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Marc Andreessen: AI coding doesn’t eliminate programmers — it redefines them. The job is no longer typing code line by line, it’s orchestrating 10 coding bots in parallel, arguing with them, debugging their output, changing the spec, and pushing them toward the right result. But here’s the catch: if...

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Ian Miles Cheong

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911,487 просмотров • 4 месяцев назад •via X (Twitter)

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Anthropic CEO: “In the next 3 to 6 months, AI will write 90% of the code, and within 12 months, nearly all code may be generated by AI.” the job isn’t coding anymore, it’s telling machines what to build.
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Anthropic CEO: “In the next 3 to 6 months, AI will write 90% of the code, and within 12 months, nearly all code may be generated by AI.” the job isn’t coding anymore, it’s telling machines what to build.

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OpenAI Greg Brockman says the fraction of code not written by AI is vanishing With the right context and structure, the actual coding is already better handled by AI than by humans "what's left for human experts is the architecture" But the actual writing of code is essentially all AI now
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Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.
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Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

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“code is cheap, anyone can code with ai” sure, but the bottleneck has never been coding and it still never is. so if you misunderstand the real differentiating factor, you’re not gonna make it.
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“code is cheap, anyone can code with ai” sure, but the bottleneck has never been coding and it still never is. so if you misunderstand the real differentiating factor, you’re not gonna make it.

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“AI isn’t just changing how we write code. It’s starting to change what we choose to build with in the first place.” The Head of GitHub Next, Idan Gazit, shares his thoughts on TypeScript's rise, Python's role, and how AI is influencing our coding practices.👇
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“AI isn’t just changing how we write code. It’s starting to change what we choose to build with in the first place.” The Head of GitHub Next, Idan Gazit, shares his thoughts on TypeScript's rise, Python's role, and how AI is influencing our coding practices.👇

GitHub

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AI coding isn’t just a hype cycle — it could represent trillions worth of developer productivity. From Copilot to Cursor, AI is already changing how millions code; not someday, but now. Listen to the full conversation on how the future of coding is being rewritten — and what comes next with Yoko, Guido Appenzeller, and Matt Bornstein:
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AI coding isn’t just a hype cycle — it could represent trillions worth of developer productivity. From Copilot to Cursor, AI is already changing how millions code; not someday, but now. Listen to the full conversation on how the future of coding is being rewritten — and what comes next with Yoko, Guido Appenzeller, and Matt Bornstein:

a16z

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🚨 UNCANNY VALLEY: LEARNING TO CODE WON’T SAVE YOU Forget Python. If you don’t know how to think like an algorithm, Danks says the future will leave you behind. Dr. David Danks: “We need students to think computationally and algorithmically—but we don’t need them to write Python scripts.” In the world of AI, it’s not what you can code—it’s how you think that matters.
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🚨 UNCANNY VALLEY: LEARNING TO CODE WON’T SAVE YOU Forget Python. If you don’t know how to think like an algorithm, Danks says the future will leave you behind. Dr. David Danks: “We need students to think computationally and algorithmically—but we don’t need them to write Python scripts.” In the world of AI, it’s not what you can code—it’s how you think that matters.

Mario Nawfal

85,486 просмотров • 1 год назад

Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:
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Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:

Santiago

296,299 просмотров • 8 месяцев назад

Jensen Huang recently said he wants his engineers to spend 0% of their time coding. At first, it sounds absurd — until you realize what he really means. Coding is becoming automated. The new value isn’t in typing instructions for machines… …it’s in teaching machines what’s worth building. Jensen’s message is clear: The future belongs to engineers who understand systems, context, and creativity — not just syntax. As AI takes over execution, human intelligence must rise to orchestration. It’s not about writing code anymore. It’s about writing the future. What do you think — will the best engineers of 2026 still code, or will they design intelligence itself? #ArtificialIntelligence #AI #Coding #Technology #FutureOfWork #Leadership #Innovation #Automation #MachineLearning #AIandHumanity
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Jensen Huang recently said he wants his engineers to spend 0% of their time coding. At first, it sounds absurd — until you realize what he really means. Coding is becoming automated. The new value isn’t in typing instructions for machines… …it’s in teaching machines what’s worth building. Jensen’s message is clear: The future belongs to engineers who understand systems, context, and creativity — not just syntax. As AI takes over execution, human intelligence must rise to orchestration. It’s not about writing code anymore. It’s about writing the future. What do you think — will the best engineers of 2026 still code, or will they design intelligence itself? #ArtificialIntelligence #AI #Coding #Technology #FutureOfWork #Leadership #Innovation #Automation #MachineLearning #AIandHumanity

Pascal Bornet

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"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."
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"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."

