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Dario Amodei warns that software may soon become so cheap it feels almost free. The old model, building software for mass distribution to justify cost may no longer apply. Apps could be generated on-demand, even for simple, one-off use cases. But this flexibility comes at a cost: entire careers... show more

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81,457 views • 6 months ago •via X (Twitter)

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Anthropic CEO Dario Amodei : "Software is going to become cheap, maybe essentially free. The premise that you need to amortize a piece of software you build across millions of users, that may start to be false. But at the same time, there are whole jobs, whole careers that we've built for decades that may not be present. And, you know, I think we can deal with it. I think we can adjust to it. But I don't, I don't think there's an awareness at all of what, of what is coming here and the magnitude of it." --- From "The Wall Street Journal" YT channel (link in comment)

Anthropic CEO Dario Amodei : "Software is going to become cheap, maybe essentially free. The premise that you need to amortize a piece of software you build across millions of users, that may start to be false. But at the same time, there are whole jobs, whole careers that we've built for decades that may not be present. And, you know, I think we can deal with it. I think we can adjust to it. But I don't, I don't think there's an awareness at all of what, of what is coming here and the magnitude of it." --- From "The Wall Street Journal" YT channel (link in comment)

Rohan Paul

798,242 views • 2 months ago

#WATCH | Mumbai, Maharashtra: On the Jevons paradox and software demand expansion, Neelkanth Mishra, Executive Director Designate of the World Bank, says, “…If you think about what the major cost of a software firm is, it is people. So, if the writing of software is getting automated, those firms will be the first to make adjustments. But on the whole, it seems that the Jevons paradox—which dictates that as the cost of writing software falls, the amount of software written goes up—is actually playing out. If an XYZ company, which is a product company, was spending, say, $100 million on writing software and can now do it for $70 million, they might need to lay off some people; that is their business priority. But if the amount of software written goes up three to four times, the number of software engineers in the ecosystem will not change… This panic and paranoia about massive job losses is something that I think is, at this stage, unfounded.”

#WATCH | Mumbai, Maharashtra: On the Jevons paradox and software demand expansion, Neelkanth Mishra, Executive Director Designate of the World Bank, says, “…If you think about what the major cost of a software firm is, it is people. So, if the writing of software is getting automated, those firms will be the first to make adjustments. But on the whole, it seems that the Jevons paradox—which dictates that as the cost of writing software falls, the amount of software written goes up—is actually playing out. If an XYZ company, which is a product company, was spending, say, $100 million on writing software and can now do it for $70 million, they might need to lay off some people; that is their business priority. But if the amount of software written goes up three to four times, the number of software engineers in the ecosystem will not change… This panic and paranoia about massive job losses is something that I think is, at this stage, unfounded.”

ANI

114,594 views • 1 month ago

Introducing Autobot 🤖 Software agencies still overcharge clients by the hour. AI has fundamentally changed how software gets built, but clients aren't seeing those savings. So we built Autobot, the first autonomous Replit ⠕ agency that reduce the cost of agency-built software by 90%. Describe what you want to build, answer a few questions, and about an hour later your first working version is ready, for free. Try it, share it and if you love it, you can keep it. If not, you don't pay anything. That's how custom software should be bought.

Introducing Autobot 🤖 Software agencies still overcharge clients by the hour. AI has fundamentally changed how software gets built, but clients aren't seeing those savings. So we built Autobot, the first autonomous Replit ⠕ agency that reduce the cost of agency-built software by 90%. Describe what you want to build, answer a few questions, and about an hour later your first working version is ready, for free. Try it, share it and if you love it, you can keep it. If not, you don't pay anything. That's how custom software should be bought.

viktorTJM

90,352 views • 16 days ago

We’re in the “centaur” moment for software: humans + AI outperform either alone, but it won’t last. Then machines will dominate, and entry-level engineering gets wrecked fast. Don’t assume “we’ll adapt like before.” The pace is different: a brief phase where humans check AI code, then a rapid shift that guts junior white-collar paths. - Anthropic's CEO Dario Amodei "So after Garry Kasparov was beaten by Deep Blue, chess went through a 15–20 year stretch where a “centaur” team, a human checking an AI’s moves, could beat any human or any AI alone. That era eventually ended, and it became mostly just the machine. My worry is that we’re already in that centaur phase for software. During this phase, demand for software engineers might even increase, but the phase could be very short. Then entry-level white-collar work, especially software engineering, gets hit with a major disruption. People compare this to older labor shifts, like farming to industry to knowledge work, and say humans adapt. But those transitions played out over decades or centuries. This one is happening in just a few years, and that speed is the real concern." ---- Video from "Interesting Times with Ross Douthat + New York Times Opinion + New York Times Podcasts" YT channel (link in comment)

