Grad Conn

Mostly true. What matters is securing the long-term future of consciousness, both on Earth and other heavenly bodies. We cannot just focus on Earth, because there are irreducible external (eg massive meteor) and internal (eg global nuclear war) cataclysmic risks. The Moon is faster to make self-growing, but is more susceptible to problems on Earth. Mars will take longer to make self-growing, because it is so hard to reach, but is more secure from Earth disasters for that same reason. Both the Moon and Mars should have self-growing civilizations. Making this happen is the prime directive of SpaceX.

JUST IN: Anthropic rolls out new Claude tools aimed at automating legal work for lawyers & law firms.

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CANCEL your Tuesday morning plans. You NEED to: Watch the Anthropic engineer who wrote "Building Effective Agents" — 14 minutes Take notes. rewind. watch again. Build your first real agent — not a chatbot. a system. Stop guessing how agents actually work — he shows you. Read the 33-page Claude Skills guide — 1 sitting Never build another agent from scratch again You had Monday to start. You didn't. Now you're two days behind. Both guides are below.

The former director of AI at Tesla stood up at Y Combinator's AI Startup School in June 2025 and said something that made half the room of young developers realize they had been preparing for the wrong future. His name is Andrej Karpathy, and he is one of the only people alive who has been in the room for all three of the paradigm shifts that built modern AI. He was a founding member of OpenAI. He led the Autopilot team at Tesla. He designed and taught the first deep learning class at Stanford, which grew from 150 students in 2015 to 750 by 2017 and then escaped onto the internet where millions of people have watched it since. When he said something had fundamentally changed, the people in that room had every reason to listen. Here is the framework he walked through, and why it is the clearest map anyone has drawn of what just happened to software. He said there have now been three distinct eras of programming, and they are not replacements of each other. They are layers on top of each other, each one eating into the work that used to require the one below it. Software 1.0 is what almost everyone still means when they say code. A human being sits down, writes explicit step-by-step instructions in Python or C or JavaScript, and the computer does exactly what those instructions say. For seventy years, this was the only kind of software there was. Software 2.0 is the shift Karpathy himself named in a 2017 essay. He watched it happen in real time at Tesla. The team stopped writing explicit rules for how the car should recognize a stop sign and started showing a neural network millions of examples until it figured the pattern out on its own. The code was no longer the instructions. The code was the dataset and the network architecture, and the actual logic lived in the weights that came out of training. He wrote at the time that Software 2.0 was eating Software 1.0 one function at a time, and inside Tesla, he was watching hand-coded computer vision logic get deleted and replaced by learned weights week after week. Software 3.0 is the one that just arrived, and it is the one almost nobody has the right framework for yet. He said the line carefully. "The hottest new programming language is English." Not a metaphor. A literal statement about how software is now being built. You no longer need to write Python to produce behavior. You write a prompt in plain language, and a large language model executes the intent. The prompt is the program. The English is the source code. And the thing that makes this more than a productivity improvement is what he said next. Software 3.0 is eating Software 1.0 and Software 2.0 at the same time. Every traditional rule-based function that used to require a team of engineers can now be replaced by a prompt and a model call. Every narrow machine learning model that used to require millions of labeled examples can be replaced by a large model that was already trained on a significant fraction of the internet. The entire stack is being compressed upward into natural language. The implication he drew from this is the one that matters most for anyone trying to figure out what to build next. He said we are living through the single biggest expansion of accessibility in the history of computing. For seventy years, programming required learning a formal language that fewer than one percent of humans could ever become fluent in. In the span of about three years, the barrier has collapsed. The only language you need to program a computer now is the one you already speak. He used a phrase for this that sounded almost silly until you realize what it actually means. Vibe coding. The act of describing the program you want in loose natural language and letting the model handle the syntax, the structure, the boilerplate, and the integration. You do not need to know Swift to describe the iOS app you want to build. You describe the vibe, and the LLM handles the rest. But he was careful not to oversell it. He said LLMs are what he calls people spirits. Stochastic simulations of human reasoning with an emergent psychology and a set of very specific weaknesses that every builder now has to design around. They have jagged intelligence, meaning they can do astonishingly hard things and then fail at something a child could handle. They have anterograde amnesia, meaning they cannot form new long-term memory the way a human coworker would. They hallucinate. They get confused. They need supervision. Which means the job of a developer is not disappearing. It is changing shape. The best developers in the Software 3.0 era are not the ones who write the most code. They are the ones who can think in systems, design the right prompts, build the validation layers that catch the model when it drifts, and orchestrate an entire pipeline of specialized AI agents the way a conductor handles an orchestra. The specific line he kept coming back to is the one I keep thinking about. We are no longer just writing code. We are managing behavior. The people who will build the important things in the next decade are not the ones with the cleanest syntax. They are the ones who figured out, earlier than everyone else, that when English becomes a programming language, the bottleneck is no longer how well you can speak to the compiler. The bottleneck is how clearly you can think about what you actually want the machine to do. And that has always been the real skill. It is just that for seventy years, we had the luxury of hiding it behind the syntax.