In November 2017, Andrej Karpathy published a 3,000-word essay arguing that neural networks were not a better algorithm but an entirely new programming paradigm; nine years later, he has arrived at Anthropic with a third-era update to that same argument. The essay, "Software 2.0," has become one of the most-cited frameworks in modern AI engineering, and the 2026 version of its author is now using that framework to direct how a frontier lab trains the models at the center of the industry.
Software 2.0: Neural Networks as the New Compiler
In his November 2017 essay, Karpathy drew a hard line between two modes of software. Software 1.0 writes explicit instructions in Python or C++. Software 2.0, he argued, specifies a desired outcome and lets an optimization process discover the program by training on data. "Neural networks are not just another classifier," he wrote. "They represent the beginning of a fundamental shift in how we develop software." He named image recognition, speech synthesis, machine translation, and game playing as domains where Software 2.0 would absorb entire engineering stacks, because in each of these domains "it is significantly easier to collect the data than to explicitly write the program."
The prediction tracked closely with what happened next. By the time Karpathy left Tesla's Autopilot director role in July 2022, the company had rebuilt its perception system almost entirely in learned weights, replacing hand-coded computer-vision rules with large neural networks trained on video. What he described in 2017 as a coming shift had, within five years, reshaped how every major technology company built its core perception products.
The essay also carried a prescient caveat: Software 2.0 programs are opaque. They can be evaluated but not easily read. That asymmetry, between capability and interpretability, would shape the safety debates that followed Karpathy across the labs he moved between.
The Eureka Interlude: Teaching the Machine to Teach
Between his second OpenAI stint and Anthropic, Karpathy spent 22 months running Eureka Labs, an AI-education startup he announced in July 2024. The company's premise was direct: AI teaching assistants could scale expert-designed courses to any student population, at any skill level, without requiring more human instructors. It was an extension of his earlier YouTube lecture series, which already ranked among the most-watched technical AI content available online. Eureka Labs raised approximately $20 million in seed funding, with investors including Conviction's Sarah Guo and Sam Altman, according to Silicon Republic and Inc.