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We’ve built a Smart GTM Agent! Your AI-powered growth co-pilot that generates company research, competitor insights, GTM playbooks, and distribution strategies - all in minutes, not weeks. Features: → auto-generated profiles, competitor analysis & funding insights → target market, ICP, messaging, pricing, growth strategy → distribution partners, sales +...

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Mr. Ånand

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Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi
5:34

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi

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We’re thrilled to share that Outset has raised $17M in Series A funding, led by 8VC to help companies better understand their customers. When michael hess and I founded Outset, we set out to solve a critical problem: truly understanding your customer is painfully slow, expensive, and at times, impossible. It shouldn’t be. We believed AI could change this by focusing on the human side of AI. So we built the world’s first AI-interviewer. An AI agent that asked questions, listened, and learned from real humans. By removing the barriers of traditional research and allowing companies to have deeper conversations with more customers more quickly, we could help teams infuse their products with genuine human understanding at a speed and scale previously impossible. So what was the moment we knew we were onto something? When WeightWatchers ran that first pilot back in early 2023. Participants shared incredible amounts of depth, all with the world’s first AI-interviewer. We got a call the next day from their head of research: "You built a thing. We want that thing. How do I pay you for it?" That validation has turned into explosive growth. We've doubled our revenue in just the last 4 months alone, with >50 enterprise customers like Nestlé, Microsoft, and WeightWatchers trusting us to unlock deeper customer insights. All of this built on the backs of a truly special early team with 0 employee turnover. This funding round was led by 8VC, with participation from an incredible group, including: Future Back Ventures by Bain & Company, Y Combinator / Garry Tan, Adverb Ventures /April Underwood / Jessica Verrilli ), Alt Capital / Jack Altman , Rebel Fund, Genius Ventures, Ritual Capital, and many more. So what's next? We're scaling our engineering and go-to-market teams to build the world's most powerful agentic customer research platform. All focused on one mission: helping companies better understand humans. To our customers, investors, and team - thank you for believing in this vision. Now check out the video to see Outset in action, testing the video itself with real people!
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We’re thrilled to share that Outset has raised $17M in Series A funding, led by 8VC to help companies better understand their customers. When michael hess and I founded Outset, we set out to solve a critical problem: truly understanding your customer is painfully slow, expensive, and at times, impossible. It shouldn’t be. We believed AI could change this by focusing on the human side of AI. So we built the world’s first AI-interviewer. An AI agent that asked questions, listened, and learned from real humans. By removing the barriers of traditional research and allowing companies to have deeper conversations with more customers more quickly, we could help teams infuse their products with genuine human understanding at a speed and scale previously impossible. So what was the moment we knew we were onto something? When WeightWatchers ran that first pilot back in early 2023. Participants shared incredible amounts of depth, all with the world’s first AI-interviewer. We got a call the next day from their head of research: "You built a thing. We want that thing. How do I pay you for it?" That validation has turned into explosive growth. We've doubled our revenue in just the last 4 months alone, with >50 enterprise customers like Nestlé, Microsoft, and WeightWatchers trusting us to unlock deeper customer insights. All of this built on the backs of a truly special early team with 0 employee turnover. This funding round was led by 8VC, with participation from an incredible group, including: Future Back Ventures by Bain & Company, Y Combinator / Garry Tan, Adverb Ventures /April Underwood / Jessica Verrilli ), Alt Capital / Jack Altman , Rebel Fund, Genius Ventures, Ritual Capital, and many more. So what's next? We're scaling our engineering and go-to-market teams to build the world's most powerful agentic customer research platform. All focused on one mission: helping companies better understand humans. To our customers, investors, and team - thank you for believing in this vision. Now check out the video to see Outset in action, testing the video itself with real people!

