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Sequoia and Nvidia back AlphaGo creator David Silver’s Ineffable Intelligence at $5.1 billion

Posted on April 27, 2026 By 164news66 No Comments on Sequoia and Nvidia back AlphaGo creator David Silver’s Ineffable Intelligence at $5.1 billion

Sequoia and Nvidia Back David Silver’s Ineffable Intelligence at $5.1 Billion

Sequoia and Nvidia are backing David Silver’s Ineffable Intelligence at a valuation of $5.1 billion.

A London Startup Betting on Reinforcement Learning Over LLMs

  • April 27, 2026 – 2:53 pm

Silver, who left Google DeepMind in late 2025 after more than a decade developing AlphaGo, AlphaZero, AlphaStar, and contributing to Gemini, founded Ineffable Intelligence in November 2025. The startup has no product, revenue, or public roadmap, but its founder’s track record is considered worth $5.1 billion to investors.

Ineffable Intelligence, a London-based AI startup led by David Silver, has secured funding from Sequoia Capital and Nvidia. The round, significant for an early-stage startup, was led by Sequoia, with partners Alfred Lin and Sonya Huang traveling to London to finalize the deal. Nvidia’s venture arm contributed at least $250 million.

Incorporating a company without products or revenue at a valuation exceeding five billion dollars is remarkable.

Silver’s contributions at Google DeepMind have defined modern AI history. AlphaGo, released in 2016, was the first AI to defeat a professional Go player without a handicap, and later, in Seoul, beat world champion Lee Sedol 4-1 in a match watched by 200 million people across Asia.

He then developed AlphaZero, which mastered games like Go, Chess, and Shogi from scratch through self-play, showcasing the potential of reinforcement learning to achieve superhuman performance across multiple complex games simultaneously. AlphaStar followed, demonstrating grandmaster-level play in StarCraft II.

Ineffable Intelligence’s core argument challenges the dominant AI development paradigm. Co-authored by Silver and Richard Sutton, a reinforcement learning pioneer, their 2025 paper suggests that large language models (LLMs) are limited because they learn exclusively from human-generated data, preventing them from discovering genuine novelty. In contrast, reinforcement learning allows an AI to learn through interaction with its environment, trial, error, and self-play.

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