Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Trump's AI EO has no heavy regulation, no permission frameworks, just a voluntary preview of models 30 days before release. Anthropic immediately filed a draft S-1 for an IPO, all while ChatGPT hit 1 billion users faster than any other product in human history. The US is telling the...

PeterDiamandis's profile picture

Peter H. Diamandis, MD

402,526 subscribers

23,253 Aufrufe • vor 1 Monat •via X (Twitter)

Nachrichten & PolitikWissenschaft & TechnologieFinanzen
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

OpenAI just admitted Anthropic is KILLING their business. Their own applications chief told employees it was a "code red." Said Anthropic was a "wake-up call." Then admitted OpenAI had been "spreading efforts across too many apps" and it was "slowing them down." This is an internal confession. Here's why Anthropic is eating up OpenAI: 12 months ago, OpenAI owned 50% of all enterprise AI spending. Today it's just 27%. Anthropic went from nearly ZERO to winning 70% of every first-time enterprise AI deal. Seven out of ten companies buying AI tools for the first time are choosing Claude over ChatGPT. A year ago, one in 25 businesses on Ramp paid for Anthropic. Today it's one in four. OpenAI just had its biggest single-month adoption decline ever recorded. And Anthropic literally charges MORE than OpenAI for roughly the same performance. And businesses are STILL choosing them. In enterprise software, that never happens. The cheaper product usually wins. But Claude became something OpenAI never figured out how to be: Cool. Celebrities publicly switched to Claude. Senators are tweeting about using it. Engineers are shipping entire products with Claude Code in hours that used to take weeks. It started to became an identity signal. Like blue bubble vs green bubble in iMessage. Choosing Claude says something about you now. Meanwhile OpenAI went the opposite direction: They took the Pentagon contract that Anthropic refused. Greg Brockman donated $25 million to fund wars. ChatGPT uninstalls jumped 295% in a single day. Reddit posts saying "Cancel and Delete ChatGPT" got 30,000 upvotes. Anthropic said no to mass surveillance and autonomous weapons. Got blacklisted by the Pentagon. Trump called them a "Radical Left AI company." And their downloads went to #1 on the App Store the next day. Turns out refusing to build weapons is good marketing. But the real damage isn't consumer downloads. It's the MONEY. Claude Code hit $2.5 billion in annual revenue in six months. OpenAI's competing product Codex just barely crossed $1 billion. And Anthropic literally cannot meet demand. They're turning away paying customers because they don't have enough compute to serve them. A company REJECTING revenue because it's growing too fast. While OpenAI scrambles to consolidate. Last week OpenAI announced they're merging ChatGPT, Codex, and their browser into one "superapp." But what this really means: "We launched too many products, none of them worked well enough alone, so now we're cramming everything together and hoping it sticks." And remember their video tool Sora? Launched standalone. Hit #1 on the App Store. Usage flatlined within weeks. Now they're forced to shut it down. Their browser Atlas? Still hasn't launched publicly. Their IPO? Polymarket odds dropped from 55% to 35%. OpenAI has 900 million users. Anthropic has maybe 10 million daily actives. But here's the thing... OpenAI won the consumer war. ChatGPT is where your mom asks about recipes and your cousin makes memes. Anthropic won the war that actually MATTERS. The developers. The engineers. The enterprises writing 7 figure checks. OpenAI built the biggest chatbot on Earth. Anthropic built the tool that companies can't stop paying for. This is Yahoo vs Google all over again. Yahoo had the users. Google had the product. And we all know how that ended. OpenAI has 12 months to prove the superapp works, land the IPO, and stop the enterprise bleeding. If they can't, the most valuable startup in history becomes the most cautionary tale in tech. 900 million users don't mean anything if the people who actually pay are walking out the door. What do you think?
2:25

OpenAI just admitted Anthropic is KILLING their business. Their own applications chief told employees it was a "code red." Said Anthropic was a "wake-up call." Then admitted OpenAI had been "spreading efforts across too many apps" and it was "slowing them down." This is an internal confession. Here's why Anthropic is eating up OpenAI: 12 months ago, OpenAI owned 50% of all enterprise AI spending. Today it's just 27%. Anthropic went from nearly ZERO to winning 70% of every first-time enterprise AI deal. Seven out of ten companies buying AI tools for the first time are choosing Claude over ChatGPT. A year ago, one in 25 businesses on Ramp paid for Anthropic. Today it's one in four. OpenAI just had its biggest single-month adoption decline ever recorded. And Anthropic literally charges MORE than OpenAI for roughly the same performance. And businesses are STILL choosing them. In enterprise software, that never happens. The cheaper product usually wins. But Claude became something OpenAI never figured out how to be: Cool. Celebrities publicly switched to Claude. Senators are tweeting about using it. Engineers are shipping entire products with Claude Code in hours that used to take weeks. It started to became an identity signal. Like blue bubble vs green bubble in iMessage. Choosing Claude says something about you now. Meanwhile OpenAI went the opposite direction: They took the Pentagon contract that Anthropic refused. Greg Brockman donated $25 million to fund wars. ChatGPT uninstalls jumped 295% in a single day. Reddit posts saying "Cancel and Delete ChatGPT" got 30,000 upvotes. Anthropic said no to mass surveillance and autonomous weapons. Got blacklisted by the Pentagon. Trump called them a "Radical Left AI company." And their downloads went to #1 on the App Store the next day. Turns out refusing to build weapons is good marketing. But the real damage isn't consumer downloads. It's the MONEY. Claude Code hit $2.5 billion in annual revenue in six months. OpenAI's competing product Codex just barely crossed $1 billion. And Anthropic literally cannot meet demand. They're turning away paying customers because they don't have enough compute to serve them. A company REJECTING revenue because it's growing too fast. While OpenAI scrambles to consolidate. Last week OpenAI announced they're merging ChatGPT, Codex, and their browser into one "superapp." But what this really means: "We launched too many products, none of them worked well enough alone, so now we're cramming everything together and hoping it sticks." And remember their video tool Sora? Launched standalone. Hit #1 on the App Store. Usage flatlined within weeks. Now they're forced to shut it down. Their browser Atlas? Still hasn't launched publicly. Their IPO? Polymarket odds dropped from 55% to 35%. OpenAI has 900 million users. Anthropic has maybe 10 million daily actives. But here's the thing... OpenAI won the consumer war. ChatGPT is where your mom asks about recipes and your cousin makes memes. Anthropic won the war that actually MATTERS. The developers. The engineers. The enterprises writing 7 figure checks. OpenAI built the biggest chatbot on Earth. Anthropic built the tool that companies can't stop paying for. This is Yahoo vs Google all over again. Yahoo had the users. Google had the product. And we all know how that ended. OpenAI has 12 months to prove the superapp works, land the IPO, and stop the enterprise bleeding. If they can't, the most valuable startup in history becomes the most cautionary tale in tech. 900 million users don't mean anything if the people who actually pay are walking out the door. What do you think?

Ricardo

35,020 Aufrufe • vor 4 Monaten

🚨 OG YOUTUBER PEWDIEPIE JUST LAUNCHED A FREE, OPEN-SOURCE AI RIVAL TO CHATGPT AND CLAUDE While ChatGPT and Claude charge users for premium features, PewDiePie’s new AI workspace, Odysseus, is FREE. In his own words, "NO tracking, NO subscriptions, and can be run on your OWN computer." Odysseus is essentially “Claude and ChatGPT’s web UI, but SELF-hosted”, allowing users to run local AI models or use APIs while keeping control of their own data. Odysseus includes: - An AI agent that can find, create, edit and run files - Memory that learns from past conversations - Deep research with follow-up chat - An email client that flags urgent messages and drafts replies - A document editor for writing, formatting and fact-checking - Calendar, to-do notes and search tools - An image editor - “Cookbook,” which scans hardware, recommends local models and helps users run them - Mobile support Its BIGGEST selling point is privacy and control. “All your data is yours, as it should be.” He also made a firm commitment on pricing: “This project will never cost any money.” The launch comes just days after PewDiePie announced he was ENDING his vlogs. Retirement arc is wild.
1:00

🚨 OG YOUTUBER PEWDIEPIE JUST LAUNCHED A FREE, OPEN-SOURCE AI RIVAL TO CHATGPT AND CLAUDE While ChatGPT and Claude charge users for premium features, PewDiePie’s new AI workspace, Odysseus, is FREE. In his own words, "NO tracking, NO subscriptions, and can be run on your OWN computer." Odysseus is essentially “Claude and ChatGPT’s web UI, but SELF-hosted”, allowing users to run local AI models or use APIs while keeping control of their own data. Odysseus includes: - An AI agent that can find, create, edit and run files - Memory that learns from past conversations - Deep research with follow-up chat - An email client that flags urgent messages and drafts replies - A document editor for writing, formatting and fact-checking - Calendar, to-do notes and search tools - An image editor - “Cookbook,” which scans hardware, recommends local models and helps users run them - Mobile support Its BIGGEST selling point is privacy and control. “All your data is yours, as it should be.” He also made a firm commitment on pricing: “This project will never cost any money.” The launch comes just days after PewDiePie announced he was ENDING his vlogs. Retirement arc is wild.

