closed development

Perhaps the most significant development emerging from Meta Superintelligence Labs is a potential abandonment of the company’s long-standing open-source philosophy. Last week, a small group of top members of the lab, including Wang, discussed abandoning the company’s most powerful open source AI model, called Behemoth, in favour of developing a closed model.

This represents a departure from Meta’s historical approach. For years, Meta has chosen to open source its AI models, making code public for other developers to build on. Meta executives have argued it is better for the technology to be built in public so that AI development will move faster and be accessible to more developers.

The shift reflects concerns about competitive positioning in the AI race. Meta had finished feeding data into its Behemoth model (training) but has delayed its release because of poor internal performance. The setback has prompted a serious reconsideration of the company’s approach.

Infrastructure as a competitive advantage

Beyond talent acquisition, Meta is making infrastructure investments to support its superintelligence ambitions. Zuckerberg revealed that the company is “building multiple, multi-gigawatt data centres” and pioneering new construction methods, including “weatherproof tents” to accelerate deployment.

The scale of these investments is staggering. Hyperion, one of Meta’s new data centres, “is going to scale up to five gigawatts over the coming years” and “the size of the site covers a significant portion of the footprint of Manhattan in terms of space.”

The infrastructure spending is enabled by Meta’s strong financial position, with Zuckerberg noting that “we can basically do this all funded from the cash flow of the company.”

The personal superintelligence vision

What distinguishes Meta’s approach from competitors is its focus on “personal superintelligence” rather than centralised AI systems. During his interview with The Information’s Lessin, Zuckerberg explained that while other labs focus on “wanting to automate all of the economically productive work in society,” Meta’s vision centres on “what are the things that people care about in their own lives… relationships and culture and creativity and having fun and enjoying life.”

The vision extends to Meta’s hardware ambitions, particularly its AR glasses initiative. In the same TITV interview, Zuckerberg predicted that “if you don’t have AI glasses, you’re going to be at a cognitive disadvantage” and described future scenarios where AI companions could “observe what’s going on in your life and be able to follow up on things for you.”

Industry implications and competitive dynamics

The implications of Meta’s superintelligence push extend beyond the company. Meta’s AI talent acquisition strategy has created salary inflation in the industry, forcing competitors to match or exceed Meta’s compensation levels to retain their researchers.

When asked about his interactions with competitors at Sun Valley, Zuckerberg acknowledged the competitive landscape, stating that “we’re not trying to target anyone individually. I want to make sure that I get to know all of the top researchers in the industry.”

The diplomatic approach masks what is fundamentally a zero-sum competition for a finite pool of top-tier superintelligence AI talent. The potential shift away from open-source development also signals a broader industry trend toward more proprietary approaches to AI development.

Conclusion: A defining moment for Meta

Meta’s superintelligence initiative represents a re-imagining of the company’s future. After the costly metaverse experiment failed to deliver results, Zuckerberg is betting even bigger on AI, with investments that could exceed US$100 billion over the coming years.

The success or failure of Meta Superintelligence Labs will likely determine not just the company’s future but the trajectory of the broader AI industry. With some employees expecting “an exodus of AI talent who were not chosen to join Wang’s superintelligence team,” the stakes are high.

Whether Meta’s AI talent acquisition campaign will yield the breakthrough technologies Zuckerberg envisions remains to be seen. What’s certain is that Silicon Valley’s most expensive talent war has begun.

See also: Apple loses key AI leader to Meta

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Artificial Intelligence

Zuckerberg outlines Meta’s AI vision for ‘personal superintelligence’

Published

20 hours ago

July 31, 2025

Hrishi

Meta CEO Mark Zuckerberg has laid out his blueprint for the future of AI, and it’s about giving you “personal superintelligence”.

In a letter, the Meta chief painted a picture of what’s coming next, and he believes it’s closer than we think. He says his teams are already seeing early signs of progress.