Patrick OShaughnessy

24,818 просмотров • 4 месяцев назад

🚨UNCANNY CLIP: CODING ISN’T THE FUTURE…DESIGN IS As AI eats the low-level tasks, Danks says the next generation of engineers won’t be writing scripts—they’ll be designing systems. Dr. David Danks: “You won’t be writing that script in six months. What you need to know is why it’s the right one to use.” In the age of agentic AI, the biggest skill isn’t technical—it’s human.
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🚨UNCANNY CLIP: CODING ISN’T THE FUTURE…DESIGN IS As AI eats the low-level tasks, Danks says the next generation of engineers won’t be writing scripts—they’ll be designing systems. Dr. David Danks: “You won’t be writing that script in six months. What you need to know is why it’s the right one to use.” In the age of agentic AI, the biggest skill isn’t technical—it’s human.

Mario Nawfal

89,779 просмотров • 1 год назад

Larry Ellison just told every software engineer on Earth their job description is dead. Not evolving. Dead. Ellison: “The code that Oracle is writing, Oracle isn’t writing. Our AI models are writing.” This is not a startup demo. This is one of the largest infrastructure monopolies on the planet telling you it already replaced the people who built it. For fifty years, building software meant translating human intent into machine instructions. Line by line. Bug by bug. Sprint by sprint. That entire layer is gone. Ellison: “We don’t write the procedure. We declare our intent.” That sentence just made the entire engineering labor market flinch. The procedure was the job. The procedure was the paycheck. The procedure was what made a developer valuable. And now the machine does it without being asked twice. Ellison: “We just tell the model what we want the program to do, and then the AI comes up with a step-by-step process to actually do it.” You are no longer paid to build. You are paid to think. And most organizations have no idea how to evaluate that. The companies still hiring armies of developers to grind through codebases are paying salaries the machine already made worthless. Not in years. In seconds. When a company worth hundreds of billions hands the keyboard to the machine and tells you the output is better, the debate is not winding down. The debate is over. The enterprise that wins this decade does not write the best code. It removes the human from the process entirely and runs on intent alone. The programmers who survive are the ones who realize the craft is no longer typing. It is architecture. It is judgment. It is knowing what to build and why. Everything else now belongs to the machine. And the machine does not negotiate severance.
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Larry Ellison just told every software engineer on Earth their job description is dead. Not evolving. Dead. Ellison: “The code that Oracle is writing, Oracle isn’t writing. Our AI models are writing.” This is not a startup demo. This is one of the largest infrastructure monopolies on the planet telling you it already replaced the people who built it. For fifty years, building software meant translating human intent into machine instructions. Line by line. Bug by bug. Sprint by sprint. That entire layer is gone. Ellison: “We don’t write the procedure. We declare our intent.” That sentence just made the entire engineering labor market flinch. The procedure was the job. The procedure was the paycheck. The procedure was what made a developer valuable. And now the machine does it without being asked twice. Ellison: “We just tell the model what we want the program to do, and then the AI comes up with a step-by-step process to actually do it.” You are no longer paid to build. You are paid to think. And most organizations have no idea how to evaluate that. The companies still hiring armies of developers to grind through codebases are paying salaries the machine already made worthless. Not in years. In seconds. When a company worth hundreds of billions hands the keyboard to the machine and tells you the output is better, the debate is not winding down. The debate is over. The enterprise that wins this decade does not write the best code. It removes the human from the process entirely and runs on intent alone. The programmers who survive are the ones who realize the craft is no longer typing. It is architecture. It is judgment. It is knowing what to build and why. Everything else now belongs to the machine. And the machine does not negotiate severance.