Coin Bureau

73,897 Aufrufe • vor 1 Monat

Microsoft is about to sue its own golden child. $14 billion invested. Exclusive cloud rights. The most important AI partnership in history. And Sam Altman just went behind their back with a $50 billion Amazon deal. Here's why they're betraying each other: When Microsoft first invested in OpenAI in 2019, they locked in ONE rule above everything else... ALL access to OpenAI's models must go through Microsoft's Azure cloud. No exceptions. That deal made Azure the backbone of the AI revolution. Every company using ChatGPT's API was paying Microsoft for the privilege. It was the smartest infrastructure play of the decade. Then last month, OpenAI quietly signed a deal with Amazon. $50 billion. AWS becomes the exclusive third-party cloud provider for Frontier, OpenAI's new enterprise AI agent platform. $138 billion committed to Amazon cloud services. Microsoft found out and got really angry.... A person familiar with Microsoft's position told the Financial Times today: "We know our contract. We will sue them if they breach it. If Amazon and OpenAI want to take a bet on the creativity of their contractual lawyers, I would back us, not them." That's basically a declaration of war. And here's where it gets crazy: OpenAI and Amazon are trying to build a technical workaround. A system called the "Stateful Runtime Environment" that runs on Amazon's Bedrock platform. Their argument is that the system "only" handles memory and context for AI agents using enterprise data on AWS. It doesn't technically "invoke" OpenAI's core models through Amazon. Microsoft's response: Bullshit. The workaround violates the spirit of the deal even if it technically dances around the letter. Amazon knows they're on thin ice too. An internal memo leaked showing Amazon told employees exactly what language they can and can't use. They can say Frontier is "powered by OpenAI" or "enabled by OpenAI." But they CANNOT say customers can "access" or "invoke" OpenAI models on AWS. When you're coaching employees on which verbs to avoid, you know you're in trouble. But here's the thing everyone seems to forget: OpenAI is planning an IPO this year. They just closed a $110 billion funding round last month. So if Microsoft sues, the IPO timeline is DEAD. You can't go public while your biggest partner and investor is suing you for breach of contract. Elon Musk is already suing OpenAI separately for abandoning its nonprofit mission. Two active lawsuits from two of the most powerful people in tech. Against one company trying to IPO. Good luck with that S-1 filing. But WHY did Altman do this? Microsoft gave OpenAI everything. Capital. Infrastructure. Distribution. Enterprise customers. And Altman's response was to secretly build an escape route through Amazon... Because he saw what was coming: Microsoft launched Copilot. Their own AI product. Competing directly with ChatGPT. Microsoft started building their own models. Hiring their own AI researchers. Reducing dependency on OpenAI. So Altman did the same thing back. Found another cloud provider. Started building leverage. Both sides were preparing for divorce while still living in the same house. So the $50 billion Amazon deal was just an insurance policy against the day Microsoft decides it doesn't need OpenAI anymore. And Microsoft caught him packing his bags. What happens next: The companies are still talking. Trying to resolve this before Frontier launches. But Microsoft has made their position clear. Litigation is on the table. If this goes to court, it sets a precedent for every AI partnership in the industry. Every cloud deal. Every exclusive licensing agreement. The entire AI infrastructure map gets redrawn. Sam Altman built OpenAI on Microsoft's money, Microsoft's cloud, and Microsoft's trust. Then he signed a $50 billion deal with their biggest competitor. In any other industry they'd call that what it is.
0:28

Microsoft is about to sue its own golden child. $14 billion invested. Exclusive cloud rights. The most important AI partnership in history. And Sam Altman just went behind their back with a $50 billion Amazon deal. Here's why they're betraying each other: When Microsoft first invested in OpenAI in 2019, they locked in ONE rule above everything else... ALL access to OpenAI's models must go through Microsoft's Azure cloud. No exceptions. That deal made Azure the backbone of the AI revolution. Every company using ChatGPT's API was paying Microsoft for the privilege. It was the smartest infrastructure play of the decade. Then last month, OpenAI quietly signed a deal with Amazon. $50 billion. AWS becomes the exclusive third-party cloud provider for Frontier, OpenAI's new enterprise AI agent platform. $138 billion committed to Amazon cloud services. Microsoft found out and got really angry.... A person familiar with Microsoft's position told the Financial Times today: "We know our contract. We will sue them if they breach it. If Amazon and OpenAI want to take a bet on the creativity of their contractual lawyers, I would back us, not them." That's basically a declaration of war. And here's where it gets crazy: OpenAI and Amazon are trying to build a technical workaround. A system called the "Stateful Runtime Environment" that runs on Amazon's Bedrock platform. Their argument is that the system "only" handles memory and context for AI agents using enterprise data on AWS. It doesn't technically "invoke" OpenAI's core models through Amazon. Microsoft's response: Bullshit. The workaround violates the spirit of the deal even if it technically dances around the letter. Amazon knows they're on thin ice too. An internal memo leaked showing Amazon told employees exactly what language they can and can't use. They can say Frontier is "powered by OpenAI" or "enabled by OpenAI." But they CANNOT say customers can "access" or "invoke" OpenAI models on AWS. When you're coaching employees on which verbs to avoid, you know you're in trouble. But here's the thing everyone seems to forget: OpenAI is planning an IPO this year. They just closed a $110 billion funding round last month. So if Microsoft sues, the IPO timeline is DEAD. You can't go public while your biggest partner and investor is suing you for breach of contract. Elon Musk is already suing OpenAI separately for abandoning its nonprofit mission. Two active lawsuits from two of the most powerful people in tech. Against one company trying to IPO. Good luck with that S-1 filing. But WHY did Altman do this? Microsoft gave OpenAI everything. Capital. Infrastructure. Distribution. Enterprise customers. And Altman's response was to secretly build an escape route through Amazon... Because he saw what was coming: Microsoft launched Copilot. Their own AI product. Competing directly with ChatGPT. Microsoft started building their own models. Hiring their own AI researchers. Reducing dependency on OpenAI. So Altman did the same thing back. Found another cloud provider. Started building leverage. Both sides were preparing for divorce while still living in the same house. So the $50 billion Amazon deal was just an insurance policy against the day Microsoft decides it doesn't need OpenAI anymore. And Microsoft caught him packing his bags. What happens next: The companies are still talking. Trying to resolve this before Frontier launches. But Microsoft has made their position clear. Litigation is on the table. If this goes to court, it sets a precedent for every AI partnership in the industry. Every cloud deal. Every exclusive licensing agreement. The entire AI infrastructure map gets redrawn. Sam Altman built OpenAI on Microsoft's money, Microsoft's cloud, and Microsoft's trust. Then he signed a $50 billion deal with their biggest competitor. In any other industry they'd call that what it is.

