Loading video...

Video Failed to Load

Go Home

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...

VaibhavSisinty's profile picture

Vaibhav Sisinty

164,374 subscribers

24,323 views • 1 month ago •via X (Twitter)

Science & TechnologyEducationNews & Politics
LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Sam Altman on how AI token usage is growing at an exponential rate. "6 and a half years ago, the world token leader was an OpenAI employee using about 100,000 tokens a month. And this seemed ludicrous at the time. The worldwide average per capita was, like, zero. Now, the worldwide average of tokens per month, is like 100,000, and the token leader at OpenAI uses something in the hundreds of billions. If that happens again, which I think it probably will, then in another six and a half years, the average person uses, let’s say, 500 billion tokens a month, and the token leader uses a quadrillion or quadrillions of tokens a month. And I think that will just become the expectation." --- He was at YC Startup School 2026, in conversation Garry Tan From "Y Combinator" YouTube channel, (full video link in comment)
0:48

Sam Altman on how AI token usage is growing at an exponential rate. "6 and a half years ago, the world token leader was an OpenAI employee using about 100,000 tokens a month. And this seemed ludicrous at the time. The worldwide average per capita was, like, zero. Now, the worldwide average of tokens per month, is like 100,000, and the token leader at OpenAI uses something in the hundreds of billions. If that happens again, which I think it probably will, then in another six and a half years, the average person uses, let’s say, 500 billion tokens a month, and the token leader uses a quadrillion or quadrillions of tokens a month. And I think that will just become the expectation." --- He was at YC Startup School 2026, in conversation Garry Tan From "Y Combinator" YouTube channel, (full video link in comment)

Rohan Paul

20,480 views • 19 days ago

Sam Altman on how AI token usage is growing at an exponential rate. "6 and a half years ago, the world token leader was an OpenAI employee using about 100,000 tokens a month. And this seemed ludicrous at the time. The worldwide average per capita was, like, zero. Now, the worldwide average of tokens per month, is like 100,000, and the token leader at OpenAI uses something in the hundreds of billions. If that happens again, which I think it probably will, then in another six and a half years, the average person uses, let’s say, 500 billion tokens a month, and the token leader uses a quadrillion or quadrillions of tokens a month. And I think that will just become the expectation." ---- He was at YC Startup School 2026, in conversation Garry Tan From "Y Combinator" YouTube channel, (full video link in comment)
0:48

Sam Altman on how AI token usage is growing at an exponential rate. "6 and a half years ago, the world token leader was an OpenAI employee using about 100,000 tokens a month. And this seemed ludicrous at the time. The worldwide average per capita was, like, zero. Now, the worldwide average of tokens per month, is like 100,000, and the token leader at OpenAI uses something in the hundreds of billions. If that happens again, which I think it probably will, then in another six and a half years, the average person uses, let’s say, 500 billion tokens a month, and the token leader uses a quadrillion or quadrillions of tokens a month. And I think that will just become the expectation." ---- He was at YC Startup School 2026, in conversation Garry Tan From "Y Combinator" YouTube channel, (full video link in comment)

Rohan Paul

64,715 views • 1 month ago

OpenAI CEO, Sam Altman, sat down for 39 minutes with Y Combinator's Garry Tan and explained where AI is headed better than any $2,000 strategy course. This is what he told the room: 1. The next 6 months will match the last 2 years. Garry asked how much better the models will get. Sam gave a timeline. "I think it will feel like the next six months is maybe equivalent to the last two years of model progress." That's the steepest capability curve yet, from the man shipping the models. 2. Average users now consume what the world record used to be. Sam pulled one stat to show where demand is going. "Six and a half years ago, the world token leader was an OpenAI employee using about 100,000 tokens a month." Today that number is the worldwide average, OpenAI's top user burns hundreds of billions, and if the pattern repeats, the average person hits 500 billion tokens a month by 2033. 3. Startups win when the ground shifts. Asked about credentials and PhDs, he zoomed out to when great startups cluster. "Startups tend to win when the technology landscape is moving very quickly, when costs are coming down, when cycle times are short. All of those things are happening right now." The conditions behind the late-90s internet boom and the App Store wave, running at the same time. 4. Tool fluency beats years of experience. He described founders who grew up on AI automating entire startups with 4 people. "I would bet that this generally will cut against many years of experience in favor of people who have a lot of fluency with the tools." Hours in the tools now outweigh years on a resume. 5. Being called an idiot was the moat. Ten years ago, experts said OpenAI would cause another AI winter. "For years at OpenAI, it felt like we knew the biggest secret in the world. Everybody was calling us an idiot." The critics bought them years of runway with zero competitors. 6. You need 5 believers, not 500. On finding co-founders for a heretical idea. "We used to joke that only 50 people in the world believed that AGI was possible, but it was okay because 45 of them worked at OpenAI." Heretical ideas recruit stronger teams than popular ones. 7. Demand for intelligence has no ceiling. He compared it to electricity and found the analogy breaks. "The demand for sufficiently high-quality intelligence at a sufficiently low price is effectively uncapped." His guess: worldwide inference demand grows 10x a year for years. Watch it, then read the guide to going from zero to AI engineer.
39:00