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LeetCode is dead. Developers don't write code line-by-line anymore. They orchestrate AI agents working in parallel, review AI-generated code, and make architectural decisions. That's the job now. But most interview processes haven't caught up. They still test algorithm memorization instead of AI fluency, code review, and judgment. We're building assessments for next-gen hiring that mirror how developers actually work. Here's how we think about it:
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LeetCode is dead. Developers don't write code line-by-line anymore. They orchestrate AI agents working in parallel, review AI-generated code, and make architectural decisions. That's the job now. But most interview processes haven't caught up. They still test algorithm memorization instead of AI fluency, code review, and judgment. We're building assessments for next-gen hiring that mirror how developers actually work. Here's how we think about it:

HackerRank

342,700 просмотров • 1 месяц назад

Marc Andreessen just coined a term that perfectly describes what's actually happening to programmers right now and it's the opposite of what the doomers predicted (Save this). He calls them AI vampires. Andreessen's says that programmers using Codex, Claude Code and AI coding tools are not being replaced but they're working harder than ever, sleeping less than ever, with massive bags under their eyes and they are completely euphoric. What's remarkable is that the phenomenon extends far beyond professional engineers. Andreessen described an a16z partner who had never written a single line of code in his career, who built an entire AI powered work system for himself and when asked if he'd ever looked at the underlying code, the answer was simply "hell no." The data behind the anecdote is extraordinary. Andreessen says the leading-edge programmers at a16z portfolio companies are now 20x more productive than they were a year ago, the most dramatic increase in programmer productivity in the history of the industry. The METR May 2026 AI usage survey found technical workers self reporting a 1.4–2x change in work value from AI tools, with 75% of software engineers using AI for at least half their work. The software engineer hiring rate is actually increasing up to 22.77% of new hires in 2025 from 19.32% in late 2023 and companies are now bidding more aggressively for senior engineers specifically because AI empowered engineers have a higher ROI than ever before. The US economy added 115,000 jobs in April 2026 alone, beating the 62,000 consensus forecast precisely as AI adoption hit its highest level on record. This is exactly what basic economics predicts and what almost no one who writes about AI and jobs bothers to say. Classic marginal productivity theory says: when you increase the productivity of a worker, you don't diminish human work, you expand it. The worker becomes more productive, gets paid more, does more, and more jobs are created in the process. Andreessen's ATM analogy holds here because ATMs were supposed to eliminate bank tellers but instead, teller employment rose because lower operating costs let banks open more branches. The no-code AI market has exploded from $4.3 billion in 2023 to $21.2 billion in 2026 not because programmers are being replaced, but because the universe of people who can now build software has expanded by orders of magnitude. The blind spot, as Andreessen notes, is that productivity is now outrunning comprehension. The a16z partner building AI systems he's never looked at the code for represents something genuinely new, software being summoned faster than it can be understood. That's not necessarily dangerous but it does mean the verification, security, and governance layer of the AI development stack is more important now than it has ever been.
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Marc Andreessen just coined a term that perfectly describes what's actually happening to programmers right now and it's the opposite of what the doomers predicted (Save this). He calls them AI vampires. Andreessen's says that programmers using Codex, Claude Code and AI coding tools are not being replaced but they're working harder than ever, sleeping less than ever, with massive bags under their eyes and they are completely euphoric. What's remarkable is that the phenomenon extends far beyond professional engineers. Andreessen described an a16z partner who had never written a single line of code in his career, who built an entire AI powered work system for himself and when asked if he'd ever looked at the underlying code, the answer was simply "hell no." The data behind the anecdote is extraordinary. Andreessen says the leading-edge programmers at a16z portfolio companies are now 20x more productive than they were a year ago, the most dramatic increase in programmer productivity in the history of the industry. The METR May 2026 AI usage survey found technical workers self reporting a 1.4–2x change in work value from AI tools, with 75% of software engineers using AI for at least half their work. The software engineer hiring rate is actually increasing up to 22.77% of new hires in 2025 from 19.32% in late 2023 and companies are now bidding more aggressively for senior engineers specifically because AI empowered engineers have a higher ROI than ever before. The US economy added 115,000 jobs in April 2026 alone, beating the 62,000 consensus forecast precisely as AI adoption hit its highest level on record. This is exactly what basic economics predicts and what almost no one who writes about AI and jobs bothers to say. Classic marginal productivity theory says: when you increase the productivity of a worker, you don't diminish human work, you expand it. The worker becomes more productive, gets paid more, does more, and more jobs are created in the process. Andreessen's ATM analogy holds here because ATMs were supposed to eliminate bank tellers but instead, teller employment rose because lower operating costs let banks open more branches. The no-code AI market has exploded from $4.3 billion in 2023 to $21.2 billion in 2026 not because programmers are being replaced, but because the universe of people who can now build software has expanded by orders of magnitude. The blind spot, as Andreessen notes, is that productivity is now outrunning comprehension. The a16z partner building AI systems he's never looked at the code for represents something genuinely new, software being summoned faster than it can be understood. That's not necessarily dangerous but it does mean the verification, security, and governance layer of the AI development stack is more important now than it has ever been.