Ricardo

209,425 Aufrufe • vor 4 Monaten

Everyone told Vicente Silveira (Vicente Silveira) that his startup—a GPT wrapper—would fail. Instead, one year later, it’s thriving—with about 500,000 registered users, nearly 3,000 paying subscribers, and over 2 million conversations in the GPT store. Vicente is the cofounder and CEO of AI PDF, a tool that can help you summarize, chat with, and organize your PDF files. When OpenAI allowed users to upload PDFs to ChatGPT, the consensus was that his startup, and all the other GPT wrappers out there, were toast. Some of his competitors even shut shop, but Vicente believed they could still create value for users as a specialized tool. The AI PDF team kept building. A year later, AI PDF is one of the most popular AI-powered PDF readers in the world—and they did it all with a five-person team, and a friends and family round. I sat down with Vicente to understand, in granular detail, the success of AI PDF. We get into: - Why staying small and specialized is a bigger advantage than you think - The power of building with your early adopters - Why lean startups are better positioned than frontier AI companies to create radical solutions - When a growing startup should think about raising venture capital - The emerging role of ‘AI managers’ who will be responsible for overseeing AI agents We even demo an agent integrated into AI PDF, prompting it to analyze recent articles from my column Chain of Thought and write a bulleted list of the core thesis statements. This is a must-watch for small teams building profitable companies at the bleeding edge of AI. Watch below! Timestamps: Introduction: 00:00:35 AI PDF’s story begins with an email to OpenAI’s Greg Brockman: 00:02:58 Why users choose AI PDF over ChatGPT: 00:05:41 How to compete—and thrive—as a GPT wrapper: 00:06:58 Why building with early adopters is key: 00:20:49 Being small and specialized is your biggest advantage: 00:27:53 When should AI startups raise capital: 00:31:47 The emerging role of humans who will manage AI agents: 00:34:53 Why AI is different from other tech revolutions: 00:45:25 A live demo of an agent integrated into AI PDF: 00:54:01
1:03:24

Everyone told Vicente Silveira (Vicente Silveira) that his startup—a GPT wrapper—would fail. Instead, one year later, it’s thriving—with about 500,000 registered users, nearly 3,000 paying subscribers, and over 2 million conversations in the GPT store. Vicente is the cofounder and CEO of AI PDF, a tool that can help you summarize, chat with, and organize your PDF files. When OpenAI allowed users to upload PDFs to ChatGPT, the consensus was that his startup, and all the other GPT wrappers out there, were toast. Some of his competitors even shut shop, but Vicente believed they could still create value for users as a specialized tool. The AI PDF team kept building. A year later, AI PDF is one of the most popular AI-powered PDF readers in the world—and they did it all with a five-person team, and a friends and family round. I sat down with Vicente to understand, in granular detail, the success of AI PDF. We get into: - Why staying small and specialized is a bigger advantage than you think - The power of building with your early adopters - Why lean startups are better positioned than frontier AI companies to create radical solutions - When a growing startup should think about raising venture capital - The emerging role of ‘AI managers’ who will be responsible for overseeing AI agents We even demo an agent integrated into AI PDF, prompting it to analyze recent articles from my column Chain of Thought and write a bulleted list of the core thesis statements. This is a must-watch for small teams building profitable companies at the bleeding edge of AI. Watch below! Timestamps: Introduction: 00:00:35 AI PDF’s story begins with an email to OpenAI’s Greg Brockman: 00:02:58 Why users choose AI PDF over ChatGPT: 00:05:41 How to compete—and thrive—as a GPT wrapper: 00:06:58 Why building with early adopters is key: 00:20:49 Being small and specialized is your biggest advantage: 00:27:53 When should AI startups raise capital: 00:31:47 The emerging role of humans who will manage AI agents: 00:34:53 Why AI is different from other tech revolutions: 00:45:25 A live demo of an agent integrated into AI PDF: 00:54:01

Dan Shipper 📧

25,622 Aufrufe • vor 1 Jahr

DeepSeek-R1 shattered the assumption that performant AI models must be built closed source with loss-leading computational costs. This is the reality that Web3 x Crypto firms have been waiting for, leading me to believe that the most performant AI models in the future will be built on-chain. Resource Requirements DeepSeek R1 (671 billion parameters), which took over a billion dollars, 2,000 Nvidia H800 GPUs, and over 55 days, beat benchmarks held by OpenAI’s o1 mode (near 2 trillion parameters)l, which required hundreds of billions of dollars to develop along with over 16,000 advanced GPUs. The idea that AI models must be closed-source and have loss-leading computational costs to succeed is crumbling. The Existing Decentralized AI Narrative AI x Crypto projects believed that crowdsourced, public, decentralized AI would eventually create better models than their centralized counterparts. This had thus far not been true, as the highest-performing models had come from closed-source companies like OpenAI and Anthropic. Crypto x AI companies have adapted to this by specializing in infrastructure rather than model-building. For example, GPU marketplaces like , The Render Network, io.net, and Exabits have developed sustainable revenues. Companies that allow users to share their network bandwidth like touch grass and Gradient have found their niche in supplying services, like distributed web scraping, to web2 clients. Storage networks like Arweave Ecosystem, Filecoin, and Ocean Protocol have also done well by being the platform on which these projects are built. Supply networks have flourished because of their ability to tailor their cheaper and more scalable services to off-chain customers. Renewed Focus Now that GPU and financial resources are no longer limitations to creating quality AI models, web3 AI companies can focus on replicating DeepSeek’s effectiveness while offering new benefits like modality, user ownership, censorship resistance, privacy, and more. Pantera Capital has funded companies in this space like and Sentient that believe they can match or exceed the performance of traditional AI companies while offering additional services or benefits. , for example, is building a platform where anyone can monetize AI models, data sets, and applications in a collaborative space. Users can permissionlessly train models manually, provide training data, and create tailored AI models with no-code tools. They are only able to cater to all these stakeholders (AI developers, users, resource providers) because everything is tied to their native Sahara blockchain. We invested in them precisely for this reason. The Future of AI will be built with Web3 Infrastructure I believe that supply-side projects will continue to grow, while consumer-facing projects can begin competing with web2 competitors by taking advantage of their ability to build networks that invite community involvement. and Sentient, for example, have begun setting up systems for users to train models based on the users’ expertise. These platforms will allow users to pick and choose the data and integrations to whatever they are applying the model towards. Sahara already has over 780,000 users on their waitlist while Sentient has over 1 million interactions. In the near future, I believe that the most performant AI models will be built on-chain. For the full blog post, read my newsletter.
0:32

DeepSeek-R1 shattered the assumption that performant AI models must be built closed source with loss-leading computational costs. This is the reality that Web3 x Crypto firms have been waiting for, leading me to believe that the most performant AI models in the future will be built on-chain. Resource Requirements DeepSeek R1 (671 billion parameters), which took over a billion dollars, 2,000 Nvidia H800 GPUs, and over 55 days, beat benchmarks held by OpenAI’s o1 mode (near 2 trillion parameters)l, which required hundreds of billions of dollars to develop along with over 16,000 advanced GPUs. The idea that AI models must be closed-source and have loss-leading computational costs to succeed is crumbling. The Existing Decentralized AI Narrative AI x Crypto projects believed that crowdsourced, public, decentralized AI would eventually create better models than their centralized counterparts. This had thus far not been true, as the highest-performing models had come from closed-source companies like OpenAI and Anthropic. Crypto x AI companies have adapted to this by specializing in infrastructure rather than model-building. For example, GPU marketplaces like , The Render Network, io.net, and Exabits have developed sustainable revenues. Companies that allow users to share their network bandwidth like touch grass and Gradient have found their niche in supplying services, like distributed web scraping, to web2 clients. Storage networks like Arweave Ecosystem, Filecoin, and Ocean Protocol have also done well by being the platform on which these projects are built. Supply networks have flourished because of their ability to tailor their cheaper and more scalable services to off-chain customers. Renewed Focus Now that GPU and financial resources are no longer limitations to creating quality AI models, web3 AI companies can focus on replicating DeepSeek’s effectiveness while offering new benefits like modality, user ownership, censorship resistance, privacy, and more. Pantera Capital has funded companies in this space like and Sentient that believe they can match or exceed the performance of traditional AI companies while offering additional services or benefits. , for example, is building a platform where anyone can monetize AI models, data sets, and applications in a collaborative space. Users can permissionlessly train models manually, provide training data, and create tailored AI models with no-code tools. They are only able to cater to all these stakeholders (AI developers, users, resource providers) because everything is tied to their native Sahara blockchain. We invested in them precisely for this reason. The Future of AI will be built with Web3 Infrastructure I believe that supply-side projects will continue to grow, while consumer-facing projects can begin competing with web2 competitors by taking advantage of their ability to build networks that invite community involvement. and Sentient, for example, have begun setting up systems for users to train models based on the users’ expertise. These platforms will allow users to pick and choose the data and integrations to whatever they are applying the model towards. Sahara already has over 780,000 users on their waitlist while Sentient has over 1 million interactions. In the near future, I believe that the most performant AI models will be built on-chain. For the full blog post, read my newsletter.