We’re in the “centaur” moment for software: humans + AI outperform either alone, but it won’t last. Then machines will dominate, and entry-level engineering gets wrecked fast. Don’t assume “we’ll adapt like before.” The pace is different: a brief phase where humans check AI code, then a rapid shift that guts junior white-collar paths. - Anthropic's CEO Dario Amodei "So after Garry Kasparov was beaten by Deep Blue, chess went through a 15–20 year stretch where a “centaur” team, a human checking an AI’s moves, could beat any human or any AI alone. That era eventually ended, and it became mostly just the machine. My worry is that we’re already in that centaur phase for software. During this phase, demand for software engineers might even increase, but the phase could be very short. Then entry-level white-collar work, especially software engineering, gets hit with a major disruption. People compare this to older labor shifts, like farming to industry to knowledge work, and say humans adapt. But those transitions played out over decades or centuries. This one is happening in just a few years, and that speed is the real concern." ---- Video from "Interesting Times with Ross Douthat + New York Times Opinion + New York Times Podcasts" YT channel (link in comment)

Rohan Paul

17,239 views • 5 months ago

In the AI era, the next great software distribution company won’t look like an app store For the last decade-plus, building a strong software business was mostly a game of scarcity. The hard part was assembling the capital, taste, engineering, and distribution to build one enduring app. The discovery systems we built matched that world: app stores, SEO, search bars, rankings, reviews, ads, marketplaces AI has blown up these constraints Soon, anyone will be able to generate software for anything: a workout injury, a work project, a personal workflow, a one-off analysis, a weird niche need only they have, maybe only in a single moment Some apps will last forever, but most won’t. Many will exist for minutes or hours, and be created even faster But just because software can be created at the speed of thought doesn’t mean everyone will become a developer. Most people won’t want to prompt, build, debug, deploy, or manage software. They’ll just want to be matched with the right tool for the job in front of them The same technological wave enabling software creation at scale is also breaking software distribution at scale. Search assumes the best answer already exists somewhere. App stores assume software is something you browse, compare, install, and keep In a world of personal software, the real question becomes: Who understands enough about me, or has earned enough trust from me, to know what software should exist for me right now? The next great app distribution companies may look less like search engines and more like trusted relationships Some will win by importing and processing personal context: your goals, constraints, calendar, body, files, workflows, history, collaborators, and intent. Others will win by exporting taste and trust: experts, creators, communities, agents, and brands that people rely on to decide what is worth using Either way, the new gateway won’t be a search box or an app store shelf. It will be the layer that recommends, assembles, routes, and personalizes software at the moment of need This is the new frontier: context, recommendation, trust, and distribution in the age of infinite software If you’re building platforms or experiences for this future, we want to meet you. Apply to a16z speedrun 🧊. Applications for SR007 close this weekend Application link below

In the AI era, the next great software distribution company won’t look like an app store For the last decade-plus, building a strong software business was mostly a game of scarcity. The hard part was assembling the capital, taste, engineering, and distribution to build one enduring app. The discovery systems we built matched that world: app stores, SEO, search bars, rankings, reviews, ads, marketplaces AI has blown up these constraints Soon, anyone will be able to generate software for anything: a workout injury, a work project, a personal workflow, a one-off analysis, a weird niche need only they have, maybe only in a single moment Some apps will last forever, but most won’t. Many will exist for minutes or hours, and be created even faster But just because software can be created at the speed of thought doesn’t mean everyone will become a developer. Most people won’t want to prompt, build, debug, deploy, or manage software. They’ll just want to be matched with the right tool for the job in front of them The same technological wave enabling software creation at scale is also breaking software distribution at scale. Search assumes the best answer already exists somewhere. App stores assume software is something you browse, compare, install, and keep In a world of personal software, the real question becomes: Who understands enough about me, or has earned enough trust from me, to know what software should exist for me right now? The next great app distribution companies may look less like search engines and more like trusted relationships Some will win by importing and processing personal context: your goals, constraints, calendar, body, files, workflows, history, collaborators, and intent. Others will win by exporting taste and trust: experts, creators, communities, agents, and brands that people rely on to decide what is worth using Either way, the new gateway won’t be a search box or an app store shelf. It will be the layer that recommends, assembles, routes, and personalizes software at the moment of need This is the new frontier: context, recommendation, trust, and distribution in the age of infinite software If you’re building platforms or experiences for this future, we want to meet you. Apply to a16z speedrun 🧊. Applications for SR007 close this weekend Application link below