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136,646 просмотров • 26 дней назад

Every time I hear “Stop learning to code. AI will do it,” I worry. That is dangerous advice. Andrew Ng nailed it. When something becomes easier, more people should do it, not fewer. When coding moved from punch cards to keyboards, demand exploded. When higher level languages replaced assembly, people predicted the end of programmers. They were wrong. Now AI assisted coding is here. For the first time, a GenAI native graduate can outperform an experienced engineer stuck in old workflows. Impressive. But here’s what matters. The real advantage belongs to those who combine deep architectural thinking with AI fluency. In the companies I advise, the pattern is clear: - Experienced engineers who master AI - Deep thinkers who combine architecture + automation - Professionals who upgrade instead of resist AI lowers the barrier. It does not lower the ceiling. So let me ask you: Are you upgrading your skills with AI… or quietly assuming it will make your skills obsolete? #AI #ArtificialIntelligence #Coding #SoftwareEngineering #FutureOfWork #Technology
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Every time I hear “Stop learning to code. AI will do it,” I worry. That is dangerous advice. Andrew Ng nailed it. When something becomes easier, more people should do it, not fewer. When coding moved from punch cards to keyboards, demand exploded. When higher level languages replaced assembly, people predicted the end of programmers. They were wrong. Now AI assisted coding is here. For the first time, a GenAI native graduate can outperform an experienced engineer stuck in old workflows. Impressive. But here’s what matters. The real advantage belongs to those who combine deep architectural thinking with AI fluency. In the companies I advise, the pattern is clear: - Experienced engineers who master AI - Deep thinkers who combine architecture + automation - Professionals who upgrade instead of resist AI lowers the barrier. It does not lower the ceiling. So let me ask you: Are you upgrading your skills with AI… or quietly assuming it will make your skills obsolete? #AI #ArtificialIntelligence #Coding #SoftwareEngineering #FutureOfWork #Technology

Pascal Bornet

18,000 просмотров • 3 месяцев назад

We've built a massive verification bottleneck: • 72% of developers use AI to generate code every day • 96% of them don't fully trust it Writing code is now free. All the work now is verifying that the code does what it's supposed to. According to a new State of Code Survey, almost half of the respondents believe the number one skill that will matter most in the coming years is "reviewing and validating AI-generated code for quality and security". Not prompt engineering or vibe-coding, but checking AI's work.
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We've built a massive verification bottleneck: • 72% of developers use AI to generate code every day • 96% of them don't fully trust it Writing code is now free. All the work now is verifying that the code does what it's supposed to. According to a new State of Code Survey, almost half of the respondents believe the number one skill that will matter most in the coming years is "reviewing and validating AI-generated code for quality and security". Not prompt engineering or vibe-coding, but checking AI's work.

Santiago

16,299 просмотров • 4 месяцев назад

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!
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I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

Andrew Ng

1,223,364 просмотров • 1 год назад

🚨just in: the first AI coding agent for serious builder is here AI copilots help you write code. ClackyAI helps you build software. your agentic coding studio → L3 agentic AI, full-stack awareness, real-time debugging, parallel tasking join the beta now:
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🚨just in: the first AI coding agent for serious builder is here AI copilots help you write code. ClackyAI helps you build software. your agentic coding studio → L3 agentic AI, full-stack awareness, real-time debugging, parallel tasking join the beta now:

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31,332 просмотров • 11 месяцев назад