paul.nft

32,465 Aufrufe • vor 1 Jahr

In 2026, I believe we'll see a Consumer AI Renaissance. Consumer spending is the lifeblood of the US economy – representing more than 65% of US GDP. AI is already rapidly transforming how we spend both our money and time. In just the past 3 years, ChatGPT has become the fastest growing consumer product in history – reaching billions of users with engagement levels similar to social media apps like WhatsApp and Instagram. And with recent advancement in AI capabilities, I predict we’ll see a new wave of breakout consumer AI applications. We’ve seen major advances in three key areas: 1. Real-time voice AI - which allows users to engage with AI products in a natural, hands-free way 2. Advanced reasoning models - which improves the complexity of questions AI products can answer 3. Embedded Memory - which allows AI products to remember context between conversations, enabling more personalized responses With these new capabilities, we can now build AI products that are 10x better than before. I’m especially excited about products that use AI to make previously expensive services cheaper and more accessible, sometimes using human-in-the-loop to start. This includes things like AI travel agents, personal assistants, matchmakers, therapists, tutors, and more. We’ve already seen a number of early-stage teams working on these at a16z a16z speedrun 🧊 – and we’re excited to meet more. If you’re building the next generation of consumer AI, we’d love to hear from you! h/t to Theresa Horne Avenir whose excellent research helped inspire some of these insights.
1:02

In 2026, I believe we'll see a Consumer AI Renaissance. Consumer spending is the lifeblood of the US economy – representing more than 65% of US GDP. AI is already rapidly transforming how we spend both our money and time. In just the past 3 years, ChatGPT has become the fastest growing consumer product in history – reaching billions of users with engagement levels similar to social media apps like WhatsApp and Instagram. And with recent advancement in AI capabilities, I predict we’ll see a new wave of breakout consumer AI applications. We’ve seen major advances in three key areas: 1. Real-time voice AI - which allows users to engage with AI products in a natural, hands-free way 2. Advanced reasoning models - which improves the complexity of questions AI products can answer 3. Embedded Memory - which allows AI products to remember context between conversations, enabling more personalized responses With these new capabilities, we can now build AI products that are 10x better than before. I’m especially excited about products that use AI to make previously expensive services cheaper and more accessible, sometimes using human-in-the-loop to start. This includes things like AI travel agents, personal assistants, matchmakers, therapists, tutors, and more. We’ve already seen a number of early-stage teams working on these at a16z a16z speedrun 🧊 – and we’re excited to meet more. If you’re building the next generation of consumer AI, we’d love to hear from you! h/t to Theresa Horne Avenir whose excellent research helped inspire some of these insights.

Kenan Saleh

15,892 Aufrufe • vor 7 Monaten

Sam Altman on Sora, Energy, and Building an AI Empire From GPT-5 to Sora, OpenAI has been making a dizzying amount of bets recently. It now acts as a frontier AGI research lab, a big tech product company with nearly a billion users, and a driver of the largest infrastructure buildout in history, all in one - plus, it’s made partnerships with Microsoft, Nvidia, Oracle, AMD, SoftBank, Oracle, and even the US government. We talked to Sam Altman about the core thesis bringing OpenAI’s bets together, why they released Sora, how they use models internally, the most informative evals for assessing AI progress, and where we’re going from here. 00:00 Introduction 00:41 OpenAI’s vision 08:01 Human-AI interfaces 09:28 AI scientists and scientific progress 17:52 Sam's operating lessons 28:33 Copyright and open source 43:03 AI talent wars 45:33 Advice for founders Sam Altman benahorowitz.eth Erik Torenberg
49:26

Sam Altman on Sora, Energy, and Building an AI Empire From GPT-5 to Sora, OpenAI has been making a dizzying amount of bets recently. It now acts as a frontier AGI research lab, a big tech product company with nearly a billion users, and a driver of the largest infrastructure buildout in history, all in one - plus, it’s made partnerships with Microsoft, Nvidia, Oracle, AMD, SoftBank, Oracle, and even the US government. We talked to Sam Altman about the core thesis bringing OpenAI’s bets together, why they released Sora, how they use models internally, the most informative evals for assessing AI progress, and where we’re going from here. 00:00 Introduction 00:41 OpenAI’s vision 08:01 Human-AI interfaces 09:28 AI scientists and scientific progress 17:52 Sam's operating lessons 28:33 Copyright and open source 43:03 AI talent wars 45:33 Advice for founders Sam Altman benahorowitz.eth Erik Torenberg

a16z

1,301,084 Aufrufe • vor 9 Monaten

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?
0:26

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 Aufrufe • vor 3 Monaten

🎙️ The Sujal Show Ep. 13: Ishan Sharma – The Blueprint for Building Wealth with AI in 2026 4M creator Ishan Sharma reveals his exact AI workflow, shares 3 skills that will save you from AI, 5 junior roles at risk from AI, how to build an AI automation agency, and drops zero‑coding money methods We Discussed on The Sujal Show:👇 - 3 skills that AI cannot replace - Biggest mistake college students make that AI can’t fix - Why he avoids AI avatars (and why you should too) - The easiest way to make ₹1 lakh per month with AI - Ways to make money with AI right now (no coding) - Degree vs personal brand – which actually matters in 2026? - Why he prefers Claude over ChatGPT - Which junior jobs are already dying in 2026? - The “2% rule” for success mindset - Why did he refuse a ₹1Cr brand deal? - Why vibe coding is a trap for serious founders - AI workflow that saves him 20 hours a week - His favorite AI models for 2026 Timestamps: 00:00 Intro 01:50 How deep he is in AI 02:12 Degree vs Personal Brand in the AI Era 04:23 Are Tier-3 College Degrees Still Worth It? 08:50 Top 10 Ways to Make Money with AI in 2026 15:18 How to market your AI product 16:38 Ishan Sharma's Highest-Earning Year 20:15 Roadmap for a 14-Year-Old to Build a Personal Brand Using AI 22:33 His Content Creation Workflow 27:51 The AI Prompts Ishan Uses Daily 29:19 Most Underrated AI Tool 47:41 Cost of Building AI Automations 48:40 How to Get Hired in the AI Age 52:04 Best Way to Learn AI, Sales & Communication Skills 54:07 Job market 2026 57:38 Top 5 AI Models He dropped out at 20. Built a 4M+ empire. His AI workflow will save you years.
1:04:05

🎙️ The Sujal Show Ep. 13: Ishan Sharma – The Blueprint for Building Wealth with AI in 2026 4M creator Ishan Sharma reveals his exact AI workflow, shares 3 skills that will save you from AI, 5 junior roles at risk from AI, how to build an AI automation agency, and drops zero‑coding money methods We Discussed on The Sujal Show:👇 - 3 skills that AI cannot replace - Biggest mistake college students make that AI can’t fix - Why he avoids AI avatars (and why you should too) - The easiest way to make ₹1 lakh per month with AI - Ways to make money with AI right now (no coding) - Degree vs personal brand – which actually matters in 2026? - Why he prefers Claude over ChatGPT - Which junior jobs are already dying in 2026? - The “2% rule” for success mindset - Why did he refuse a ₹1Cr brand deal? - Why vibe coding is a trap for serious founders - AI workflow that saves him 20 hours a week - His favorite AI models for 2026 Timestamps: 00:00 Intro 01:50 How deep he is in AI 02:12 Degree vs Personal Brand in the AI Era 04:23 Are Tier-3 College Degrees Still Worth It? 08:50 Top 10 Ways to Make Money with AI in 2026 15:18 How to market your AI product 16:38 Ishan Sharma's Highest-Earning Year 20:15 Roadmap for a 14-Year-Old to Build a Personal Brand Using AI 22:33 His Content Creation Workflow 27:51 The AI Prompts Ishan Uses Daily 29:19 Most Underrated AI Tool 47:41 Cost of Building AI Automations 48:40 How to Get Hired in the AI Age 52:04 Best Way to Learn AI, Sales & Communication Skills 54:07 Job market 2026 57:38 Top 5 AI Models He dropped out at 20. Built a 4M+ empire. His AI workflow will save you years.

