Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

In my recent interactions with CTOs and Eng managers, this is coming up a lot: The SDLC is changing shape. We are moving from the "Middle Heavy: Coding" era to the Hourglass. Let me explain! Here is the breakdown of the Agentic SDLC: 🔻 THE TOP: INTENT (Heavy) The...

pvergadia's profile picture

Priyanka Vergadia

48,586 subscribers

18,085 görüntüleme • 7 ay önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

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)
0:59

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 görüntüleme • 2 ay önce

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 you don’t understand how to write code yourself, you can’t evaluate what the AI gives you. The next layer of programming isn’t writing scripts — it’s supervising AI that writes them. Today’s best programmers spend their day jumping between terminals, managing multiple coding bots, fixing mistakes, and refining instructions. The irony? You still need deep fundamentals, because without them, you won’t know when the AI is wrong. The job of the programmer has changed. Now it’s about arguing with coding bots, debugging AI-generated code, and understanding why something doesn’t work or isn’t fast enough. AI abstracts the work — but only people who truly understand code can tell if the abstraction is doing the right thing. Programmers aren’t going away — they’re becoming 10x, 100x, even 1,000x more productive. Tasks are changing, the job is changing, but humans are still overseeing the process, evaluating results, fixing errors, and making judgment calls. AI changes how we code, not who is responsible. The future programmer isn’t replaced by AI — they’re upgraded by it. You still need to learn how to write and understand code, because when the AI gets it wrong, humans are the ones who have to know why. That up-leveling of capability is the real revolution.
1:50

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 you don’t understand how to write code yourself, you can’t evaluate what the AI gives you. The next layer of programming isn’t writing scripts — it’s supervising AI that writes them. Today’s best programmers spend their day jumping between terminals, managing multiple coding bots, fixing mistakes, and refining instructions. The irony? You still need deep fundamentals, because without them, you won’t know when the AI is wrong. The job of the programmer has changed. Now it’s about arguing with coding bots, debugging AI-generated code, and understanding why something doesn’t work or isn’t fast enough. AI abstracts the work — but only people who truly understand code can tell if the abstraction is doing the right thing. Programmers aren’t going away — they’re becoming 10x, 100x, even 1,000x more productive. Tasks are changing, the job is changing, but humans are still overseeing the process, evaluating results, fixing errors, and making judgment calls. AI changes how we code, not who is responsible. The future programmer isn’t replaced by AI — they’re upgraded by it. You still need to learn how to write and understand code, because when the AI gets it wrong, humans are the ones who have to know why. That up-leveling of capability is the real revolution.

Ian Miles

913,087 görüntüleme • 6 ay önce

Sam Altman says soon everyone will be a software engineer and he might be right Sam Altman just casually dropped one of the biggest takes about the future of work and software and people are still sleeping on it. Here’s the core idea, and it’s honestly wild: Natural language is the new syntax You won’t write code. You’ll describe what you want in plain English. Talking to computers becomes the default programming interface. The end of the “army of developers” No product managers writing specs. No giant dev teams for v1. You describe the app and the AI builds it. The overnight app You explain your idea, go to sleep, and the AI spends the night writing, testing, and wiring everything together. You wake up and the product exists. Coffee optional but recommended ☕ Autonomous software agents For complex systems, AI agents live inside the codebase itself. They crawl the repo, fix bugs, write tests, refactor code, and commit changes on their own. A digital workforce that never sleeps Not copilots. Not autocomplete. Actual agents doing ongoing engineering work without supervision. Beyond coding: total company automation Once software is automated, the same logic applies to operations, planning, and even parts of management. Code is just the first domino. If this plays out, “learning to code” becomes less important than learning to think clearly, describe intent, and spot good ideas. Question for you 👀 If everyone can build software, what actually becomes scarce: ideas, taste, or execution?
1:12