OpenAI CEO, Sam Altman, sat down for 39 minutes with Y Combinator's Garry Tan and explained where AI is headed better than any $2,000 strategy course. This is what he told the room: 1. The next 6 months will match the last 2 years. Garry asked how much better the models will get. Sam gave a timeline. "I think it will feel like the next six months is maybe equivalent to the last two years of model progress." That's the steepest capability curve yet, from the man shipping the models. 2. Average users now consume what the world record used to be. Sam pulled one stat to show where demand is going. "Six and a half years ago, the world token leader was an OpenAI employee using about 100,000 tokens a month." Today that number is the worldwide average, OpenAI's top user burns hundreds of billions, and if the pattern repeats, the average person hits 500 billion tokens a month by 2033. 3. Startups win when the ground shifts. Asked about credentials and PhDs, he zoomed out to when great startups cluster. "Startups tend to win when the technology landscape is moving very quickly, when costs are coming down, when cycle times are short. All of those things are happening right now." The conditions behind the late-90s internet boom and the App Store wave, running at the same time. 4. Tool fluency beats years of experience. He described founders who grew up on AI automating entire startups with 4 people. "I would bet that this generally will cut against many years of experience in favor of people who have a lot of fluency with the tools." Hours in the tools now outweigh years on a resume. 5. Being called an idiot was the moat. Ten years ago, experts said OpenAI would cause another AI winter. "For years at OpenAI, it felt like we knew the biggest secret in the world. Everybody was calling us an idiot." The critics bought them years of runway with zero competitors. 6. You need 5 believers, not 500. On finding co-founders for a heretical idea. "We used to joke that only 50 people in the world believed that AGI was possible, but it was okay because 45 of them worked at OpenAI." Heretical ideas recruit stronger teams than popular ones. 7. Demand for intelligence has no ceiling. He compared it to electricity and found the analogy breaks. "The demand for sufficiently high-quality intelligence at a sufficiently low price is effectively uncapped." His guess: worldwide inference demand grows 10x a year for years. Watch it, then read the guide to going from zero to AI engineer.

Alex Prompter

84,654 views • 1 month ago

Nvidia $NVDA catches and passes China's open models by the end of 2027. That's Jason Calacanis @jason on All-In, and the mechanism is pricing. Jensen sells a company a full rack of compute, soup to nuts, total sovereignty, at 80 to 90% below what that company pays for tokens from OpenAI and Anthropic. He doesn't need margin on the tokens because he already made it on the hardware. Calacanis's read: starting now Nvidia is the number one competitor to OpenAI and Anthropic, not only for tokens but for enterprise compute, and the leading open source provider in America. What the rack-versus-tokens shift does to AI margins: Source: All-In Podcast -
1:16

Nvidia $NVDA catches and passes China's open models by the end of 2027. That's Jason Calacanis @jason on All-In, and the mechanism is pricing. Jensen sells a company a full rack of compute, soup to nuts, total sovereignty, at 80 to 90% below what that company pays for tokens from OpenAI and Anthropic. He doesn't need margin on the tokens because he already made it on the hardware. Calacanis's read: starting now Nvidia is the number one competitor to OpenAI and Anthropic, not only for tokens but for enterprise compute, and the leading open source provider in America. What the rack-versus-tokens shift does to AI margins: Source: All-In Podcast -