Aaron Cannon

34,386 Aufrufe • vor 1 Jahr

Nebius will be a trillion dollar company (Save this). The neocloud market, purpose-built AI cloud infrastructure, separate from legacy hyperscalers generated roughly $25 billion in revenue in 2025, up 223% year over year. Synergy Research projects it will approach $400 billion by 2031, compounding at 58% annually one of the fastest sustained growth rates ever recorded for an infrastructure category of this scale. The CEO's explanation for why they win is worth understanding in detail. GPU compute is scarce and that part everyone knows but Nebius is not simply renting GPUs by the hour and marking them up, which is what most neocloud imitators do. They have built their own physical capacity for inference, optimized the full technology stack from the software layer all the way down to the rack hardware and recently acquired a company called Agen specifically to push inference latency even lower and throughput even higher. The CEO frames the core problem directly that in 2026, every product you build is powered by tokens, AI intelligence and while you can get those tokens from OpenAI or Anthropic via a simple API call, the moment you want to run open source models, specialized vertical models, or anything other than the two dominant frontier labs, you run into a wall. You can download the weights from Hugging Face and assemble the pieces. But getting those workloads to run at scale, at the economics you need, with the reliability your product requires, is an extraordinarily complex engineering challenge that most companies cannot staff or afford to solve in-house. That is the problem Nebius is solving, and that is why their inference product called Token Factory exists. The financial results are among the most dramatic growth numbers reported by any public company this year. In Q1 2026, Nebius posted $399 million in revenue, a 684% increase from the same quarter a year earlier. In the span of twelve months, the company swung from a $104 million net loss to $621 million in net income. Cash from operations went from negative $184 million to positive $2.26 billion in the same period meaning this is not growth funded by burning investor capital, it is growth that is now generating its own fuel. For the full year 2026, Nebius is guiding for an annualized revenue run rate of $7 billion to $9 billion, with pipeline creation tracking to surpass $4 billion. The contracted backlog sits at $49 billion, anchored by a $27 billion agreement with Meta, a deal worth up to $19.4 billion with Microsoft, and a public endorsement from Jensen Huang at NVIDIA's GTC conference in 2026. The current market cap is approximately $56 billion. A company with $7 to $9 billion in annualized revenue, growing at 684%, turning cash-flow positive, sitting on $49 billion in contracted backlog, operating in a market compounding at 58% annually toward $400 billion, that company has a credible path to 20x from its current valuation if execution holds. That is the trillion dollar case, and it does not require any heroic assumptions and it requires Nebius to keep doing what it is already demonstrably doing. Milk Road Pro called this one early. Our analysts added Nebius to the portfolio when it was still flying under the radar, and we are sitting on a massive gain on that position right now. If you want to see what else we are building conviction on before the rest of the market catches up, come join us at Milk Road Pro using the link below!
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Nebius will be a trillion dollar company (Save this). The neocloud market, purpose-built AI cloud infrastructure, separate from legacy hyperscalers generated roughly $25 billion in revenue in 2025, up 223% year over year. Synergy Research projects it will approach $400 billion by 2031, compounding at 58% annually one of the fastest sustained growth rates ever recorded for an infrastructure category of this scale. The CEO's explanation for why they win is worth understanding in detail. GPU compute is scarce and that part everyone knows but Nebius is not simply renting GPUs by the hour and marking them up, which is what most neocloud imitators do. They have built their own physical capacity for inference, optimized the full technology stack from the software layer all the way down to the rack hardware and recently acquired a company called Agen specifically to push inference latency even lower and throughput even higher. The CEO frames the core problem directly that in 2026, every product you build is powered by tokens, AI intelligence and while you can get those tokens from OpenAI or Anthropic via a simple API call, the moment you want to run open source models, specialized vertical models, or anything other than the two dominant frontier labs, you run into a wall. You can download the weights from Hugging Face and assemble the pieces. But getting those workloads to run at scale, at the economics you need, with the reliability your product requires, is an extraordinarily complex engineering challenge that most companies cannot staff or afford to solve in-house. That is the problem Nebius is solving, and that is why their inference product called Token Factory exists. The financial results are among the most dramatic growth numbers reported by any public company this year. In Q1 2026, Nebius posted $399 million in revenue, a 684% increase from the same quarter a year earlier. In the span of twelve months, the company swung from a $104 million net loss to $621 million in net income. Cash from operations went from negative $184 million to positive $2.26 billion in the same period meaning this is not growth funded by burning investor capital, it is growth that is now generating its own fuel. For the full year 2026, Nebius is guiding for an annualized revenue run rate of $7 billion to $9 billion, with pipeline creation tracking to surpass $4 billion. The contracted backlog sits at $49 billion, anchored by a $27 billion agreement with Meta, a deal worth up to $19.4 billion with Microsoft, and a public endorsement from Jensen Huang at NVIDIA's GTC conference in 2026. The current market cap is approximately $56 billion. A company with $7 to $9 billion in annualized revenue, growing at 684%, turning cash-flow positive, sitting on $49 billion in contracted backlog, operating in a market compounding at 58% annually toward $400 billion, that company has a credible path to 20x from its current valuation if execution holds. That is the trillion dollar case, and it does not require any heroic assumptions and it requires Nebius to keep doing what it is already demonstrably doing. Milk Road Pro called this one early. Our analysts added Nebius to the portfolio when it was still flying under the radar, and we are sitting on a massive gain on that position right now. If you want to see what else we are building conviction on before the rest of the market catches up, come join us at Milk Road Pro using the link below!

