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Guillermo Rauch

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392,736 views • 9 months ago •via X (Twitter)

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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 views • 1 year ago

David Sacks: NO Federal Bailout for AI – “Build out, not bailout” “My entire feed was full of comments saying that we can't allow OpenAI to have a federal bailout.” “Look, there's not gonna be a federal bailout for AI, not gonna happen.” “We have five major frontier model companies right now, and there are new companies being formed all the time.” “And if one of them fails, hey, it's gonna go out of business and the other ones are gonna replace it.” “So nobody is talking about a bailout.” “I do think that we want to make it easier to build infrastructure in this country.” “And that means making permitting easier, making power generation easier. That's all about regulatory reform.” “And I think that the goal here is to enable a rapid infrastructure build out without increasing residential rates for electricity, that's the thing we have to combat.” “But the way that you solve that problem is by making it easier for these AI companies to stand up their own power generation behind the meter, and that requires regulatory reform. That's what the president has called for.” “So no one's talking about a bailout. We are talking about making permitting easier, and making it easier to do build outs.” “So ‘Build out, not bailout’ should be our motto here.”
1:07

David Sacks: NO Federal Bailout for AI – “Build out, not bailout” “My entire feed was full of comments saying that we can't allow OpenAI to have a federal bailout.” “Look, there's not gonna be a federal bailout for AI, not gonna happen.” “We have five major frontier model companies right now, and there are new companies being formed all the time.” “And if one of them fails, hey, it's gonna go out of business and the other ones are gonna replace it.” “So nobody is talking about a bailout.” “I do think that we want to make it easier to build infrastructure in this country.” “And that means making permitting easier, making power generation easier. That's all about regulatory reform.” “And I think that the goal here is to enable a rapid infrastructure build out without increasing residential rates for electricity, that's the thing we have to combat.” “But the way that you solve that problem is by making it easier for these AI companies to stand up their own power generation behind the meter, and that requires regulatory reform. That's what the president has called for.” “So no one's talking about a bailout. We are talking about making permitting easier, and making it easier to do build outs.” “So ‘Build out, not bailout’ should be our motto here.”

The All-In Podcast

115,786 views • 8 months ago

United States Attorney of Minnesota “Minnesota is drowning in fraud” - Taxpayer funded housing stabilization services companies are creating fake programs and making fake claims to bill for services that never happen - Over $100 million per year is stolen - Fraudulent health care companies set up - Fraudulent housing programs set up - Rehab patient fraud - Fake Medicare and Medicaid claims “Many of these companies operated out of dilapidated storefronts or run down office buildings that were full of other fraudulent health care companies” “They identified vulnerable individuals, Medicaid eligible individuals, many of whom were being released from drug or alcohol rehab facilities. They sign them up to receive housing stabilization services to help them, purportedly to help them find stable housing. Then they bill them for services that they did not, or they build Medicaid for services they did not actually provide. Despite the hundreds of millions of dollars in payments that have gone out from the state of Minnesota to housing stabilization services companies, many of these, most of these individuals did not receive stable housing they so desperately needed. The money was just simply stolen” “The level of fraud in these programs is staggering.” Now imagine this same fraud in every major city in every state. We can’t even comprehend the real amount of theft taking place against taxpayers
7:14

United States Attorney of Minnesota “Minnesota is drowning in fraud” - Taxpayer funded housing stabilization services companies are creating fake programs and making fake claims to bill for services that never happen - Over $100 million per year is stolen - Fraudulent health care companies set up - Fraudulent housing programs set up - Rehab patient fraud - Fake Medicare and Medicaid claims “Many of these companies operated out of dilapidated storefronts or run down office buildings that were full of other fraudulent health care companies” “They identified vulnerable individuals, Medicaid eligible individuals, many of whom were being released from drug or alcohol rehab facilities. They sign them up to receive housing stabilization services to help them, purportedly to help them find stable housing. Then they bill them for services that they did not, or they build Medicaid for services they did not actually provide. Despite the hundreds of millions of dollars in payments that have gone out from the state of Minnesota to housing stabilization services companies, many of these, most of these individuals did not receive stable housing they so desperately needed. The money was just simply stolen” “The level of fraud in these programs is staggering.” Now imagine this same fraud in every major city in every state. We can’t even comprehend the real amount of theft taking place against taxpayers

Wall Street Apes

918,095 views • 9 months ago

The firms leading in market making figured out early that infrastructure compounds. Austin Federa | 🇺🇸 sits down with Rahul Jain, Rahul Jain (Ellipsis Labs), to trace how crypto market making evolved: why being early matters, how MEV and transaction landing reshaped execution, and what it will take for onchain finance to scale beyond crypto-native users. Understanding the infrastructure layer is a prerequisite for competing with traditional trading environments. Timestamps and links below ↓ 00:00 - Introduction 03:53 - What Makes Solana Trading Different 07:17 - Trust and Retail Outcomes 12:04 - Infra Arms Race 2025 14:27 - Prop AMMs vs Order Books 17:05 - Spot Market Equilibrium 18:12 - MCP and Perps Security 21:22 - Why MCP Matters 24:25 - ACE For Safer Perps 25:47 - RWAs And Oracle Risk 29:28 - Why Retail Loves Perps 34:23 - Hypothetical VIX perps 35:29 - The Different MCP Camps 37:07 - Latency And Block Full 40:41 - Conclusion
42:09

The firms leading in market making figured out early that infrastructure compounds. Austin Federa | 🇺🇸 sits down with Rahul Jain, Rahul Jain (Ellipsis Labs), to trace how crypto market making evolved: why being early matters, how MEV and transaction landing reshaped execution, and what it will take for onchain finance to scale beyond crypto-native users. Understanding the infrastructure layer is a prerequisite for competing with traditional trading environments. Timestamps and links below ↓ 00:00 - Introduction 03:53 - What Makes Solana Trading Different 07:17 - Trust and Retail Outcomes 12:04 - Infra Arms Race 2025 14:27 - Prop AMMs vs Order Books 17:05 - Spot Market Equilibrium 18:12 - MCP and Perps Security 21:22 - Why MCP Matters 24:25 - ACE For Safer Perps 25:47 - RWAs And Oracle Risk 29:28 - Why Retail Loves Perps 34:23 - Hypothetical VIX perps 35:29 - The Different MCP Camps 37:07 - Latency And Block Full 40:41 - Conclusion

Validated

27,660 views • 2 months ago

How to build a $100K/month iOS app (playbook below): Over the next 18 months, we'll see 200+ apps hit $100k/month solving problems that were impossible to solve until right now. AI can now solve hyper-specific problems for tiny audiences. Problems so specific only 50,000 people on earth have them. But those 50,000 will pay $40/month for a perfect solution. Meanwhile, the "For You Page" killed the need for followers. Any creator with zero audience can go viral tomorrow. One video about your niche app can deliver 1,000 paying customers overnight. This is the perfect storm. Micro-problems can finally be solved. Micro-apps can finally find their people. *Disclaimer* No, this won't work for everyone. Not everyone will make $100k MRR app. But there is a tremendous amount of opportunity, and my point is this is how i'd approach it: The playbook: 1) Daily habit (not occasional use) Not "track calories." Think "photograph your psoriasis patches every morning to track flare triggers." Not "meditate" but "record your stutter severity after each phone call." The winning apps solve micro-problems inside daily routines. The person checking if their dog's food has ingredients that trigger seizures. The runner who needs to know if today's pollen will trigger their exercise-induced asthma. Find the tiny, specific thing someone does at the exact same time every day. 2) AI-powered narrow wedge One problem. One perfect solution. "AI that tells if this supplement will interfere with your Adderall." "AI that identifies which FODMAP ingredients are in this restaurant dish." The narrower you go, the more people will pay. Start with one use case that takes 10 seconds to understand. 3) One channel, 3 formats TikTok or Instagram. Pick one. Test three content types daily. When something hits, make 50 versions. The apps making $100K/month mastered one platform before touching another. 4) 100 obsessed users 100 people who use it daily and would riot if you shut down. They'll bring the next 100. Those bring 400. But without the first 100 fanatics, you have nothing. Use organic audience to bring these people there. 5) Charge immediately Paywall goes up week one. $7-40/month. Free users give garbage feedback. Paying users tell you exactly what to build. If nobody pays for your broken MVP, they won't pay for your polished version either. 90-day timeline: Days 1-10: Find the habit, validate demand (reddit, tiktok, Idea Browser) Days 11-30: Ship the ugliest working version (use bolt/lovable/vibecode app/rork) While you're doing this you're building organic audience. Figuring out formats, focusing on 1 channel, working with creators. Days 31-60: Get 100 users, obsess over them Days 61-90: Double down on what's working TLDR; Pick a daily micro-problem this weekend. Ship something that solves it by next weekend. Charge for it the weekend after. (full episode is below or on the latest episode The Startup Ideas Podcast (SIP) 🧃 on yt etc) Enjoy the sauce. People charge for this sorta sauce. It's free for you. The App Store is open for the first time in years. Go claim your piece. Im rooting for you.
32:49

