Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

Microsoft canceling its internal Claude Code licenses this week likely has almost nothing to do with AI costs becoming untenable. Microsoft's own statement says Claude models remain accessible through Copilot CLI and Anthropic's Claude still runs inside Microsoft 365. The real story is that Microsoft's own product (GitHub Copilot)... was being embarrassed by a competitor internally this is a platform strategy play, not a cost crisis call. And the post assumes token prices are rising and unsustainable but the actual data goes the other way. As Sam Altman said, a hard reasoning problem that cost X on the OpenAI API 18 months ago now costs 1,000x less. 1,000x in 18 months is just that doesn't happen very often. His stated mission is to relentlessly drive the cost of intelligence down as close to zero as possible and make it a low-cost asset available to the world. A16z separately documented a 10x cost decline per year for equivalent model performance and Nvidia's Blackwell platform delivered 4x to 10x inference cost reductions in production deployments in early 2026. Yes, Uber burned through its 2026 AI budget in four months. But look at why, 95% of engineers were using AI tools monthly, ~70% of committed code was AI-generated, and 11% of real-time backend updates were made autonomously by agents with zero human intervention. That's not an AI cost problem. That's a CFO who budgeted for a pilot and got a revolution. Also, this post ignores the Jevons Paradox, when a resource gets cheaper, total consumption rises, not falls. Tokens inferenced on platforms like OpenRouter grew 25-fold since December 2024, OpenAI’s revenue grew at roughly a 250% annual rate to around a $20B run rate, while Anthropic’s surged from roughly $9B to over $30B in just a few months. Those aren't the numbers of companies whose economics are imploding. Even Gartner projects inference on a 1-trillion parameter model will cost 90%+ less by 2030. The actual Gartner warning is that agentic AI costs more per task because agents run longer chains, that's a design and scoping problem, not a structural industry collapse.show more

Milk Road AI

41,743 subscribers

251,150 просмотров • 2 месяцев назад •via X (Twitter)

Новости и политика Образование Наука и технологии

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

Microsoft just banned its own engineers from using AI. The tool was literally costing MORE than the humans it was supposed to replace. They lied to you about AI adoption and now the whole narrative is blowing up: Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it. Engineers loved it and adoption exploded. But then the invoices arrived. Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead. The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much. Uber's story is even worse... Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April. Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems. Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session. The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money. Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote: "For my team, the cost of compute is far beyond the costs of the employees." This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans. Think about what this means for the entire AI narrative. Every CEO on every earnings call for the past two years has said the same thing: AI will make us more efficient, reduce headcount, and cut costs. The stock market rewarded every company that said it. Fired workers, stock goes up. Announced AI adoption, stock goes up. But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill. Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools. Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible. Both companies are spending hundreds of billions on AI infrastructure this year alone. And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control. The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP. This is the gap nobody on Wall Street is pricing in. $725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work. What do you think?

Ricardo

2,967,677 просмотров • 2 месяцев назад

🚨 SAM ALTMAN: "A REDUCTION IN COST OF ABOUT A 1000X" 🤯 Sam Altman revealed the cost to solve a hard reasoning problem has plummeted by 1,000x since the release of o1 just 16 months ago (comparing o1 to the new 5.4 model). How is this possible? Altman credits two main factors: 1️⃣ We're Still in the Early Days: “We are so new to this paradigm that there is massive room for improvement. We're still doing things in "dumb ways" and getting better fast.” 2️⃣ Human Ingenuity is Underrated: “It's not just the models getting better. It's kernel engineers, power engineers, and data center designers finding brilliant ways to optimize every step of the process.” The compounding effect of better models + better human engineering is driving an exponential decrease in the cost of AI reasoning.

🚨 SAM ALTMAN: "A REDUCTION IN COST OF ABOUT A 1000X" 🤯 Sam Altman revealed the cost to solve a hard reasoning problem has plummeted by 1,000x since the release of o1 just 16 months ago (comparing o1 to the new 5.4 model). How is this possible? Altman credits two main factors: 1️⃣ We're Still in the Early Days: “We are so new to this paradigm that there is massive room for improvement. We're still doing things in "dumb ways" and getting better fast.” 2️⃣ Human Ingenuity is Underrated: “It's not just the models getting better. It's kernel engineers, power engineers, and data center designers finding brilliant ways to optimize every step of the process.” The compounding effect of better models + better human engineering is driving an exponential decrease in the cost of AI reasoning.

Chris

283,126 просмотров • 5 месяцев назад

Chamath is making one of the most important business arguments of 2026. Half of large US companies right now cannot generate returns that exceed their cost of capital, which has normalized back to its long run average of 8 to 11%. Another one in seven companies globally is stuck generating persistent returns between 1 and 5% and most businesses don't have room for error and in this environment walks every frontier AI lab saying the same thing, give us your data, your workflows, your processes and our model will make everything better. And companies by the millions said yes. What they didn't fully account for is what happens on the other side of that door. Every time an employee runs a query through a frontier model API, the prompt goes through external servers, workflows, customer data, pricing logic, internal processes, all of it transmitted through a third party. As Alex Karp said companies are spending on tokens while handing over the exact proprietary advantages that make their business worth owning. Microsoft blocked internal use of Anthropic's Claude Fable 5 but over its 30-day data retention policy and the largest software company in the world decided a frontier model's data handling was too risky for its own employees. A US government action revoked access to another frontier model for foreign nationals overnight. Now here's where the cost math becomes impossible to ignore. Deutsche Bank calculated a roughly 65x cost gap between frontier models like Claude Fable 5 at ~$3.25 per task and open-source alternatives at ~$0.05. For 90% of everyday enterprise tasks, performance is comparable. Open-weight models now match closed frontier systems on core agent tasks at roughly one-tenth the cost, a high-volume deployment that costs $250/day on Claude runs at $12/day on an open-source equivalent. Chamath Palihapitiya tested this directly by running a standard enterprise code migration task through an orchestration layer wrapping an open-source model came in 16.4x cheaper than using a frontier model directly.