Sam Altman says soon everyone will be a software engineer and he might be right Sam Altman just casually dropped one of the biggest takes about the future of work and software and people are still sleeping on it. Here’s the core idea, and it’s honestly wild: Natural language is the new syntax You won’t write code. You’ll describe what you want in plain English. Talking to computers becomes the default programming interface. The end of the “army of developers” No product managers writing specs. No giant dev teams for v1. You describe the app and the AI builds it. The overnight app You explain your idea, go to sleep, and the AI spends the night writing, testing, and wiring everything together. You wake up and the product exists. Coffee optional but recommended ☕ Autonomous software agents For complex systems, AI agents live inside the codebase itself. They crawl the repo, fix bugs, write tests, refactor code, and commit changes on their own. A digital workforce that never sleeps Not copilots. Not autocomplete. Actual agents doing ongoing engineering work without supervision. Beyond coding: total company automation Once software is automated, the same logic applies to operations, planning, and even parts of management. Code is just the first domino. If this plays out, “learning to code” becomes less important than learning to think clearly, describe intent, and spot good ideas. Question for you 👀 If everyone can build software, what actually becomes scarce: ideas, taste, or execution?

VraserX e/acc

70,659 görüntüleme • 7 ay önce

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.
0:31

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.

Dustin

535,724 görüntüleme • 5 ay önce

Andrej Karpathy: "The future is not writing code. The future is writing the loops that write the code. The leverage point has moved one floor up." This is the shift that defines the next era of coding and quant building. 1 hour of pure insight on agentic AI coding and loop engineering from the OpenAI co-founder who just joined Anthropic to build self improving Claude. Worth more than every $500 vibe coding course combined. Watch it, then read the full guide on how to build your first self improving quant trading loop below.
1:04:50

Andrej Karpathy: "The future is not writing code. The future is writing the loops that write the code. The leverage point has moved one floor up." This is the shift that defines the next era of coding and quant building. 1 hour of pure insight on agentic AI coding and loop engineering from the OpenAI co-founder who just joined Anthropic to build self improving Claude. Worth more than every $500 vibe coding course combined. Watch it, then read the full guide on how to build your first self improving quant trading loop below.

Roan

37,862 görüntüleme • 1 ay önce

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.
0:35

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.

Dustin

82,757 görüntüleme • 5 ay önce

Jensen Huang just told the world something nobody wants to hear. AI is not coming for your job. It is coming for the part of your job you mistakenly believe IS your job. Huang: “The purpose of your job and the tasks that you do in your job are related but not the same.” That one sentence is the fault line between the people who thrive in the next decade and the people who vanish from it. Huang used himself as proof. Reduce the CEO of Nvidia to his raw outputs and his entire career is typing and talking. Both have been automated to superhuman levels. Huang: “Typing and talking have both been automated to a superhuman level by AI. And yet, I’m busier than ever.” The man building the infrastructure that automates human labor has never worked harder. That should stop you cold. We look at a profession and see the tasks. The motions. The mechanical friction. We never see the intent underneath. And when AI arrives, we panic. Because we confuse the task with the job. The task was never the job. It was always the bottleneck between a human and their actual purpose. Now the bottleneck is dissolving. Years ago, the experts declared radiology dead. The algorithm could read a scan better than any human. A generation of medical students listened. They walked away from the field. The result was catastrophic. Huang: “We need more radiologists than ever, and we don’t have enough.” The algorithm did not replace the doctor. It armed the doctor. Suddenly the department could see more patients. Catch more anomalies. Generate more revenue. The hospital did not fire the radiologists. It tried to hire more. And could not find them. Because we terrified an entire generation out of a career with a prediction that landed exactly backwards. Now the same hysteria is consuming software engineering. The timeline is screaming that coding is dead. Meanwhile, inside the very company building the hardware that automates code. Huang: “The software engineers that know how to use AI, know how to work with agentic systems, are the most popular and the most successful.” The tool did not replace the architect. It replaced the shovel. This is the pattern nobody wants to confront. AI does not eliminate the human. It eliminates the friction that made the human slow. And when the friction disappears, demand for the human explodes. But only if the human shows up. The ones who defined themselves by the mechanical act of writing code are fading. The ones who defined themselves by what the code was meant to build are now the most valuable people on the planet. That is not a nuance. That is the entire dividing line. The machine will write the script. Read the scan. Draft the brief. It will never possess the reason any of it needed to exist. The task was never the job. And nobody who figures that out last gets the privilege of figuring it out twice.
2:00