How to build a $100K/month iOS app (playbook below): Over the next 18 months, we'll see 200+ apps hit $100k/month solving problems that were impossible to solve until right now. AI can now solve hyper-specific problems for tiny audiences. Problems so specific only 50,000 people on earth have them. But those 50,000 will pay $40/month for a perfect solution. Meanwhile, the "For You Page" killed the need for followers. Any creator with zero audience can go viral tomorrow. One video about your niche app can deliver 1,000 paying customers overnight. This is the perfect storm. Micro-problems can finally be solved. Micro-apps can finally find their people. *Disclaimer* No, this won't work for everyone. Not everyone will make $100k MRR app. But there is a tremendous amount of opportunity, and my point is this is how i'd approach it: The playbook: 1) Daily habit (not occasional use) Not "track calories." Think "photograph your psoriasis patches every morning to track flare triggers." Not "meditate" but "record your stutter severity after each phone call." The winning apps solve micro-problems inside daily routines. The person checking if their dog's food has ingredients that trigger seizures. The runner who needs to know if today's pollen will trigger their exercise-induced asthma. Find the tiny, specific thing someone does at the exact same time every day. 2) AI-powered narrow wedge One problem. One perfect solution. "AI that tells if this supplement will interfere with your Adderall." "AI that identifies which FODMAP ingredients are in this restaurant dish." The narrower you go, the more people will pay. Start with one use case that takes 10 seconds to understand. 3) One channel, 3 formats TikTok or Instagram. Pick one. Test three content types daily. When something hits, make 50 versions. The apps making $100K/month mastered one platform before touching another. 4) 100 obsessed users 100 people who use it daily and would riot if you shut down. They'll bring the next 100. Those bring 400. But without the first 100 fanatics, you have nothing. Use organic audience to bring these people there. 5) Charge immediately Paywall goes up week one. $7-40/month. Free users give garbage feedback. Paying users tell you exactly what to build. If nobody pays for your broken MVP, they won't pay for your polished version either. 90-day timeline: Days 1-10: Find the habit, validate demand (reddit, tiktok, Idea Browser) Days 11-30: Ship the ugliest working version (use bolt/lovable/vibecode app/rork) While you're doing this you're building organic audience. Figuring out formats, focusing on 1 channel, working with creators. Days 31-60: Get 100 users, obsess over them Days 61-90: Double down on what's working TLDR; Pick a daily micro-problem this weekend. Ship something that solves it by next weekend. Charge for it the weekend after. (full episode is below or on the latest episode The Startup Ideas Podcast (SIP) 🧃 on yt etc) Enjoy the sauce. People charge for this sorta sauce. It's free for you. The App Store is open for the first time in years. Go claim your piece. Im rooting for you.

GREG ISENBERG

102,168 views • 9 months ago

🎉BERT Semantic Interlinker App V2 (Free App)🎉 Free Streamlit App to Semantically Interlink Pages using Sentence Transformers. Last week Emilia tagged me in this excellent article that she had linked to the original version of the BERT Interlinking App. I checked the stats and over it had 17,000 unique visitors with no promotion at all. I spent the last week re-writing it from the ground up with a ton more features, visualisations and polish. What's the Point? “There are tons of page Interlinkers out there, why not just use those?” It’s a fair point, this script doesn’t even use anchor text or link metrics when making recommendations. What it does differently is make logical connections to pages that will please your users and increase revenue. (link to related helpful content). Take the Page 'HP Original Ink' Using this interlinker it'll match to the following related pages: 'Printer Ink' 'Printers' 'Printer Paper' Even thought syntactically those words are a million miles away from each other. I originally wrote it to surface User Guides / Buyer's Guides at a category level. But realised there are so many other uses for it. ✅ Interlink Related Products Within Category Text ✅ Show Related Categories ✅ Surface Related Content to Assist Conversion (Blogs, Buyers’ Guides, etc.) ✅ Display Products Most Closely Related to Category Page for Increased Relevancy ✅ Feature Related Blog Posts If you haven't tried it before I'd love you to give it a go, if you have tried it before you should revisit it as it's been completed overhauled. (The original version actually had a bug that prevented a large number of results from being returned 🤦‍♂️) Full Write Up with New Features Instructions: Direct Link to the App: Retweets for reach most appreciated!
0:58

🎉BERT Semantic Interlinker App V2 (Free App)🎉 Free Streamlit App to Semantically Interlink Pages using Sentence Transformers. Last week Emilia tagged me in this excellent article that she had linked to the original version of the BERT Interlinking App. I checked the stats and over it had 17,000 unique visitors with no promotion at all. I spent the last week re-writing it from the ground up with a ton more features, visualisations and polish. What's the Point? “There are tons of page Interlinkers out there, why not just use those?” It’s a fair point, this script doesn’t even use anchor text or link metrics when making recommendations. What it does differently is make logical connections to pages that will please your users and increase revenue. (link to related helpful content). Take the Page 'HP Original Ink' Using this interlinker it'll match to the following related pages: 'Printer Ink' 'Printers' 'Printer Paper' Even thought syntactically those words are a million miles away from each other. I originally wrote it to surface User Guides / Buyer's Guides at a category level. But realised there are so many other uses for it. ✅ Interlink Related Products Within Category Text ✅ Show Related Categories ✅ Surface Related Content to Assist Conversion (Blogs, Buyers’ Guides, etc.) ✅ Display Products Most Closely Related to Category Page for Increased Relevancy ✅ Feature Related Blog Posts If you haven't tried it before I'd love you to give it a go, if you have tried it before you should revisit it as it's been completed overhauled. (The original version actually had a bug that prevented a large number of results from being returned 🤦‍♂️) Full Write Up with New Features Instructions: Direct Link to the App: Retweets for reach most appreciated!

Lee Foot 🐍📈

34,701 views • 2 years ago

Even though Plume just launched its mainnet in June, it already has $5B assets in the pipeline and over half of all RWA holders in crypto Its cofounder Chris Yin shares on Talking Tokens Podcast how Plume went from a product app to a full chain, why most tokenized assets fail, and what it takes to make RWAs usable. Timestamps 0:00 – What is Plume Network? 1:32 – Chris’s background: DeFi summer, yield farming, and app-layer insights 2:15 – Why most tokenized assets fail: no usage, no velocity 4:03 – Why Plume had to build its own chain 5:42 – Tokenization isn’t enough — here’s what else matters 8:01 – RWAs as crypto’s biggest opportunity (bigger than all DeFi) 9:33 – Stable yield, composability, and how users interact with assets 12:16 – What makes tokenized assets actually usable 14:00 – Top-down vs bottoms-up tokenization 16:00 – Mineral Vault: onboarded 80K holders in a month 19:12 – Plume’s Phase 2: onboarding non-crypto users 20:30 – Why real traction > flashy launches 22:41 – The real numbers behind RWA growth 25:50 – Plume’s 2025 goal: $1B in clean, onchain RWA value 27:48 – Structured products will unlock the next wave 30:50 – Long-term vision: an onchain BlackRock 32:10 – It’s not about one asset — it’s the full stack 36:36 – Honoring Co-Founder Eugene Chen 39:25 – Final advice: focus long-term, but adapt fast
51:44

Even though Plume just launched its mainnet in June, it already has $5B assets in the pipeline and over half of all RWA holders in crypto Its cofounder Chris Yin shares on Talking Tokens Podcast how Plume went from a product app to a full chain, why most tokenized assets fail, and what it takes to make RWAs usable. Timestamps 0:00 – What is Plume Network? 1:32 – Chris’s background: DeFi summer, yield farming, and app-layer insights 2:15 – Why most tokenized assets fail: no usage, no velocity 4:03 – Why Plume had to build its own chain 5:42 – Tokenization isn’t enough — here’s what else matters 8:01 – RWAs as crypto’s biggest opportunity (bigger than all DeFi) 9:33 – Stable yield, composability, and how users interact with assets 12:16 – What makes tokenized assets actually usable 14:00 – Top-down vs bottoms-up tokenization 16:00 – Mineral Vault: onboarded 80K holders in a month 19:12 – Plume’s Phase 2: onboarding non-crypto users 20:30 – Why real traction > flashy launches 22:41 – The real numbers behind RWA growth 25:50 – Plume’s 2025 goal: $1B in clean, onchain RWA value 27:48 – Structured products will unlock the next wave 30:50 – Long-term vision: an onchain BlackRock 32:10 – It’s not about one asset — it’s the full stack 36:36 – Honoring Co-Founder Eugene Chen 39:25 – Final advice: focus long-term, but adapt fast