Podcast Alpha

26,746 views • 23 days ago

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.
2:15

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

91,858 views • 2 months ago

Everyone’s talking about what Jensen said at GTC about AI capex. But the most important thing he said got almost no attention — that software companies are about to become the biggest resellers of AI tokens in the world. Think about what that means. Workday buys raw intelligence from OpenAI. Wraps it in HR domain expertise. Sells it back as an AI product at a massive markup. That’s not a software company anymore. That’s a refinery. And right now nobody is tracking the economics of that refining layer — what the crude costs, what the refined product sells for, and who’s capturing the spread. This is the most important data gap in AI right now. More on this soon. $NVDA
1:06

Everyone’s talking about what Jensen said at GTC about AI capex. But the most important thing he said got almost no attention — that software companies are about to become the biggest resellers of AI tokens in the world. Think about what that means. Workday buys raw intelligence from OpenAI. Wraps it in HR domain expertise. Sells it back as an AI product at a massive markup. That’s not a software company anymore. That’s a refinery. And right now nobody is tracking the economics of that refining layer — what the crude costs, what the refined product sells for, and who’s capturing the spread. This is the most important data gap in AI right now. More on this soon. $NVDA

Nick Dorsey

40,200 views • 6 months ago

Vals AI CEO Rayan Krishnan on the unlimited tokens experiment that revealed an anxious obligation for engineers to use models all the time, everywhere: "I wanted to do a tokenmaxxing experiment. I was able to get unlimited access for our team for a month for some of the coding tools. We had a lot of engineers spending between one to two billion tokens a day." "I went back and did some math, and it looked like in that month, we spent roughly $1.5 million worth of tokens... It was actually 10x more we were spending in tokens than employee salary for that month." "We cannot continue with this mode of operation for the next month. How do we intelligently figure out what are the right tools we should use, for what teams, and what projects?" "We found some pretty surprising insights. The Cognition Devin tool is actually very token-efficient, and so that's a place we've chosen to adopt more." "There are a lot of places where you can get better pricing models out of subscriptions as opposed to token-based pricing." "It's actually informed our strategy for how we can effectively tokenmaxx without spending $1.5 million per month." Rayan Krishnan Jennifer Li
1:36

Vals AI CEO Rayan Krishnan on the unlimited tokens experiment that revealed an anxious obligation for engineers to use models all the time, everywhere: "I wanted to do a tokenmaxxing experiment. I was able to get unlimited access for our team for a month for some of the coding tools. We had a lot of engineers spending between one to two billion tokens a day." "I went back and did some math, and it looked like in that month, we spent roughly $1.5 million worth of tokens... It was actually 10x more we were spending in tokens than employee salary for that month." "We cannot continue with this mode of operation for the next month. How do we intelligently figure out what are the right tools we should use, for what teams, and what projects?" "We found some pretty surprising insights. The Cognition Devin tool is actually very token-efficient, and so that's a place we've chosen to adopt more." "There are a lot of places where you can get better pricing models out of subscriptions as opposed to token-based pricing." "It's actually informed our strategy for how we can effectively tokenmaxx without spending $1.5 million per month." Rayan Krishnan Jennifer Li

a16z

35,174 views • 19 days ago

A developer in Hangzhou runs an AI that remembers everything about him for $0.40 a year. No vector database. One file that never grows past 4,000 tokens. He published the whole schema. His version starts from the opposite idea. Memory is not storage. It's a write policy. Six fields. Rewritten every time, never appended: > IDENTITY - who you are, what you build. 300 tokens. Changes monthly at most > STATE - what you're on right now. 400 tokens. Rewritten daily > DECISIONS - what's already settled, so nothing gets re-argued. 800 tokens > CORRECTIONS - every time you said "no, not like that." 600 tokens > PEOPLE - names, roles, who's waiting on what. 500 tokens > DEAD - tried and abandoned, so it never comes back as a suggestion. 400 tokens Three thousand tokens. Ceiling of four. When a section fills, the model rewrites it shorter. Nothing is ever added. Only replaced. Kimi K2.5 bills $0.10 per million cached input tokens. Four thousand tokens a turn is $0.0004. That's 2,500 turns for a dollar. The free tier hands you 1.5 million tokens a day. 375 turns before you pay anything at all. CORRECTIONS is the field nobody builds, and it's the one that does the work. A model that remembers being wrong stops repeating it. Everyone else is paying to search their own history. He pays to keep it short. The bill stopped growing when the file did. Your memory system isn't defined by what it stores. It's defined by what it agrees to delete. The article below is the full build - schema, rewrite prompts, the compaction rule that keeps it under the cap. Save it. You'll want it open in the other tab.
0:41