Sujal Jethwani

33,994 Aufrufe • vor 2 Monaten

AI models currently have a 50% chance of doing something that takes a human expert one hour. This doubles every 7 months. In 2 years? They could automate full workdays. In 4 years? A full month. I discuss the most important graph in AI today with Beth Barnes, the CEO of METR, which uncovered this rule of AI progress. Her bottom line: "It really doesn't seem like 2 years would be surprising for recursively self-improving AI." Beth also explains: where company safety testing fails, why there are no true closed-weight models, AI undermines leading powers, why she's come around on open weighting, and why models might be about to start playing dumb much more often. Enjoy! Available on the 80,000 Hours Podcast in all apps. Links below. 1:51 Can we see AI scheming in the chain of thought? 12:50 Alignment faking 17:33 We have to test models before they're even used inside AI companies 31:56 Each 7 months models can do tasks twice as long 51:31 METR's research finds AIs are solid at AI research already 58:18 AI may turn out to be strong at novel and creative research 1:07:55 Recursively self-improving AI might even be here in two years 1:14:29 Could evaluations backfire? 1:39:55 Do we need external auditors doing AI safety tests? 1:54:09 Why not work at AI companies 2:08:40 The new more dire situation has forced changes to METR's strategy 2:21:49 Overrated: Interpretability research 2:32:55 Overrated: Major AI companies' contributions to safety research 2:39:15 Could we ban using AI to enhance AI, or is that just naive? 2:45:31 Open-weighting models is often good 2:50:22 What we can learn about AGI from the nuclear arms race 3:10:43 AI is more like bioweapons because it undermines the leading power 3:42:09 What research METR plans to do next
3:57:39

AI models currently have a 50% chance of doing something that takes a human expert one hour. This doubles every 7 months. In 2 years? They could automate full workdays. In 4 years? A full month. I discuss the most important graph in AI today with Beth Barnes, the CEO of METR, which uncovered this rule of AI progress. Her bottom line: "It really doesn't seem like 2 years would be surprising for recursively self-improving AI." Beth also explains: where company safety testing fails, why there are no true closed-weight models, AI undermines leading powers, why she's come around on open weighting, and why models might be about to start playing dumb much more often. Enjoy! Available on the 80,000 Hours Podcast in all apps. Links below. 1:51 Can we see AI scheming in the chain of thought? 12:50 Alignment faking 17:33 We have to test models before they're even used inside AI companies 31:56 Each 7 months models can do tasks twice as long 51:31 METR's research finds AIs are solid at AI research already 58:18 AI may turn out to be strong at novel and creative research 1:07:55 Recursively self-improving AI might even be here in two years 1:14:29 Could evaluations backfire? 1:39:55 Do we need external auditors doing AI safety tests? 1:54:09 Why not work at AI companies 2:08:40 The new more dire situation has forced changes to METR's strategy 2:21:49 Overrated: Interpretability research 2:32:55 Overrated: Major AI companies' contributions to safety research 2:39:15 Could we ban using AI to enhance AI, or is that just naive? 2:45:31 Open-weighting models is often good 2:50:22 What we can learn about AGI from the nuclear arms race 3:10:43 AI is more like bioweapons because it undermines the leading power 3:42:09 What research METR plans to do next

Rob Wiblin

93,669 Aufrufe • vor 1 Jahr

SpaceX just agreed to buy Cursor for $60 billion. Everyone is talking about the product, the models, the valuation. I’m interested in the human side of this AI story. Who built the team behind one of the fastest-growing software companies in history, and how do they think about talent at the frontier of AI? For the first episode of my new podcast, Below the Surface, I sat down with Adam Ward, Cursor 's Head of Talent, to find out. Before Cursor, Adam helped scale Facebook through its IPO, ran global recruiting at Pinterest from 200 to 2000+ people, and built Growth by Design Talent, the advisory firm behind OpenAI, Anthropic, Stripe, Notion, and Figma … until Cursor acquired it last year. We talked about what hiring looks like when the market is moving this fast, how Cursor does it differently, and what it actually takes to build a high-performance team right now. One of my biggest takeaways: the companies that win in AI won’t just have better models. They’ll have better systems for finding, assessing, and enabling exceptional people. Watch the full episode here: Or subscribe wherever you get podcasts: 🎧 🎤
0:53

SpaceX just agreed to buy Cursor for $60 billion. Everyone is talking about the product, the models, the valuation. I’m interested in the human side of this AI story. Who built the team behind one of the fastest-growing software companies in history, and how do they think about talent at the frontier of AI? For the first episode of my new podcast, Below the Surface, I sat down with Adam Ward, Cursor 's Head of Talent, to find out. Before Cursor, Adam helped scale Facebook through its IPO, ran global recruiting at Pinterest from 200 to 2000+ people, and built Growth by Design Talent, the advisory firm behind OpenAI, Anthropic, Stripe, Notion, and Figma … until Cursor acquired it last year. We talked about what hiring looks like when the market is moving this fast, how Cursor does it differently, and what it actually takes to build a high-performance team right now. One of my biggest takeaways: the companies that win in AI won’t just have better models. They’ll have better systems for finding, assessing, and enabling exceptional people. Watch the full episode here: Or subscribe wherever you get podcasts: 🎧 🎤

Joelle Emerson

25,352 Aufrufe • vor 1 Monat

After taking some time off post-Rapid, I'm excited to share what I’ve been up to since: Datawizz AI! We’ve raised a $12.5M Seed led by Human Capital to make AI 10x cheaper, 2x more accurate and 15x faster by transitioning from LLMs to SLMs. AI is eating the world. But unit economics are eating AI. Looking at the fastest growing AI products, they all share two traits - growing fast, and painful inference bills. General-purpose LLMs are just too expensive to run. A big reason for that is we train LLMs to be good at everything - answer any question, be an expert on any topic. The big labs dub this "generalisation", but for real-world applications, it is unnecessary. In reality - many AI applications need models to be experts in one thing - and do that thing extremely well. Your coding model doesn’t need to memorize ancient recipes for Garum sauce. This is where Datawizz comes in - we sit between the AI applications and automatically create smaller (100x-1,000x) specialized models to handle specific aspects of your work. By focusing the model and combining industry-data in the distillation process - we end up with models that beat SOTA LLMs at a fraction of the cost. We created Datawizz to make AI specialized and scalable. We’re early in the journey, but have already been able to save companies 90%+ on their inference bill and speed up their apps by 10x. Excited to build better AI platforms? Join the Datawizz team (link in first comment)
1:33

After taking some time off post-Rapid, I'm excited to share what I’ve been up to since: Datawizz AI! We’ve raised a $12.5M Seed led by Human Capital to make AI 10x cheaper, 2x more accurate and 15x faster by transitioning from LLMs to SLMs. AI is eating the world. But unit economics are eating AI. Looking at the fastest growing AI products, they all share two traits - growing fast, and painful inference bills. General-purpose LLMs are just too expensive to run. A big reason for that is we train LLMs to be good at everything - answer any question, be an expert on any topic. The big labs dub this "generalisation", but for real-world applications, it is unnecessary. In reality - many AI applications need models to be experts in one thing - and do that thing extremely well. Your coding model doesn’t need to memorize ancient recipes for Garum sauce. This is where Datawizz comes in - we sit between the AI applications and automatically create smaller (100x-1,000x) specialized models to handle specific aspects of your work. By focusing the model and combining industry-data in the distillation process - we end up with models that beat SOTA LLMs at a fraction of the cost. We created Datawizz to make AI specialized and scalable. We’re early in the journey, but have already been able to save companies 90%+ on their inference bill and speed up their apps by 10x. Excited to build better AI platforms? Join the Datawizz team (link in first comment)

Iddo Gino 🐙

21,915 Aufrufe • vor 10 Monaten

Leopold Aschenbrenner, the 24 year old who wrote a 165 page AGI manifesto, got it right on the money, and turned it into a $5.5 billion hedge fund. And he's identifying the single most important milestone to watch for in all of AI. The question is can AI automate AI research itself? Here's why that question matters so much. Right now, a few thousand human researchers at the frontier labs are driving all the progress. They design experiments, write papers, propose architectural improvements, build the next generation of models and it's an incredibly small workforce doing incredibly high-leverage work. If an AI system can do that job even partially, the feedback loop changes completely. The AI makes algorithmic improvements, which produces more powerful AI, which makes better improvements, faster. You go from linear progress to compounding returns and a decade of research could compress into a year. Aschenbrenner says there's a "pretty reasonable chance" this happens within five years. He's not alone, Anthropic says they're on track to fully automate AI R&D as soon as early 2027. OpenAI has publicly targeted a fully autonomous AI researcher by March 2028 and Sam Altman has said a research intern level AI will exist before the end of this year. If he's right, the next few years won't look like the last few years but they'll look like nothing we've seen before. The future is bright!
0:49

Leopold Aschenbrenner, the 24 year old who wrote a 165 page AGI manifesto, got it right on the money, and turned it into a $5.5 billion hedge fund. And he's identifying the single most important milestone to watch for in all of AI. The question is can AI automate AI research itself? Here's why that question matters so much. Right now, a few thousand human researchers at the frontier labs are driving all the progress. They design experiments, write papers, propose architectural improvements, build the next generation of models and it's an incredibly small workforce doing incredibly high-leverage work. If an AI system can do that job even partially, the feedback loop changes completely. The AI makes algorithmic improvements, which produces more powerful AI, which makes better improvements, faster. You go from linear progress to compounding returns and a decade of research could compress into a year. Aschenbrenner says there's a "pretty reasonable chance" this happens within five years. He's not alone, Anthropic says they're on track to fully automate AI R&D as soon as early 2027. OpenAI has publicly targeted a fully autonomous AI researcher by March 2028 and Sam Altman has said a research intern level AI will exist before the end of this year. If he's right, the next few years won't look like the last few years but they'll look like nothing we've seen before. The future is bright!