Jacquelyn Melinek

24,629 views • 11 months ago

Everyone's talking, reading, or writing about how much is changing in how we build products in the future, but very few people have actually experienced what the most cutting edge tools are capable of. In a new series on my podcast, I'm going to sit down with the founders of the most advanced product development tools to live demo what these tools can do, and then talk about the implications of this on product people's careers and lives. To kick things off, I sat down with Amjad Masad (Amjad Masad), co-founder and CEO of Replit ⠕, a browser-based coding environment that allows anyone to write and deploy code. Replit has 34 million users globally and is one of the fastest-growing developer communities in the world. In our conversation, Amjad shares: 🔸 A live demo of Replit building a full-stack web app from a text prompt 🔸 The implications of AI-powered development for product managers, designers, and engineers 🔸 What skills will matter more, and less, in the future 🔸 How this might reshape companies and careers 🔸 Why being “generative” will become an increasingly valuable skill 🔸 “Amjad’s law” and how learning to debug AI-generated code is becoming ever more valuable 🔸 Much more Listen now 👇 - YouTube: - Spotify: - Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 — A global leader in digital identity verification: 🏆 @LinkedInMktg — Reach professionals and drive results for your business:
1:06

Everyone's talking, reading, or writing about how much is changing in how we build products in the future, but very few people have actually experienced what the most cutting edge tools are capable of. In a new series on my podcast, I'm going to sit down with the founders of the most advanced product development tools to live demo what these tools can do, and then talk about the implications of this on product people's careers and lives. To kick things off, I sat down with Amjad Masad (Amjad Masad), co-founder and CEO of Replit ⠕, a browser-based coding environment that allows anyone to write and deploy code. Replit has 34 million users globally and is one of the fastest-growing developer communities in the world. In our conversation, Amjad shares: 🔸 A live demo of Replit building a full-stack web app from a text prompt 🔸 The implications of AI-powered development for product managers, designers, and engineers 🔸 What skills will matter more, and less, in the future 🔸 How this might reshape companies and careers 🔸 Why being “generative” will become an increasingly valuable skill 🔸 “Amjad’s law” and how learning to debug AI-generated code is becoming ever more valuable 🔸 Much more Listen now 👇 - YouTube: - Spotify: - Apple: Thank you to our wonderful sponsors for supporting the podcast: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: 🏆 — A global leader in digital identity verification: 🏆 @LinkedInMktg — Reach professionals and drive results for your business:

Lenny Rachitsky

81,955 views • 1 year ago

GT Protocol AI Digest No.83: Sora Is Dead. What Now? Here are five stories worth your attention 👇 🎬 Sora Is Shut Down OpenAI abruptly shut down its AI video generator Sora after heavy hype and investment. The move shows that even flagship AI products can fail to reach real-world viability, raising questions about the scalability of generative video. 🎵 Gemini Expands Into AI Music Gemini introduced Lyria 3 Pro, a model that generates longer and more structured music. AI audio is moving closer to professional production, shifting from short demos to usable creative output. 📱 Pixel Ads Create AI Confusion Google’s Pixel 10 ads left users unclear about what is being sold: the phone or its AI features. As AI becomes core to products, communicating its value is becoming harder for companies. 🧠 “AGI Is Already Here” Nvidia CEO Jensen Huang said he believes AGI has effectively been achieved. The claim reflects growing confidence in AI progress, but also shows how unclear and flexible the definition of AGI has become. ⚖️ Anthropic vs Pentagon Anthropic is heading to court against the Pentagon over restrictions on its AI models. The case highlights rising tension between AI safety policies and government demand for broader access. AI is still moving fast, but not everything is working as expected. Read the full digest on our Medium 👉
0:46

GT Protocol AI Digest No.83: Sora Is Dead. What Now? Here are five stories worth your attention 👇 🎬 Sora Is Shut Down OpenAI abruptly shut down its AI video generator Sora after heavy hype and investment. The move shows that even flagship AI products can fail to reach real-world viability, raising questions about the scalability of generative video. 🎵 Gemini Expands Into AI Music Gemini introduced Lyria 3 Pro, a model that generates longer and more structured music. AI audio is moving closer to professional production, shifting from short demos to usable creative output. 📱 Pixel Ads Create AI Confusion Google’s Pixel 10 ads left users unclear about what is being sold: the phone or its AI features. As AI becomes core to products, communicating its value is becoming harder for companies. 🧠 “AGI Is Already Here” Nvidia CEO Jensen Huang said he believes AGI has effectively been achieved. The claim reflects growing confidence in AI progress, but also shows how unclear and flexible the definition of AGI has become. ⚖️ Anthropic vs Pentagon Anthropic is heading to court against the Pentagon over restrictions on its AI models. The case highlights rising tension between AI safety policies and government demand for broader access. AI is still moving fast, but not everything is working as expected. Read the full digest on our Medium 👉

GT Protocol

34,954 views • 3 months ago

Bolt Africa Bolt💔⁉️🇿🇦 I am submitting a formal complaint regarding a Bolt trip during which I was placed in serious danger due to the drvers negligent and unsafe conduct.... I was picked up in Johannesburg and noticed while we were travelling on the N1 towards Randburg that the driver appeared extremely tired. The vehicle repeatedly moved onto the white lane markings, and I had to alert the driver that we were driving on top of the lines..... Shortly afterwards, i observed through the rear-view mirror that the driver was repeatedly closing his eyes while driving. Concerned for my safety, l recorded a video because I intended to report the incident. .... "In the videos attached, you can clearly see that the driver's eyes were closing while he was driving. The footage shows him repeatedly struggling to keep his eyes open, which put my safety and the safety of other road users at risk." The driver's eyes were visibly closing while the vehicle was moving at speed on a busy highway At one point, the driver appeared to fall asleep while driving and the vehicle started moving into other lanes while his eyes were closed. I had to tap his shoulder to wake him up. A few minutes later, I believed he had recovered, but as we approached a traffic light l noticed that the traffic signal was red and the driver was not slowing down. Instead, the vehicle continued accelerating. When I looked again, I saw that his eyes were closed. I immediately tapped him again, but he was unable to avoid a collision and crashed into a Ford vehicle Fortunately, I was seated in the back and was not seriously injured, although! experienced pain in my knee immediately after the collision. The police attended the scene following the accident. What concerns me most is that this accident appears to have been caused by the driver's fatigue and repeated loss of attention while operating the vehicle. leven provided video evidence to the driver of the Ford because the Bolt driver was blaming the other driver despite having fallen asleep behind the wheel Passengers trust Bolt to provide safe transportation. A driver who is too tired to remain awake should not be accepting trips, as this places passengers and other road users at risk of serious injury or death. I request that Bolt conduct a full investigation into this incident, review any available trip data and the video evidence, and take appropriate action regarding the driver's conduct. I would also appreciate confirmation that this complaint has been received and information on the outcome of the investigation.
0:26

Bolt Africa Bolt💔⁉️🇿🇦 I am submitting a formal complaint regarding a Bolt trip during which I was placed in serious danger due to the drvers negligent and unsafe conduct.... I was picked up in Johannesburg and noticed while we were travelling on the N1 towards Randburg that the driver appeared extremely tired. The vehicle repeatedly moved onto the white lane markings, and I had to alert the driver that we were driving on top of the lines..... Shortly afterwards, i observed through the rear-view mirror that the driver was repeatedly closing his eyes while driving. Concerned for my safety, l recorded a video because I intended to report the incident. .... "In the videos attached, you can clearly see that the driver's eyes were closing while he was driving. The footage shows him repeatedly struggling to keep his eyes open, which put my safety and the safety of other road users at risk." The driver's eyes were visibly closing while the vehicle was moving at speed on a busy highway At one point, the driver appeared to fall asleep while driving and the vehicle started moving into other lanes while his eyes were closed. I had to tap his shoulder to wake him up. A few minutes later, I believed he had recovered, but as we approached a traffic light l noticed that the traffic signal was red and the driver was not slowing down. Instead, the vehicle continued accelerating. When I looked again, I saw that his eyes were closed. I immediately tapped him again, but he was unable to avoid a collision and crashed into a Ford vehicle Fortunately, I was seated in the back and was not seriously injured, although! experienced pain in my knee immediately after the collision. The police attended the scene following the accident. What concerns me most is that this accident appears to have been caused by the driver's fatigue and repeated loss of attention while operating the vehicle. leven provided video evidence to the driver of the Ford because the Bolt driver was blaming the other driver despite having fallen asleep behind the wheel Passengers trust Bolt to provide safe transportation. A driver who is too tired to remain awake should not be accepting trips, as this places passengers and other road users at risk of serious injury or death. I request that Bolt conduct a full investigation into this incident, review any available trip data and the video evidence, and take appropriate action regarding the driver's conduct. I would also appreciate confirmation that this complaint has been received and information on the outcome of the investigation.