Milk Road AI

28,622 Aufrufe • vor 2 Monaten

new chapter begins: a terminal for the agentic future, built on blockchain, powered by AI. This is our marketplace—a glimpse of what AGI will mean for crypto. Today, we launch 3 agents—Image Generation, Token Swap Agent, & Blockchain Tax Estimate Agent—out of hundreds to come. We see an agentic future where AI guides every step: buying online, managing finances, transacting globally. FOMO’s here to make that real, with experts at your side. Our Model Context Protocol (MCP) ties it together—agents talking, reasoning, scaling across crypto and DeFi. It’s orchestration with a brain, evolving daily. FOMO’s not just building tools; we’re pushing intelligent automation into blockchain’s core. Our Model Context Protocol (MCP) is the backbone. Think of it as a conductor for AI agents—each runs its own logic (workflows, API calls, LLMs), but MCP syncs them on-chain. Agents share context via a lightweight event bus, logged to a blockchain ledger. Agent A (say, Market Analysis) pulls stock data, flags trends. Agent B (Email Sales) reads that, drafts outreach—both talk through MCP’s orchestration layer. We use Web3 hooks to settle fees or split revenue, all transparent. It’s messy, but it scales. Under the hood: MCP leans on a pub-sub model—agents publish tasks, others subscribe. We’re training them with RL loops to optimize gas costs and response times. Goal? A self-tuning swarm of agents reasoning over DeFi, NFTs, whatever’s next. This is FOMO’s bet on AGI. Welcome to the new FOMO. We’re not just building tools—we’re wiring AI into crypto’s future, agent by agent. A leader in blockchain intelligence, starting here. Join us as we push the boundaries.
1:16

new chapter begins: a terminal for the agentic future, built on blockchain, powered by AI. This is our marketplace—a glimpse of what AGI will mean for crypto. Today, we launch 3 agents—Image Generation, Token Swap Agent, & Blockchain Tax Estimate Agent—out of hundreds to come. We see an agentic future where AI guides every step: buying online, managing finances, transacting globally. FOMO’s here to make that real, with experts at your side. Our Model Context Protocol (MCP) ties it together—agents talking, reasoning, scaling across crypto and DeFi. It’s orchestration with a brain, evolving daily. FOMO’s not just building tools; we’re pushing intelligent automation into blockchain’s core. Our Model Context Protocol (MCP) is the backbone. Think of it as a conductor for AI agents—each runs its own logic (workflows, API calls, LLMs), but MCP syncs them on-chain. Agents share context via a lightweight event bus, logged to a blockchain ledger. Agent A (say, Market Analysis) pulls stock data, flags trends. Agent B (Email Sales) reads that, drafts outreach—both talk through MCP’s orchestration layer. We use Web3 hooks to settle fees or split revenue, all transparent. It’s messy, but it scales. Under the hood: MCP leans on a pub-sub model—agents publish tasks, others subscribe. We’re training them with RL loops to optimize gas costs and response times. Goal? A self-tuning swarm of agents reasoning over DeFi, NFTs, whatever’s next. This is FOMO’s bet on AGI. Welcome to the new FOMO. We’re not just building tools—we’re wiring AI into crypto’s future, agent by agent. A leader in blockchain intelligence, starting here. Join us as we push the boundaries.

