Google Named Its London HQ 'Platform 37.' Nine Years Ago, AlphaGo's Move 37 Rewrote AI History

Google DeepMind CEO Demis Hassabis announced London's new HQ will be named 'Platform 37,' honoring AlphaGo's legendary Move 37 against Lee Sae-Dol in 2016. The 330-meter building, designed by Heatherwick and Bjarke Ingels, is Google's first self-designed facility outside the US and will feature 'The AI Exchange' public space for AI education.

Platform 37: A Building, A Move, A Manifesto for the AI Civilization

Google DeepMind CEO Demis Hassabis announced London's new King's Cross headquarters will be named "Platform 37," honoring AlphaGo's legendary Move 37 against Lee Sedol in 2016—the moment AI first demonstrated creativity beyond human intuition.

The Building

  • 330-meter "landscraper" design by Thomas Heatherwick + Bjarke Ingels
  • Google's first wholly-owned and designed facility outside the US (~£1B cost)
  • Low-carbon materials, "suspended" column-free interior structure
  • Teams move in summer 2026

The AI Exchange (Public Space)

The building's most distinctive feature is "The AI Exchange"—a free, public AI education and interactive exhibition space offering:

  • AI fundamentals courses for the public
  • Interactive exhibitions explaining AI technology
  • Cultural events
  • Access to AI researchers and engineers

This breaks the traditional "fortress" model of AI labs, embodying DeepMind's commitment to AI democratization.

The 10th Anniversary of AlphaGo

Platform 37's naming coincides with the 10th anniversary of the AlphaGo-Lee Sedol match. Hassabis described AGI as "the ultimate tool to accelerate solving humanity's grand challenges—climate, disease, poverty."

Strategic Significance

  • Consolidates 5,000+ UK team members under one iconic campus
  • Reinforces London's position as a top global AI talent hub
  • Supports UK's "AI superpower" national strategy
  • The "Platform" double meaning: railway platform (near King's Cross) + AI as humanity's launch platform

In-Depth Analysis and Industry Outlook

From a broader perspective, this development reflects the accelerating trend of AI technology transitioning from laboratories to industrial applications. Industry analysts widely agree that 2026 will be a pivotal year for AI commercialization. On the technical front, large model inference efficiency continues to improve while deployment costs decline, enabling more SMEs to access advanced AI capabilities. On the market front, enterprise expectations for AI investment returns are shifting from long-term strategic value to short-term quantifiable gains.

However, the rapid proliferation of AI also brings new challenges: increasing complexity of data privacy protection, growing demands for AI decision transparency, and difficulties in cross-border AI governance coordination. Regulatory authorities across multiple countries are closely monitoring these developments, attempting to balance innovation promotion with risk prevention. For investors, identifying AI companies with truly sustainable competitive advantages has become increasingly critical as the market transitions from hype to value validation.