Black-Jesus💧🇿🇦

16,986 views • 1 month ago

In the latest episode of Professionally Curious 🧠, Joanna Cook @ SOON📍🇺🇸 🗽 (AI 🦞Arc) sits down with John, the founder of @Finch, a loyalty marketing and payment app on Solana that is reshaping how bars and restaurants interact with their customers. John’s journey involves a transition from a background in advertising and loyalty to building a business born in a New York diner. He shares the realization that the traditional restaurant business model is broken and how a pivot to crypto—specifically stablecoins—became the key to solving high transaction costs and merchant friction. Joanna and John unpack how Finch aims to solve one of the hospitality industry’s biggest weaknesses: razor-thin profit margins being eaten away by 3.5%–4% credit card fees. Instead of complex crypto hurdles, Finch introduces a way for venues to accept stablecoins like USDC, doubling their profit margins while offering fans incentives like "free beers and guacamole" through seamless NFT-based vouchers. Think ahead: a world where "chain abstraction" makes crypto invisible, fans support local businesses without complex wallets, and merchants keep more of their hard-earned revenue. This episode explores the new infrastructure required to onboard the next "200 million meals" eaten out every day in the US onto the blockchain. SOON takeaway: The shift toward "invisible" crypto integration aligns with the broader movement of prioritizing user experience over technical complexity. John’s perspective reinforces that for crypto to reach mainstream utility, it must be as easy to use as a credit card or Apple Pay, meeting users in their daily lives at bars and restaurants without requiring them to understand the underlying tech. ⏱ Timestamps • 00:00 – Introduction to Finch and the mission to fix the restaurant business • 01:06 – John’s career story: From NY diners to crypto discovery • 06:54 – Moving from Web2 advertising to building a Web3 payment app • 13:52 – What Finch actually is: Solving the 4% credit card fee problem • 21:18 – On-chain mechanics: Using USDC and NFT vouchers for rewards • 23:26 – Insights on the US dining economy and the scale of the opportunity • 26:20 – How Finch differs from competitors by using in-venue hardware • 37:56 – The future of UX: Chain abstraction and the "Apple" approach to crypto
17:21

In the latest episode of Professionally Curious 🧠, Joanna Cook @ SOON📍🇺🇸 🗽 (AI 🦞Arc) sits down with John, the founder of @Finch, a loyalty marketing and payment app on Solana that is reshaping how bars and restaurants interact with their customers. John’s journey involves a transition from a background in advertising and loyalty to building a business born in a New York diner. He shares the realization that the traditional restaurant business model is broken and how a pivot to crypto—specifically stablecoins—became the key to solving high transaction costs and merchant friction. Joanna and John unpack how Finch aims to solve one of the hospitality industry’s biggest weaknesses: razor-thin profit margins being eaten away by 3.5%–4% credit card fees. Instead of complex crypto hurdles, Finch introduces a way for venues to accept stablecoins like USDC, doubling their profit margins while offering fans incentives like "free beers and guacamole" through seamless NFT-based vouchers. Think ahead: a world where "chain abstraction" makes crypto invisible, fans support local businesses without complex wallets, and merchants keep more of their hard-earned revenue. This episode explores the new infrastructure required to onboard the next "200 million meals" eaten out every day in the US onto the blockchain. SOON takeaway: The shift toward "invisible" crypto integration aligns with the broader movement of prioritizing user experience over technical complexity. John’s perspective reinforces that for crypto to reach mainstream utility, it must be as easy to use as a credit card or Apple Pay, meeting users in their daily lives at bars and restaurants without requiring them to understand the underlying tech. ⏱ Timestamps • 00:00 – Introduction to Finch and the mission to fix the restaurant business • 01:06 – John’s career story: From NY diners to crypto discovery • 06:54 – Moving from Web2 advertising to building a Web3 payment app • 13:52 – What Finch actually is: Solving the 4% credit card fee problem • 21:18 – On-chain mechanics: Using USDC and NFT vouchers for rewards • 23:26 – Insights on the US dining economy and the scale of the opportunity • 26:20 – How Finch differs from competitors by using in-venue hardware • 37:56 – The future of UX: Chain abstraction and the "Apple" approach to crypto

SOON - Solana Optimistic Network (Mainnet Arc)

222,250 views • 6 months ago

Jensen Huang just reframed the entire history of computing in two minutes. The argument is deceptively simple, but once you see it you can't unsee it. Every single piece of software ever built, every app, every website, every search engine, every platform operated on exactly the same fundamental principle. Someone creates content, it gets stored somewhere and when you ask for it, the system retrieves it. Google indexes the web and retrieves the right page, YouTube encodes your video and retrieves it when someone clicks, Amazon photographs every product in its catalog and retrieves the listing that matches your search. Every recommender system, every ad platform, every social feed, all of it, without exception, is a retrieval operation dressed up in a user interface and we called it the Information Age. But strip away the branding and what you had, for 30 consecutive years, was an extraordinarily sophisticated filing cabinet. The smartest engineers in the world spent their careers optimizing how fast you could put things in and pull things out. Generative AI doesn't just improve that system but rather replaces the entire premise of it. Instead of retrieving content that was pre-recorded by someone else, AI generates it from scratch, in real time, calibrated to your exact context, your specific intent, the precise ground truth of that moment. The same question asked twice gets two different answers, both tailored to what the system knows about you right now. There is no file being pulled or a pre-recorded version, the content is being synthesized on the fly from a compressed model of human knowledge, shaped to fit exactly what you need. The implications of this for the companies that built the retrieval era are profound and already starting to show. Google's click-through rates on organic search results have dropped 61% since AI Overviews rolled out, because users are getting answers directly instead of clicking through to files. Gartner projects traditional search engine query volume drops 25% by the end of 2026 as users migrate to generative interfaces. And yet this is exactly what Jensen predicted, in the old world, the computing bottleneck was storage and retrieval, you needed hard drives, bandwidth, and CDNs. In the new world, the bottleneck is computation, you need the raw processing power to generate tokens at scale, millions of times per second, for millions of simultaneous users. Inference computing demand has grown roughly ten thousand times in the last two years alone. That shift is precisely why Nvidia's revenue opportunity forecast just jumped from $500 billion through 2026 to $1 trillion through 2027. The retrieval era needed CPUs and storage and the generative era needs GPUs, token factories, and inference infrastructure at a scale never built before and Nvidia builds the engine underneath all of it. Jensen has been making this argument since 2024. Most people wrote it off as a chip salesman talking his book but two years later, it's the architecture of the entire industry.
1:54

Jensen Huang just reframed the entire history of computing in two minutes. The argument is deceptively simple, but once you see it you can't unsee it. Every single piece of software ever built, every app, every website, every search engine, every platform operated on exactly the same fundamental principle. Someone creates content, it gets stored somewhere and when you ask for it, the system retrieves it. Google indexes the web and retrieves the right page, YouTube encodes your video and retrieves it when someone clicks, Amazon photographs every product in its catalog and retrieves the listing that matches your search. Every recommender system, every ad platform, every social feed, all of it, without exception, is a retrieval operation dressed up in a user interface and we called it the Information Age. But strip away the branding and what you had, for 30 consecutive years, was an extraordinarily sophisticated filing cabinet. The smartest engineers in the world spent their careers optimizing how fast you could put things in and pull things out. Generative AI doesn't just improve that system but rather replaces the entire premise of it. Instead of retrieving content that was pre-recorded by someone else, AI generates it from scratch, in real time, calibrated to your exact context, your specific intent, the precise ground truth of that moment. The same question asked twice gets two different answers, both tailored to what the system knows about you right now. There is no file being pulled or a pre-recorded version, the content is being synthesized on the fly from a compressed model of human knowledge, shaped to fit exactly what you need. The implications of this for the companies that built the retrieval era are profound and already starting to show. Google's click-through rates on organic search results have dropped 61% since AI Overviews rolled out, because users are getting answers directly instead of clicking through to files. Gartner projects traditional search engine query volume drops 25% by the end of 2026 as users migrate to generative interfaces. And yet this is exactly what Jensen predicted, in the old world, the computing bottleneck was storage and retrieval, you needed hard drives, bandwidth, and CDNs. In the new world, the bottleneck is computation, you need the raw processing power to generate tokens at scale, millions of times per second, for millions of simultaneous users. Inference computing demand has grown roughly ten thousand times in the last two years alone. That shift is precisely why Nvidia's revenue opportunity forecast just jumped from $500 billion through 2026 to $1 trillion through 2027. The retrieval era needed CPUs and storage and the generative era needs GPUs, token factories, and inference infrastructure at a scale never built before and Nvidia builds the engine underneath all of it. Jensen has been making this argument since 2024. Most people wrote it off as a chip salesman talking his book but two years later, it's the architecture of the entire industry.

