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The creator of Ruby on Rails just said programmers may be worse at AI coding than people who cannot code. Lex Fridman asked David Heinemeier Hansson directly. His answer: 100%. Because once the AI handles the implementation, knowing how to write a loop is no longer the rare skill.... The valuable skills become: What should this product do? Who is it for? What should version one include? What needs to be removed? What should the AI build first? A traditional programmer may tell the agent exactly how to solve the problem. That experience can become a trap. The agent follows the old path instead of finding a better one. Meanwhile, someone who understands users, products, priorities, and taste can describe the desired result and let the agent choose the route. Programming knowledge still matters when the system breaks. But AI is moving the bottleneck away from writing code. The advantage now belongs to the person who knows what should exist, not merely how to implement it. WATCH FULL PODCAST ON Lex Fridman YT CHANNELshow more

Ihtesham Ali

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143,315 Aufrufe • vor 2 Monaten •via X (Twitter)

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41 Kommentare

Profilbild von Adrian Novischi

Adrian Novischivor 2 Monaten

LOL. That's assumes that AI agents perfectly implement what you tell them, and if you just give them a general ask they will precisely fulfill your wishes and the language we speak is perfectly clear with no ambiguities and all words have a single clear sense.

Profilbild von Achim Friedland 🏴󠁧󠁢󠁥󠁮󠁧󠁿

Achim Friedland 🏴󠁧󠁢󠁥󠁮󠁧󠁿vor 2 Monaten

Yes… that‘s why ruby-on-rails was always nice but slow as hell! It is NOT enough to specify what the user wants, you also have to specify the technical details sometimes down to registers and database indices.

Profilbild von Ihtesham Ali

Ihtesham Alivor 2 Monaten

Very true and accurate

Profilbild von Andrew Apell | Software Generalist

Andrew Apell | Software Generalistvor 2 Monaten

Skill issue... Next he will blame a hammer for not knowing how to drive a nail into wood.

Profilbild von Ihtesham Ali

Ihtesham Alivor 2 Monaten

🤣 you are talking about a legend here

Profilbild von Andrew Apell | Software Generalist

Andrew Apell | Software Generalistvor 2 Monaten

😂🤣😂 I know but since 2016 I have been ignoring medals one might have and looking at what is right before me. You would be surprised how many times that has helped me.

Profilbild von Ihtesham Ali

Ihtesham Alivor 2 Monaten

yes, i can see where you are pointing at. Got it

Profilbild von scLLptr

scLLptrvor 2 Monaten

I call this hyper-vibe ai evangelism.

Profilbild von Ihtesham Ali

Ihtesham Alivor 2 Monaten

Are you aware of DHH?

Profilbild von scLLptr

scLLptrvor 2 Monaten

A am aware of ridiculousity of his claim. AI is 5 years from replacing whole SWE. It's too early now. This short phase is the phase of AI boosted fake confidence.

Profilbild von sajid 💫

sajid 💫vor 2 Monaten

there are good product guys who are also programmers.

Profilbild von Lucidos

Lucidosvor 2 Monaten

I agree for the coder if they're a jira factory worker. But a good software developer has always been the one with an architectural eye and that is still needed. Along with knowing what product to build and what makes it great. Architecture has no value on its own. It's what lets you evolve a great product.

Profilbild von Durbek

Durbekvor 2 Monaten

This is so confusing. You are saying that programmers are worse at AI because they can't do the product design? But even before AI, programmers were not doing product design. You are mixing up product design and programming. And adding AI here out of nowhere like wth.

Profilbild von ArthurMxn 🇲🇽🇺🇲

ArthurMxn 🇲🇽🇺🇲vor 2 Monaten

Totally agree! 💯 Working with my nephew, a programmer, on construction takeoff software. I'm the estimator, and I've used other software. He's full of technical jargon about all his implementation ideas – some great, some not so much. Ultimately, as the user, I know what needs to be built. 👷‍♂️💻

Profilbild von Nick Stewart

Nick Stewartvor 2 Monaten

This starts to break down when you are unable to describe the problem since you don’t have the pre-requisite knowledge needed

Profilbild von José Manuel

José Manuelvor 2 Monaten

This is the most retard take that I've seen.