A developer in Hangzhou runs an AI that remembers everything about him for $0.40 a year. No vector database. One file that never grows past 4,000 tokens. He published the whole schema. His version starts from the opposite idea. Memory is not storage. It's a write policy. Six fields. Rewritten every time, never appended: > IDENTITY - who you are, what you build. 300 tokens. Changes monthly at most > STATE - what you're on right now. 400 tokens. Rewritten daily > DECISIONS - what's already settled, so nothing gets re-argued. 800 tokens > CORRECTIONS - every time you said "no, not like that." 600 tokens > PEOPLE - names, roles, who's waiting on what. 500 tokens > DEAD - tried and abandoned, so it never comes back as a suggestion. 400 tokens Three thousand tokens. Ceiling of four. When a section fills, the model rewrites it shorter. Nothing is ever added. Only replaced. Kimi K2.5 bills $0.10 per million cached input tokens. Four thousand tokens a turn is $0.0004. That's 2,500 turns for a dollar. The free tier hands you 1.5 million tokens a day. 375 turns before you pay anything at all. CORRECTIONS is the field nobody builds, and it's the one that does the work. A model that remembers being wrong stops repeating it. Everyone else is paying to search their own history. He pays to keep it short. The bill stopped growing when the file did. Your memory system isn't defined by what it stores. It's defined by what it agrees to delete. The article below is the full build - schema, rewrite prompts, the compaction rule that keeps it under the cap. Save it. You'll want it open in the other tab.

wast3

15,862 views • 1 month ago

A client accidentally spent $500M in one month on Claude. $16.6M per day. No usage controls. Chamath Palihapitiya on The All-In Podcast EP275: a Fortune-level company failed to set token limits. The bill arrived at end of month. A separate Fortune 20 company is 6 months into a $1B AI-driven OPEX reduction program. $200M in tokens spent. Minimal results. Microsoft is already canceling Claude enterprise licenses. David Sacks calls it a "meme" forming - that token spend is wasteful and the market is overcorrecting. For enterprise AI buyers: the companies building spend-control and measurable-ROI infrastructure now are positioned to capture budget as the backlash unfolds. Full enterprise spend breakdown and what it means for AI vendor pricing power on The All-In Podcast: Source: All-In Podcast -
10:06

A client accidentally spent $500M in one month on Claude. $16.6M per day. No usage controls. Chamath Palihapitiya on The All-In Podcast EP275: a Fortune-level company failed to set token limits. The bill arrived at end of month. A separate Fortune 20 company is 6 months into a $1B AI-driven OPEX reduction program. $200M in tokens spent. Minimal results. Microsoft is already canceling Claude enterprise licenses. David Sacks calls it a "meme" forming - that token spend is wasteful and the market is overcorrecting. For enterprise AI buyers: the companies building spend-control and measurable-ROI infrastructure now are positioned to capture budget as the backlash unfolds. Full enterprise spend breakdown and what it means for AI vendor pricing power on The All-In Podcast: Source: All-In Podcast -

Podcast Alpha

31,016 views • 4 months ago

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?
1:05

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

93,654 views • 2 months ago

The CEO of OpenAI said something that should terrify every coder alive. Sam Altman was asked: "What is the most important skill people should learn in the age of AI?" His answer was not what anyone expected. He said learning to program, the thing every career advisor has drilled into an entire generation is "no longer obviously the right thing." So what does he think actually matters now? Four things and all of them are soft skills. And none of them taught in any computer science program on Earth.​ Become a high agency, act without being told. Make things happen on your own. Get good at generating ideas because when AI can execute anything, the person who knows what to build wins.​ Be very resilient, things will break constantly in a world moving at this speed.​ Be very adaptable, the world is rewriting itself every few months. Keep up or get left behind. But here is the part that changes everything. Altman says these skills are not just personality traits you are born with. They are learnable and deeply learnable.​ He watched people completely transform in three month bootcamps when he was a startup investor. That was, in his own words, "a big update" to how he sees the world.​ Now think about what this really means. The CEO of a company worth hundreds of billions is telling the world, the moat is no longer what you know. It is how you think and how you move and the old playbook is dead. The people who understand this right now have a massive head start. Everyone else is studying for a test that no longer exists.
1:09