Milk Road AI

17,890 views • 2 months ago

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 views • 3 months ago

Ryan Cohen: “Why Does Everyone Want GameStop to Fail?” $GME CEO Ryan Cohen: “The media is an example. Why is it that you've got a ($EBAY) management team with no skin in the game, they're not builders, they haven't built anything themselves before, they've basically just been employees at major companies, they’ve been overpaid, I don't think they've ever broken out a sweat in their entire lives, why does everyone want them to succeed? But when you have someone that, and by the way, I'm putting $500M of my own money into this transaction, I haven't pulled a penny out of GameStop, and it seems like everyone in the media basically wants us to fail, and wants them to succeed. And you've got a board that's making hundreds of thousands of dollars a year. They don't buy stock with their own money. They end up showing up to a handful of board meetings, and they're making a fortune. You've got a management team that is grossly overpaid, taking zero risk. There's nothing more American than basically risking your own capital. So why does everyone want us to fail? david friedberg: “I do think that the media, in order to give you credibility, they're gonna have to acknowledge that all of their takes on GameStop just being a meme stock were wrong, and that there is actually a business here, and that there is value being created here, and that they missed that, and they got the story completely wrong.” -------------------------------------- Thanks to our partners! Most advertisers have never heard of the platform with an $11B annual run rate in ad spend. AppLovin Ads — 1B+ daily active users, full-screen video ads watched for a median of 35 seconds, and businesses are profitably spending hundreds of thousands of dollars a day on it. Advertiser access is in closed beta. The window is open at Nasdaq - Positioned at the nexus of technology and the capital markets, Nasdaq provides premier platforms and services for global capital markets and beyond with unmatched technology, insights and markets expertise.
1:16

Ryan Cohen: “Why Does Everyone Want GameStop to Fail?” $GME CEO Ryan Cohen: “The media is an example. Why is it that you've got a ($EBAY) management team with no skin in the game, they're not builders, they haven't built anything themselves before, they've basically just been employees at major companies, they’ve been overpaid, I don't think they've ever broken out a sweat in their entire lives, why does everyone want them to succeed? But when you have someone that, and by the way, I'm putting $500M of my own money into this transaction, I haven't pulled a penny out of GameStop, and it seems like everyone in the media basically wants us to fail, and wants them to succeed. And you've got a board that's making hundreds of thousands of dollars a year. They don't buy stock with their own money. They end up showing up to a handful of board meetings, and they're making a fortune. You've got a management team that is grossly overpaid, taking zero risk. There's nothing more American than basically risking your own capital. So why does everyone want us to fail? david friedberg: “I do think that the media, in order to give you credibility, they're gonna have to acknowledge that all of their takes on GameStop just being a meme stock were wrong, and that there is actually a business here, and that there is value being created here, and that they missed that, and they got the story completely wrong.” -------------------------------------- Thanks to our partners! Most advertisers have never heard of the platform with an $11B annual run rate in ad spend. AppLovin Ads — 1B+ daily active users, full-screen video ads watched for a median of 35 seconds, and businesses are profitably spending hundreds of thousands of dollars a day on it. Advertiser access is in closed beta. The window is open at Nasdaq - Positioned at the nexus of technology and the capital markets, Nasdaq provides premier platforms and services for global capital markets and beyond with unmatched technology, insights and markets expertise.

The All-In Podcast

168,239 views • 20 days ago

Friedberg’s Datacenter Wake-Up Call: If We Don't Build Them Here, Other Countries Will david friedberg explains why a datacenter moratorium would be a disaster for the US: The data coming in and out of datacenters moves at roughly the speed of light, so you could put them anywhere. And I think that our policymakers need to be very cognizant of that fact. You have, and we do, connect to the internet using high speed cable, high speed fiber optic throughout the world, and so theoretically, if we don't embrace and allow the economic development of the datacenter industry, and it will fundamentally be an industry because it is almost like the new oil, if we don't put them here, someone else will put them on their shores. Someone else will put them in their country, someone else will put them in their jurisdiction, and a lot of the economic value that arises from the people that will build those facilities, the energy that will be installed to produce power for those facilities, and then all of the second and third order industries that emerge as a result of those installations, that value will accrue elsewhere. The demand is there. The economy's moving forward, AI's moving forward. Datacenters do not take up a lot of space. They're very small relative to the economic value that they produce. If you zoom out on the map of the world, all the datacenters in the world fit under the tip of a pin, and so this is a very small footprint. If we're going to give up hundreds of thousands of jobs and many billions of dollars of economic value creation, we're being pretty silly and pretty obtuse in our view of the world. Provided datacenters are producing their own electricity, that means that you're taking electricity consumption off the grid because they otherwise are not being used on the grid, and that will reduce the cost of electricity for other residential and industrial users. So it's silly to think that we need to put a moratorium on datacenters. As soon as you do that, the companies that use datacenters are not going to slow down. They're going to go put them somewhere else, and we're going to miss out.
1:47

Friedberg’s Datacenter Wake-Up Call: If We Don't Build Them Here, Other Countries Will david friedberg explains why a datacenter moratorium would be a disaster for the US: The data coming in and out of datacenters moves at roughly the speed of light, so you could put them anywhere. And I think that our policymakers need to be very cognizant of that fact. You have, and we do, connect to the internet using high speed cable, high speed fiber optic throughout the world, and so theoretically, if we don't embrace and allow the economic development of the datacenter industry, and it will fundamentally be an industry because it is almost like the new oil, if we don't put them here, someone else will put them on their shores. Someone else will put them in their country, someone else will put them in their jurisdiction, and a lot of the economic value that arises from the people that will build those facilities, the energy that will be installed to produce power for those facilities, and then all of the second and third order industries that emerge as a result of those installations, that value will accrue elsewhere. The demand is there. The economy's moving forward, AI's moving forward. Datacenters do not take up a lot of space. They're very small relative to the economic value that they produce. If you zoom out on the map of the world, all the datacenters in the world fit under the tip of a pin, and so this is a very small footprint. If we're going to give up hundreds of thousands of jobs and many billions of dollars of economic value creation, we're being pretty silly and pretty obtuse in our view of the world. Provided datacenters are producing their own electricity, that means that you're taking electricity consumption off the grid because they otherwise are not being used on the grid, and that will reduce the cost of electricity for other residential and industrial users. So it's silly to think that we need to put a moratorium on datacenters. As soon as you do that, the companies that use datacenters are not going to slow down. They're going to go put them somewhere else, and we're going to miss out.

The All-In Podcast

89,297 views • 4 months ago

Google just announced the biggest upgrade to Search in over 25 years. For brands the opportunity here is pretty enormous. Here is what the new Search actually looks like and how you should take advantage: The search box now accepts text, images, files, videos, and open Chrome tabs. It expands dynamically as you type. It also anticipates your intent before you finish asking. This is the version of Search that SEO Stuff has been helping customers build for. The biggest opportunity here is what happens after the search. Google's new information agents run 24/7 in the background on behalf of your buyer. And that's why it has never been more important to understand how Google, ChatGPT, Claude and every other AI platform sees your brand. (If you want to see where your site stands across Google and AI search, start here: Here is exactly how Google's new information agents work: Step 1: The buyer does a total brain dump of what they want to stay updated on. Essentially a full description of their problem, their category, their needs. Step 2: The agent breaks down that question and maps out a plan across every relevant sub-topic. Step 3: It determines urgency and what kind of intel the buyer needs right now versus later. Step 4: It sets triggers and monitors the web continuously, scanning blogs, news sites, and social posts for relevant information as it changes. Step 5: It sends the buyer an intelligent synthesized update with links and the ability to take action. Here is why this is a massive opportunity: AI Mode already has 1 billion monthly users. Queries are more than doubling every quarter. And multiple studies have shown that users arriving via AI search are more likely to convert. With information agents running continuously for over a billion users, the brands in that cited source pool are being recommended around the clock, automatically, to buyers who are actively monitoring their category. The brands that build content depth and editorial authority now are building a presence that buildings on itself 24 hours a day. This is what SEO Stuff builds for every customer. Content that covers every sub-question a buyer in your category asks, so the agent finds you at every step of its plan. Authority building from trusted websites that signal credibility to every retrieval system Google has ever built. One investment. Continuous recommendations. Around the clock. Check it out: Our most popular done-for-you package: Our done-for-you "content only" package: There is a reason more than 80 percent of SEO Stuff customers reorder. The results continue long after the work is done. #GoogleIO📷📷📷 #Google📷📷📷
1:26