Chamath is making one of the most important business arguments of 2026. Half of large US companies right now cannot generate returns that exceed their cost of capital, which has normalized back to its long run average of 8 to 11%. Another one in seven companies globally is stuck generating persistent returns between 1 and 5% and most businesses don't have room for error and in this environment walks every frontier AI lab saying the same thing, give us your data, your workflows, your processes and our model will make everything better. And companies by the millions said yes. What they didn't fully account for is what happens on the other side of that door. Every time an employee runs a query through a frontier model API, the prompt goes through external servers, workflows, customer data, pricing logic, internal processes, all of it transmitted through a third party. As Alex Karp said companies are spending on tokens while handing over the exact proprietary advantages that make their business worth owning. Microsoft blocked internal use of Anthropic's Claude Fable 5 but over its 30-day data retention policy and the largest software company in the world decided a frontier model's data handling was too risky for its own employees. A US government action revoked access to another frontier model for foreign nationals overnight. Now here's where the cost math becomes impossible to ignore. Deutsche Bank calculated a roughly 65x cost gap between frontier models like Claude Fable 5 at ~$3.25 per task and open-source alternatives at ~$0.05. For 90% of everyday enterprise tasks, performance is comparable. Open-weight models now match closed frontier systems on core agent tasks at roughly one-tenth the cost, a high-volume deployment that costs $250/day on Claude runs at $12/day on an open-source equivalent. Chamath Palihapitiya tested this directly by running a standard enterprise code migration task through an orchestration layer wrapping an open-source model came in 16.4x cheaper than using a frontier model directly.

Milk Road AI

280,929 просмотров • 1 месяц назад

Elon Musk just pulled off the biggest AI power grab of 2026. Tesla is capping every employee at $200 a week on AI spending starting Monday, July 6. Media's celebrating it as cost control. But what Elon actually built is an expense policy that redirects his own engineering workforce off Claude and onto Grok, while every competitor gets throttled by internal procurement rules. Here's what happened: Tesla spent the last six months pushing engineers to use AI as aggressively as possible. Leadership built an internal platform called Bottle Rocket that gave employees access to Claude, GPT, Gemini, Grok, and Cursor. They gamified adoption by ranking engineers on internal leaderboards by how many AI tokens they consumed. The strategy worked. Software engineers started burning THOUSANDS of dollars a week on Claude and Cursor. Then the invoices arrived and Tesla panicked. But they didn't pull the standard cost-control response... The loophole: The $200 weekly cap does not apply to beta products from xAI. Grok is completely exempt from the cap. Anthropic's Claude, OpenAI's GPT, and Google's Gemini all get throttled at the same $200 line. Four Tesla engineers told Electrek that internal usage overwhelmingly favors Claude over Grok. That preference is about to become financially punishing overnight. The genius part: This quarter SpaceX is closing a $60 billion all-stock acquisition of Anysphere, the parent company of Cursor. The moment that deal closes, Cursor's Composer coding model falls under the same Musk-controlled ecosystem, and any Tesla engineer choosing between a capped Claude session and an uncapped Composer session will pay a financial penalty for using the tool they actually prefer. By exempting only his own products from the cap, Elon is using Tesla shareholder money to build market share for xAI without ever having to disclose that is what he is doing. Because on paper, it is cost control. Now zoom out to what this signals for the wider AI narrative: Uber capped employees at $1,500 a month after burning $3.4 billion in four months. Meta introduced spending caps. Amazon and Walmart pushed staff toward cheaper models. Microsoft canceled Claude Code licenses across 100,000 engineers. Every Fortune 500 that pushed heavy AI adoption in 2025 is now rationing it in 2026. Meanwhile Nvidia is trading at a $5 trillion market cap. That entire valuation assumes enterprise AI consumption is about to explode across the economy. But every company actually deploying AI at scale is telling their own engineers to slow down. One of these narratives is lying. Goldman Sachs still forecasts a 24x increase in token consumption by 2030. Gartner says total enterprise AI costs will keep climbing because agents consume exponentially more tokens per task. Jensen Huang keeps repeating that 100 AI agents will work alongside every employee. And now the CEO of the most agentic company on the planet just told his own engineers they cannot spend more than $200 a week on the tools those agents need to run. Retail investors buying Nvidia and Palantir today are betting enterprise AI adoption compounds without limit. The CEOs deploying AI inside those same enterprises are betting the exact opposite, in writing, by internal memo. Thoughts?

Elon Musk just pulled off the biggest AI power grab of 2026. Tesla is capping every employee at $200 a week on AI spending starting Monday, July 6. Media's celebrating it as cost control. But what Elon actually built is an expense policy that redirects his own engineering workforce off Claude and onto Grok, while every competitor gets throttled by internal procurement rules. Here's what happened: Tesla spent the last six months pushing engineers to use AI as aggressively as possible. Leadership built an internal platform called Bottle Rocket that gave employees access to Claude, GPT, Gemini, Grok, and Cursor. They gamified adoption by ranking engineers on internal leaderboards by how many AI tokens they consumed. The strategy worked. Software engineers started burning THOUSANDS of dollars a week on Claude and Cursor. Then the invoices arrived and Tesla panicked. But they didn't pull the standard cost-control response... The loophole: The $200 weekly cap does not apply to beta products from xAI. Grok is completely exempt from the cap. Anthropic's Claude, OpenAI's GPT, and Google's Gemini all get throttled at the same $200 line. Four Tesla engineers told Electrek that internal usage overwhelmingly favors Claude over Grok. That preference is about to become financially punishing overnight. The genius part: This quarter SpaceX is closing a $60 billion all-stock acquisition of Anysphere, the parent company of Cursor. The moment that deal closes, Cursor's Composer coding model falls under the same Musk-controlled ecosystem, and any Tesla engineer choosing between a capped Claude session and an uncapped Composer session will pay a financial penalty for using the tool they actually prefer. By exempting only his own products from the cap, Elon is using Tesla shareholder money to build market share for xAI without ever having to disclose that is what he is doing. Because on paper, it is cost control. Now zoom out to what this signals for the wider AI narrative: Uber capped employees at $1,500 a month after burning $3.4 billion in four months. Meta introduced spending caps. Amazon and Walmart pushed staff toward cheaper models. Microsoft canceled Claude Code licenses across 100,000 engineers. Every Fortune 500 that pushed heavy AI adoption in 2025 is now rationing it in 2026. Meanwhile Nvidia is trading at a $5 trillion market cap. That entire valuation assumes enterprise AI consumption is about to explode across the economy. But every company actually deploying AI at scale is telling their own engineers to slow down. One of these narratives is lying. Goldman Sachs still forecasts a 24x increase in token consumption by 2030. Gartner says total enterprise AI costs will keep climbing because agents consume exponentially more tokens per task. Jensen Huang keeps repeating that 100 AI agents will work alongside every employee. And now the CEO of the most agentic company on the planet just told his own engineers they cannot spend more than $200 a week on the tools those agents need to run. Retail investors buying Nvidia and Palantir today are betting enterprise AI adoption compounds without limit. The CEOs deploying AI inside those same enterprises are betting the exact opposite, in writing, by internal memo. Thoughts?