Milk Road AI

628,892 Aufrufe • vor 3 Monaten

The fastest company in history to $1B did it with no VC money and fewer than 100 people. Surge AI has become the secret weapon behind Anthropic and Google's best models. Founder Edwin Chen (echen) built it without playing the Silicon Valley game—no viral posts, no fundraising treadmill. In my powerful conversation with Edwin Chen, we discuss: 🔸 How Claude got so good at coding and writing 🔸 Why the AI industry is optimizing for "dopamine instead of truth"—and delaying AGI 🔸 The problems with AI benchmarks and leaderboards 🔸 Why RL environments are the next frontier in AI training 🔸 Why taste and human judgment determine which AI models win 🔸 Why Edwin believes we’re still a decade away from AGI Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Vanta — Automate compliance. Simplify security: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 Coda (Superhuman Docs) — The all-in-one collaborative workspace:
1:38

The fastest company in history to $1B did it with no VC money and fewer than 100 people. Surge AI has become the secret weapon behind Anthropic and Google's best models. Founder Edwin Chen (echen) built it without playing the Silicon Valley game—no viral posts, no fundraising treadmill. In my powerful conversation with Edwin Chen, we discuss: 🔸 How Claude got so good at coding and writing 🔸 Why the AI industry is optimizing for "dopamine instead of truth"—and delaying AGI 🔸 The problems with AI benchmarks and leaderboards 🔸 Why RL environments are the next frontier in AI training 🔸 Why taste and human judgment determine which AI models win 🔸 Why Edwin believes we’re still a decade away from AGI Listen now 👇 • YouTube: • Spotify: • Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 Vanta — Automate compliance. Simplify security: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 Coda (Superhuman Docs) — The all-in-one collaborative workspace:

Lenny Rachitsky

481,159 Aufrufe • vor 7 Monaten

AI instead of UI The last few years radically changed how we think about digital products & product design process in general. What we are witnessing right now is a transition where AI is no longer just a feature; it is becoming the infrastructure of interaction. For decades, UI design was rooted in the concept of “Happy Path,” a series of static, linear screens (routes from A to B) designed to funnel users toward a goal. This “one-size-fits-all” approach assumes that all users have the same mental model, which is far from the truth. The rise of AI tools like ChatGPT and Gemini is showing that we are moving into an era of Generative Interfaces. Instead of a designer pre-determining every button and menu, the interface is synthesized in real-time based on user actions. Back in 2019, Gleb Kuznetsov and I were working on the concept of morphing Interface, the idea of a UI that adapts its structure based on user intent. The great thing about this UI is that it was highly dynamic: different users saw different content/contextual actions based on their behavior. This concept was crazy in 2019, but it is highly relevant to the modern state of the product design process. And I strongly believe that it represents the future of UI design. We will move beyond the “AI chatbox" crutch Yes, many products today treat AI as a sidecar (think of Copilots or chatbots pinned to the side of a traditional dashboard), but this is a transitional phase. Why? Because adding a chat window to a 20-year-old software layout is like putting a jet engine on a horse-drawn carriage. That's why the future isn't "AI as an add-on"; it is AI as the Operating System. AI will power generative interfaces that are rooted in anticipatory design: UI won’t wait for a command; it will surface tools based on the user's current environmental context and historical behavior. This evolution changes the very nature of product design, and we will move away from designing pages/screens and toward designing systems of logic.
0:23

AI instead of UI The last few years radically changed how we think about digital products & product design process in general. What we are witnessing right now is a transition where AI is no longer just a feature; it is becoming the infrastructure of interaction. For decades, UI design was rooted in the concept of “Happy Path,” a series of static, linear screens (routes from A to B) designed to funnel users toward a goal. This “one-size-fits-all” approach assumes that all users have the same mental model, which is far from the truth. The rise of AI tools like ChatGPT and Gemini is showing that we are moving into an era of Generative Interfaces. Instead of a designer pre-determining every button and menu, the interface is synthesized in real-time based on user actions. Back in 2019, Gleb Kuznetsov and I were working on the concept of morphing Interface, the idea of a UI that adapts its structure based on user intent. The great thing about this UI is that it was highly dynamic: different users saw different content/contextual actions based on their behavior. This concept was crazy in 2019, but it is highly relevant to the modern state of the product design process. And I strongly believe that it represents the future of UI design. We will move beyond the “AI chatbox" crutch Yes, many products today treat AI as a sidecar (think of Copilots or chatbots pinned to the side of a traditional dashboard), but this is a transitional phase. Why? Because adding a chat window to a 20-year-old software layout is like putting a jet engine on a horse-drawn carriage. That's why the future isn't "AI as an add-on"; it is AI as the Operating System. AI will power generative interfaces that are rooted in anticipatory design: UI won’t wait for a command; it will surface tools based on the user's current environmental context and historical behavior. This evolution changes the very nature of product design, and we will move away from designing pages/screens and toward designing systems of logic.

Nick Babich

53,320 Aufrufe • vor 5 Monaten

The Trump administration just did a complete 180 on AI regulation. 16 months ago, Trump killed Biden's AI executive order on DAY ONE. Called AI "a beautiful baby" that shouldn't be stopped with rules. His AI czar David Sacks went to every conference saying deregulation was the only path. JD Vance flew to Paris and told world leaders the future is won "by building, not by hand-wringing about safety." That was the whole pitch. Regulation is for losers. But the same White House just started briefing Anthropic, Google, and OpenAI executives on plans for MANDATORY government review of AI models before public release. The exact policy they destroyed 16 months ago. Fortune called it a "head-spinning policy pirouette." So what happened? ONE AI model happened: In April, Anthropic announced a model called Mythos. During internal testing, it found THOUSANDS of unknown security vulnerabilities across every major operating system and browser on earth including a 27yo bug in OpenBSD, an OS literally famous for being unhackable, and a 16yo flaw in FFmpeg that survived 5 million automated security tests. NOBODY asked it to do any of this. The capabilities emerged on their own as the model got smarter at coding. Anthropic's researchers said they found more bugs in weeks than they'd found in their entire careers combined. The UK's AI Security Institute confirmed Mythos could autonomously execute multi-stage cyberattacks on networks. Tasks that take human professionals DAYS. Anthropic refused to release it. Formed "Project Glasswing" with Apple, Microsoft, Google, JPMorgan, and 40 other organizations to use it defensively before attackers develop similar tools. Their estimate: Competing labs will have comparable capabilities within 6 to 18 months. That timeline is what scared Washington. Because here's what nobody in the White House considered while removing safety rules: What happens when a devastating AI-enabled cyberattack hits American infrastructure and the government has ZERO oversight in place? No safety testing, pre-release review, or reporting. They literally burned all of it. David Sacks quietly left in March. Treasury Secretary Bessent and Chief of Staff Susie Wiles took over AI policy. They're now drafting an executive order for an AI working group that would vet models before release. Some officials want the government to get FIRST ACCESS to new models. The same government that said 16 months ago it had no business being involved. But here's where it gets really insane: The company that triggered all of this was BANNED by the Trump administration from government contracts. Labeled Anthropic a "supply-chain risk." They tried to punish them for refusing to let their AI target US citizens autonomously. Anthropic is currently fighting the Pentagon in federal court. So the timeline reads like this: January 2025: Trump kills Biden's AI oversight. July 2025: Calls AI a "beautiful baby," signs orders to fast-track AI with zero safety guardrails. March 2026: Bans Anthropic from government work. April 2026: Anthropic's Mythos demonstrates it can hack every major OS on earth. May 2026: Same administration rebuilds the oversight it destroyed BECAUSE of the company it banned. This is what happens when ideology meets reality. Every government told itself AI regulation could wait. Mythos proved them wrong overnight. Open-weight models with similar capabilities are even closer. Once those tools are in the wild, no executive order puts them back.
1:00