“Over the last few months we have begun to see glimpses of our AI systems improving themselves,” Zuckerberg wrote. “The improvement is slow for now, but undeniable. Developing superintelligence is now in sight.”

So, what does he want to do with it? Forget AI that just automates boring office work, Zuckerberg and Meta’s vision for personal superintelligence is far more intimate. He imagines a future where technology serves our individual growth, not just our productivity.

In his words, the real revolution will be “everyone having a personal superintelligence that helps you achieve your goals, create what you want to see in the world, experience any adventure, be a better friend to those you care about, and grow to become the person you aspire to be.”

But here’s where it gets interesting. He drew a clear line in the sand, contrasting his vision against a very different, almost dystopian alternative that he believes others are pursuing.

“This is distinct from others in the industry who believe superintelligence should be directed centrally towards automating all valuable work, and then humanity will live on a dole of its output,” he stated.

Meta, Zuckerberg says, is betting on the individual when it comes to AI superintelligence. The company believes that progress has always come from people chasing their own dreams, not from living off the scraps of a hyper-efficient machine.

If he’s right, we’ll spend less time wrestling with software and more time creating and connecting. This personal AI would live in devices like smart glasses, understanding our world because they can “see what we see, hear what we hear.”

Of course, he knows this is powerful, even dangerous, stuff. Zuckerberg admits that superintelligence will bring new safety concerns and that Meta will have to be careful about what they release to the world. Still, he argues that the goal must be to empower people as much as possible.

Zuckerberg believes we’re at a crossroads right now. The choices we make in the next few years will decide everything.

“The rest of this decade seems likely to be the decisive period for determining the path this technology will take,” he warned, framing it as a choice between “personal empowerment or a force focused on replacing large swaths of society.”

Zuckerberg has made his choice. He’s focusing Meta’s enormous resources on building this personal superintelligence future.

See also: Forget the Turing Test, AI’s real challenge is communication

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Artificial Intelligence

Google’s Veo 3 AI video creation tools are now widely available

Published

2 days ago

July 30, 2025

Hrishi

Google has made its most powerful AI video creator, Veo 3, available for everyone to use on its Vertex AI platform. And for those who need to work quickly, a speedier version called Veo 3 Fast is also ready-to-go for quick creative work.

Ever had a brilliant idea for a video but found yourself held back by the cost, time, or technical skills needed to create it? This tool aims to offer a faster way to turn your text ideas into everything from short films to product demos.

70 million videos have been created since May, showing a huge global appetite for these AI video creation tools. Businesses are diving in as well, generating over 6 million videos since they got early access in June.

The real-world applications for Veo 3

So, what does this look like in the real world? From global design platforms to major advertising agencies, companies are already putting Veo 3 to work. Take design platform Canva, they are building Veo directly into their software to make video creation simple for their users.

Cameron Adams, Co-Founder and Chief Product Officer at Canva, said: “Enabling anyone to bring their ideas to life – especially their most creative ones – has been core to Canva’s mission ever since we set out to empower the world to design.

“By democratising access to a powerful technology like Google’s Veo 3 inside Canva AI, your big ideas can now be brought to life in the highest quality video and sound, all from within your existing Canva subscription. In true Canva fashion, we’ve built this with an intuitive interface and simple editing tools in place, all backed by Canva Shield.”

For creative agencies like BarkleyOKRP, the big wins are speed and quality. They claim to have been so impressed with the latest version that they went back and remade videos.

Julie Ray Barr, Senior Vice President Client Experience at BarkleyOKRP, commented: “The rapid advancements from Veo 2 to Veo 3 within such a short time frame on this project have been nothing short of remarkable.

“Our team undertook the task of re-creating numerous music videos initially produced with Veo 2 once Veo 3 was released, primarily due to the significantly improved synchronization between voice and mouth movements. The continuous daily progress we are witnessing is truly extraordinary.”

It’s even changing how global companies connect with local customers. The investing platform eToro used Veo 3 to create 15 different, fully AI-generated versions of a single advertisement, each customised to a specific country with its own native language.