Profilbild von SoftwareGuy

SoftwareGuyvor 2 Monaten

I get his point, my take is micromanagement. But I think I'd like my AI surgery to be overseen by a "surgeon" and not a "prompt surgeon".

Profilbild von 0xful 🇪🇺

0xful 🇪🇺vor 2 Monaten

He forgot that the coding agents were trained on human written code... which is average...

Profilbild von FreeRoamer

FreeRoamervor 2 Monaten

yes and no. For an excellent result you need to know what is going on behind the seams. Also, once the product reaches the masses you NEED to understand the system and tech stack, one mistake and your db is gone, you get hacked, get denials of services, nasty bugs etc.

Profilbild von Philip

Philipvor 2 Monaten

People with good taste will create the best products.

Profilbild von Nick Losciuto

Nick Losciutovor 2 Monaten

Well, that guy makes an awesome guest

Profilbild von P@ulLuo

P@ulLuovor 2 Monaten

You do realize ai generated code gets it wrong too sometimes? Somebody has to review generated code. Lol

Profilbild von luan au

luan auvor 2 Monaten

Any evidence so far?

Profilbild von dreadnaught

dreadnaughtvor 2 Monaten

hahahahahaha we've finally made it! so the nerds they employ are totally smart enough to use it, just not the other billion nerds out there. it's almost as stupid as the "opus 5 isn't bad you just have to purge everything from your environment for it to be good" stuff from the other month lol

Profilbild von David Joffe ⌨🖌🎨

David Joffe ⌨🖌🎨vor 2 Monaten

Yes and no. As an experienced developer I know software is complex and AIs also may add bugs or sometimes subtly incorrect behaviors that take valuable time to check. Just assuming some code works is a recipe for trouble. Software is a lot more complex than just "writing loops".

Profilbild von Crypto Cabrón

Crypto Cabrónvor 2 Monaten

Product owners are kings

Profilbild von Hussain Hashim | Building SundayBack

Hussain Hashim | Building SundayBackvor 2 Monaten

@ihteshamali honestly, it's wild to think about. feels like we're all just learning how to speak the AI's language now

Profilbild von wensei🔥

wensei🔥vor 2 Monaten

Meu vizinho, dev senior, obeso, mora com a mãe em Sapopemba, 45 anos, disse que ele é insubstituível e que em breve vai ficar rico arrumando bugs em códigos feito por vibe coders.

Profilbild von Vishnu Chilamakuru

Vishnu Chilamakuruvor 2 Monaten

Absolutely. Now writing code is not a moat. Understanding domain knowledge, knowing what should be the expected behaviour, what are all flows needs particularl change, all the domain level context became much important.

Profilbild von Pockets Full ⭕f NFT's

Pockets Full ⭕f NFT'svor 2 Monaten

Final someone not afraid to tell the truth

Profilbild von Nav Toor

Nav Toorvor 2 Monaten

Code is becoming the easy part

Profilbild von Alfred

Alfredvor 2 Monaten

I am a Solution Architect and I agree with DHH

Profilbild von Ihtesham Ali

Ihtesham Alivor 2 Monaten

Many would agree.

Profilbild von Simon Smith

Simon Smithvor 2 Monaten

It’s not like product managers with excellent product understanding have made unreasonable requests for decades already. The problem with today’s coding agents is that they won’t say no, and will move mountains of code to satisfy any architectural disaster.

Profilbild von Brian Gastón

Brian Gastónvor 2 Monaten

This tracks hard. I don’t win because I can code better than a senior. I win because I know the bar, the rush, the boring problems that actually matter. AI can implement. I decide what deserves to exist.

Profilbild von KnightHawk

KnightHawkvor 2 Monaten

Great for people who understand intuitive UX and product but also used to code.

Profilbild von Zack

Zackvor 2 Monaten

A lot of non tech founders are realizing this and jumping in the pool themselves

Profilbild von ppalme Cont.Learning

ppalme Cont.Learningvor 2 Monaten

What advantage now belongs to the person ….

Profilbild von Religion is poison

Religion is poisonvor 2 Monaten

Twisting his words, he said "there are problems" (not all problems), which any good programmer knows already.