FOMO

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I've been with Firstock since day one, and we’ve worked closely with Vikram, the founder. In 2023, Firstock set out to build the fastest, most user-friendly trading app completely in-house. After countless meetings, iterations, and late nights, I’m proud to present the latest version of the Firstock web and mobile app—designed to transform your trading experience. 🚀 You can open your account here: (Open your account today for exciting offers) Experience Demo Account: Zero Hassle. Zero Charges. Maximum Power. With Firstock, you get: * ₹0 Delivery Charges * ₹0 API Fees * ₹0 Account Opening Charges * ₹0 Pledge Charges * ₹0 AMC * ₹0 Pay-in Charges * Just ₹20/order for F&O trades What do we have? For Investors: Let’s begin with what Firstock offers to investors: 1. Fundamentals at Your Fingertips Access detailed stock charts and fundamental data directly within the app—no need to go elsewhere. 2. Holding Performance Overview Track your portfolio's performance with full visibility into all corporate actions affecting your holdings. 3. Complete Holding Analysis Gain a holistic view of your portfolio through our intuitive holdings dashboard. 4. Instant Pledge for Instant Margin Need margin quickly? Instantly pledge your stocks and start trading within minutes—no delays. Confused about what to invest in? 5. Curated Investment Ideas Explore top-performing market movers, sectoral trends, and international ETFs to make informed investment choices. 6. Custom Screeners Build your own stock screeners to filter out investments that match your strategy and risk appetite. --- For Traders: Built by traders, for traders—our platform is designed to meet your high-speed, high-efficiency needs. 1. Sticky Orders & Bulk Slicing*l Place large orders with ease. Our bulk slicing and sticky order window make placing, modifying, and exiting large quantities seamless. 2. Options Strategy Builder Design strategies directly from the option chain, view the payoff graph, and execute instantly. 3. Live Position Analysis Analyze and tweak your open positions directly from the position book—no switching screens. 4. Custom Strategy Execution Save your favorite strategies and execute them when the timing is right. 5. Advanced Option Analytics View real-time data like OI, Max Pain, and synthetic futures to make quicker, smarter trading decisions. 6. Pre-Built Straddle and Strangle Tools Trade straddles or strangles effortlessly using our dedicated strategy screens. --- Now on Mobile: Enjoy the same powerful features on our brand-new mobile app—designed for ease, speed, and convenience. --- Try it Today: Experience the platform with our demo—explore all features before you commit. Explore Demo Account: You can open your account here: --- This is just the beginning. We’re continuously building features that will redefine the way you trade. Have suggestions or feedback? I’d love to hear from you personally. Let’s grow together.
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I've been with Firstock since day one, and we’ve worked closely with Vikram, the founder. In 2023, Firstock set out to build the fastest, most user-friendly trading app completely in-house. After countless meetings, iterations, and late nights, I’m proud to present the latest version of the Firstock web and mobile app—designed to transform your trading experience. 🚀 You can open your account here: (Open your account today for exciting offers) Experience Demo Account: Zero Hassle. Zero Charges. Maximum Power. With Firstock, you get: * ₹0 Delivery Charges * ₹0 API Fees * ₹0 Account Opening Charges * ₹0 Pledge Charges * ₹0 AMC * ₹0 Pay-in Charges * Just ₹20/order for F&O trades What do we have? For Investors: Let’s begin with what Firstock offers to investors: 1. Fundamentals at Your Fingertips Access detailed stock charts and fundamental data directly within the app—no need to go elsewhere. 2. Holding Performance Overview Track your portfolio's performance with full visibility into all corporate actions affecting your holdings. 3. Complete Holding Analysis Gain a holistic view of your portfolio through our intuitive holdings dashboard. 4. Instant Pledge for Instant Margin Need margin quickly? Instantly pledge your stocks and start trading within minutes—no delays. Confused about what to invest in? 5. Curated Investment Ideas Explore top-performing market movers, sectoral trends, and international ETFs to make informed investment choices. 6. Custom Screeners Build your own stock screeners to filter out investments that match your strategy and risk appetite. --- For Traders: Built by traders, for traders—our platform is designed to meet your high-speed, high-efficiency needs. 1. Sticky Orders & Bulk Slicing*l Place large orders with ease. Our bulk slicing and sticky order window make placing, modifying, and exiting large quantities seamless. 2. Options Strategy Builder Design strategies directly from the option chain, view the payoff graph, and execute instantly. 3. Live Position Analysis Analyze and tweak your open positions directly from the position book—no switching screens. 4. Custom Strategy Execution Save your favorite strategies and execute them when the timing is right. 5. Advanced Option Analytics View real-time data like OI, Max Pain, and synthetic futures to make quicker, smarter trading decisions. 6. Pre-Built Straddle and Strangle Tools Trade straddles or strangles effortlessly using our dedicated strategy screens. --- Now on Mobile: Enjoy the same powerful features on our brand-new mobile app—designed for ease, speed, and convenience. --- Try it Today: Experience the platform with our demo—explore all features before you commit. Explore Demo Account: You can open your account here: --- This is just the beginning. We’re continuously building features that will redefine the way you trade. Have suggestions or feedback? I’d love to hear from you personally. Let’s grow together.

