Siddharth Pai: Meta is going all GPUs blazing to win the ‘superintelligence’ race
Mark Zuckerberg is doing all he can to leapfrog Generative AI and develop machines that can ‘think’. The challenge is of another order of magnitude, but the resources he’s pouring into it means he’s in the race alright.
Meta’s audacious pivot towards what it calls ‘superintelligence’ marks more than a renewal of its AI ambitions; it signals a philosophical recalibration. A few days ago, Meta unveiled a nearly $15 billion campaign to chase a future beyond conventional AI—an initiative that has seen the recruitment of Scale AI’s prodigy founder Alexandr Wang and the launch of a dedicated ‘superintelligence’ lab under the CEO’s own gaze (bit.ly/3ZHgYIh).
This is not merely an attempt to catch up; it is a strategic gambit to leapfrog competitors like OpenAI, Google DeepMind, Anthropic and xAI.
Currently, Meta’s AI offerings, its Llama family, primarily reside within the predictive and Generative AI paradigm. These systems excel at forecasting text sequences or generating images and dialogue, but they lack the structural scaffolding required for reasoning, planning and understanding the physical world.
Meta’s chief AI scientist Yann LeCun has been eloquent on this front, arguing in a 2024 Financial Times interview that large language models, while powerful, are fundamentally constrained—they grasp patterns but not underlying logic, memory or causal inference (bit.ly/3SVYYGi). For LeCun and his team, superintelligence denotes AI that transcends such limitations and is capable of building internal world models and achieving reasoning comparable to—or exceeding—human cognition.
This definition distances itself sharply from today’s predictive AI, which statistically extrapolates from patterns, as well as GenAI, which crafts plausible outputs, such as text or images. Superintelligence, by contrast, aspires for general-purpose cognitive ability. Unsiloed and flexible, it will be able to plan hierarchically and form persistent internal representations.
It is not alone in this quest. Ilya Sutskever, the former chief scientist at OpenAI who believes powerful AI could harm humanity, has co-founded Safe Superintelligence. It has no plans to release products but its stated mission is to build superintelligence and release the technology once it has been proven to be completely safe (bit.ly/3Tvyprx).
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Meta has established a cadre of roughly 50 elite researchers, luring them with huge compensation packages, to work with Scale AI to create a vertically integrated stack of data labelling, model alignment and deployment. Meta chief Mark Zuckerberg’s combative leadership style—marked by intense micromanagement and 24/7 messaging—hints at both the urgency and stakes.
In comparison with rivals, Meta lags on the AI developmental curve. Its Llama-4 release has faced delays and scrutiny, while its competitors have sped ahead—OpenAI moved quickly to GPT-4 and Google countered it with Gemini-based multimodal agents. Nevertheless, Meta brings distinctive assets to the table: its social graph, an enormous user base, its sprawling compute resources, which include hundreds of thousands of Nvidia H100 GPUs, and a renewed impetus underpinned by its Scale AI partnership.
Yet, beyond the material strength of its stack lies the more profound question: Can Meta, with its social media heritage, really deliver on superintelligence? LeCun muses that a decade may pass before systems capable of hierarchical reasoning, sustained memory and world modelling come to fruition (bit.ly/3HOt0ti). Meta’s pursuit is an investment in a bold vision as much as engineering muscle.
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The differences between predictive, generative and superintelligent systems are consequential. An AI tool that merely predicts or synthesizes text operates within a bounded comfort zone, finding patterns, optimizing loss and generating output. However, a superintelligent AI must contend with the open-ended unpredictability of real-world tasks—reasoning across contexts, planning with foresight and adapting to novel situations. It requires an architecture qualitatively different from pattern matching.
In this sense, Meta is not joining the arms race to outdo competitors in generative benchmarks. Instead, it aims to leapfrog that race for a big stake in a future where AI systems begin to think, plan, learn and remember far better. The risk is high: billions of dollars are invested, talent battles are underway and there is no guarantee that such advancements will fully materialize. Critics note that AI today fails at some straightforward tasks that any competent Class 10 school student would pass with ease.
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But Meta views this as a strategic inflection point. Zuckerberg is personally setting the pace, scouting for top minds and restructuring teams to align with his lofty ambition. If Meta can transition from crafting better chatbots to instilling AI with coherent, persistent models of the world, it just might recalibrate the AI hierarchy entirely (shorturl.at/5j707). Whether this would mark Meta’s renaissance remains to be seen. Yet, the narrative shift is unmistakable. Where once Meta chased after generative prowess, it now envisions cognitive machines that supposedly actually ‘think.’
The challenge lies not only in engineering capability, but in philosophical restraint. Superintelligent systems demand new ethics, not just new math. If Meta achieves its goal, it will not merely change AI—it will redefine our expectations of intelligence itself. In this quest, the company must navigate both technical intricacies and the social repercussions of creating minds that learn, adapt and may surpass us. Whether such minds can be safely steered is a question that no GPU cluster can answer definitively.
The author is co-founder of Siana Capital, a venture fund manager.
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