Josh Lu

11,092 views • 2 months ago

Evan Spiegel on the lesson that killed his first startup and led to Snapchat: "We focused on building the perfect product for way too long before we got feedback. We worked for like eighteen months to build this perfect full-featured product, which was in direct contravention to how I was always taught to build things. Build a prototype, build an MVP, get it in front of people, learn as quickly as possible. But we had spent all this time building this perfect piece of software and we hadn't thought enough about distribution. While we built this great piece of software, our competitor at the time, Naviance, had secured distribution through all the different college counselors. What piece of software are you going to choose to help your kid get into college? The one recommended by the college counselors or the one from two kids at Stanford? I think it's a pretty easy choice. So we saw very early that we had no distribution advantage. Even if we loved our software, people weren't going to use it because we didn't have a scalable way to get it in people's hands. Around that time when we saw the emergence of the App Store on iPhone, it was very clear that was a distribution channel we could really use and benefit from. But we also needed to build things we could build quickly, things we really were going to use together with our friends so we could be the first early customers. Ultimately Peekaboo and Snapchat represented that." This is exactly the asymmetry Peter Thiel describes in Zero to One: superior distribution by itself can create a monopoly, even with no product differentiation. The converse is not true. Thiel puts an even finer point on it: most businesses get zero distribution channels to work. Poor sales rather than bad product is the most common cause of failure. If you can get just one distribution channel to work, you have a great business. Estée Lauder said: it's not enough to have the most wonderful product in the world. You must be able to sell it. Many founders over-index on product perfection and under-invest in distribution. The world is full of great products nobody knows about, and mediocre products with massive distribution that dominate markets. Evan Spiegel lived it. He built the better product, watched it lose, and then built something he could actually distribute.

Evan Spiegel on the lesson that killed his first startup and led to Snapchat: "We focused on building the perfect product for way too long before we got feedback. We worked for like eighteen months to build this perfect full-featured product, which was in direct contravention to how I was always taught to build things. Build a prototype, build an MVP, get it in front of people, learn as quickly as possible. But we had spent all this time building this perfect piece of software and we hadn't thought enough about distribution. While we built this great piece of software, our competitor at the time, Naviance, had secured distribution through all the different college counselors. What piece of software are you going to choose to help your kid get into college? The one recommended by the college counselors or the one from two kids at Stanford? I think it's a pretty easy choice. So we saw very early that we had no distribution advantage. Even if we loved our software, people weren't going to use it because we didn't have a scalable way to get it in people's hands. Around that time when we saw the emergence of the App Store on iPhone, it was very clear that was a distribution channel we could really use and benefit from. But we also needed to build things we could build quickly, things we really were going to use together with our friends so we could be the first early customers. Ultimately Peekaboo and Snapchat represented that." This is exactly the asymmetry Peter Thiel describes in Zero to One: superior distribution by itself can create a monopoly, even with no product differentiation. The converse is not true. Thiel puts an even finer point on it: most businesses get zero distribution channels to work. Poor sales rather than bad product is the most common cause of failure. If you can get just one distribution channel to work, you have a great business. Estée Lauder said: it's not enough to have the most wonderful product in the world. You must be able to sell it. Many founders over-index on product perfection and under-invest in distribution. The world is full of great products nobody knows about, and mediocre products with massive distribution that dominate markets. Evan Spiegel lived it. He built the better product, watched it lose, and then built something he could actually distribute.

David Senra

27,043 views • 3 months ago

🚨 ELON MUSK MAY HAVE JUST EXPOSED THE NEXT BIG PROBLEM FOR THE GLOBAL ECONOMY. Musk says AI demand is now so extreme that production shortages have become “insane.” At the same time, Tim Cook says tech cost inflation is the worst he has seen in 40 years. Now AI is starting to push up the cost of chips, electricity, software, smartphones, and data centers across the entire economy. The AI boom is no longer just a Bubble. It is starting to become an inflation problem too.