Saketh R

15,618 Aufrufe • vor 1 Jahr

Nebius is one of the most undervalued AI infrastructure companies in the public markets right now (Save this). Leopold Aschenbrenner, the former OpenAI researcher who wrote the 165-page essay predicting AGI within this decade and then launched the $13.7 billion Situational Awareness Fund around that thesis just filed a 13G disclosing a 5.6% stake in Nebius, representing 12.41 million Class A shares. This is the man whose entire investment framework is built on one core conviction, AI will advance faster than anyone expects, and the binding constraint will not be algorithms or model architectures, it will be physical computing infrastructure, data center capacity, and energy. Now look at what Nebius actually is and why this conviction is justified by the numbers alone. Nebius is a GPU native AI cloud platform, a neocloud built from the ground up specifically for AI training and inference workloads, founded by Arkady Volozh, the former CEO of Yandex who divested all non-Russian assets and left Russia in direct opposition to Putin before relisting the company on Nasdaq. In Q1 2026, Nebius reported $399 million in revenue, a 684% increase year over year from just $50.9 million while also delivering EBITDA and adjusted EPS that beat consensus estimates by 43% and 50% respectively, in a quarter where analysts had already built in aggressive assumptions. The scale of the infrastructure buildout is what makes the valuation argument so compelling. Nebius has raised its contracted power capacity guidance to over 4 gigawatts for 2026, with a target of 5 gigawatts of AI computing capacity deployed by 2030, including multiple gigawatt-scale AI factories across the United States and Europe. The Finland campus coming soon to Lappeenranta will be 310 megawatts powered by low-carbon energy, making it one of the largest AI data centers in Europe, specifically located in a cold-climate, energy-stable region that dramatically reduces cooling costs and carbon intensity. The 2026 capacity is already effectively sold out according to management disclosures, which means every megawatt Nebius brings online has a revenue contract attached to it before the facility opens. The strategic backing validates the thesis at every level. NVIDIA committed a $2 billion strategic investment in Nebius by 2030, with the two companies co-developing an inference stack, implementing NVIDIA's GPU health monitoring systems, and deploying next-generation architectures including Rubin GPUs, Vera CPUs, and Bluefield storage systems meaning Nebius gets preferential access to the hardware that every other AI company is begging Jensen Huang for. Meta signed a $27 billion agreement with Nebius, with $12 billion in dedicated computing resources confirmed and up to $15 billion in additional capacity over the coming years. And Nebius just partnered with Bloom Energy on a $2.6 billion deal guaranteeing 328 megawatts of installed capacity through modular fuel cell systems behind the meter power that eliminates grid dependency and accelerates deployment timelines. The forward valuation math is where the undervaluation case becomes undeniable. Nebius is pricing in $3.5 billion in revenue for 2026 and $11 billion for 2027, which puts the forward price-to-sales ratio at 16.6 times for this year and just 5.3 times for next year for a company growing revenue at 684% year over year with sold out capacity, NVIDIA backing, a $27 billion Meta contract, and a path to 4+ gigawatts of contracted power. Milk Road has been positioned in Nebius and we believe the convergence of Leopold's conviction stake, NVIDIA's $2 billion endorsement, Meta's $27 billion commitment, and a physical infrastructure buildout that is sold out before it opens represents one of the highest-quality risk-reward setups in AI infrastructure today. Come join Milk Road Pro and get our full Nebius thesis including the exact framework we use to think about neocloud valuation, the power capacity math that determines when revenue accelerates, and every catalyst we are watching through 2027. Link in bio/below.
0:20