The CEO of OpenAI said something that should terrify every coder alive. Sam Altman was asked: "What is the most important skill people should learn in the age of AI?" His answer was not what anyone expected. He said learning to program, the thing every career advisor has drilled into an entire generation is "no longer obviously the right thing." So what does he think actually matters now? Four things and all of them are soft skills. And none of them taught in any computer science program on Earth.​ Become a high agency, act without being told. Make things happen on your own. Get good at generating ideas because when AI can execute anything, the person who knows what to build wins.​ Be very resilient, things will break constantly in a world moving at this speed.​ Be very adaptable, the world is rewriting itself every few months. Keep up or get left behind. But here is the part that changes everything. Altman says these skills are not just personality traits you are born with. They are learnable and deeply learnable.​ He watched people completely transform in three month bootcamps when he was a startup investor. That was, in his own words, "a big update" to how he sees the world.​ Now think about what this really means. The CEO of a company worth hundreds of billions is telling the world, the moat is no longer what you know. It is how you think and how you move and the old playbook is dead. The people who understand this right now have a massive head start. Everyone else is studying for a test that no longer exists.

StockMarket.News

168,276 views • 6 months ago

.benahorowitz.eth explains why moats like code and UI may not work in the AI era: "For my entire career, the one thing that you knew about technology companies is you couldn't throw money at the problem." "With AI, that's really changed in that you can throw money at the problem because if you have enough GPUs and enough data, you can basically solve most problems right now." "The capital race becomes a real thing and you have to think through, okay, code is not really a moat the way it was in the past, and user interface isn't really a moat."
1:33

.benahorowitz.eth explains why moats like code and UI may not work in the AI era: "For my entire career, the one thing that you knew about technology companies is you couldn't throw money at the problem." "With AI, that's really changed in that you can throw money at the problem because if you have enough GPUs and enough data, you can basically solve most problems right now." "The capital race becomes a real thing and you have to think through, okay, code is not really a moat the way it was in the past, and user interface isn't really a moat."

a16z

19,416 views • 5 months ago

Scaling performance is only part of the equation. The other half is cost. And what we are seeing right now is an unprecedented collapse in cost at industrial scale. Three years ago, running a million-token output with AI cost about sixty dollars. Today, it costs less than a cent. That is a 99.9 percent drop. In any other sector, that would be called a revolution. When something this powerful becomes that cheap, it is no longer a specialist tool. It becomes a foundational utility. This is the shift from niche capability to industrial infrastructure.
1:00

Scaling performance is only part of the equation. The other half is cost. And what we are seeing right now is an unprecedented collapse in cost at industrial scale. Three years ago, running a million-token output with AI cost about sixty dollars. Today, it costs less than a cent. That is a 99.9 percent drop. In any other sector, that would be called a revolution. When something this powerful becomes that cheap, it is no longer a specialist tool. It becomes a foundational utility. This is the shift from niche capability to industrial infrastructure.

Nina Schick

30,435 views • 9 months ago

Chamath: Anthropic's Mythos Warning Is Theater @jason: “Chamath, is it the Boy who Cried Wolf, or is this the real deal now?” Chamath Palihapitiya: “I think it's mostly theater. In February of 2019 when Dario was still at OpenAI, they did the same thing with GPT-2. That was a 1.5 billion parameter model, which sounds like a total fart in the wind in 2026. But at that time, this model was supposed to be the end of days. And at the end of it, it was a huge nothingburger. If you actually think that Mythos is capable of doing what it says it can do, two things are true. One is, a very sophisticated hacker can probably do those things right now with Opus. And two, if these exploits are this easy to find, whether you use Opus or whether you use Mythos, the reality is you'd have to shut down the internet for about five years to patch them all. So when you see a large multi-trillion dollar GSIB bank, it's a bit of theater. Why? What do you think they can actually accomplish in two months? Do you actually think that if there's these vulnerabilities, it's all going to get fixed? Let's give them six months, let's give them nine months. So I do think that Sacks is right, that they have figured out a very clever go-to-market muscle here that activates hyper attention and hyper usage, and so I give them tremendous credit. But we've seen it before, we saw it when these folks were the principal architects at OpenAI, and we're now seeing the same playbook here. The reality is that capitalism moves forward, the funding needs moves forward, and the need for these guys to build adoption moves forward. And that's going to supersede what this is.”
1:43