Google just announced the biggest upgrade to Search in over 25 years. For brands the opportunity here is pretty enormous. Here is what the new Search actually looks like and how you should take advantage: The search box now accepts text, images, files, videos, and open Chrome tabs. It expands dynamically as you type. It also anticipates your intent before you finish asking. This is the version of Search that SEO Stuff has been helping customers build for. The biggest opportunity here is what happens after the search. Google's new information agents run 24/7 in the background on behalf of your buyer. And that's why it has never been more important to understand how Google, ChatGPT, Claude and every other AI platform sees your brand. (If you want to see where your site stands across Google and AI search, start here: Here is exactly how Google's new information agents work: Step 1: The buyer does a total brain dump of what they want to stay updated on. Essentially a full description of their problem, their category, their needs. Step 2: The agent breaks down that question and maps out a plan across every relevant sub-topic. Step 3: It determines urgency and what kind of intel the buyer needs right now versus later. Step 4: It sets triggers and monitors the web continuously, scanning blogs, news sites, and social posts for relevant information as it changes. Step 5: It sends the buyer an intelligent synthesized update with links and the ability to take action. Here is why this is a massive opportunity: AI Mode already has 1 billion monthly users. Queries are more than doubling every quarter. And multiple studies have shown that users arriving via AI search are more likely to convert. With information agents running continuously for over a billion users, the brands in that cited source pool are being recommended around the clock, automatically, to buyers who are actively monitoring their category. The brands that build content depth and editorial authority now are building a presence that buildings on itself 24 hours a day. This is what SEO Stuff builds for every customer. Content that covers every sub-question a buyer in your category asks, so the agent finds you at every step of its plan. Authority building from trusted websites that signal credibility to every retrieval system Google has ever built. One investment. Continuous recommendations. Around the clock. Check it out: Our most popular done-for-you package: Our done-for-you "content only" package: There is a reason more than 80 percent of SEO Stuff customers reorder. The results continue long after the work is done. #GoogleIO📷📷📷 #Google📷📷📷

Alex Groberman

157,952 views • 1 month ago

In honor of Y Combinator's 20th anniversary, here are my 6 favorite lessons: 1. The 7 minute espresso rule: Our first meeting with Sam Altman lasted just seven minutes. The batch hadn’t even officially started yet and my cofounder Ryan Rowe and I drove down from Mountain View to the tiny SF YC outpost to meet him. Sam was making an espresso when we walked in. He opened with – “have you launched”? We said no, too many bugs. Our product, kimono, made it easy to just point and click to build a web scraper. Our promise was to get you an API in 60 seconds, without writing a single line of code. But, the web is vast, and websites are very different. We needed it to work on a large enough spectrum of sites so that our first users would have a good experience. It worked on just a handful of sites at the time. Sam pushed us to make sure we were launched in less than 2 weeks. We debated, highlighting the complexity of the bugs and the limits of underlying headless browsing technology. The espresso finished brewing, he picked it up, looked at us and said, “well, you better get going then and fix those bugs”. We left, launched within those next two weeks and learned one of the most important lessons that day - speed matters. Ship something you’re embarrassed by. 2. There are no experts. Ryan and I were not prepared for the rapid influx of user on launch day. We did 88 user interviews to validate our product idea, and everyone basically said they wouldn’t use it. We thought they were wrong and built it anyway. It got tons of traffic on day 1. We didn’t sleep in the 48 hours following because our servers and database kept crashing. We hadn’t indexed it properly. We realized we weren’t the experts and needed to hire one. We went over to Michael Seibel's apartment for advice. Michael smiled and told us about the early days at SocialCam, that eventually became Twitch – experts thought the streaming video problem was too hard to be possible. The answer wasn’t hiring an expert, but hiring someone young, capable and naïve enough to give it an earnest try. So we opted to just figure it out ourselves. 3. Messages in Pizza Boxes. Startups win through incredible customer service, then through product, not the other way around. Michael Seibel told us how SocialCam’s streaming infrastructure went down while a key teammate was unreachable off-grid in a Tahoe cabin for the weekend. Normal people would have waited until Monday. Not Michael. He called a local pizza delivery place and asked the delivery person to send a large pizza with an urgent message in the box to the cabin. Their infrastructure was back up in hours. 4. The Twinkle can matter more than the TAM. Ambition matters just as much as practicality. Before demo day, we were struggling with the end note of our pitch. We knew the value of our product, and had a fanatical and fast-growing user base, but the market size we calculated either seemed so ridiculously big that it was not plausible, or so narrow that it was equally silly. Paul Graham and Geoff Ralston sat with us and showed us that if we can really pull it off at scale, it would be bigger than Google. So, we could skip the market size, if we really believed it. PG suggested not talking about market size, but just making sure people could see the twinkle in our eyes when we talked about what you might be able to do with a structured copy of the internet that’s larger than Google’s. 5. You’re always at the Origin. Our demo day was successful beyond our wildest beliefs. Afterwards, Geoff Ralston drew a chart for us on a whiteboard. It was a hockey stick. He asked us where we thought we were. It was a rhetorical question. He said we were at the origin, and that the most important thing is to remember that. Sam Altman doubled down. When he invested in Kimono, he gave us a Zimbabwean Trillion dollar note (the result of extreme hyperinflation in Zimbabwe), as a cautionary reminder that the fundraising and valuation mean nothing. We have channeled this into our culture at @TheArenaAI with our ritual around neon shoelaces and a pair of neon track spikes hanging on the wall to remind us that we’re at the Olympic starting line, but we don’t have any medals yet. 6. High bandwidth discussions with users don’t happen over email. The Collison brothers, who founded Stripe, took user engagement to the next level – so much so that “Collison installation” is part of YC lore. John Collison told us that talking to users was essential because email and chat conversations were not “high bandwidth” enough. He emphasized the importance of what you can learn being with users in person or talking to them over Skype (remember, this was early 2014!). We took this to heart, and spoke to hundreds of users at Kimono regularly over Skype. One power user Alexander Chung, who I met this way, has become a close friend and even attended my wedding in India. At Arena, we now go a step further and regularly fly to our users. We over-invest to a degree that is almost crazy in order to be more than a supplier — a true partner to our early customers. YC taught us it’s the only way to understand their problems on the ground and really make sure our products work for them. YC taught us to outsource very little. Own the problem. Own the outcome. And if you do, you get to be stupidly ambitious. If you’re curious about Kimono Labs (which we later sold to Palantir ), here was our launch demo. No-code web scraping before “no code” was even a term: Here's our demo day pitch, from 2014. Forever grateful to those above + Garry Tan Jessica Livingston Trevor Blackwell
2:37

In honor of Y Combinator's 20th anniversary, here are my 6 favorite lessons: 1. The 7 minute espresso rule: Our first meeting with Sam Altman lasted just seven minutes. The batch hadn’t even officially started yet and my cofounder Ryan Rowe and I drove down from Mountain View to the tiny SF YC outpost to meet him. Sam was making an espresso when we walked in. He opened with – “have you launched”? We said no, too many bugs. Our product, kimono, made it easy to just point and click to build a web scraper. Our promise was to get you an API in 60 seconds, without writing a single line of code. But, the web is vast, and websites are very different. We needed it to work on a large enough spectrum of sites so that our first users would have a good experience. It worked on just a handful of sites at the time. Sam pushed us to make sure we were launched in less than 2 weeks. We debated, highlighting the complexity of the bugs and the limits of underlying headless browsing technology. The espresso finished brewing, he picked it up, looked at us and said, “well, you better get going then and fix those bugs”. We left, launched within those next two weeks and learned one of the most important lessons that day - speed matters. Ship something you’re embarrassed by. 2. There are no experts. Ryan and I were not prepared for the rapid influx of user on launch day. We did 88 user interviews to validate our product idea, and everyone basically said they wouldn’t use it. We thought they were wrong and built it anyway. It got tons of traffic on day 1. We didn’t sleep in the 48 hours following because our servers and database kept crashing. We hadn’t indexed it properly. We realized we weren’t the experts and needed to hire one. We went over to Michael Seibel's apartment for advice. Michael smiled and told us about the early days at SocialCam, that eventually became Twitch – experts thought the streaming video problem was too hard to be possible. The answer wasn’t hiring an expert, but hiring someone young, capable and naïve enough to give it an earnest try. So we opted to just figure it out ourselves. 3. Messages in Pizza Boxes. Startups win through incredible customer service, then through product, not the other way around. Michael Seibel told us how SocialCam’s streaming infrastructure went down while a key teammate was unreachable off-grid in a Tahoe cabin for the weekend. Normal people would have waited until Monday. Not Michael. He called a local pizza delivery place and asked the delivery person to send a large pizza with an urgent message in the box to the cabin. Their infrastructure was back up in hours. 4. The Twinkle can matter more than the TAM. Ambition matters just as much as practicality. Before demo day, we were struggling with the end note of our pitch. We knew the value of our product, and had a fanatical and fast-growing user base, but the market size we calculated either seemed so ridiculously big that it was not plausible, or so narrow that it was equally silly. Paul Graham and Geoff Ralston sat with us and showed us that if we can really pull it off at scale, it would be bigger than Google. So, we could skip the market size, if we really believed it. PG suggested not talking about market size, but just making sure people could see the twinkle in our eyes when we talked about what you might be able to do with a structured copy of the internet that’s larger than Google’s. 5. You’re always at the Origin. Our demo day was successful beyond our wildest beliefs. Afterwards, Geoff Ralston drew a chart for us on a whiteboard. It was a hockey stick. He asked us where we thought we were. It was a rhetorical question. He said we were at the origin, and that the most important thing is to remember that. Sam Altman doubled down. When he invested in Kimono, he gave us a Zimbabwean Trillion dollar note (the result of extreme hyperinflation in Zimbabwe), as a cautionary reminder that the fundraising and valuation mean nothing. We have channeled this into our culture at @TheArenaAI with our ritual around neon shoelaces and a pair of neon track spikes hanging on the wall to remind us that we’re at the Olympic starting line, but we don’t have any medals yet. 6. High bandwidth discussions with users don’t happen over email. The Collison brothers, who founded Stripe, took user engagement to the next level – so much so that “Collison installation” is part of YC lore. John Collison told us that talking to users was essential because email and chat conversations were not “high bandwidth” enough. He emphasized the importance of what you can learn being with users in person or talking to them over Skype (remember, this was early 2014!). We took this to heart, and spoke to hundreds of users at Kimono regularly over Skype. One power user Alexander Chung, who I met this way, has become a close friend and even attended my wedding in India. At Arena, we now go a step further and regularly fly to our users. We over-invest to a degree that is almost crazy in order to be more than a supplier — a true partner to our early customers. YC taught us it’s the only way to understand their problems on the ground and really make sure our products work for them. YC taught us to outsource very little. Own the problem. Own the outcome. And if you do, you get to be stupidly ambitious. If you’re curious about Kimono Labs (which we later sold to Palantir ), here was our launch demo. No-code web scraping before “no code” was even a term: Here's our demo day pitch, from 2014. Forever grateful to those above + Garry Tan Jessica Livingston Trevor Blackwell