Nebius is one of the most undervalued AI infrastructure companies in the public markets right now (Save this). Leopold Aschenbrenner, the former OpenAI researcher who wrote the 165-page essay predicting AGI within this decade and then launched the $13.7 billion Situational Awareness Fund around that thesis just filed a 13G disclosing a 5.6% stake in Nebius, representing 12.41 million Class A shares. This is the man whose entire investment framework is built on one core conviction, AI will advance faster than anyone expects, and the binding constraint will not be algorithms or model architectures, it will be physical computing infrastructure, data center capacity, and energy. Now look at what Nebius actually is and why this conviction is justified by the numbers alone. Nebius is a GPU native AI cloud platform, a neocloud built from the ground up specifically for AI training and inference workloads, founded by Arkady Volozh, the former CEO of Yandex who divested all non-Russian assets and left Russia in direct opposition to Putin before relisting the company on Nasdaq. In Q1 2026, Nebius reported $399 million in revenue, a 684% increase year over year from just $50.9 million while also delivering EBITDA and adjusted EPS that beat consensus estimates by 43% and 50% respectively, in a quarter where analysts had already built in aggressive assumptions. The scale of the infrastructure buildout is what makes the valuation argument so compelling. Nebius has raised its contracted power capacity guidance to over 4 gigawatts for 2026, with a target of 5 gigawatts of AI computing capacity deployed by 2030, including multiple gigawatt-scale AI factories across the United States and Europe. The Finland campus coming soon to Lappeenranta will be 310 megawatts powered by low-carbon energy, making it one of the largest AI data centers in Europe, specifically located in a cold-climate, energy-stable region that dramatically reduces cooling costs and carbon intensity. The 2026 capacity is already effectively sold out according to management disclosures, which means every megawatt Nebius brings online has a revenue contract attached to it before the facility opens. The strategic backing validates the thesis at every level. NVIDIA committed a $2 billion strategic investment in Nebius by 2030, with the two companies co-developing an inference stack, implementing NVIDIA's GPU health monitoring systems, and deploying next-generation architectures including Rubin GPUs, Vera CPUs, and Bluefield storage systems meaning Nebius gets preferential access to the hardware that every other AI company is begging Jensen Huang for. Meta signed a $27 billion agreement with Nebius, with $12 billion in dedicated computing resources confirmed and up to $15 billion in additional capacity over the coming years. And Nebius just partnered with Bloom Energy on a $2.6 billion deal guaranteeing 328 megawatts of installed capacity through modular fuel cell systems behind the meter power that eliminates grid dependency and accelerates deployment timelines. The forward valuation math is where the undervaluation case becomes undeniable. Nebius is pricing in $3.5 billion in revenue for 2026 and $11 billion for 2027, which puts the forward price-to-sales ratio at 16.6 times for this year and just 5.3 times for next year for a company growing revenue at 684% year over year with sold out capacity, NVIDIA backing, a $27 billion Meta contract, and a path to 4+ gigawatts of contracted power. Milk Road has been positioned in Nebius and we believe the convergence of Leopold's conviction stake, NVIDIA's $2 billion endorsement, Meta's $27 billion commitment, and a physical infrastructure buildout that is sold out before it opens represents one of the highest-quality risk-reward setups in AI infrastructure today. Come join Milk Road Pro and get our full Nebius thesis including the exact framework we use to think about neocloud valuation, the power capacity math that determines when revenue accelerates, and every catalyst we are watching through 2027. Link in bio/below.