🚨 ELON MUSK MAY HAVE JUST EXPOSED THE NEXT BIG PROBLEM FOR THE GLOBAL ECONOMY. Musk says AI demand is now so extreme that production shortages have become “insane.” At the same time, Tim Cook says tech cost inflation is the worst he has seen in 40 years. Now AI is starting to push up the cost of chips, electricity, software, smartphones, and data centers across the entire economy. The AI boom is no longer just a Bubble. It is starting to become an inflation problem too.

Crypto Rover

371,713 views • 1 month ago

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 views • 2 months ago

Anthropic CEO: "there are jobs that took generations to build that may disappear" this is one of the best interviews I've seen in a long time Dario Amodei talks about how to prepare for what's coming here's what to expect: > high GDP growth and high unemployment at the same time > software becoming essentially free to build > the gap between people who use AI and people who don't the scariest part this is not a prediction, this is already happening to stay competitive you need to adapt fast and you can't do that while ignoring AI that's why I put together a guide on Claude features that 99% of users have no idea exist it will completely change how you work with Claude you can find it below

Anthropic CEO: "there are jobs that took generations to build that may disappear" this is one of the best interviews I've seen in a long time Dario Amodei talks about how to prepare for what's coming here's what to expect: > high GDP growth and high unemployment at the same time > software becoming essentially free to build > the gap between people who use AI and people who don't the scariest part this is not a prediction, this is already happening to stay competitive you need to adapt fast and you can't do that while ignoring AI that's why I put together a guide on Claude features that 99% of users have no idea exist it will completely change how you work with Claude you can find it below

Anatoli Kopadze

661,897 views • 2 months ago

Dario Amodei just told software engineers exactly how long they have. Six to twelve 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, I do the things around it.” The people building the most powerful AI in history have already stopped writing code. That is not a forecast. That is the current working condition inside the lab closest to the frontier. Amodei: “We might be six to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” The tech industry spent a decade making software engineers its highest-paid, most protected class. That era has a last day now. When a model can execute an entire software build end-to-end, the ability to write syntax stops being a skill. It becomes a credential for a job that no longer exists. Amodei: “And then it’s a question of how fast does that loop close.” That is the sentence everyone skipped. The code was never the hard part. The hard part was everything around it. The model just learned everything around it. Writing the code is already nearly gone. Testing is next. Deployment is next. When all three collapse into a single autonomous execution loop, the machine no longer needs a human in the chain at all. The corporation or sovereign state that closes that loop first does not gain a competitive advantage. It gains a category of speed that biological engineers cannot match, track, or reverse. That is not disruption. That is replacement at a systems level. Amodei is not describing a future disruption. He is describing the current state of his own building. The loop is already closing. The only question is whether you are inside it or outside it when it seals.

Dario Amodei just told software engineers exactly how long they have. Six to twelve 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, I do the things around it.” The people building the most powerful AI in history have already stopped writing code. That is not a forecast. That is the current working condition inside the lab closest to the frontier. Amodei: “We might be six to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” The tech industry spent a decade making software engineers its highest-paid, most protected class. That era has a last day now. When a model can execute an entire software build end-to-end, the ability to write syntax stops being a skill. It becomes a credential for a job that no longer exists. Amodei: “And then it’s a question of how fast does that loop close.” That is the sentence everyone skipped. The code was never the hard part. The hard part was everything around it. The model just learned everything around it. Writing the code is already nearly gone. Testing is next. Deployment is next. When all three collapse into a single autonomous execution loop, the machine no longer needs a human in the chain at all. The corporation or sovereign state that closes that loop first does not gain a competitive advantage. It gains a category of speed that biological engineers cannot match, track, or reverse. That is not disruption. That is replacement at a systems level. Amodei is not describing a future disruption. He is describing the current state of his own building. The loop is already closing. The only question is whether you are inside it or outside it when it seals.

Dustin

318,457 views • 5 months ago

🆕 Marc Andreessen’s 2026 AI Thesis: Agents, Open Source, and Why This Time Is Different Marc Andreessen 🇺🇸 of a16z says AI people keep swinging between utopian and apocalyptic for one simple reason: this field has been “almost here” for 80 years. But now, the breakthroughs are no longer theoretical. Reasoning, coding, agents, and self-improvement are all starting to work at once. This episode goes deep on AI winters, OpenAI + OpenClaw, infrastructure overbuild risk, proof-of-human, why software may soon be written mostly for bots, and why the real bottleneck may be society adopting AI rather than the models improving.