Ricardo

188,797 просмотров • 1 месяц назад

Liquid AI (Liquid AI) CEO Ramin Hasani (Ramin) says: "We're bringing the cost of tokens to zero." "The axis was maximizing intelligence at all costs." Foundation models need to optimize across 3 axes: 1.) Intelligence and capability 2.) Efficiency and cost 3.) Substrate: where the intelligence actually runs "Efficiency is not an afterthought. Energy is not abundant." "If you maximize intelligence at all costs, that axis alone is not going to get you to the place that you want to go." "Efficiency and cost of intelligence as a first-class citizen, and not an afterthought." "Where does this intelligence system go? AI is majorly getting hosted in data centers, but you could also bring intelligence on phones, on laptops, on airplanes, on cars." "Imagine if it's not tokens that are actually important. It's just the outcome that actually matters." "Not just thinking about foundation models as the token machines that are generating money and revenue for the foundation model companies that are useless tokens, and getting them into the place where they can actually unlock true value for enterprises."

Liquid AI (Liquid AI) CEO Ramin Hasani (Ramin) says: "We're bringing the cost of tokens to zero." "The axis was maximizing intelligence at all costs." Foundation models need to optimize across 3 axes: 1.) Intelligence and capability 2.) Efficiency and cost 3.) Substrate: where the intelligence actually runs "Efficiency is not an afterthought. Energy is not abundant." "If you maximize intelligence at all costs, that axis alone is not going to get you to the place that you want to go." "Efficiency and cost of intelligence as a first-class citizen, and not an afterthought." "Where does this intelligence system go? AI is majorly getting hosted in data centers, but you could also bring intelligence on phones, on laptops, on airplanes, on cars." "Imagine if it's not tokens that are actually important. It's just the outcome that actually matters." "Not just thinking about foundation models as the token machines that are generating money and revenue for the foundation model companies that are useless tokens, and getting them into the place where they can actually unlock true value for enterprises."

Molly O’Shea

12,816 просмотров • 1 месяц назад

Gavin Baker: "Anthropic is roughly four times more capital efficient than OpenAI - they're growing really fast and they're not burning that much cash" this is him explaining why he thinks token efficiency decides who wins, why xAI has the lowest cost per token of anyone, and what a Tesla board member says is actually the most advanced AI in the world right now "an Anthropic model can give you a comparable quality answer to a Google model using half as many tokens - and each token is literally cost" "xAI has the lowest cost per token because they build data centers faster and at a lower cost than everyone, and they run GPUs at a higher utilization" Tesla board member Antonio Gracias: "Tesla actually is today the most advanced AI in the world - it does the most complex thing a human does, drive a car" bookmark & watch the full conversation ↓

Gavin Baker: "Anthropic is roughly four times more capital efficient than OpenAI - they're growing really fast and they're not burning that much cash" this is him explaining why he thinks token efficiency decides who wins, why xAI has the lowest cost per token of anyone, and what a Tesla board member says is actually the most advanced AI in the world right now "an Anthropic model can give you a comparable quality answer to a Google model using half as many tokens - and each token is literally cost" "xAI has the lowest cost per token because they build data centers faster and at a lower cost than everyone, and they run GPUs at a higher utilization" Tesla board member Antonio Gracias: "Tesla actually is today the most advanced AI in the world - it does the most complex thing a human does, drive a car" bookmark & watch the full conversation ↓

Сarm1ne

124,996 просмотров • 12 дней назад

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.

Anatoli Kopadze

91,518 просмотров • 1 месяц назад

Chamath said AI is not like the internet. Every new user costs real money. And the infrastructure making it possible was built by everyone. His argument was the clearest case for government ownership of AI labs I have ever heard. And it had nothing to do with Bernie Sanders. Start with the internet comparison. Google and Facebook became the most profitable companies in human history because of one number. The marginal cost of adding a new user was effectively zero. One more search query cost Google nothing. One more Facebook profile cost Meta nothing. They could serve a billion people and the incremental cost of that billion person was rounding error. That is the money printer. Infinite scale at zero marginal cost. AI breaks that model completely. Every single user taxes a GPU. Every query costs electricity. Every response requires memory and compute. The marginal cost of AI is real, significant, and does not disappear at scale. You cannot print money the same way. Then Chamath made the point that landed hardest. The infrastructure these companies depend on, the power grid, the land, the data centers, the permitting, the national security apparatus that protects their chips from being stolen, none of that was built by Anthropic or OpenAI. It was built by the public. By taxpayers. By decades of government investment in the physical and legal foundation these companies are now running on. He compared it to the interstate highway system. If the federal government built the roads and two companies transported all the goods on them, a logical question at that point would be how much of that should I own? You are riding on my rails. His conclusion was direct. If he were running a sovereign wealth fund and had the negotiating leverage of the US government, he would own 75% of these companies when he was done. The internet had zero marginal cost. That is why the founders captured almost all of the value. AI has real marginal cost and runs on public infrastructure. That changes who has a claim on what gets built. WATCH THE FULL PODCAST ON The All-In Podcast