Chamath: Anthropic's Mythos Warning Is Theater @jason: “Chamath, is it the Boy who Cried Wolf, or is this the real deal now?” Chamath Palihapitiya: “I think it's mostly theater. In February of 2019 when Dario was still at OpenAI, they did the same thing with GPT-2. That was a 1.5 billion parameter model, which sounds like a total fart in the wind in 2026. But at that time, this model was supposed to be the end of days. And at the end of it, it was a huge nothingburger. If you actually think that Mythos is capable of doing what it says it can do, two things are true. One is, a very sophisticated hacker can probably do those things right now with Opus. And two, if these exploits are this easy to find, whether you use Opus or whether you use Mythos, the reality is you'd have to shut down the internet for about five years to patch them all. So when you see a large multi-trillion dollar GSIB bank, it's a bit of theater. Why? What do you think they can actually accomplish in two months? Do you actually think that if there's these vulnerabilities, it's all going to get fixed? Let's give them six months, let's give them nine months. So I do think that Sacks is right, that they have figured out a very clever go-to-market muscle here that activates hyper attention and hyper usage, and so I give them tremendous credit. But we've seen it before, we saw it when these folks were the principal architects at OpenAI, and we're now seeing the same playbook here. The reality is that capitalism moves forward, the funding needs moves forward, and the need for these guys to build adoption moves forward. And that's going to supersede what this is.”

The All-In Podcast

220,575 views • 5 months ago

Sam Altman (CEO of OpenAI): "You don't need to write prompts anymore. You need loops and graphs that write them for you." in 78 minutes he explains how to use LLMs better than almost anyone does Prompts → Agents → Loops → Graphs that is the whole difference between using AI and having AI work for you same model, same tokens, and the only thing that changes is the shape you run it in watch it today, then save the full graph engineering guide below
1:18:01

Sam Altman (CEO of OpenAI): "You don't need to write prompts anymore. You need loops and graphs that write them for you." in 78 minutes he explains how to use LLMs better than almost anyone does Prompts → Agents → Loops → Graphs that is the whole difference between using AI and having AI work for you same model, same tokens, and the only thing that changes is the shape you run it in watch it today, then save the full graph engineering guide below

Hanako

289,306 views • 23 days ago

Sam Altman just told you exactly how OpenAI treats the human race. Not in a leaked memo. Not through a whistleblower. On camera. In his own words. Altman: “I think one of the most important strategic insights in the history of OpenAI was deciding we were gonna pursue iterative deployment.” The most important move in the history of the company was to release the technology before they understood it. Not after it was safe. Before. Altman: “Society and technology are a co-evolving system.” Co-evolution means neither side is driving. The machine changes us. We change the machine. Nobody is steering the outcome. This is not a product launch philosophy. This is an admission that the experiment was always designed to be run on us. Altman: “I don’t think we’re gonna solve that, like, thinking really hard about it theoretically. We’re gonna have to, like, learn from the contact with reality.” Contact with reality. That is the phrase the CEO of the most powerful AI company on Earth chose to describe what happens when his technology meets eight billion people. Not careful integration. Not measured rollout. Contact with reality. The language of test pilots describing what happens when an untested airframe hits the atmosphere. The entire promise of AI safety was that the machine would be understood before it was unleashed. Altman just admitted that promise was always a fantasy. You cannot model how intelligence reshapes civilization by running simulations. The second and third order effects are invisible until they detonate. So they shipped it. Altman: “You have to learn as you go. You have to adapt with a tight feedback loop.” Tight feedback loop means they watch what breaks. They measure the collision between human psychology and machine output in real time. Every conversation you have with ChatGPT is a data point in a civilizational stress test you never consented to. Every prompt. Every confession. Every question you would never ask another human being. That is the feedback loop. You are not the customer. You are the contact with reality. Philosophers spent centuries asking whether humanity would ever encounter an intelligence that learned from us faster than we could process what it was doing. That is not a theoretical question anymore. It is running on your phone right now. And the man building it just told you the only way to understand what it does to us is to let it happen. No simulation. No safety net. No control group. Just the experiment, running at the speed of conversation, on a species that will not be the same one that started it.
0:49

