AlphaEvolve: AI Meets Algorithm Discovery

In March 2016, professional GoAn ancient two-player board game in which black and white stones are placed on a 19 × 19 grid to surround more territory than the opponent. player Lee Sedol was shocked during a match. Lee was playing against AlphaGo, a Go-playing computer program developed by DeepMind. On the 37th move, AlphaGo made a move that seemed to everyone like a mistake.

But later, people realized it was actually a brilliant strategy.

That move, now known as “Move 37,” showed how AI could think in surprising and creative ways, changing how people see the game.

Jump ahead to 2025. The “board” is no longer a 19 × 19 grid of Go—it’s the entire landscape of computer science, mathematics, and large-scale systems engineering.

The new contender isn’t exploring a few hundred game positions per second; it’s AlphaEvolve: a squad of frontier large language models (LLMs) that rewrite, critique, and evolve complete codebases while an automated test harness keeps score. Where AlphaGo blended Monte-Carlo tree searchA heuristic search algorithm that guides decision-making by running many random simulations from each game state and using their outcomes to grow a search tree. with deep neural networks, AlphaEvolve fuses the creative spark of LLMs with evolutionary algorithms—think natural selection for software. It spins up thousands of program variants in parallel, tests them, and carries only the fittest ideas into the next generation.

Watching AlphaEvolve gives the impression of experiencing numerous Move 37 moments in quick succession. First, it discovered a shortcut for multiplying two 4 × 4 grids of numbers, needing just 48 basic calculations—breaking the long-standing record of 49 steps set in the late 1960s. Next, it tackled over fifty stubborn math puzzles and solved roughly one-fifth of them with brand-new answers. On the engineering front, it fine-tuned a key routine inside Google’s Gemini training software, making it 23% faster and shaving days off every training run. These breakthroughs weren’t flashes of human inspiration; they emerged from an automated laboratory that never sleeps and is already optimizing parts of the very infrastructure that powers it.

The Go board taught us that a machine’s odd-looking move might be genius in disguise; AlphaEvolve suggests the same can be true for code.

In this newsletter, we’ll dissect:

• How AlphaEvolve works.

• What it has achieved so far.

• Why its evolutionary feedback loop could make software, algorithms, and even hardware design the next great frontier of automated discovery.

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