Ihtesham Ali

79,066 просмотров • 1 месяц назад

Gavin on how Blackwell and GB300 will end Google's position as the lowest-cost producer of tokens: "I think it's important that Google has been the lowest cost producer of tokens. And this is really important because AI is the first time in my career as a tech investor that being the low cost producer has ever mattered. What Google has been doing as the low cost producer is they have been sucking the economic oxygen out of the AI ecosystem, which is an extremely rational strategy for them -- let's make life really hard for our competitors. We'll see the first models trained on Blackwell in early 2026. I think the first Blackwell model will come from xAI because they build the data centers the fastest. The GB300 is a great chip. It is drop-in compatible in every way with those GB200 racks, and the companies that use the GB300s -- they're going to be the low cost producer of tokens. I think this has pretty profound implications because it has to change Google's strategic calculus. If you have decisive cost advantage, why not AI at a -30% margin. You make it hard for your competitors who need funding to raise the capital that they need. On the other side of that maybe you have an extremely dominant share position. Well, all of that changes once Google is no longer the low-cost producer."

Gavin on how Blackwell and GB300 will end Google's position as the lowest-cost producer of tokens: "I think it's important that Google has been the lowest cost producer of tokens. And this is really important because AI is the first time in my career as a tech investor that being the low cost producer has ever mattered. What Google has been doing as the low cost producer is they have been sucking the economic oxygen out of the AI ecosystem, which is an extremely rational strategy for them -- let's make life really hard for our competitors. We'll see the first models trained on Blackwell in early 2026. I think the first Blackwell model will come from xAI because they build the data centers the fastest. The GB300 is a great chip. It is drop-in compatible in every way with those GB200 racks, and the companies that use the GB300s -- they're going to be the low cost producer of tokens. I think this has pretty profound implications because it has to change Google's strategic calculus. If you have decisive cost advantage, why not AI at a -30% margin. You make it hard for your competitors who need funding to raise the capital that they need. On the other side of that maybe you have an extremely dominant share position. Well, all of that changes once Google is no longer the low-cost producer."

Patrick OShaughnessy

174,657 просмотров • 8 месяцев назад

$Microsoft just betrayed OpenAI and Anthropic, the two companies it helped build. And it could break the entire AI trade... Here's what happened: Inside Excel and Outlook, two of the most used business apps on Earth, Microsoft has started routing tens of thousands of AI requests every week to its own in-house models instead of OpenAI and Anthropic. Microsoft's own AI chief, Mustafa Suleyman, said himself: "We pay a lot of money to Anthropic, so our goal is to reduce and ultimately ELIMINATE that cost." This is the company that poured $13 billion into OpenAI and effectively created the modern AI industry, and it just decided the most advanced models on the market are NOT worth paying for. And here's the thing... Microsoft is not just ripping out OpenAI everywhere - it is being surgical about it. The hardest and rarest tasks can still go to OpenAI or Anthropic. What Microsoft is taking back is the boring, high-volume work, like the email replies, the thread summaries, and the simple spreadsheet formulas. Why does that matter so much? Because that boring, repetitive work is where the actual money lives. The frontier labs assumed businesses would push BILLIONS of these tiny requests through expensive models forever. That endless river of tokens is the entire reason OpenAI and Anthropic are valued in the hundreds of billions of dollars. Microsoft looked at that river, decided it was massively overpaying, and rerouted it to models it owns outright. So the single biggest customer in the industry just walked off with the most profitable part of the business. And it is not only Microsoft: That same week, CNBC reported that American companies have been escaping to Chinese AI models to dodge rising US prices. Chinese models now handle more than 30% of US companies' AI usage on one major platform, peaking at 46%, up from an average of 11% a year earlier. They cost 60 to 90% less, and on some benchmarks they land within a single point of the best American model. One US startup moved ALL of its AI traffic off Claude and onto China's DeepSeek, and expects to save millions. Meanwhile Meta just admitted it has "excess" AI compute it wants to sell, becoming the first giant to concede it built far too much. Do you see the pattern forming? For two years, the entire AI story rested on one assumption: Every company on Earth would happily pay premium prices for the best model, forever. That assumption literally died in a single week. And the market noticed. More than a trillion dollars has been wiped off AI and chip stocks in a matter of days, as Wall Street finally started asking whether all of this spending will ever pay for itself. What this means for OpenAI and Anthropic: Their models are extraordinary, and it may not matter because their own biggest customers have decided they do not NEED the best model in the world to answer an email, and "good enough" now costs a fraction of the price. When even Microsoft refuses to pay full price for AI, the real question becomes who exactly IS left to pay it. What do you think?$