Shay Chikotay, Head of Creative & Content at eToro, said: “With Veo 3, we produced 15 fully AI‑generated versions of our ad, each in the native language of its market, all while capturing real emotion at scale.

“Ironically, AI didn’t reduce humanity; it amplified it. Veo 3 lets us tell more stories, in more tongues, with more impact.”

Google gives creators a powerful AI video creation tool

Veo 3 and Veo 3 Fast are packed with features designed to give you the control to tell complete stories.

Create scenes with sound. The AI generates video and audio at the same time, so you can have characters that speak with accurate lip-syncing and sound effects that fit the scene.

High quality results. The models produce video in high-definition (1080p), making it good enough for professional marketing campaigns and demos.

Reach a global audience easily. Veo 3’s ability to generate dialogue natively makes it much simpler to produce a video once and then translate the dialogue for many different languages.

Bring still images to life. A new feature, coming in August, will let you take a single photo, add a text prompt, and watch as Veo animates it into an 8-second video clip.

Of course, with such powerful technology, safety is a key concern. Google has built Veo 3 for responsible enterprise use. Every video frame is embedded with an invisible digital watermark from SynthID to help combat misinformation. The service is also covered by Google’s indemnity for generative AI, giving businesses that extra layer of security.

See also: Google’s newest Gemini 2.5 model aims for ‘intelligence per dollar’

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Artificial Intelligence

Forget the Turing Test, AI’s real challenge is communication

Published

3 days ago

July 29, 2025

Hrishi

While the development of increasingly powerful AI models grabs headlines, the big challenge is getting intelligent agents to communicate.

Right now, we have all these capable systems, but they’re all speaking different languages. It’s a digital Tower of Babel, and it’s holding back the true potential of what AI can achieve.

To move forward, we need a common tongue; a universal translator that will allow these different systems to connect and collaborate. Several contenders have stepped up to the plate, each with their own ideas about how to solve this communication puzzle.

Anthropic’s Model Context Protocol, or MCP, is one of the big names in the ring. It attempts to create a secure and organised way for AI models to use external tools and data. MCP has become popular because it’s relatively simple and has the backing of a major AI player. However, it’s really designed for a single AI to use different tools, not for a team of AIs to work together.

And that’s where other protocols like the Agent Communication Protocol (ACP) and the Agent-to-Agent Protocol (A2A) come in.

ACP, an open-source project from IBM, is all about enabling AI agents to communicate as peers. It’s built on familiar web technologies that developers are already comfortable with, which makes it easy to adopt. It’s a flexible and powerful solution that allows for a more decentralised and collaborative approach to AI.

Google’s A2A protocol, meanwhile, takes a slightly different tack. It’s designed to work alongside MCP, rather than replace it. A2A is focused on how a team of AIs can work together on complex tasks, passing information and responsibilities back and forth. It uses a system of ‘Agent Cards,’ like digital business cards, to help AIs find and understand each other.

The real difference between these protocols is their vision for the future of how AI agents communicate. MCP is for a world where a single, powerful AI is at the centre, using a variety of tools to get things done. ACP and A2A are designed for distributed intelligence, where teams of specialised AIs work together to solve problems.

A universal language for AI would open the door to a whole new world of possibilities. Imagine a team of AIs working together to design a new product, with one agent handling the market research, another the design, and a third the manufacturing process. Or a network of medical AIs collaborating to analyse patient data and develop personalised treatment plans.

But we’re not there yet. The “protocol wars” are in full swing, and there’s a real risk that we could end up with even more fragmentation than we have now.

It’s likely that the future of how AI communicates won’t be a one-size-fits-all solution. We may see different protocols, each used for what it does best. One thing is for sure: figuring out how to get AIs to talk to each other is among the next great challenges in the field.

(Photo by Theodore Poncet)

See also: Anthropic deploys AI agents to audit models for safety

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