Jensen Huang just told the world something nobody wants to hear. AI is not coming for your job. It is coming for the part of your job you mistakenly believe IS your job. Huang: “The purpose of your job and the tasks that you do in your job are related but not the same.” That one sentence is the fault line between the people who thrive in the next decade and the people who vanish from it. Huang used himself as proof. Reduce the CEO of Nvidia to his raw outputs and his entire career is typing and talking. Both have been automated to superhuman levels. Huang: “Typing and talking have both been automated to a superhuman level by AI. And yet, I’m busier than ever.” The man building the infrastructure that automates human labor has never worked harder. That should stop you cold. We look at a profession and see the tasks. The motions. The mechanical friction. We never see the intent underneath. And when AI arrives, we panic. Because we confuse the task with the job. The task was never the job. It was always the bottleneck between a human and their actual purpose. Now the bottleneck is dissolving. Years ago, the experts declared radiology dead. The algorithm could read a scan better than any human. A generation of medical students listened. They walked away from the field. The result was catastrophic. Huang: “We need more radiologists than ever, and we don’t have enough.” The algorithm did not replace the doctor. It armed the doctor. Suddenly the department could see more patients. Catch more anomalies. Generate more revenue. The hospital did not fire the radiologists. It tried to hire more. And could not find them. Because we terrified an entire generation out of a career with a prediction that landed exactly backwards. Now the same hysteria is consuming software engineering. The timeline is screaming that coding is dead. Meanwhile, inside the very company building the hardware that automates code. Huang: “The software engineers that know how to use AI, know how to work with agentic systems, are the most popular and the most successful.” The tool did not replace the architect. It replaced the shovel. This is the pattern nobody wants to confront. AI does not eliminate the human. It eliminates the friction that made the human slow. And when the friction disappears, demand for the human explodes. But only if the human shows up. The ones who defined themselves by the mechanical act of writing code are fading. The ones who defined themselves by what the code was meant to build are now the most valuable people on the planet. That is not a nuance. That is the entire dividing line. The machine will write the script. Read the scan. Draft the brief. It will never possess the reason any of it needed to exist. The task was never the job. And nobody who figures that out last gets the privilege of figuring it out twice.

Dustin

52,882 görüntüleme • 3 ay önce

“AI is changing the value prop of software.” We spoke with Metronome CEO Scott Woody about why AI requires a new business model. “In the cloud era…the value of the software scales as your org scales.” “In AI, the software is now doing work on your behalf…so the core value of the software is not who in my org has access to the data, it's what can this software do for me? When the value shifts from access to output, the business model has to follow.
1:36

“AI is changing the value prop of software.” We spoke with Metronome CEO Scott Woody about why AI requires a new business model. “In the cloud era…the value of the software scales as your org scales.” “In AI, the software is now doing work on your behalf…so the core value of the software is not who in my org has access to the data, it's what can this software do for me? When the value shifts from access to output, the business model has to follow.

a16z

23,455 görüntüleme • 1 yıl önce

New podcast on AI (full episode). Links below. A Motorcycle for the Mind 0:00 If you want to learn, do 2:13 Vibe coding is the new product management 6:49 Training models is the new coding 10:13 Is traditional software engineering dead? 13:07 There is no demand for average 14:12 The hottest new programming language is English 18:36 AI is adapting to us faster than we are adapting to it 22:56 No entrepreneur is worried about AI taking their job 26:46 The goal is not to have a job 29:49 AIs are not alive 32:55 AI fails the only true test of intelligence 36:49 Early adopters of AI have an enormous edge 39:37 AI meets you exactly where you are 43:02 Always leverage the best intelligence 44:37 If you can't define it, you can't program it 49:37 The solution to AI anxiety is action
52:05

New podcast on AI (full episode). Links below. A Motorcycle for the Mind 0:00 If you want to learn, do 2:13 Vibe coding is the new product management 6:49 Training models is the new coding 10:13 Is traditional software engineering dead? 13:07 There is no demand for average 14:12 The hottest new programming language is English 18:36 AI is adapting to us faster than we are adapting to it 22:56 No entrepreneur is worried about AI taking their job 26:46 The goal is not to have a job 29:49 AIs are not alive 32:55 AI fails the only true test of intelligence 36:49 Early adopters of AI have an enormous edge 39:37 AI meets you exactly where you are 43:02 Always leverage the best intelligence 44:37 If you can't define it, you can't program it 49:37 The solution to AI anxiety is action