The Trump administration just did a complete 180 on AI regulation. 16 months ago, Trump killed Biden's AI executive order on DAY ONE. Called AI "a beautiful baby" that shouldn't be stopped with rules. His AI czar David Sacks went to every conference saying deregulation was the only path. JD Vance flew to Paris and told world leaders the future is won "by building, not by hand-wringing about safety." That was the whole pitch. Regulation is for losers. But the same White House just started briefing Anthropic, Google, and OpenAI executives on plans for MANDATORY government review of AI models before public release. The exact policy they destroyed 16 months ago. Fortune called it a "head-spinning policy pirouette." So what happened? ONE AI model happened: In April, Anthropic announced a model called Mythos. During internal testing, it found THOUSANDS of unknown security vulnerabilities across every major operating system and browser on earth including a 27yo bug in OpenBSD, an OS literally famous for being unhackable, and a 16yo flaw in FFmpeg that survived 5 million automated security tests. NOBODY asked it to do any of this. The capabilities emerged on their own as the model got smarter at coding. Anthropic's researchers said they found more bugs in weeks than they'd found in their entire careers combined. The UK's AI Security Institute confirmed Mythos could autonomously execute multi-stage cyberattacks on networks. Tasks that take human professionals DAYS. Anthropic refused to release it. Formed "Project Glasswing" with Apple, Microsoft, Google, JPMorgan, and 40 other organizations to use it defensively before attackers develop similar tools. Their estimate: Competing labs will have comparable capabilities within 6 to 18 months. That timeline is what scared Washington. Because here's what nobody in the White House considered while removing safety rules: What happens when a devastating AI-enabled cyberattack hits American infrastructure and the government has ZERO oversight in place? No safety testing, pre-release review, or reporting. They literally burned all of it. David Sacks quietly left in March. Treasury Secretary Bessent and Chief of Staff Susie Wiles took over AI policy. They're now drafting an executive order for an AI working group that would vet models before release. Some officials want the government to get FIRST ACCESS to new models. The same government that said 16 months ago it had no business being involved. But here's where it gets really insane: The company that triggered all of this was BANNED by the Trump administration from government contracts. Labeled Anthropic a "supply-chain risk." They tried to punish them for refusing to let their AI target US citizens autonomously. Anthropic is currently fighting the Pentagon in federal court. So the timeline reads like this: January 2025: Trump kills Biden's AI oversight. July 2025: Calls AI a "beautiful baby," signs orders to fast-track AI with zero safety guardrails. March 2026: Bans Anthropic from government work. April 2026: Anthropic's Mythos demonstrates it can hack every major OS on earth. May 2026: Same administration rebuilds the oversight it destroyed BECAUSE of the company it banned. This is what happens when ideology meets reality. Every government told itself AI regulation could wait. Mythos proved them wrong overnight. Open-weight models with similar capabilities are even closer. Once those tools are in the wild, no executive order puts them back.

Ricardo

75,445 Aufrufe • vor 2 Monaten

🚨THEY CALLED HIM CRAZY FOR 20 YEARS.. HE'S ABOUT TO FILE THE BIGGEST IPO IN HISTORY.. SpaceX is quietly preparing to go public.. targeting a $1.75 trillion valuation.. bigger than Saudi Aramco.. the biggest IPO in human history.. but that's not even the real story.. in february elon merged xAI into SpaceX.. so now one single company owns the rockets.. owns about 65% of every satellite in orbit.. owns Starlink with over 10 million users.. and owns the AI.. think about that for a second.. OpenAI rents its servers from Microsoft.. Anthropic rents from Amazon.. they don't own anything.. they're tenants.. elon owns the infrastructure.. they're putting AI data centers in space.. solar powered.. no electric grid.. no cooling problems.. just satellites running AI in orbit while everyone else is fighting over GPU shipments on the ground.. the pentagon just handed them $2 billion for a defense satellite network.. starlink aviation customers are paying $300K a year.. NASA used to be their biggest customer.. now NASA is only 5% of their revenue.. they outgrew the entire US government.. this man built a company that launches the rockets.. owns the satellites.. provides the internet.. runs the AI.. and is about to go public at the highest valuation in history.. nobody is connecting the dots.. i got into xAI before the merger.. that converts to SpaceX equity before it even hits public markets.. sometimes the play is obvious.. you just have to be paying attention.
0:28

🚨THEY CALLED HIM CRAZY FOR 20 YEARS.. HE'S ABOUT TO FILE THE BIGGEST IPO IN HISTORY.. SpaceX is quietly preparing to go public.. targeting a $1.75 trillion valuation.. bigger than Saudi Aramco.. the biggest IPO in human history.. but that's not even the real story.. in february elon merged xAI into SpaceX.. so now one single company owns the rockets.. owns about 65% of every satellite in orbit.. owns Starlink with over 10 million users.. and owns the AI.. think about that for a second.. OpenAI rents its servers from Microsoft.. Anthropic rents from Amazon.. they don't own anything.. they're tenants.. elon owns the infrastructure.. they're putting AI data centers in space.. solar powered.. no electric grid.. no cooling problems.. just satellites running AI in orbit while everyone else is fighting over GPU shipments on the ground.. the pentagon just handed them $2 billion for a defense satellite network.. starlink aviation customers are paying $300K a year.. NASA used to be their biggest customer.. now NASA is only 5% of their revenue.. they outgrew the entire US government.. this man built a company that launches the rockets.. owns the satellites.. provides the internet.. runs the AI.. and is about to go public at the highest valuation in history.. nobody is connecting the dots.. i got into xAI before the merger.. that converts to SpaceX equity before it even hits public markets.. sometimes the play is obvious.. you just have to be paying attention.

Evan Luthra

101,861 Aufrufe • vor 4 Monaten

This is the biggest irony in tech history. Microsoft beat revenue estimates. Stock plunged 11%, wiped out $400 BILLION in market cap. Salesforce reported growth. Stock fell 5.6%. ServiceNow beat earnings. Stock crashed 11%. SAP beat projections. Stock dropped 16%. Entire software sector entered bear market territory. Down 22% from peak. These are the companies everyone said would WIN from AI. They spent billions BUYING AI companies. ServiceNow: $7.75 billion for Armis. Salesforce: $8 billion for Informatica. They launched AI products. Built AI workflows. Hired AI teams. And the market said: You're all dead. Because investors just realized something nobody wanted to admit: AI doesn't make software companies stronger. AI makes software companies OBSOLETE. Morgan Stanley: "In an environment of heightened investor skepticism, stable growth falls short of shifting the narrative." Good earnings aren't enough anymore. The market is pricing in a world where AI replaces the software these companies sell. ServiceNow CEO tried defending on the earnings call: "AI needs workflow orchestration. ServiceNow is the gateway to this shift." Market response: 11% crash. Because here's what he didn't say: If AI can write code, automate workflows, and generate apps at a fraction of the cost, why would anyone pay $50,000 per year for enterprise software licenses? The per-seat pricing model that made SaaS companies rich is getting murdered by AI efficiency. One AI agent replaces 10 seats. One prompt replaces months of custom development. One LLM call replaces entire software categories. Klarna already proved it. CEO said they pulled Salesforce out of their stack. Built everything themselves using AI. And that's just the beginning. The software apocalypse hit hardest on companies that INVESTED IN AI: Atlassian: down 12.6% Intuit: down 7.8% HubSpot: down 11.5% Zscaler: down 6.3% Meanwhile, the companies ENABLING AI made money: Nvidia: up Semiconductor stocks: surging Memory firms: rallying The divide is brutal. Hardware companies print cash. Software companies get destroyed. Because in an AI-first world, you need GPUs to build the models. But you don't need software subscriptions when the AI builds the software for you. Jim Cramer called it the "P/E multiple compression crisis." Translation: Investors don't care about earnings anymore. They care about whether your business model survives the next 5 years. And right now software business models look doomed. They're literally stuck: If they DON'T invest in AI, they fall behind. If they DO invest in AI, they cannibalize their own products. It's a death spiral with no exit. ServiceNow spent $12 BILLION on acquisitions in 2025 alone. Trying to buy their way into relevance. And yesterday the market cooked them. The craziest thing to me tho... Most software companies beat earnings. Revenue was solid. Growth was fine. But it didn't matter. Because the market stopped pricing software on what it earns TODAY. It's pricing software on what it's worth in a world where AI does the job for free. And in that world these companies are worth nothing. This is the biggest sector repricing since 2008. $500 billion in market value gone in ONE DAY. And it's not stopping. Because every company watching this is thinking the same thing: "If I can replace ServiceNow with 3 AI agents and save $10 million per year, why wouldn't I?" The answer used to be: "Because you need enterprise-grade reliability." But now? AI agents are getting reliable. Fast. Software companies just realized they're competing with open-source models that cost $0.02 per 1,000 tokens. You can't win a pricing war against free. The companies that spent BILLIONS preparing for AI are getting killed BY AI. What an irony.
1:54