🆕 Marc Andreessen’s 2026 AI Thesis: Agents, Open Source, and Why This Time Is Different Marc Andreessen 🇺🇸 of a16z says AI people keep swinging between utopian and apocalyptic for one simple reason: this field has been “almost here” for 80 years. But now, the breakthroughs are no longer theoretical. Reasoning, coding, agents, and self-improvement are all starting to work at once. This episode goes deep on AI winters, OpenAI + OpenClaw, infrastructure overbuild risk, proof-of-human, why software may soon be written mostly for bots, and why the real bottleneck may be society adopting AI rather than the models improving.

Latent.Space

351,146 views • 4 months ago

Software may be starting to show early signs of life but I still think the “hard” side of AI has the cleaner lane for 2026 and probably most of 2027. We're nowhere close to done with the physical AI buildout since compute demand is still ahead of supply, power is becoming the bottleneck and every layer of AI infrastructure still needs more capacity. I own some software names but I still see this as more of an oversold bounce than a true leadership rotation away from the companies building the AI infrastructure layer. Software probably becomes a cleaner leadership group once inference costs fall, enterprise AI budgets expand and AI features start flowing through margins but that still feels more like a 2027 story to me.

Software may be starting to show early signs of life but I still think the “hard” side of AI has the cleaner lane for 2026 and probably most of 2027. We're nowhere close to done with the physical AI buildout since compute demand is still ahead of supply, power is becoming the bottleneck and every layer of AI infrastructure still needs more capacity. I own some software names but I still see this as more of an oversold bounce than a true leadership rotation away from the companies building the AI infrastructure layer. Software probably becomes a cleaner leadership group once inference costs fall, enterprise AI budgets expand and AI features start flowing through margins but that still feels more like a 2027 story to me.

Shay Boloor

122,716 views • 2 months ago

Marc Andreesen on why building companies will become more expensive in the AI Era. There's a notion that AI will help cut down costs and increase efficiency. However, Marc presented a strong counterargument by referencing Jevon's paradox. Jevon's paradox: The Jevon's paradox occurs when technological progress increases the efficiency with which a resource is used (reducing the amount necessary for any one use), but the falling cost of use induces increases in demand enough that resource use is increased, rather than reduced. He gave some eg of Jevon's Paradox: ->Building roads will only lead to more cars & resulting in traffic. ->CGI in hollywood was developed to reduce the cost of film making but people's expectation increased, so cost involved in CGI also increased. ->Coal consumption increased in Industrial Revolution when the coal prices decreased. He says,The paradox here making cost of a given piece of software would be reduced, but the massive surge of demand for more powerful softwares will actually increase the cost of building a software company. Customers will start seeking for more & more powerful features.

Marc Andreesen on why building companies will become more expensive in the AI Era. There's a notion that AI will help cut down costs and increase efficiency. However, Marc presented a strong counterargument by referencing Jevon's paradox. Jevon's paradox: The Jevon's paradox occurs when technological progress increases the efficiency with which a resource is used (reducing the amount necessary for any one use), but the falling cost of use induces increases in demand enough that resource use is increased, rather than reduced. He gave some eg of Jevon's Paradox: ->Building roads will only lead to more cars & resulting in traffic. ->CGI in hollywood was developed to reduce the cost of film making but people's expectation increased, so cost involved in CGI also increased. ->Coal consumption increased in Industrial Revolution when the coal prices decreased. He says,The paradox here making cost of a given piece of software would be reduced, but the massive surge of demand for more powerful softwares will actually increase the cost of building a software company. Customers will start seeking for more & more powerful features.

