Meta AI

Zuckerberg in a Panic! Leaked Meta Files Reveal: Prefer Competitors Over Ditching Legacy System

Honghao Wang

19 Oct 2025 — 4 min read

Meta CEO Zuckerberg Bets Billions on AI — Deployment Speeds Cut from Hours to Minutes

Racing Against OpenAI and Google

[Xinzhiyuan Editorial Summary]

In the global AI race, Meta CEO Mark Zuckerberg sees time as the only true enemy. Backed by tens of billions of dollars and record-breaking salaries to attract top talent, Meta is undergoing a rapid engineering overhaul — slashing deployment times from hours to minutes. The goal: close the gap with OpenAI and Google, and possibly hasten the march toward superintelligence.

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Meta's Billion-Dollar AI Push and Workforce Transformation

Billions Invested, Nine-Digit Salaries Offered

Meta’s Superintelligence Lab (MSL) is spearheading a sweeping internal revolution.
Executive teams are moving to fast, modern engineering tools for accelerated AI development.
Internal dashboards now track AI adoption across departments, with set targets.

Key initiatives include:

Enforcing AI tool usage in daily workflows.
Monitoring AI usage rates by team.

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Zuckerberg’s Gamble: Speed Over Caution

Meta has consolidated all AI work under MSL, hiring aggressively with unprecedented pay:

Example: Matt Deitke, 24-year-old “AI prodigy,” reportedly received up to $250M over multiple years.

Zuckerberg’s philosophy:

> “If superintelligence becomes real in 3 years and you’re on a 5-year plan, you miss AI — the most important technology in history… The bigger risk is moving too slowly.”

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Engineering System Overhaul

Leadership Changes

Nat Friedman, ex-GitHub CEO, now leads Product and Applied Research (PAR).
Driving a shift away from Meta’s slow, legacy infrastructure toward mainstream platforms like Vercel.

Why Change?

Legacy systems were designed for billions of users but slowed small, AI-centric teams.
“Vibe coding” — using AI to assist in development — requires faster iteration.

New Solutions:

Nest: Meta’s in-house platform for quicker data and app integration.
Vercel + GitHub integration for streamlined workflows.

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Obstacles and Acceleration Plans

Ramani’s September Memo

Aparna Ramani, MSL Head of Infrastructure, said deployments took 99 minutes vs. target ≤ 2 minutes.
Proposed two pathways:
Use Vercel for immediate relief.
Build Nest internally to host TypeScript-based applications.

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External Tools Filling Gaps

Examples:

Code Llama underperformed on complex tasks → Introduced Devmate using Anthropic’s Claude.
Image generation: Vibes feature powered by Midjourney — advised by Nat Friedman.

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AI Usage KPIs: The Push to Adopt

Dashboards and Gamification

Real-time AI usage tracking.
“Level Up” program turns adoption into a game inside Metamate chatbot.
Badges awarded for milestones.

Industry Trends

Google: Monitors productivity hours gained via AI.
Microsoft: Links AI usage to performance reviews.
Some firms track “AI reliance” directly.

Meta’s Reality Labs division has an AI usage target >75%.

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AI Content Ecosystem Potential

Platforms like AiToEarn官网 show how rapid AI workflows can extend beyond engineering:

AI content generation + multi-platform publishing.
Analytics + AI model ranking (AI模型排名).
Supports publishing to Douyin, Kwai, WeChat, Bilibili, Xiaohongshu, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, and X.

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Conclusion: Every Minute Counts

Meta’s aggressive adoption drive — from record salaries to faster tools — reflects the high stakes in the race toward superintelligence.

Whether in global corporations or among independent creators, speed, efficiency, and deep AI integration are fast becoming the decisive factors in shaping the future of intelligent systems.

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