Naval

2,364,659 görüntüleme • 6 ay önce

"the people who struggle with coding agents are those who try to push their way too hard. partially why I find coding with agents easy is that I've led engineering teams before". this is a unique view on AI coding, that to maximize your output you have to "let go" a bit and accept that the way AI will build the project might not be 100% aligned with your way, it might not even be 100% correct, but it will push the project forward. and if you realize later that you don't like an approach, you can always go back to it later and iterate over it. so there's not always an absolute total gain, but the overall performance is what matters in the end.
1:33

"the people who struggle with coding agents are those who try to push their way too hard. partially why I find coding with agents easy is that I've led engineering teams before". this is a unique view on AI coding, that to maximize your output you have to "let go" a bit and accept that the way AI will build the project might not be 100% aligned with your way, it might not even be 100% correct, but it will push the project forward. and if you realize later that you don't like an approach, you can always go back to it later and iterate over it. so there's not always an absolute total gain, but the overall performance is what matters in the end.

ℏεsam

165,093 görüntüleme • 6 ay önce

We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens
41:29

We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens

Y Combinator

67,145 görüntüleme • 3 ay önce

Anthropic CEO Dario Amodei just revealed the hidden bottleneck that will kill most AI companies in the next 18 months (Save this). The insight comes from a principle in computer science called Amdahl's Law. Dario's argument is simple when something starts working really well inside an organization, you have to immediately ask what isn't working well around it. Amdahl's Law states that the maximum speedup of any system is capped by the fraction you haven't improved and that applies to companies just as brutally as it applies to processors. If you can suddenly write three or four times as many pull requests as before, you don't get three or four times the output but you rather get a pile of code no one can review, verify, or trust. The data makes this impossible to ignore. Teams with heavy AI coding adoption are merging 98% more pull requests but PR review time has ballooned 91%, deployment velocity is effectively flat and 96% of developers don't fully trust AI-generated code reaching production. AI generated code produces 1.7x more issues per pull request than human written code, 0.83 issues per PR versus 6.45. Veracode's 2026 State of Software Security report found that 82% of organizations now carry security debt, up 11% year over year, with critical security debt surging 36% in a single year driven directly by AI-generated code reaching production faster than security teams can handle. What Dario is describing is a systems problem, not a software problem and coding is roughly 20% of the software delivery cycle. Even at infinite coding speed, you're still bottlenecked by review, security, verification, testing, and deployment which make up the other 80%. The enterprises that win are the ones that identify which part of their system is the new constraint after AI accelerates the old one and fix that next. This is why Anthropic's Claude Code focuses on the full development loop, not just generation, and why the verification and security layer of the AI stack is where the next wave of enterprise value gets created. This is also why Anthropic as a company is positioned differently than most people realize. Anthropic's 2026 Agentic Coding Trends Report found that organizations using full-loop agentic coding workflows where AI handles not just generation but testing, review, and deployment validation reduced their software defect rates by 43% while increasing velocity by 2.8x. Claude Code now authors 4% of all GitHub commits and is on track to hit 20%+ by year-end, with the full-loop use case growing 3x faster than pure code generation. Dario has been building Anthropic around the exact insight he's describing publicly ,the constraint isn't writing code but rather everything that has to happen after.
0:48

Anthropic CEO Dario Amodei just revealed the hidden bottleneck that will kill most AI companies in the next 18 months (Save this). The insight comes from a principle in computer science called Amdahl's Law. Dario's argument is simple when something starts working really well inside an organization, you have to immediately ask what isn't working well around it. Amdahl's Law states that the maximum speedup of any system is capped by the fraction you haven't improved and that applies to companies just as brutally as it applies to processors. If you can suddenly write three or four times as many pull requests as before, you don't get three or four times the output but you rather get a pile of code no one can review, verify, or trust. The data makes this impossible to ignore. Teams with heavy AI coding adoption are merging 98% more pull requests but PR review time has ballooned 91%, deployment velocity is effectively flat and 96% of developers don't fully trust AI-generated code reaching production. AI generated code produces 1.7x more issues per pull request than human written code, 0.83 issues per PR versus 6.45. Veracode's 2026 State of Software Security report found that 82% of organizations now carry security debt, up 11% year over year, with critical security debt surging 36% in a single year driven directly by AI-generated code reaching production faster than security teams can handle. What Dario is describing is a systems problem, not a software problem and coding is roughly 20% of the software delivery cycle. Even at infinite coding speed, you're still bottlenecked by review, security, verification, testing, and deployment which make up the other 80%. The enterprises that win are the ones that identify which part of their system is the new constraint after AI accelerates the old one and fix that next. This is why Anthropic's Claude Code focuses on the full development loop, not just generation, and why the verification and security layer of the AI stack is where the next wave of enterprise value gets created. This is also why Anthropic as a company is positioned differently than most people realize. Anthropic's 2026 Agentic Coding Trends Report found that organizations using full-loop agentic coding workflows where AI handles not just generation but testing, review, and deployment validation reduced their software defect rates by 43% while increasing velocity by 2.8x. Claude Code now authors 4% of all GitHub commits and is on track to hit 20%+ by year-end, with the full-loop use case growing 3x faster than pure code generation. Dario has been building Anthropic around the exact insight he's describing publicly ,the constraint isn't writing code but rather everything that has to happen after.