Milk Road AI

61,932 Aufrufe • vor 2 Monaten

Drew Bredvick compressed Vercel's sales team from 20 people to 2. And I think it's one of the best case studies in the history of AI and GTM. the problem: Sales development doesn't compound. Headcount does. Every additional SDR brings another salary, another ramp period, another personal definition of what "qualified" actually means. the solution: Drew built an AI agent that evaluates every inbound lead: researching the company, scoring intent, and routing only the credible opportunities to the sales team. Everything else is handled automatically. the result: Now two people, focused exclusively on edge cases and high-touch accounts handle the entire sales operation at the $10B company. Andddd the previous team wasn't let go. They were moved into "higher-value work" within the company. here's the play in six steps: 1. Shadow your best performer 2. Pull 90 days of historical data 3. Iterate until 95% agreement 4. Run in parallel with people 5. Get co-sign 6. Hand your top dogs the controller 1. shadow your best performer Sit next to your best SDR for a full day and document every decision: when they qualify, when they disqualify, every signal they check, every button they click. Drew found the real qualification criteria was not in process docs. Reps were checking LinkedIn profiles, scanning websites for tech stack indicators, and pattern-matching on how leads found Vercel. None of it was documented. 2. pull 90 days of historical data Export 90 days of contact form submissions with outcomes attached. Did they close? Ghost? Become a $500K whale? 3. iterate until 95% agreement Open any code editor with AI built in. Drop your CSV into a new project and start a conversation: "Look at this lead data. I'm going to give you a prompt to evaluate leads. Tell me if each one is qualified or not." Run this prompt against a batch. Compare the agent's calls to what actually happened—not what humans decided, but whether the lead converted. Find disagreements. Fix the prompt. Repeat. You're aiming for 95%+ agreement with historical outcomes. starter prompt: You are a lead qualification agent. For each lead, analyze the following signals and provide your reasoning BEFORE your decision: Company signals: website quality, tech stack, company stage, employee count Intent signals: how they found us, what they asked for, urgency indicators Fit signals: ICP match, use case alignment, budget indicators Structure your response as: REASONING: [Your analysis of each signal category] CONFIDENCE: [High/Medium/Low] DECISION: [Qualified/Not Qualified] NEXT ACTION: [Route to sales / Auto-respond / Request more info] Be conservative. You naturally want to qualify leads to make humans happy. Resist that urge. A false positive wastes sales time. A false negative just means we follow up later. 4. run in parallel with people Once the prompt works with historical data, prove it works live with your sales team: Here's what to track: - agreement rate: Agent vs. human decisions. - accuracy rate: Agent vs. actual outcomes. - processing time: Lead received → decision made. - confidence distribution: How often the agent is certain vs. uncertain - error log: When it got it wrong, and why. 5. get co-sign Drew started chatting with individual contributors. He got them to validate that the agent was making good calls. Then he partnered closely with the leader of the SDR team. He then let leaders of the sales team tweak qualification criteria, the leaders of the marketing team adjust scoring weights, and let leaders of the ops team define routing rules. b/c when leadership builds alongside you, they stop being gatekeepers and start being advocates. 6. hand your top dog the controller Flip the switch!! The agent processes every lead, makes a qualification decision, and even drafts the response. But a person reviews before anything goes out and has more time to check the genuinely f******* tough and weird cases. The system recommends; humans decide. The same loop should work In other parts of the org too: customer support triage, contract review, expense approvals, and even content moderation. Full playbook below w/ prompts and Drew's handholding. 👇
1:00:11