Microsoft just betrayed OpenAI and Anthropic, the two companies it helped build. And it could break the entire AI trade... Here's what happened: Inside Excel and Outlook, two of the most used business apps on Earth, Microsoft has started routing tens of thousands of AI requests every week to its own in-house models instead of OpenAI and Anthropic. Microsoft's own AI chief, Mustafa Suleyman, said himself: "We pay a lot of money to Anthropic, so our goal is to reduce and ultimately ELIMINATE that cost." This is the company that poured $13 billion into OpenAI and effectively created the modern AI industry, and it just decided the most advanced models on the market are NOT worth paying for. And here's the thing... Microsoft is not just ripping out OpenAI everywhere - it is being surgical about it. The hardest and rarest tasks can still go to OpenAI or Anthropic. What Microsoft is taking back is the boring, high-volume work, like the email replies, the thread summaries, and the simple spreadsheet formulas. Why does that matter so much? Because that boring, repetitive work is where the actual money lives. The frontier labs assumed businesses would push BILLIONS of these tiny requests through expensive models forever. That endless river of tokens is the entire reason OpenAI and Anthropic are valued in the hundreds of billions of dollars. Microsoft looked at that river, decided it was massively overpaying, and rerouted it to models it owns outright. So the single biggest customer in the industry just walked off with the most profitable part of the business. And it is not only Microsoft: That same week, CNBC reported that American companies have been escaping to Chinese AI models to dodge rising US prices. Chinese models now handle more than 30% of US companies' AI usage on one major platform, peaking at 46%, up from an average of 11% a year earlier. They cost 60 to 90% less, and on some benchmarks they land within a single point of the best American model. One US startup moved ALL of its AI traffic off Claude and onto China's DeepSeek, and expects to save millions. Meanwhile Meta just admitted it has "excess" AI compute it wants to sell, becoming the first giant to concede it built far too much. Do you see the pattern forming? For two years, the entire AI story rested on one assumption: Every company on Earth would happily pay premium prices for the best model, forever. That assumption literally died in a single week. And the market noticed. More than a trillion dollars has been wiped off AI and chip stocks in a matter of days, as Wall Street finally started asking whether all of this spending will ever pay for itself. What this means for OpenAI and Anthropic: Their models are extraordinary, and it may not matter because their own biggest customers have decided they do not NEED the best model in the world to answer an email, and "good enough" now costs a fraction of the price. When even Microsoft refuses to pay full price for AI, the real question becomes who exactly IS left to pay it. What do you think?

Ricardo

92,971 просмотров • 1 месяц назад

Jensen Huang, CEO of NVIDIA, laid out the compute math almost nobody priced in: reasoning AI needs 1,000x more compute than generative AI. Then you multiply by 100x more users. "In order for AI to go through understanding, reasoning, planning, using tools to take action, the amount of computation necessary compared to generative AI is like a thousand times more." "In two years time, the number of cars you need in the world grew by a thousand times. In two years time, the number of airplanes you need in the world grew by 2,000 times. Whatever metric you want to use, the thousand times difference is incredible." "But then you multiply that by the number of people who now want to use it by a hundred times. Which is the reason why GPU consumption is going through the roof." "Even GPUs we sold 4 or 5 years ago are now rising in price faster than good wine. It has defied what everybody said was going to happen." The reasoning era did not make intelligence cheaper to run. It made a single answer cost 1,000x more compute, then handed that answer to 100x more people. Anyone still budgeting AI around "inference gets cheap" is planning for a curve that already snapped.

Jensen Huang, CEO of NVIDIA, laid out the compute math almost nobody priced in: reasoning AI needs 1,000x more compute than generative AI. Then you multiply by 100x more users. "In order for AI to go through understanding, reasoning, planning, using tools to take action, the amount of computation necessary compared to generative AI is like a thousand times more." "In two years time, the number of cars you need in the world grew by a thousand times. In two years time, the number of airplanes you need in the world grew by 2,000 times. Whatever metric you want to use, the thousand times difference is incredible." "But then you multiply that by the number of people who now want to use it by a hundred times. Which is the reason why GPU consumption is going through the roof." "Even GPUs we sold 4 or 5 years ago are now rising in price faster than good wine. It has defied what everybody said was going to happen." The reasoning era did not make intelligence cheaper to run. It made a single answer cost 1,000x more compute, then handed that answer to 100x more people. Anyone still budgeting AI around "inference gets cheap" is planning for a curve that already snapped.

Karl Mehta

54,717 просмотров • 1 месяц назад

$Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.$

Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.

Milk Road AI

27,908 просмотров • 1 месяц назад

$🚨🇨🇳 China could crash the U.S. stock market Not with tariffs With AI MODEL DUMPING The same playbook they used in SOLAR and EVs Make it almost as good Sell it much cheaper Take the market Now they are doing it with AI Open models are getting close to U.S. frontier models But some run at just a fraction of the cost If a company can cut a $100M AI bill to a few million It will switch And that is the real problem OpenAI and Anthropic are not priced like normal companies They are priced like they have unlimited PRICING POWER China is trying to destroy that The AI BUBBLE was never about whether AI works It was about how much companies can charge for it$

🚨🇨🇳 China could crash the U.S. stock market Not with tariffs With AI MODEL DUMPING The same playbook they used in SOLAR and EVs Make it almost as good Sell it much cheaper Take the market Now they are doing it with AI Open models are getting close to U.S. frontier models But some run at just a fraction of the cost If a company can cut a $100M AI bill to a few million It will switch And that is the real problem OpenAI and Anthropic are not priced like normal companies They are priced like they have unlimited PRICING POWER China is trying to destroy that The AI BUBBLE was never about whether AI works It was about how much companies can charge for it

Leshka.eth ⛩

11,737 просмотров • 1 месяц назад

$MSFT is trading at a decade-low valuation because the market is focused on the near-term cost of AI while underpricing Microsoft’s role as one of the most important enterprise AI platforms in the world. The bigger shift is that Microsoft may move from only monetizing users to monetizing work where companies eventually pay not just for employee licenses but for the AI work being performed around each employee. That creates a ton of upside if Microsoft can monetize agents, tokens, workflows, security, governance and productivity across the enterprise stack in the new AI economy.

$MSFT is trading at a decade-low valuation because the market is focused on the near-term cost of AI while underpricing Microsoft’s role as one of the most important enterprise AI platforms in the world. The bigger shift is that Microsoft may move from only monetizing users to monetizing work where companies eventually pay not just for employee licenses but for the AI work being performed around each employee. That creates a ton of upside if Microsoft can monetize agents, tokens, workflows, security, governance and productivity across the enterprise stack in the new AI economy.