Profilbild von 0x21

0x21vor 2 Monaten

Absolutely nailed it

Profilbild von Muhammad Suleman

Muhammad Sulemanvor 2 Monaten

i see his point first you make code work like an average joe but after that you also need to challenge it....LLM will usually contradict itself in longer discussion ...you give it few more lines of new context and it may totally abandon previous strategy

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Patrick OShaughnessy

24,818 Aufrufe • vor 9 Monaten

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

195,059 Aufrufe • vor 6 Monaten

Sam Altman told the world exactly what skills will matter when AI takes over 30 to 40 percent of the global economy. He was asked what his own kids should do to survive it. His answer was surprisingly human. He said the single most valuable thing anyone can build right now is the meta-skill of learning how to learn. Not a degree or a certification but the raw ability to adapt when everything around you changes. He also said learning to understand what other people actually want and building useful things for them will be more valuable than almost any technical knowledge. That skill has never been automated and is not close to being automated. He said human creativity and the desire to express it are, in his words, limitless. Every major technological revolution increased the demand for creative, curious, and socially intelligent people, not decreased it. The Industrial Revolution is the clearest parallel. Machines replaced physical labor and people were terrified. The next generation took those machines and built industries, art forms, and institutions nobody had conceived of before. The people who thrived were not the ones who competed with the machines. They were the ones who learned to direct them toward something new. That dynamic is already playing out right now with AI. The practical implication is this, depth in a single rigid skill is becoming less valuable. The ability to move across domains, pick up new tools quickly, and apply judgment in ambiguous situations is becoming more valuable. Altman also pointed to something most career advice ignores entirely, learning how to interact with the world, build relationships, and earn trust from other people. Those are things AI can simulate but cannot replace. The honest opportunity in this moment is not to outrun AI. It is to focus on the things that make you irreducibly human. Curiosity, judgment, empathy and the ability to ask the right question before anyone knows what the right question is. The people who will matter most in an AI-driven economy are not necessarily the ones who understand the technology deepest. They are the ones who can figure out what the technology should actually be used for. Altman has spent his career betting on human potential in the face of technological disruption. Based on every historical precedent, that is still the right bet to make.

Sam Altman told the world exactly what skills will matter when AI takes over 30 to 40 percent of the global economy. He was asked what his own kids should do to survive it. His answer was surprisingly human. He said the single most valuable thing anyone can build right now is the meta-skill of learning how to learn. Not a degree or a certification but the raw ability to adapt when everything around you changes. He also said learning to understand what other people actually want and building useful things for them will be more valuable than almost any technical knowledge. That skill has never been automated and is not close to being automated. He said human creativity and the desire to express it are, in his words, limitless. Every major technological revolution increased the demand for creative, curious, and socially intelligent people, not decreased it. The Industrial Revolution is the clearest parallel. Machines replaced physical labor and people were terrified. The next generation took those machines and built industries, art forms, and institutions nobody had conceived of before. The people who thrived were not the ones who competed with the machines. They were the ones who learned to direct them toward something new. That dynamic is already playing out right now with AI. The practical implication is this, depth in a single rigid skill is becoming less valuable. The ability to move across domains, pick up new tools quickly, and apply judgment in ambiguous situations is becoming more valuable. Altman also pointed to something most career advice ignores entirely, learning how to interact with the world, build relationships, and earn trust from other people. Those are things AI can simulate but cannot replace. The honest opportunity in this moment is not to outrun AI. It is to focus on the things that make you irreducibly human. Curiosity, judgment, empathy and the ability to ask the right question before anyone knows what the right question is. The people who will matter most in an AI-driven economy are not necessarily the ones who understand the technology deepest. They are the ones who can figure out what the technology should actually be used for. Altman has spent his career betting on human potential in the face of technological disruption. Based on every historical precedent, that is still the right bet to make.