Milk Road AI

52,190 görüntüleme • 3 ay önce

Right now, most developers are still doing the job AI should be doing. Assign a task. Wait. Review the result. Fix it. Repeat. You're not building with AI. You're babysitting it. The shift happening now is much bigger than better coding agents. It's loop engineering. Instead of managing every AI agent yourself, you build a system that manages them for you. One loop assigns the work. Another checks the output. A third retries failures. Everything keeps moving without waiting for your next prompt. Your job doesn't disappear. It changes. You stop acting like a project manager... ...and start designing the system that manages the entire team. That's exactly the transition I break down in the article below.
1:41

Right now, most developers are still doing the job AI should be doing. Assign a task. Wait. Review the result. Fix it. Repeat. You're not building with AI. You're babysitting it. The shift happening now is much bigger than better coding agents. It's loop engineering. Instead of managing every AI agent yourself, you build a system that manages them for you. One loop assigns the work. Another checks the output. A third retries failures. Everything keeps moving without waiting for your next prompt. Your job doesn't disappear. It changes. You stop acting like a project manager... ...and start designing the system that manages the entire team. That's exactly the transition I break down in the article below.

Pure

11,257 görüntüleme • 1 ay önce

When it comes to AI, the new hot thing everyone is talking about is Claude Code. Noah Brier, co-founder of Alephic, joins Joe Weisenthal and @‌tracyalloway to discuss the evolution of AI-assisted coding, what Claude Code actually is, and why traditional software firms have been getting destroyed in the stock market lately
1:29

When it comes to AI, the new hot thing everyone is talking about is Claude Code. Noah Brier, co-founder of Alephic, joins Joe Weisenthal and @‌tracyalloway to discuss the evolution of AI-assisted coding, what Claude Code actually is, and why traditional software firms have been getting destroyed in the stock market lately

Bloomberg

13,275 görüntüleme • 7 ay önce

I built dev tools at GitHub for 7 years before AI coding became the default. AI made code gen fast, but software still depends on the human mind knowing which details matter. AI only made that harder. That's why I built Nuanced, the AI coding app I want. Nuanced is a macOS app for building software with agents around a living spec, keeping intent and implementation connected as the agent builds. Try it:
1:52

I built dev tools at GitHub for 7 years before AI coding became the default. AI made code gen fast, but software still depends on the human mind knowing which details matter. AI only made that harder. That's why I built Nuanced, the AI coding app I want. Nuanced is a macOS app for building software with agents around a living spec, keeping intent and implementation connected as the agent builds. Try it:

ayman nadeem

12,822 görüntüleme • 1 ay önce

Marc Andreessen explains why AI coding won't replace programmers, but fundamentally change what they do. He argues that AI coding is just the latest abstraction layer, and the job of a programmer has always evolved with each one. Andreessen's key reframe of what's actually happening: "AI coding actually abstracts away the process of actually writing the scripting code... This is the next layer of the task redefinition under the job of programmer." He's clear that the best programmers aren't being replaced. They're already adapting, even if their day-to-day looks radically different now. Their job has shifted from writing code line by line to managing dozens of AI agents working in parallel. "The world's best programmers today will tell you, 'My job is I'm sitting there and I'm orchestrating 10 code bots running in parallel.' Their day job now is kind of arguing with the AI bots to try to get them to write the right code." But Marc Andreessen 🇺🇸 is adamant this doesn't make foundational knowledge obsolete — it makes it more important. "You need to still fully understand and learn how to write and understand code, because if it doesn't work or it's not doing what you expect, you need to be able to understand the results of what the AI is giving you." He draws a direct parallel: Just as someone writing scripting languages still needs to understand how a microprocessor works, someone orchestrating AI bots needs to understand the code those bots produce. "It's this upleveling of capability where you actually want the depth to go down and understand what the thing is actually doing, even if you're not spending your day doing that by hand." The result, in his view, is transformative: "Now programmers are going to be 10 times or 100 times or a thousand times more productive. And that is overwhelmingly a good thing." The pattern: New abstraction layer emerges → tasks change → the job gets redefined upward → productivity explodes It raises a question every programmer should be sitting with... Are you building the depth to evaluate what AI gives you, or just accepting the output?
1:50