Shay Boloor

606,243 просмотров • 1 месяц назад

Morgan Stanley just raised their 2027 AI capex forecast to $1.1 trillion and that number still doesn't include SpaceX or a lot of the other AI companies (Save this). When you factor those in, the real 2027 figure is probably closer to $1.5 trillion and AI lab inference revenue combined is tracking toward $300 billion in 2027. On its surface that ratio sounds alarming, spending $1.5 trillion in capex to generate $300 billion in revenue. But the framing collapses the moment you examine two things the bears consistently ignore, gross margins and the revenue trajectory. Gross margins on inference revenue are running at 60 to 70 percent. That means the $300 billion in inference revenue generates $180 to $210 billion in gross profit and that number compounds rapidly as utilization scales on infrastructure that is already built and paid for. The Capex is not being deployed against today's revenue but rather being deployed against a revenue trajectory that has shown no signs of decelerating. To understand how aggressive that trajectory actually is, consider that Morgan Stanley's $1.1 trillion hyperscaler forecast is nearly double what analysts projected for the same year just twelve months ago And they described the demand as inelastic, meaning it is not slowing down regardless of rising costs, tighter financing conditions or geopolitical risk. The AI industry ended 2025 tracking well over $200 billion in combined inference revenue and the growth rate since then has continued to accelerate rather than flatten. Anthropic alone scaled from negligible revenue to a $30 billion annualized run rate in approximately 18 months while OpenAI is tracking toward $280 billion in annual revenue by 2030 from $13 billion in 2025. There is also a structural reality in the capex number that the bears never account for. Roughly 35 percent of total AI spending goes toward training, building the next model generation which is not revenue-generating in the current period. That means only about 65 percent of the $1.5 trillion in capex is actually deployed against the inference infrastructure that earns revenue today. When you apply the 60 to 70 percent gross margin to the revenue that sits on top of that 65 percent figure, the economics look substantially better than the headline capex to revenue ratio implies. Every CEO who has been closest to this buildout has consistently underestimated it and Jensen Huang projected $1 trillion in AI capex two years ago and was called delusional. Dario Amodei said in early 2026 that AI revenues would reach the low hundreds of billions by 2028 and trillions before 2030 and given where Anthropic's own revenue trajectory is today, he is likely revising those numbers upward. The pattern here is consistent, every time someone models the revenue ceiling, the actual number breaks through it faster than expected. Come join Milk Road Pro for our full breakdown, the real unit economics of the AI inference buildout, how the capex to revenue ratio evolves over the next three years, and our entire AI thesis! Link below!

Morgan Stanley just raised their 2027 AI capex forecast to $1.1 trillion and that number still doesn't include SpaceX or a lot of the other AI companies (Save this). When you factor those in, the real 2027 figure is probably closer to $1.5 trillion and AI lab inference revenue combined is tracking toward $300 billion in 2027. On its surface that ratio sounds alarming, spending $1.5 trillion in capex to generate $300 billion in revenue. But the framing collapses the moment you examine two things the bears consistently ignore, gross margins and the revenue trajectory. Gross margins on inference revenue are running at 60 to 70 percent. That means the $300 billion in inference revenue generates $180 to $210 billion in gross profit and that number compounds rapidly as utilization scales on infrastructure that is already built and paid for. The Capex is not being deployed against today's revenue but rather being deployed against a revenue trajectory that has shown no signs of decelerating. To understand how aggressive that trajectory actually is, consider that Morgan Stanley's $1.1 trillion hyperscaler forecast is nearly double what analysts projected for the same year just twelve months ago And they described the demand as inelastic, meaning it is not slowing down regardless of rising costs, tighter financing conditions or geopolitical risk. The AI industry ended 2025 tracking well over $200 billion in combined inference revenue and the growth rate since then has continued to accelerate rather than flatten. Anthropic alone scaled from negligible revenue to a $30 billion annualized run rate in approximately 18 months while OpenAI is tracking toward $280 billion in annual revenue by 2030 from $13 billion in 2025. There is also a structural reality in the capex number that the bears never account for. Roughly 35 percent of total AI spending goes toward training, building the next model generation which is not revenue-generating in the current period. That means only about 65 percent of the $1.5 trillion in capex is actually deployed against the inference infrastructure that earns revenue today. When you apply the 60 to 70 percent gross margin to the revenue that sits on top of that 65 percent figure, the economics look substantially better than the headline capex to revenue ratio implies. Every CEO who has been closest to this buildout has consistently underestimated it and Jensen Huang projected $1 trillion in AI capex two years ago and was called delusional. Dario Amodei said in early 2026 that AI revenues would reach the low hundreds of billions by 2028 and trillions before 2030 and given where Anthropic's own revenue trajectory is today, he is likely revising those numbers upward. The pattern here is consistent, every time someone models the revenue ceiling, the actual number breaks through it faster than expected. Come join Milk Road Pro for our full breakdown, the real unit economics of the AI inference buildout, how the capex to revenue ratio evolves over the next three years, and our entire AI thesis! Link below!

Milk Road AI

21,141 просмотров • 1 месяц назад

NVIDIA'S BIGGEST COMPETITOR AMD BUILT A BOX THAT RUNS A 235B MODEL AND KILLS $200 OPENAI AND $200 CLAUDE CODE FOR $9 A MONTH amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on a single piece of silicon with 128gb of unified memory the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months ollama installs in one command and claude code points at localhost so nothing leaves the machine and nothing costs per request bookmark this and read the article below

NVIDIA'S BIGGEST COMPETITOR AMD BUILT A BOX THAT RUNS A 235B MODEL AND KILLS $200 OPENAI AND $200 CLAUDE CODE FOR $9 A MONTH amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on a single piece of silicon with 128gb of unified memory the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months ollama installs in one command and claude code points at localhost so nothing leaves the machine and nothing costs per request bookmark this and read the article below

starmex

93,465 просмотров • 1 месяц назад

Sam Altman shared a number that should change how you think about AI. Six and a half years ago, the heaviest AI user on the planet was an OpenAI employee. Using 100,000 tokens a month. That was considered insane at the time. The global average was basically zero. Today the global average is 100,000 tokens a month. What was insane is now normal. And the heaviest user at OpenAI right now? Hundreds of billions of tokens a month. Now here's where it gets wild. If the same pattern repeats, in another six and a half years the average person will use 500 billion tokens a month. That's a 5 million x increase from today. Think about what that means. → AI is not something you open once a day to ask a question anymore. It becomes always on. Running in the background across your work, education, personal life. → Agentic workflows, coding assistants, multi-step reasoning — that's what consumes billions of tokens. Not chatting. → OpenAI is already building for this. Hundreds of billions in infrastructure spend through 2030. Stargate. Partnerships with Microsoft, Oracle, Nvidia. And cost per token keeps falling roughly 10x every 12 months for the same capability level. So usage explodes because it keeps getting cheaper. The pattern is the same one we saw with computing, internet, and mobile. What the power user does today, the average person does in six years. Right now you are the power user. Act like it.

