Meta, the parent company of Facebook, Instagram, and WhatsApp, is aggressively pushing its artificial intelligence (AI) initiatives through its newly formed Meta Superintelligence Labs (MSL). However, despite pouring millions into AI research and recruiting top talent, the company is reportedly struggling with slow internal tools, prompting CEO Mark Zuckerberg to direct teams to use external platforms like Vercel and GitHub to accelerate AI development.
This strategic pivot highlights both the urgency of the AI race and the challenges large tech firms face in adapting legacy infrastructure to fast-moving AI innovation.
Meta’s AI Ambitions
Over the past few years, Meta has made AI a central focus of its business strategy:
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Hundreds of millions of dollars have been invested into AI research and development.
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Top AI talent has been recruited with nine-figure pay packages to develop advanced models and work towards Artificial General Intelligence (AGI), which could potentially mimic human cognitive abilities.
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Meta has reorganized its AI efforts under MSL, aiming to compete with other tech giants in the AI race, including OpenAI, Google, and Microsoft.
The goal is not just incremental improvement but creating human-like AI systems that can understand, reason, and interact intelligently across a wide range of tasks.
Internal Tools Lag Behind
Despite these investments, internal documents obtained by Business Insider reveal that Meta’s existing systems are too slow to support the rapid iteration needed for AI development.
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Meta’s Product and Applied Research (PAR) group, led by former GitHub CEO Nat Friedman, has reportedly urged teams to move away from internal systems.
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Internal platforms take hours to deploy even small changes, which significantly slows down vibe coding — a practice where AI assists engineers in generating and refining code.
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A memo from Aparna Ramani, head of infrastructure at MSL, noted that Meta’s tools take “too long” to deploy changes and are “not conducive to vibe coding.”
The delays are a problem because AI projects demand rapid testing and iteration. Speed is critical for validating models, running experiments, and deploying applications efficiently.
Turning to External Platforms
To address these challenges, Meta has instructed teams to adopt external developer platforms:
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Vercel, a cloud platform that allows developers to build and deploy web applications quickly.
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GitHub, Microsoft’s code-hosting and collaboration tool, widely used for managing large codebases.
Documents suggest that at least 10 active projects were already running on Vercel by mid-September, allowing teams to deploy updates in minutes instead of hours.
“The strategy is two-pronged,” Ramani wrote. “We immediately adopt Vercel to unblock MSL’s progress while developing an internal alternative, code-named Nest, optimized for TypeScript applications.”
The adoption of Vercel is significant because it provides high-performance, scalable, and secure infrastructure that supports modern web and AI applications. Its global network enables faster deployments, which is critical for Meta’s AI teams.
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Internal Alternative: Project Nest
While turning to Vercel offers an immediate solution, Meta is simultaneously developing an internal platform codenamed Nest.
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Nest is intended to be optimized for TypeScript apps, a primary language for Meta’s AI and web development projects.
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The platform aims to reduce deployment times from 99 minutes to under 2 minutes, allowing internal teams to iterate quickly without relying on external systems.
This dual approach ensures that Meta can maintain control over sensitive AI projects while leveraging external tools for speed and efficiency.
Vercel and GitHub Partnership
Meta’s use of Vercel and GitHub together represents a broader trend in the tech industry: leveraging best-in-class external platforms to accelerate internal workflows.
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Vercel specializes in front-end frameworks and full-stack apps, enabling rapid development, testing, and deployment.
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GitHub provides version control, collaboration tools, and code management at scale, crucial for large engineering teams.
Interestingly, both Nat Friedman and Alexandr Wang, Meta’s new chief AI officer, are investors in Vercel. The platform has raised $300 million at a $9.3 billion valuation and already serves major clients such as Netflix, Adobe, and Stripe.
By pairing Vercel with GitHub, Meta aims to streamline AI workflows, reduce delays, and maintain agility despite the company’s massive scale.
Challenges of Legacy Systems
Meta’s reliance on legacy internal tools highlights a common problem among tech giants:
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Large-scale platforms designed for billions of users are often sluggish for smaller, specialized teams.
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Internal deployment pipelines, optimized for stability, can hinder speed and innovation, especially in cutting-edge AI research.
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Without faster tools, engineers are limited in their ability to iterate quickly, test models, and respond to new research.
By integrating external tools, Meta can bypass these bottlenecks while continuing to invest in long-term internal solutions.
Importance of Speed in AI Development
In the current AI landscape, speed is a competitive advantage:
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Faster iteration allows teams to test hypotheses and deploy models rapidly.
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Quick feedback loops help researchers improve accuracy, reduce bias, and enhance safety.
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Agile workflows are essential for developing AI applications for billions of users across social media, messaging, and enterprise products.
Meta’s decision to use external platforms underscores the urgency of staying competitive in the AI arms race.
Zuckerberg’s Vision
Mark Zuckerberg has repeatedly emphasized that AI is the next frontier for Meta. By combining MSL’s research capabilities with agile external tools, he hopes to:
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Achieve Artificial General Intelligence (AGI).
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Integrate AI into Meta’s ecosystem of apps, including Facebook, Instagram, WhatsApp, and Horizon Worlds.
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Maintain Meta’s position as a leader in AI research amid intense competition from OpenAI, Google DeepMind, and Microsoft.
The dual approach of external platforms for speed and internal systems for control reflects a pragmatic strategy to balance innovation and security.
Conclusion
Meta’s pivot to Vercel and GitHub highlights the challenges of running cutting-edge AI research inside a large-scale corporate infrastructure. By adopting external tools, Meta aims to accelerate development, reduce deployment times, and improve efficiency.
Simultaneously, the development of Nest ensures that Meta retains long-term control over AI projects, combining speed with security and scalability.
This move is a reminder that even tech giants face operational hurdles, and adapting workflows is critical in the race to achieve Artificial General Intelligence.