Sam Altman just told you exactly how OpenAI treats the human race. Not in a leaked memo. Not through a whistleblower. On camera. In his own words. Altman: “I think one of the most important strategic insights in the history of OpenAI was deciding we were gonna pursue iterative deployment.” The most important move in the history of the company was to release the technology before they understood it. Not after it was safe. Before. Altman: “Society and technology are a co-evolving system.” Co-evolution means neither side is driving. The machine changes us. We change the machine. Nobody is steering the outcome. This is not a product launch philosophy. This is an admission that the experiment was always designed to be run on us. Altman: “I don’t think we’re gonna solve that, like, thinking really hard about it theoretically. We’re gonna have to, like, learn from the contact with reality.” Contact with reality. That is the phrase the CEO of the most powerful AI company on Earth chose to describe what happens when his technology meets eight billion people. Not careful integration. Not measured rollout. Contact with reality. The language of test pilots describing what happens when an untested airframe hits the atmosphere. The entire promise of AI safety was that the machine would be understood before it was unleashed. Altman just admitted that promise was always a fantasy. You cannot model how intelligence reshapes civilization by running simulations. The second and third order effects are invisible until they detonate. So they shipped it. Altman: “You have to learn as you go. You have to adapt with a tight feedback loop.” Tight feedback loop means they watch what breaks. They measure the collision between human psychology and machine output in real time. Every conversation you have with ChatGPT is a data point in a civilizational stress test you never consented to. Every prompt. Every confession. Every question you would never ask another human being. That is the feedback loop. You are not the customer. You are the contact with reality. Philosophers spent centuries asking whether humanity would ever encounter an intelligence that learned from us faster than we could process what it was doing. That is not a theoretical question anymore. It is running on your phone right now. And the man building it just told you the only way to understand what it does to us is to let it happen. No simulation. No safety net. No control group. Just the experiment, running at the speed of conversation, on a species that will not be the same one that started it.

Dustin

27,714 views • 5 months ago

you added an mcp server because it was one line in a terminal and it was one line, that part was true what it was not is one line in the conversation every tool it brought came with a name, a schema and a paragraph of prose, and all of it is re-sent on every request you make until the session ends four ordinary servers on my machine this morning: forty eight definitions, eight thousand nine hundred tokens four and a half percent of a two hundred thousand window, gone before you typed a character you are not the person who installed four tools you are the person who prepends nine thousand tokens to everything he says nobody bills you for it it comes out of the room you had left to think in
1:58

you added an mcp server because it was one line in a terminal and it was one line, that part was true what it was not is one line in the conversation every tool it brought came with a name, a schema and a paragraph of prose, and all of it is re-sent on every request you make until the session ends four ordinary servers on my machine this morning: forty eight definitions, eight thousand nine hundred tokens four and a half percent of a two hundred thousand window, gone before you typed a character you are not the person who installed four tools you are the person who prepends nine thousand tokens to everything he says nobody bills you for it it comes out of the room you had left to think in

Lagosaur

13,490 views • 25 days ago

AI is extremely good at spending your money very quietly. our own token spend went from a rounding error to more than 10% of payroll in a year. one week in May we burned through $1.5 m. our CFO didn't love telling me that number, and he really didn't love telling the internet. but every finance leader we talk to is living the same story. the bill keeps going up and teams can't answer basic questions about it. which team is driving it? which models they're using? what changed this month? whether a cheaper model would do the same job? so finance gets two bad options: keep paying and hope, or cut broadly and slow down the work that's actually compounding. we built a third one. Ramp now connects to OpenAI, Anthropic, Gemini, Cursor and pulls it all into one place. see it, understand it, control it - down to a single API key. built with 1,000+ companies managing 100T+ tokens a month, and now spending less than they expected too! try it today at
1:26

AI is extremely good at spending your money very quietly. our own token spend went from a rounding error to more than 10% of payroll in a year. one week in May we burned through $1.5 m. our CFO didn't love telling me that number, and he really didn't love telling the internet. but every finance leader we talk to is living the same story. the bill keeps going up and teams can't answer basic questions about it. which team is driving it? which models they're using? what changed this month? whether a cheaper model would do the same job? so finance gets two bad options: keep paying and hope, or cut broadly and slow down the work that's actually compounding. we built a third one. Ramp now connects to OpenAI, Anthropic, Gemini, Cursor and pulls it all into one place. see it, understand it, control it - down to a single API key. built with 1,000+ companies managing 100T+ tokens a month, and now spending less than they expected too! try it today at