Sam Altman shared a number that should change how you think about AI. Six and a half years ago, the heaviest AI user on the planet was an OpenAI employee. Using 100,000 tokens a month. That was considered insane at the time. The global average was basically zero. Today the global average is 100,000 tokens a month. What was insane is now normal. And the heaviest user at OpenAI right now? Hundreds of billions of tokens a month. Now here's where it gets wild. If the same pattern repeats, in another six and a half years the average person will use 500 billion tokens a month. That's a 5 million x increase from today. Think about what that means. → AI is not something you open once a day to ask a question anymore. It becomes always on. Running in the background across your work, education, personal life. → Agentic workflows, coding assistants, multi-step reasoning — that's what consumes billions of tokens. Not chatting. → OpenAI is already building for this. Hundreds of billions in infrastructure spend through 2030. Stargate. Partnerships with Microsoft, Oracle, Nvidia. And cost per token keeps falling roughly 10x every 12 months for the same capability level. So usage explodes because it keeps getting cheaper. The pattern is the same one we saw with computing, internet, and mobile. What the power user does today, the average person does in six years. Right now you are the power user. Act like it.

Vaibhav Sisinty

24,323 просмотров • 6 дней назад

Two data points dropped in the last few months that should terrify every software company that thinks its codebase is a moat. First, one engineer at Cloudflare, working with Claude via AI agents, rebuilt 94% of Next.js, one of the most widely used frontend frameworks on the internet, built over 10 years by a large engineering team in a single week. Total cost was $1,100 in API tokens. The result, called Vinext, is a drop-in replacement that builds production apps up to 4x faster and produces client bundles 57% smaller and customers are already running it in production. Second is Cursor CEO Michael Truell deployed a swarm of hundreds of GPT-5.2 agents that ran uninterrupted for an entire week and built a fully functional web browser from scratch called FastRender. 3 million lines of code, thousands of files and a custom Rust rendering engine with HTML parsing, CSS layout, text shaping, and a custom JavaScript VM. Total cost was roughly $30,000. For context, Google has spent billions of dollars and decades of engineering building Chrome. And the benchmarks say by next year, you will be able to one-shot prompt anything. The moat that software companies spent decades building, the complexity of their codebase, the years it would take a competitor to replicate it, the switching costs that moat assumed humans were the unit of production. AI does not care how long it took you to build it, it only cares how long it takes to rebuild it. And right now, the answer is one week.

Two data points dropped in the last few months that should terrify every software company that thinks its codebase is a moat. First, one engineer at Cloudflare, working with Claude via AI agents, rebuilt 94% of Next.js, one of the most widely used frontend frameworks on the internet, built over 10 years by a large engineering team in a single week. Total cost was $1,100 in API tokens. The result, called Vinext, is a drop-in replacement that builds production apps up to 4x faster and produces client bundles 57% smaller and customers are already running it in production. Second is Cursor CEO Michael Truell deployed a swarm of hundreds of GPT-5.2 agents that ran uninterrupted for an entire week and built a fully functional web browser from scratch called FastRender. 3 million lines of code, thousands of files and a custom Rust rendering engine with HTML parsing, CSS layout, text shaping, and a custom JavaScript VM. Total cost was roughly $30,000. For context, Google has spent billions of dollars and decades of engineering building Chrome. And the benchmarks say by next year, you will be able to one-shot prompt anything. The moat that software companies spent decades building, the complexity of their codebase, the years it would take a competitor to replicate it, the switching costs that moat assumed humans were the unit of production. AI does not care how long it took you to build it, it only cares how long it takes to rebuild it. And right now, the answer is one week.

Milk Road AI

16,781 просмотров • 3 месяцев назад

The first fully AI-generated movie just went viral in Cannes. The film is called Hell Grind. According to reports, it was made using Higgsfield AI in around 14 days, cost about $500,000 and roughly $400,000 of that went to compute. For context, a traditional Hollywood feature can take 1-3 years and cost tens of millions to produce. This one was created by a team of about 15 directors, cinematographers, and editors using AI tools like Higgsfield’s Soul Cinema, Seedance 2.0 and other video models. The craziest part? For just the first 25 minutes, CineD reported they generated 16,181 clips to get 253 final shots. That’s roughly 64 attempts for every shot. Some prompts were reportedly around 3,000 words each, just to get 15 seconds of usable footage. So yes, AI is getting faster. But the real takeaway is not “humans are useless now” It’s actually the opposite The people who understand storytelling, directing, editing, taste and tools are about to get insane leverage.

The first fully AI-generated movie just went viral in Cannes. The film is called Hell Grind. According to reports, it was made using Higgsfield AI in around 14 days, cost about $500,000 and roughly $400,000 of that went to compute. For context, a traditional Hollywood feature can take 1-3 years and cost tens of millions to produce. This one was created by a team of about 15 directors, cinematographers, and editors using AI tools like Higgsfield’s Soul Cinema, Seedance 2.0 and other video models. The craziest part? For just the first 25 minutes, CineD reported they generated 16,181 clips to get 253 final shots. That’s roughly 64 attempts for every shot. Some prompts were reportedly around 3,000 words each, just to get 15 seconds of usable footage. So yes, AI is getting faster. But the real takeaway is not “humans are useless now” It’s actually the opposite The people who understand storytelling, directing, editing, taste and tools are about to get insane leverage.