This is the biggest irony in tech history. Microsoft beat revenue estimates. Stock plunged 11%, wiped out $400 BILLION in market cap. Salesforce reported growth. Stock fell 5.6%. ServiceNow beat earnings. Stock crashed 11%. SAP beat projections. Stock dropped 16%. Entire software sector entered bear market territory. Down 22% from peak. These are the companies everyone said would WIN from AI. They spent billions BUYING AI companies. ServiceNow: $7.75 billion for Armis. Salesforce: $8 billion for Informatica. They launched AI products. Built AI workflows. Hired AI teams. And the market said: You're all dead. Because investors just realized something nobody wanted to admit: AI doesn't make software companies stronger. AI makes software companies OBSOLETE. Morgan Stanley: "In an environment of heightened investor skepticism, stable growth falls short of shifting the narrative." Good earnings aren't enough anymore. The market is pricing in a world where AI replaces the software these companies sell. ServiceNow CEO tried defending on the earnings call: "AI needs workflow orchestration. ServiceNow is the gateway to this shift." Market response: 11% crash. Because here's what he didn't say: If AI can write code, automate workflows, and generate apps at a fraction of the cost, why would anyone pay $50,000 per year for enterprise software licenses? The per-seat pricing model that made SaaS companies rich is getting murdered by AI efficiency. One AI agent replaces 10 seats. One prompt replaces months of custom development. One LLM call replaces entire software categories. Klarna already proved it. CEO said they pulled Salesforce out of their stack. Built everything themselves using AI. And that's just the beginning. The software apocalypse hit hardest on companies that INVESTED IN AI: Atlassian: down 12.6% Intuit: down 7.8% HubSpot: down 11.5% Zscaler: down 6.3% Meanwhile, the companies ENABLING AI made money: Nvidia: up Semiconductor stocks: surging Memory firms: rallying The divide is brutal. Hardware companies print cash. Software companies get destroyed. Because in an AI-first world, you need GPUs to build the models. But you don't need software subscriptions when the AI builds the software for you. Jim Cramer called it the "P/E multiple compression crisis." Translation: Investors don't care about earnings anymore. They care about whether your business model survives the next 5 years. And right now software business models look doomed. They're literally stuck: If they DON'T invest in AI, they fall behind. If they DO invest in AI, they cannibalize their own products. It's a death spiral with no exit. ServiceNow spent $12 BILLION on acquisitions in 2025 alone. Trying to buy their way into relevance. And yesterday the market cooked them. The craziest thing to me tho... Most software companies beat earnings. Revenue was solid. Growth was fine. But it didn't matter. Because the market stopped pricing software on what it earns TODAY. It's pricing software on what it's worth in a world where AI does the job for free. And in that world these companies are worth nothing. This is the biggest sector repricing since 2008. $500 billion in market value gone in ONE DAY. And it's not stopping. Because every company watching this is thinking the same thing: "If I can replace ServiceNow with 3 AI agents and save $10 million per year, why wouldn't I?" The answer used to be: "Because you need enterprise-grade reliability." But now? AI agents are getting reliable. Fast. Software companies just realized they're competing with open-source models that cost $0.02 per 1,000 tokens. You can't win a pricing war against free. The companies that spent BILLIONS preparing for AI are getting killed BY AI. What an irony.

Ricardo

1,814,711 Aufrufe • vor 6 Monaten

AMD invited us to their biggest launch of the year to check out the behind-the-scenes. At Advancing AI 2026, Anush Elangovan (VP, AI Software at AMD) and Quentin Anthony (VP, AI Engineering at Zyphra) join sophia dew on the floor to break down Helios, ROCm AI, and the Anthropic partnership, live the day it all shipped: 1:05 What actually changed today: AMD goes rack-scale 3:03 ROCm skills land in Claude Code and Codex 3:51 Why Zyphra bet its whole stack on AMD 3:57 Sparse mixture-of-experts and the HBM advantage 6:25 Why an open stack beats a closed one 8:52 The wall of logos: Anthropic, OpenAI, Meta 9:27 Why the frontier labs are committing gigawatts 11:30 Why the next wave will exceed expectations 17:02 Frontier models running on your own laptop + DeepSeek V4 Pro at 8-10 tokens/sec 17:41 How ROCm AI hits 3.3x inference gains 19:38 A kernel rewritten overnight with no humans in the loop 21:40 Do you still need kernel experts? 23:47 Why CPUs are critical for the agentic AI era 25:40 An agent that's been rewriting a driver for four months 28:14 The internet parallel, and the dark-fiber lesson
29:48

AMD invited us to their biggest launch of the year to check out the behind-the-scenes. At Advancing AI 2026, Anush Elangovan (VP, AI Software at AMD) and Quentin Anthony (VP, AI Engineering at Zyphra) join sophia dew on the floor to break down Helios, ROCm AI, and the Anthropic partnership, live the day it all shipped: 1:05 What actually changed today: AMD goes rack-scale 3:03 ROCm skills land in Claude Code and Codex 3:51 Why Zyphra bet its whole stack on AMD 3:57 Sparse mixture-of-experts and the HBM advantage 6:25 Why an open stack beats a closed one 8:52 The wall of logos: Anthropic, OpenAI, Meta 9:27 Why the frontier labs are committing gigawatts 11:30 Why the next wave will exceed expectations 17:02 Frontier models running on your own laptop + DeepSeek V4 Pro at 8-10 tokens/sec 17:41 How ROCm AI hits 3.3x inference gains 19:38 A kernel rewritten overnight with no humans in the loop 21:40 Do you still need kernel experts? 23:47 Why CPUs are critical for the agentic AI era 25:40 An agent that's been rewriting a driver for four months 28:14 The internet parallel, and the dark-fiber lesson

MTS

69,613 Aufrufe • vor 8 Tagen

SITUATION EXPLAINED: 70% of frontier model queries could run locally for free. clem 🤗, co-founder and CEO of Hugging Face: "There was an interesting study from Stanford published last year showing that 70% of the queries that people ask to ChatGPT could be accurately answered locally on your laptop. For free." "Most of the AI workloads that people do today with frontier models could be done by models that are cheaper, faster, more customizable, more controllable. And they don't do it because frankly it's a pain to pick the right model." "You're subsidized so you don't have to care because you have your subscription, so you route everything to Einstein. 'Hey Einstein, what's the weather today?'" "In normal life, he would be like, 'I'm not answering your silly question.' But because it's AI and subsidized by the AI labs, all the questions are getting routed to Einstein versus in an ideal world you have different models that are more specialized and better at answering your questions in different domains." "It's possible that routing is gonna redistribute a lot of the value capture from frontier models to a more long tail of models. It's like AI maturing... we are in the first phase where it's very simple, everyone using just one giant model. Now we're moving to the second phase."
3:35

SITUATION EXPLAINED: 70% of frontier model queries could run locally for free. clem 🤗, co-founder and CEO of Hugging Face: "There was an interesting study from Stanford published last year showing that 70% of the queries that people ask to ChatGPT could be accurately answered locally on your laptop. For free." "Most of the AI workloads that people do today with frontier models could be done by models that are cheaper, faster, more customizable, more controllable. And they don't do it because frankly it's a pain to pick the right model." "You're subsidized so you don't have to care because you have your subscription, so you route everything to Einstein. 'Hey Einstein, what's the weather today?'" "In normal life, he would be like, 'I'm not answering your silly question.' But because it's AI and subsidized by the AI labs, all the questions are getting routed to Einstein versus in an ideal world you have different models that are more specialized and better at answering your questions in different domains." "It's possible that routing is gonna redistribute a lot of the value capture from frontier models to a more long tail of models. It's like AI maturing... we are in the first phase where it's very simple, everyone using just one giant model. Now we're moving to the second phase."

MTS

49,884 Aufrufe • vor 1 Monat