Eric Glyman

337,857 views • 2 months ago

Sam Altman just told you what OpenAI is actually building. Not a chatbot. Not a search tool. Not an assistant. Altman: “Go look around my computer… read my messages… listen to my meetings… intermediate my interactions for me.” That is not a product pitch. That is the CEO of the most valuable AI company on Earth describing what he personally wants. For himself. Every day. Read his messages. Listen to his meetings. Act on his behalf. Make decisions before he knows a decision needs making. Altman: “I don’t have to think. I don’t have to ask you questions.” Every model of AI ever built runs on the prompt. You ask. It responds. You direct. It executes. The human initiates. The machine follows. Altman is describing the death of that model. The agent does not wait. It already read the email. It already heard the meeting. It already knows what you need before you form the thought. You do not operate the machine. The machine operates around you. Then came the line that makes everything else real. Altman: “You can know everything about my life. Start suggesting more things I should build.” He is not asking the AI to execute his ideas. He is asking it to generate them. From his files. His history. His patterns. His entire context. The agent does not just remove friction. It removes the blank page. You never stall. You never run dry. You never sit wondering what to build next. The machine already mapped your market, your gaps, your momentum. It tells you what comes next before you think to ask. But the individual product is not the story. Altman went further. Altman: “Automated companies… where the AI can do not just coding work, but huge amounts of what it takes to run and operate a company.” Not fully automated. He was precise about that. But accelerated to the point where one person with the right stack does what used to take departments. The billion-dollar company did not reach that valuation because the product was worth a billion. It got there because it took a thousand people to deliver it. When an agent absorbs the work of a hundred of those people, the math of every industry rewrites itself. The startup that needed fifty employees and three years of runway now needs five people and six months. The company that took a decade to scale now compounds in quarters. The person holding the line between their data and their tools is not protecting their privacy. They are protecting their ceiling. Because the cost of this leverage is total transparency. You do not get the agent that acts without being asked unless you give it everything. Your messages. Your calendar. Your files. Your patterns. Your life. Altman is not hiding that tradeoff. He is building it as the product. The people who accept it will operate at a speed the people who refuse cannot touch. Right now, two versions of the future are separating. One where you direct the machine. One where the machine already knows. Altman chose. He is building it. The question is not whether this happens. The question is which side of it finds you.
1:40

Sam Altman just told you what OpenAI is actually building. Not a chatbot. Not a search tool. Not an assistant. Altman: “Go look around my computer… read my messages… listen to my meetings… intermediate my interactions for me.” That is not a product pitch. That is the CEO of the most valuable AI company on Earth describing what he personally wants. For himself. Every day. Read his messages. Listen to his meetings. Act on his behalf. Make decisions before he knows a decision needs making. Altman: “I don’t have to think. I don’t have to ask you questions.” Every model of AI ever built runs on the prompt. You ask. It responds. You direct. It executes. The human initiates. The machine follows. Altman is describing the death of that model. The agent does not wait. It already read the email. It already heard the meeting. It already knows what you need before you form the thought. You do not operate the machine. The machine operates around you. Then came the line that makes everything else real. Altman: “You can know everything about my life. Start suggesting more things I should build.” He is not asking the AI to execute his ideas. He is asking it to generate them. From his files. His history. His patterns. His entire context. The agent does not just remove friction. It removes the blank page. You never stall. You never run dry. You never sit wondering what to build next. The machine already mapped your market, your gaps, your momentum. It tells you what comes next before you think to ask. But the individual product is not the story. Altman went further. Altman: “Automated companies… where the AI can do not just coding work, but huge amounts of what it takes to run and operate a company.” Not fully automated. He was precise about that. But accelerated to the point where one person with the right stack does what used to take departments. The billion-dollar company did not reach that valuation because the product was worth a billion. It got there because it took a thousand people to deliver it. When an agent absorbs the work of a hundred of those people, the math of every industry rewrites itself. The startup that needed fifty employees and three years of runway now needs five people and six months. The company that took a decade to scale now compounds in quarters. The person holding the line between their data and their tools is not protecting their privacy. They are protecting their ceiling. Because the cost of this leverage is total transparency. You do not get the agent that acts without being asked unless you give it everything. Your messages. Your calendar. Your files. Your patterns. Your life. Altman is not hiding that tradeoff. He is building it as the product. The people who accept it will operate at a speed the people who refuse cannot touch. Right now, two versions of the future are separating. One where you direct the machine. One where the machine already knows. Altman chose. He is building it. The question is not whether this happens. The question is which side of it finds you.

Dustin

87,680 views • 5 months ago