Aish

810,390 views • 2 years ago

Dario Amodei, CEO of Anthropic, just shortened your career timeline. His own engineers have stopped writing code. 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, I do the things around it.” The people building the most advanced AI on Earth are already being replaced by what they built. Not in theory. Not in a forecast. Inside the building. Right now. Amodei: “I think we might be 6 to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” Six to twelve months. Not from automating busywork. From replacing the full scope of what a software engineer does. Architecture. Logic. Debugging. Deployment. The entire chain. Software engineering is not some fading trade. It is the highest-paid, highest-demand, most protected skill the modern economy ever produced. And the man running a frontier lab just gave it a six-month shelf life. If the most technically sophisticated job in the economy falls first, nothing beneath it is safe. That is the inversion no one saw coming. The assumption was always that AI would eat from the bottom. Routine work. Data entry. Simple automation. It started at the top. Engineers first. Then analysts. Then strategists. Then the managers overseeing work that no longer needs them. The displacement doesn’t crawl upward. It cascades downward. Starting with the people closest to the technology itself. Amodei: “If I had to guess, I would guess that this goes faster than people imagine, and that that key element of code, and increasingly research, going faster than we imagine.” Not just code. Research. Hypotheses. Experiments. Interpretation. Discovery itself. If AI closes that loop, it doesn’t just write software. It improves itself. Every iteration compresses the timeline further. Amodei: “It’s very hard for me to see how it could take longer than a few years.” He is not selling optimism. He is setting a ceiling. A few years. Maximum. For AI to absorb the two most important intellectual functions in the economy. The window to position yourself is not a decade. It is already closing.

Dario Amodei, CEO of Anthropic, just shortened your career timeline. His own engineers have stopped writing code. 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, I do the things around it.” The people building the most advanced AI on Earth are already being replaced by what they built. Not in theory. Not in a forecast. Inside the building. Right now. Amodei: “I think we might be 6 to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” Six to twelve months. Not from automating busywork. From replacing the full scope of what a software engineer does. Architecture. Logic. Debugging. Deployment. The entire chain. Software engineering is not some fading trade. It is the highest-paid, highest-demand, most protected skill the modern economy ever produced. And the man running a frontier lab just gave it a six-month shelf life. If the most technically sophisticated job in the economy falls first, nothing beneath it is safe. That is the inversion no one saw coming. The assumption was always that AI would eat from the bottom. Routine work. Data entry. Simple automation. It started at the top. Engineers first. Then analysts. Then strategists. Then the managers overseeing work that no longer needs them. The displacement doesn’t crawl upward. It cascades downward. Starting with the people closest to the technology itself. Amodei: “If I had to guess, I would guess that this goes faster than people imagine, and that that key element of code, and increasingly research, going faster than we imagine.” Not just code. Research. Hypotheses. Experiments. Interpretation. Discovery itself. If AI closes that loop, it doesn’t just write software. It improves itself. Every iteration compresses the timeline further. Amodei: “It’s very hard for me to see how it could take longer than a few years.” He is not selling optimism. He is setting a ceiling. A few years. Maximum. For AI to absorb the two most important intellectual functions in the economy. The window to position yourself is not a decade. It is already closing.

Dustin

16,132 views • 3 months ago

Marc Bhargava (Marc Bhargava) Managing Director at General Catalyst on how 'AI Roll-Ups' are unlocking software-like margins for a much larger TAM "Why hasn't everyone in VC rushed to invest in services businesses? like accounting legal IT insurance? Well they historically have not been that profitable. So they sort of break-even generally in many cases, well obviously as you get to scale in that $1T of revenue in software the marginal cost is really low & they can become very profitable & have amazing cash flow Companies like CrowdStrike or ServiceNow or others you know kind of show this model And of course the hyper scalers like Amazon, Google, Microsoft There are all these amazing proven examples in the software field of having very strong free cash flow & on high margins. Our thesis somewhat is well these un-sexy services industries that were break even or 15 to 20% EBITDA margins net income obviously much lower they can look like software from a margin profile 'Cause once you take out the repetitive tasks & you take out 30-40-50% of what people do & free them up to do the hard stuff with the same cost basis you can 50% grow the revenue or double the revenue ..your margin profile now looks much much more similar to software. So we think in this much larger TAM than software, you can have software-like margins."

Molly O’Shea

27,038 views • 8 months ago

"We don't have enough machinists. We don't have enough quality inspectors. We don't have enough welders in the country." Hadrian CEO Chris Power on how a modern software-driven factory works differently from traditional manufacturing: "At that task level, we have to get them 90% more productive." "If you want to train a Navy welder today it's going to take you a decade. So we have to augment the skills with software." "The flexibility to constructing something like a submarine demands is incredibly what we call high-mix, low-volume." "If you're building a Toyota Camry you're making 20,000 of them a year... You can easily automate a million iPhones." "What the software enables is running at that level of factory productivity but with the flexibility that something like a submarine demands." "So it's really the velocity and agility and reducing the amount of people because we just don't have the people in the US." "We have to get this productivity uplift with software and a new workforce." Chris Power Hadrian David Ulevitch 🇺🇸

