Andrej Karpathy on the Future of Software: The Rise of Software 3.0 and the Agent Era
At a packed AI event, Andrej Karpathy—former Director of AI at Tesla and founding member of OpenAI—delivered a compelling address outlining a tectonic shift in how we write, interact with, and deploy software. “Software is changing again,” Karpathy declared, positioning today’s shift as more radical than anything the industry has seen in 70 years.
From Software 1.0 to 3.0
Karpathy breaks down the evolution of software into three stages:
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Software 1.0: Traditional code written explicitly by developers in programming languages like Python or C++.
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Software 2.0: Neural networks trained via data and optimized using backpropagation—no explicit code, just learned weights.
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Software 3.0: Large Language Models (LLMs) like GPT-4 and Claude, where natural language prompts become the new form of programming.
“We are now programming computers in English,” Karpathy said, highlighting how the interface between humans and machines is becoming increasingly intuitive and accessible.
GitHub, Hugging Face, and the Rise of LLM Ecosystems
Karpathy draws powerful parallels between historical shifts in tooling: GitHub was the hub for Software 1.0; Hugging Face and similar platforms are now becoming the repositories for Software 2.0 and 3.0. Prompting an LLM is no longer just a trick—it’s a paradigm. And increasingly, tools like Cursor and Perplexity represent what he calls partial autonomy apps, with sliding scales of control for the user.
In these apps, humans perform verification while AIs handle generation, and GUIs become crucial for maintaining speed and safety.
AI as Utilities, Fabs, and Operating Systems
Karpathy introduced a powerful metaphor: LLMs as a new form of operating system. Just as Windows or Linux manage memory and processes, LLMs orchestrate knowledge and tasks. He explains that while LLMs operate with the reliability and ubiquity of utilities (like electricity), they also require the massive capex and infrastructure akin to semiconductor fabs.
But the most accurate analogy, he claims, is that LLMs are emerging operating systems, with multimodal abilities, memory management (context windows), and apps running across multiple providers—just like early days of Linux vs. Windows.
Vibe Coding and Natural Language Development
Vibe coding—the concept of programming through intuition and natural language—has exploded, thanks in part to Karpathy’s now-famous tweet. “I can’t program in Swift,” he said, “but I built an iOS app with an LLM in a day.”
The viral idea is about empowerment: anyone who speaks English can now create software. And this unlocks massive creative and economic potential, especially for young developers and non-programmers.
The Next Frontier: Building for AI Agents
Karpathy argues that today’s digital infrastructure was designed for humans and GUIs—not for autonomous agents. He proposes tools like llm.txt (analogous to robots.txt) to make content agent-readable, and praises platforms like Vercel and Stripe that are transitioning documentation and tooling to be LLM-native.
“You can’t just say ‘click this’ anymore,” he explains. Agents need precise, machine-readable instructions—not vague human UX metaphors.
He also showcases tools like Deep Wiki and Ingest to convert GitHub repos into digestible formats for LLMs. In short, we must rethink developer experience not just for humans, but for machine collaborators.
Iron Man Suits, Not Iron Man Robots
Karpathy closes with a compelling analogy: most AI applications today should act more like Iron Man suits (human-augmented intelligence) rather than fully autonomous Iron Man robots. We need GUIs for oversight, autonomy sliders to control risk, and workflows that let humans verify, adjust, and approve AI suggestions in tight loops.
“It’s not about replacing developers,” he emphasizes. “It’s about rewriting the stack, building intelligent tools, and creating software that collaborates with us.”
Takeaway:
The future of software isn’t just about writing better code. It’s about redefining what code is, who gets to write it, and how machines will interact with the web. Whether you’re a developer, founder, or student, learning to work with and build for LLMs isn’t optional—it’s the next operating system of the world.