Pratap Ranade

177,308 views • 1 year ago

YouTube JUST announced this new thing called Hype and it's lowkey genius imagine you're scrolling and see a wonderful video from some small creator. you wanna do more than just like or share. enter Hype. okay, here's how it works you get 3 "Hypes" a week to boost videos you dig. it's like a supercharged upvote. more Hypes = higher on a special leaderboard. but here's the thing it only works for creators under 500K subs and vids less than a week old. plus there's a "small creator bonus" so the little guys can compete. why's this smart 1. turns passive viewers into active supporters (people want to support the up and comers!) 2. gives smaller creators a shot at blowing up 3. YouTube gets more engagement without messing with the algorithm 4. promotes niches so everything isn't watching the same thing (increase watch time) "In just the first four weeks of our beta tests in Turkey, Taiwan, and Brazil, users hyped over 5 million times across more than 50,000 unique channels" - YT Team it's not about killing the big creators. it's about giving the underdogs a chance to be seen. it currently being tested let's be real, social's been feeling stale. same big accounts, same content, rinse and repeat. you either dont get seen or you go ballistic. Hype. it's like CPR for the internet dream. suddenly, that person making weird videos in their bedroom could be tomorrow's viral sensation. just like the good ole days. it's bringing back that wild west vibe of early YouTube. when any random video could blow up overnight. i totally can see a hype-type feature for platforms like X/Twitter too. i hope it takes off. it's about keeping the internet weird, creative, and unpredictable. so yeah, Hype might just save us from an endless feed of polished corporate content and put the "social" back in social media. maybe its the domino we need to get other social platforms to do the same. let's see who/what the internet decides to make famous next. time to make the internet weird again.
0:24

YouTube JUST announced this new thing called Hype and it's lowkey genius imagine you're scrolling and see a wonderful video from some small creator. you wanna do more than just like or share. enter Hype. okay, here's how it works you get 3 "Hypes" a week to boost videos you dig. it's like a supercharged upvote. more Hypes = higher on a special leaderboard. but here's the thing it only works for creators under 500K subs and vids less than a week old. plus there's a "small creator bonus" so the little guys can compete. why's this smart 1. turns passive viewers into active supporters (people want to support the up and comers!) 2. gives smaller creators a shot at blowing up 3. YouTube gets more engagement without messing with the algorithm 4. promotes niches so everything isn't watching the same thing (increase watch time) "In just the first four weeks of our beta tests in Turkey, Taiwan, and Brazil, users hyped over 5 million times across more than 50,000 unique channels" - YT Team it's not about killing the big creators. it's about giving the underdogs a chance to be seen. it currently being tested let's be real, social's been feeling stale. same big accounts, same content, rinse and repeat. you either dont get seen or you go ballistic. Hype. it's like CPR for the internet dream. suddenly, that person making weird videos in their bedroom could be tomorrow's viral sensation. just like the good ole days. it's bringing back that wild west vibe of early YouTube. when any random video could blow up overnight. i totally can see a hype-type feature for platforms like X/Twitter too. i hope it takes off. it's about keeping the internet weird, creative, and unpredictable. so yeah, Hype might just save us from an endless feed of polished corporate content and put the "social" back in social media. maybe its the domino we need to get other social platforms to do the same. let's see who/what the internet decides to make famous next. time to make the internet weird again.

GREG ISENBERG

755,262 views • 1 year ago

I find this explanation of the Chinese system by Prof Keyu Jin (in a recent lecture at Harvard’s Fairbank center) absolutely fascinating. Keyu Jin is a professor of economics at LSE (London School of Economics) and serves on the board of companies like Credit Suisse. She’s also the daughter of Jin Liqun, former Vice Minister of finance of China so she’s a rare West-based academic (maybe even the only one) who actually has insight into the Chinese system from the inside. Essentially what she’s explaining is that a key reason why China was so successful economically is because of its decentralized nature, which creates two mutually compounding loops of competition, as opposed to one loop in the West. What does that mean? Well, contrary to popular belief that imagines China as being this centrally planned economy where almost everything is decided in Beijing, the inverse is actually true: China is actually one of the most decentralized countries in the world. To illustrate this, a metric that’s always amazed me is the fact that in China local governments (provinces, cities, villages, etc.) control a crazy 85% of the country's expenditures. On average that same metric for OECD countries is 33% (as in 64% of the expenditures are controlled at the federal/national level to China’s 15%). In the US for instance, which is already more decentralized than most given it’s a federation with states, only 45% of the country’s expenditures happen at the state and local level: almost twice less than in China! The effect of this, as Keyu Jin explains, is that provinces and larger municipalities in China have an immense degree of autonomy over the way they run their respective economies and fiercely compete with each other. This is the first loop. And then of course the second loop is that you have companies competing with each other in the market. As a result what constantly evolves in China is not only companies themselves but the environment in which they evolve: you constantly have this or that province running a new policy that proves very effective, making them gain an advantage vs other localities, initiative which is then copied by other localities. This makes the economic environment incredibly dynamic as it allows the state to move in unison with the economy, as opposed to slowing it down as is often the case in other countries. So what’s the role of the central government in all this? The key role, Keyu Jin argues, is setting broad objectives as well as personal management and promotion. And this is what makes the whole system work as therein lies the incentive for localities to compete with each other: because local officials know that if they do a better job than their peers, they’re on track for promotion by the central government. In “China Inc”, the central government is the board of directors and HR, presiding over an army of local CEOs with immense degrees of autonomy over their own “companies”. Keyu Jin gives the example of the solar industry. There was at some point (around 2005) a directive by the central government to develop the solar industry. The graph she shares in her talk is incredible: within a few years you had solar companies as well as patents related to research on solar technology pop up literally everywhere in China. With the result we all know about today: China today completely dominates the solar industry and solar technology (according to the International Energy Agency China's share in all the manufacturing stages of solar panels exceeds 80%). As she explains, this makes the Chinese system somewhat paradoxical as it is at the same time incredibly decentralized but also incredibly effective at mobilizing the country for centrally-decided objectives, in fact she goes as far as comparing this effectiveness to the country being in a constant state of “wartime mobilization”. An interesting comparison would be if you had all the countries in North America, the EU and North Africa (altogether roughly the population of China) all united under a common leadership deciding on common objectives and on the career path of all these countries’ officials, based on how well they achieve these objectives in their respective countries. We’re seeing this system being mobilized in its full strength today on leading edge semiconductors after US sanctions, and this is why these sanctions will undoubtedly ultimately prove so self-defeating: once the Chinese “wartime mobilization” machine is given an objective - and you can be sure this objective is prioritized very highly - the fight is essentially over, you can consider it done. Once you have hundreds of thousands of PhDs, companies and officials all at the same time competing and working within the same broad “China Inc” roof to make something happen, it will ultimately get done. If you want China NOT to develop a technology, the very last thing you want is to make them mobilize the full strength of the machine on it. With the sanctions the U.S. effectively told China: “please we beg you, do dedicate your formidable economic mobilization power to becoming a semiconductors powerhouse as fast as possible” 🤦 Another particularity of the system that Keyu Jin highlights - and I’ll end on this - is that this system also allows China to “allocate losses to certain groups of people, interest groups and sectors” in order to “enact system-level changes'', something she says is “very difficult for other governments with more political constraints to do”. For instance we’re seeing this play out in real-time with the real-estate industry: China recognized there was a housing bubble and Xi issued its “houses are for living in, not for speculation” directive. We’re since witnessing an engineered deflating of the bubble, ensuring to the extent possible that the losses are borne out by real estate developers and speculators, and not too much by society as a whole. This is part of the reason why China has never suffered a recession in the modern era: it does controlled demolition when necessary but tries to ensure it doesn’t suffer massive crises like we’ve repeatedly witnessed in the U.S. for instance. Of course no system is perfect. Weaknesses of the Chinese system include for instance local protectionism: there’s a perverse incentive for local officials to protect their local companies in order to give them a leg up vs companies from other provinces, which ultimately comes at the detriment of everyone. Another weakness is corruption, a sempiternal problem in China, where local officials - who are extremely powerful due to the nature of the system - will decide that getting promoted isn’t incentive enough and will try to cash in on their position of power. Cracking down on this is also a key remit of the central government and of course one of the major initiatives of Xi since he came to power. Lastly, another clear weakness is obviously that everything ultimately relies on the wisdom of what the system gets mobilized for, on the wisdom of these broader objectives coming from the central government. If they’re ill-thought, you effectively have a whole country working towards the wrong objectives… On this we’re often told that this problem doesn’t happen in countries where what the economy works towards is set more organically by the “invisible hand of the market” but if you think about it, it actually happens just the same as the “invisible hand of the market” actually equates “what’s good for shareholders” and what’s good for shareholders isn’t exactly always a perfect proxy for what’s good for society, to say the least... For instance it’s absolutely insane that we’ve just had 2-3 generations in the West where the best and brightest went to work for the finance industry to engineer ever more convoluted schemes to make money out of nothing, simply because it’s insanely profitable to do so. Anyone looking at this rationally can see it’s not exactly the best use of our precious human resources as a society… So all things considered, if I had to choose I’d much rather have our broad societal objectives set by human beings rather than by the theoretical concept of “what makes the most money deserves the most focus”. And as it turns out the Chinese system actually fares decently well against capitalism: human beings aren’t evidently too bad at deciding what human beings should work on if they’re being thoughtful and strategic about it.
5:28