Jade 💋

153,264 просмотров • 2 месяцев назад

AI is the first technology in history where more customers makes you POORER. Every tech company in history got cheaper as it scaled. More users meant lower costs per user. That's the entire model. That's why Microsoft prints money. That's why Google prints money. That's why Meta prints money. Software has near-zero marginal cost. Build it once. Sell it a billion times. The 100 millionth user costs basically nothing to serve. This is the single most important rule in tech economics. But AI completely broke it. Every single query costs real compute. Every interaction burns real electricity. Every response depreciates real hardware. There is no "build once, sell forever." There is only "burn money every time someone asks a question." And the numbers prove it: OpenAI hit $20 billion in annualized revenue. Losses? $14 billion. For every dollar they earn, they spend $1.69 delivering it. Their losses TRIPLED as their revenue grew. Not because they're bad at business, but simply because the model itself is broken. Anthropic crossed $30 billion in annualized revenue. Still burning billions. Still not profitable. Still raising tens of billions just to keep the lights on. xAI is burning $1 billion every single month. Perplexity spent 164% of its revenue on compute costs from AWS, They literally spent more on running the AI than they made from selling it. This is not how technology is supposed to work. Google once estimated that adding AI to every search query would require 500,000 A100 servers. The cost of answering a single AI query is 10x MORE than a traditional search result. Traditional software: Serving 1 million users costs roughly the same as serving 100,000. The marginal cost is basically zero. AI: Serving 1 million users can cost 10 times what 100,000 costs. Every new user is a new expense. Every new query is a new dollar burned. This is reverse economics. The more successful you become, the faster you die. And nobody in the industry wants to talk about it because the entire narrative depends on you believing AI companies work like software companies. But they don't. They NEVER will. Software scales to infinity. AI scales to bankruptcy. HSBC ran the numbers on OpenAI specifically. Their conclusion: Even after every funding round, every investment, every deal, OpenAI still faces a $207 BILLION shortfall to reach profitability. The industry response has been to raise prices. ChatGPT went from free to $20 to $200 for the Pro plan. And it's still not enough because the cost of running these models grows FASTER than any price increase consumers will accept. Meanwhile 966 AI startups died in 2024. A 25.6% jump from the year before. AI startups burn cash twice as fast as non-AI tech companies. And the ones building on TOP of OpenAI and Anthropic are in even worse shape. Every wrapper app. Every "AI-powered" SaaS tool. Every startup whose entire product is someone else's model with a different skin on it. They're all margin-negative. Every single one. And these are the companies about to IPO. SpaceX, OpenAI, Anthropic, and Cerebras. $240 billion in combined raises planned for 2026. They're asking you to invest in an industry where the fundamental unit economics don't work. Where the MORE customers you get, the MORE money you lose. Where no company has figured out how to make the math positive. The dot-com bubble had the same pitch: "Revenue is growing. Profitability comes later." For most of them, later never came. The question isn't whether AI will change the world. It will. The question is whether it can do it without going broke first. And right now, every single number literally says no. How can they become profitable?

AI is the first technology in history where more customers makes you POORER. Every tech company in history got cheaper as it scaled. More users meant lower costs per user. That's the entire model. That's why Microsoft prints money. That's why Google prints money. That's why Meta prints money. Software has near-zero marginal cost. Build it once. Sell it a billion times. The 100 millionth user costs basically nothing to serve. This is the single most important rule in tech economics. But AI completely broke it. Every single query costs real compute. Every interaction burns real electricity. Every response depreciates real hardware. There is no "build once, sell forever." There is only "burn money every time someone asks a question." And the numbers prove it: OpenAI hit $20 billion in annualized revenue. Losses? $14 billion. For every dollar they earn, they spend $1.69 delivering it. Their losses TRIPLED as their revenue grew. Not because they're bad at business, but simply because the model itself is broken. Anthropic crossed $30 billion in annualized revenue. Still burning billions. Still not profitable. Still raising tens of billions just to keep the lights on. xAI is burning $1 billion every single month. Perplexity spent 164% of its revenue on compute costs from AWS, They literally spent more on running the AI than they made from selling it. This is not how technology is supposed to work. Google once estimated that adding AI to every search query would require 500,000 A100 servers. The cost of answering a single AI query is 10x MORE than a traditional search result. Traditional software: Serving 1 million users costs roughly the same as serving 100,000. The marginal cost is basically zero. AI: Serving 1 million users can cost 10 times what 100,000 costs. Every new user is a new expense. Every new query is a new dollar burned. This is reverse economics. The more successful you become, the faster you die. And nobody in the industry wants to talk about it because the entire narrative depends on you believing AI companies work like software companies. But they don't. They NEVER will. Software scales to infinity. AI scales to bankruptcy. HSBC ran the numbers on OpenAI specifically. Their conclusion: Even after every funding round, every investment, every deal, OpenAI still faces a $207 BILLION shortfall to reach profitability. The industry response has been to raise prices. ChatGPT went from free to $20 to $200 for the Pro plan. And it's still not enough because the cost of running these models grows FASTER than any price increase consumers will accept. Meanwhile 966 AI startups died in 2024. A 25.6% jump from the year before. AI startups burn cash twice as fast as non-AI tech companies. And the ones building on TOP of OpenAI and Anthropic are in even worse shape. Every wrapper app. Every "AI-powered" SaaS tool. Every startup whose entire product is someone else's model with a different skin on it. They're all margin-negative. Every single one. And these are the companies about to IPO. SpaceX, OpenAI, Anthropic, and Cerebras. $240 billion in combined raises planned for 2026. They're asking you to invest in an industry where the fundamental unit economics don't work. Where the MORE customers you get, the MORE money you lose. Where no company has figured out how to make the math positive. The dot-com bubble had the same pitch: "Revenue is growing. Profitability comes later." For most of them, later never came. The question isn't whether AI will change the world. It will. The question is whether it can do it without going broke first. And right now, every single number literally says no. How can they become profitable?

Ricardo

167,414 просмотров • 3 месяцев назад