Marc Andreessen explains why AI coding won't replace programmers, but fundamentally change what they do. He argues that AI coding is just the latest abstraction layer, and the job of a programmer has always evolved with each one. Andreessen's key reframe of what's actually happening: "AI coding actually abstracts away the process of actually writing the scripting code... This is the next layer of the task redefinition under the job of programmer." He's clear that the best programmers aren't being replaced. They're already adapting, even if their day-to-day looks radically different now. Their job has shifted from writing code line by line to managing dozens of AI agents working in parallel. "The world's best programmers today will tell you, 'My job is I'm sitting there and I'm orchestrating 10 code bots running in parallel.' Their day job now is kind of arguing with the AI bots to try to get them to write the right code." But Marc Andreessen 🇺🇸 is adamant this doesn't make foundational knowledge obsolete — it makes it more important. "You need to still fully understand and learn how to write and understand code, because if it doesn't work or it's not doing what you expect, you need to be able to understand the results of what the AI is giving you." He draws a direct parallel: Just as someone writing scripting languages still needs to understand how a microprocessor works, someone orchestrating AI bots needs to understand the code those bots produce. "It's this upleveling of capability where you actually want the depth to go down and understand what the thing is actually doing, even if you're not spending your day doing that by hand." The result, in his view, is transformative: "Now programmers are going to be 10 times or 100 times or a thousand times more productive. And that is overwhelmingly a good thing." The pattern: New abstraction layer emerges → tasks change → the job gets redefined upward → productivity explodes It raises a question every programmer should be sitting with... Are you building the depth to evaluate what AI gives you, or just accepting the output?

Big Brain AI

45,325 görüntüleme • 6 ay önce

Don't waste 2 years building AI agents the wrong way. An Anthropic engineer dropped a 1 hour course on the exact way to build AI agents from scratch. • 00:00 - building a Claude AI agent live • 34:01 - taking AI agents to production • 52:20 - AI agent harness Prompting is the old job. Building AI agentic loops is the new one. This one video replaces 10 paid courses on vibe coding. Bookmark & watch it now. Then build your own AI agent with the guide below.
1:05:54

Don't waste 2 years building AI agents the wrong way. An Anthropic engineer dropped a 1 hour course on the exact way to build AI agents from scratch. • 00:00 - building a Claude AI agent live • 34:01 - taking AI agents to production • 52:20 - AI agent harness Prompting is the old job. Building AI agentic loops is the new one. This one video replaces 10 paid courses on vibe coding. Bookmark & watch it now. Then build your own AI agent with the guide below.

Roan

14,167 görüntüleme • 14 gün önce

Re-introducing 8090... Two ways to work with us: → [BUILD] Software Factory: the platform for consulting and technology businesses building software with AI agents. The documentation stays alive because the system keeps it alive (no one ever updates the PRD on their own). → [BUY] 8090 Enterprise: AI-native custom software we design, build, and host tailored to your exact business needs. Give us a problem and we go execute. What part of your current SDLC is the most overdue for a rebuild?
3:40

Re-introducing 8090... Two ways to work with us: → [BUILD] Software Factory: the platform for consulting and technology businesses building software with AI agents. The documentation stays alive because the system keeps it alive (no one ever updates the PRD on their own). → [BUY] 8090 Enterprise: AI-native custom software we design, build, and host tailored to your exact business needs. Give us a problem and we go execute. What part of your current SDLC is the most overdue for a rebuild?

8090

301,221 görüntüleme • 3 ay önce

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch
56:43

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

GREG ISENBERG

84,497 görüntüleme • 2 ay önce