I find this explanation of the Chinese system by Prof Keyu Jin (in a recent lecture at Harvard’s Fairbank center) absolutely fascinating. Keyu Jin is a professor of economics at LSE (London School of Economics) and serves on the board of companies like Credit Suisse. She’s also the daughter of Jin Liqun, former Vice Minister of finance of China so she’s a rare West-based academic (maybe even the only one) who actually has insight into the Chinese system from the inside. Essentially what she’s explaining is that a key reason why China was so successful economically is because of its decentralized nature, which creates two mutually compounding loops of competition, as opposed to one loop in the West. What does that mean? Well, contrary to popular belief that imagines China as being this centrally planned economy where almost everything is decided in Beijing, the inverse is actually true: China is actually one of the most decentralized countries in the world. To illustrate this, a metric that’s always amazed me is the fact that in China local governments (provinces, cities, villages, etc.) control a crazy 85% of the country's expenditures. On average that same metric for OECD countries is 33% (as in 64% of the expenditures are controlled at the federal/national level to China’s 15%). In the US for instance, which is already more decentralized than most given it’s a federation with states, only 45% of the country’s expenditures happen at the state and local level: almost twice less than in China! The effect of this, as Keyu Jin explains, is that provinces and larger municipalities in China have an immense degree of autonomy over the way they run their respective economies and fiercely compete with each other. This is the first loop. And then of course the second loop is that you have companies competing with each other in the market. As a result what constantly evolves in China is not only companies themselves but the environment in which they evolve: you constantly have this or that province running a new policy that proves very effective, making them gain an advantage vs other localities, initiative which is then copied by other localities. This makes the economic environment incredibly dynamic as it allows the state to move in unison with the economy, as opposed to slowing it down as is often the case in other countries. So what’s the role of the central government in all this? The key role, Keyu Jin argues, is setting broad objectives as well as personal management and promotion. And this is what makes the whole system work as therein lies the incentive for localities to compete with each other: because local officials know that if they do a better job than their peers, they’re on track for promotion by the central government. In “China Inc”, the central government is the board of directors and HR, presiding over an army of local CEOs with immense degrees of autonomy over their own “companies”. Keyu Jin gives the example of the solar industry. There was at some point (around 2005) a directive by the central government to develop the solar industry. The graph she shares in her talk is incredible: within a few years you had solar companies as well as patents related to research on solar technology pop up literally everywhere in China. With the result we all know about today: China today completely dominates the solar industry and solar technology (according to the International Energy Agency China's share in all the manufacturing stages of solar panels exceeds 80%). As she explains, this makes the Chinese system somewhat paradoxical as it is at the same time incredibly decentralized but also incredibly effective at mobilizing the country for centrally-decided objectives, in fact she goes as far as comparing this effectiveness to the country being in a constant state of “wartime mobilization”. An interesting comparison would be if you had all the countries in North America, the EU and North Africa (altogether roughly the population of China) all united under a common leadership deciding on common objectives and on the career path of all these countries’ officials, based on how well they achieve these objectives in their respective countries. We’re seeing this system being mobilized in its full strength today on leading edge semiconductors after US sanctions, and this is why these sanctions will undoubtedly ultimately prove so self-defeating: once the Chinese “wartime mobilization” machine is given an objective - and you can be sure this objective is prioritized very highly - the fight is essentially over, you can consider it done. Once you have hundreds of thousands of PhDs, companies and officials all at the same time competing and working within the same broad “China Inc” roof to make something happen, it will ultimately get done. If you want China NOT to develop a technology, the very last thing you want is to make them mobilize the full strength of the machine on it. With the sanctions the U.S. effectively told China: “please we beg you, do dedicate your formidable economic mobilization power to becoming a semiconductors powerhouse as fast as possible” 🤦 Another particularity of the system that Keyu Jin highlights - and I’ll end on this - is that this system also allows China to “allocate losses to certain groups of people, interest groups and sectors” in order to “enact system-level changes'', something she says is “very difficult for other governments with more political constraints to do”. For instance we’re seeing this play out in real-time with the real-estate industry: China recognized there was a housing bubble and Xi issued its “houses are for living in, not for speculation” directive. We’re since witnessing an engineered deflating of the bubble, ensuring to the extent possible that the losses are borne out by real estate developers and speculators, and not too much by society as a whole. This is part of the reason why China has never suffered a recession in the modern era: it does controlled demolition when necessary but tries to ensure it doesn’t suffer massive crises like we’ve repeatedly witnessed in the U.S. for instance. Of course no system is perfect. Weaknesses of the Chinese system include for instance local protectionism: there’s a perverse incentive for local officials to protect their local companies in order to give them a leg up vs companies from other provinces, which ultimately comes at the detriment of everyone. Another weakness is corruption, a sempiternal problem in China, where local officials - who are extremely powerful due to the nature of the system - will decide that getting promoted isn’t incentive enough and will try to cash in on their position of power. Cracking down on this is also a key remit of the central government and of course one of the major initiatives of Xi since he came to power. Lastly, another clear weakness is obviously that everything ultimately relies on the wisdom of what the system gets mobilized for, on the wisdom of these broader objectives coming from the central government. If they’re ill-thought, you effectively have a whole country working towards the wrong objectives… On this we’re often told that this problem doesn’t happen in countries where what the economy works towards is set more organically by the “invisible hand of the market” but if you think about it, it actually happens just the same as the “invisible hand of the market” actually equates “what’s good for shareholders” and what’s good for shareholders isn’t exactly always a perfect proxy for what’s good for society, to say the least... For instance it’s absolutely insane that we’ve just had 2-3 generations in the West where the best and brightest went to work for the finance industry to engineer ever more convoluted schemes to make money out of nothing, simply because it’s insanely profitable to do so. Anyone looking at this rationally can see it’s not exactly the best use of our precious human resources as a society… So all things considered, if I had to choose I’d much rather have our broad societal objectives set by human beings rather than by the theoretical concept of “what makes the most money deserves the most focus”. And as it turns out the Chinese system actually fares decently well against capitalism: human beings aren’t evidently too bad at deciding what human beings should work on if they’re being thoughtful and strategic about it.

Arnaud Bertrand

193,688 views • 2 years ago