"We don't have enough machinists. We don't have enough quality inspectors. We don't have enough welders in the country." Hadrian CEO Chris Power on how a modern software-driven factory works differently from traditional manufacturing: "At that task level, we have to get them 90% more productive." "If you want to train a Navy welder today it's going to take you a decade. So we have to augment the skills with software." "The flexibility to constructing something like a submarine demands is incredibly what we call high-mix, low-volume." "If you're building a Toyota Camry you're making 20,000 of them a year... You can easily automate a million iPhones." "What the software enables is running at that level of factory productivity but with the flexibility that something like a submarine demands." "So it's really the velocity and agility and reducing the amount of people because we just don't have the people in the US." "We have to get this productivity uplift with software and a new workforce." Chris Power Hadrian David Ulevitch 🇺🇸

a16z

46,195 views • 4 months ago

Founders who never built companies pre-AI have at least one big advantage: they have fewer preconceived notions about how to build a company. This applies to how software should be built, when a product that people worked really hard on should get dissolved, what tools finance should use, how GTM should work, or really anything and everything else. "The culture is: you don't maximize for your function, you maximize for the company."

Founders who never built companies pre-AI have at least one big advantage: they have fewer preconceived notions about how to build a company. This applies to how software should be built, when a product that people worked really hard on should get dissolved, what tools finance should use, how GTM should work, or really anything and everything else. "The culture is: you don't maximize for your function, you maximize for the company."

Jack Altman

46,483 views • 4 months ago

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 views • 5 months ago

Cognition founder Scott Wu says most software never gets built because the economics don't work: "The math only really works out to create software if it's going to be used at least a million times or something. Maybe it's 10,000 times or whatever." "You need that software to be used enough times or to create enough value for that to be worth it." "There are so many things out there which only need to be used a few times or even only need to be used once." "I'm going to go look through these 15 LinkedIn profiles. Or fill out these forms. Or do this data analysis and put this Excel sheet together—all of these things are things that could be done with software." "It obviously doesn't make sense to hire a whole team of people to go make you that piece of software which you're going to use one time and never again—versus just having the human go and do that themselves."

Cognition founder Scott Wu says most software never gets built because the economics don't work: "The math only really works out to create software if it's going to be used at least a million times or something. Maybe it's 10,000 times or whatever." "You need that software to be used enough times or to create enough value for that to be worth it." "There are so many things out there which only need to be used a few times or even only need to be used once." "I'm going to go look through these 15 LinkedIn profiles. Or fill out these forms. Or do this data analysis and put this Excel sheet together—all of these things are things that could be done with software." "It obviously doesn't make sense to hire a whole team of people to go make you that piece of software which you're going to use one time and never again—versus just having the human go and do that themselves."

David Senra

37,919 views • 29 days ago

🚨 ELON MUSK: "AI will outperform humans at most jobs." “I do think it’s going to be a bumpy road, because AI will be able to do any job better than any person can do that job. That will be true very quickly for jobs that are digital, or really anything that involves a person at a computer or on a phone. AI will be able to do all of that very soon. For software engineering, we already have a situation where AI is better than at least 90% of humans at writing software, including 90% of professional software engineers. It really is getting to the point where it will be better than 99%, and then it will get to the point where there’s just no way to compete. It will get to what I call Stockfish level. Stockfish is a chess program that is so good at chess that it will easily beat Magnus Carlsen, or anyone, even while running on a small computer. I suspect you could probably run Stockfish on your phone and beat Magnus Carlsen. That’s how good it is at chess, and that’s how good AI will be at writing software. And that applies to everything.”

🚨 ELON MUSK: "AI will outperform humans at most jobs." “I do think it’s going to be a bumpy road, because AI will be able to do any job better than any person can do that job. That will be true very quickly for jobs that are digital, or really anything that involves a person at a computer or on a phone. AI will be able to do all of that very soon. For software engineering, we already have a situation where AI is better than at least 90% of humans at writing software, including 90% of professional software engineers. It really is getting to the point where it will be better than 99%, and then it will get to the point where there’s just no way to compete. It will get to what I call Stockfish level. Stockfish is a chess program that is so good at chess that it will easily beat Magnus Carlsen, or anyone, even while running on a small computer. I suspect you could probably run Stockfish on your phone and beat Magnus Carlsen. That’s how good it is at chess, and that’s how good AI will be at writing software. And that applies to everything.”

DogeDesigner

61,112 views • 15 days ago