StockMarket.News

377,183 Aufrufe • vor 7 Monaten

CEO of Nvidia said something that stopped me cold: "You may or may not lose a job to an AI, but you will absolutely lose a job to someone who uses AI" this 41 minutes is the most important talk on AI you'll watch this year so the question is not whether AI is coming for your job it's whether you're the one using it or the one being replaced by someone who does Nvidia already has hundreds of thousands of agents running inside the company right now agents are not the future, they are how the best teams work today I wrote a step by step guide on how to build your first AI agent with Claude Code you can find it in the article below

CEO of Nvidia said something that stopped me cold: "You may or may not lose a job to an AI, but you will absolutely lose a job to someone who uses AI" this 41 minutes is the most important talk on AI you'll watch this year so the question is not whether AI is coming for your job it's whether you're the one using it or the one being replaced by someone who does Nvidia already has hundreds of thousands of agents running inside the company right now agents are not the future, they are how the best teams work today I wrote a step by step guide on how to build your first AI agent with Claude Code you can find it in the article below

Anatoli Kopadze

14,783 Aufrufe • vor 4 Monaten

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)

Rohan Paul

16,584 Aufrufe • vor 5 Monaten

MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓

MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓

slash1s

236,758 Aufrufe • vor 5 Monaten

Max Tegmark says the way through the AI black box may be to deploy its outputs instead. A cat can learn, but it cannot export what it learned. A human scientist can. As intelligence rises, more knowledge may become externalized into code and proofs. Deploy what the mind can verify, not the mind itself.

Max Tegmark says the way through the AI black box may be to deploy its outputs instead. A cat can learn, but it cannot export what it learned. A human scientist can. As intelligence rises, more knowledge may become externalized into code and proofs. Deploy what the mind can verify, not the mind itself.

vitrupo

49,717 Aufrufe • vor 6 Monaten

Dario Amodei just announced the end of software engineering as a profession. The timeline is 6 to 12 months. Amodei: “I have engineers within Anthropic who say, I don’t write any code anymore. I just let the model write the code. I edit it.” Not a prediction. Current reality inside the frontier lab. The engineers who built the most advanced AI in the world have stopped writing code. They supervise. They edit. They manage architecture. The craft they spent careers mastering has been handed to the system they built. Amodei says models will do most, maybe all, of what software engineers do end-to-end within six to twelve months. Not assisting. Not autocompleting. Handling the entire development process independently. If you are learning syntax today, you are learning a dead language. Amodei: “Then it’s a question of how fast does that loop close?” The loop is this. AI writes code. Code builds better AI. Better AI writes better code. Faster. Without sleep. Without the cognitive limits that cap how quickly any human engineer can work. Once that loop closes, technological progress stops being constrained by human output. It becomes self-sustaining. Exponential. Operating at a pace no human workforce can match or direct. Software engineering isn’t ending. It’s becoming supervision. The developers who survive won’t be the best coders. They’ll be the best supervisors. The ones who can direct AI output, catch its failures, and architect what it builds toward. The skill that matters stops being implementation. It becomes judgment. Most developers are still optimizing for a skillset about to become as obsolete as stenography. While the people who built the systems replacing them already stopped doing the work themselves. The window to develop that judgment before the loop closes is exactly as long as Amodei’s timeline. Six to twelve months.

Dario Amodei just announced the end of software engineering as a profession. The timeline is 6 to 12 months. Amodei: “I have engineers within Anthropic who say, I don’t write any code anymore. I just let the model write the code. I edit it.” Not a prediction. Current reality inside the frontier lab. The engineers who built the most advanced AI in the world have stopped writing code. They supervise. They edit. They manage architecture. The craft they spent careers mastering has been handed to the system they built. Amodei says models will do most, maybe all, of what software engineers do end-to-end within six to twelve months. Not assisting. Not autocompleting. Handling the entire development process independently. If you are learning syntax today, you are learning a dead language. Amodei: “Then it’s a question of how fast does that loop close?” The loop is this. AI writes code. Code builds better AI. Better AI writes better code. Faster. Without sleep. Without the cognitive limits that cap how quickly any human engineer can work. Once that loop closes, technological progress stops being constrained by human output. It becomes self-sustaining. Exponential. Operating at a pace no human workforce can match or direct. Software engineering isn’t ending. It’s becoming supervision. The developers who survive won’t be the best coders. They’ll be the best supervisors. The ones who can direct AI output, catch its failures, and architect what it builds toward. The skill that matters stops being implementation. It becomes judgment. Most developers are still optimizing for a skillset about to become as obsolete as stenography. While the people who built the systems replacing them already stopped doing the work themselves. The window to develop that judgment before the loop closes is exactly as long as Amodei’s timeline. Six to twelve months.

Dustin

44,304 Aufrufe • vor 8 Monaten