Drew Bredvick compressed Vercel's sales team from 20 people to 2. And I think it's one of the best case studies in the history of AI and GTM. the problem: Sales development doesn't compound. Headcount does. Every additional SDR brings another salary, another ramp period, another personal definition of what "qualified" actually means. the solution: Drew built an AI agent that evaluates every inbound lead: researching the company, scoring intent, and routing only the credible opportunities to the sales team. Everything else is handled automatically. the result: Now two people, focused exclusively on edge cases and high-touch accounts handle the entire sales operation at the $10B company. Andddd the previous team wasn't let go. They were moved into "higher-value work" within the company. here's the play in six steps: 1. Shadow your best performer 2. Pull 90 days of historical data 3. Iterate until 95% agreement 4. Run in parallel with people 5. Get co-sign 6. Hand your top dogs the controller 1. shadow your best performer Sit next to your best SDR for a full day and document every decision: when they qualify, when they disqualify, every signal they check, every button they click. Drew found the real qualification criteria was not in process docs. Reps were checking LinkedIn profiles, scanning websites for tech stack indicators, and pattern-matching on how leads found Vercel. None of it was documented. 2. pull 90 days of historical data Export 90 days of contact form submissions with outcomes attached. Did they close? Ghost? Become a $500K whale? 3. iterate until 95% agreement Open any code editor with AI built in. Drop your CSV into a new project and start a conversation: "Look at this lead data. I'm going to give you a prompt to evaluate leads. Tell me if each one is qualified or not." Run this prompt against a batch. Compare the agent's calls to what actually happened—not what humans decided, but whether the lead converted. Find disagreements. Fix the prompt. Repeat. You're aiming for 95%+ agreement with historical outcomes. starter prompt: You are a lead qualification agent. For each lead, analyze the following signals and provide your reasoning BEFORE your decision: Company signals: website quality, tech stack, company stage, employee count Intent signals: how they found us, what they asked for, urgency indicators Fit signals: ICP match, use case alignment, budget indicators Structure your response as: REASONING: [Your analysis of each signal category] CONFIDENCE: [High/Medium/Low] DECISION: [Qualified/Not Qualified] NEXT ACTION: [Route to sales / Auto-respond / Request more info] Be conservative. You naturally want to qualify leads to make humans happy. Resist that urge. A false positive wastes sales time. A false negative just means we follow up later. 4. run in parallel with people Once the prompt works with historical data, prove it works live with your sales team: Here's what to track: - agreement rate: Agent vs. human decisions. - accuracy rate: Agent vs. actual outcomes. - processing time: Lead received → decision made. - confidence distribution: How often the agent is certain vs. uncertain - error log: When it got it wrong, and why. 5. get co-sign Drew started chatting with individual contributors. He got them to validate that the agent was making good calls. Then he partnered closely with the leader of the SDR team. He then let leaders of the sales team tweak qualification criteria, the leaders of the marketing team adjust scoring weights, and let leaders of the ops team define routing rules. b/c when leadership builds alongside you, they stop being gatekeepers and start being advocates. 6. hand your top dog the controller Flip the switch!! The agent processes every lead, makes a qualification decision, and even drafts the response. But a person reviews before anything goes out and has more time to check the genuinely f******* tough and weird cases. The system recommends; humans decide. The same loop should work In other parts of the org too: customer support triage, contract review, expense approvals, and even content moderation. Full playbook below w/ prompts and Drew's handholding. 👇

Alex Lieberman

81,801 Aufrufe • vor 6 Monaten

Alistair Croll (Alistair Croll) is the co-author of the best-selling book Lean Analytics, and a longtime product manager, entrepreneur, and startup advisor. He also profoundly changed my life over a decade ago by convincing me to start a company, funding it, and helping us exit to Airbnb. More recently, Alistar has been running events such as O’Reilly’s Strata, Cloud Connect, FWD50, and Startupfest, and is about to release a book with co-author Emily Ross that I'm very excited about: Just Evil Enough: The Subversive Marketing Handbook. In our conversation, we discuss: 🔸 The role of subversive marketing strategies in most startups’ growth 🔸 11 specific subversive tactics 🔸 Examples of companies like Netflix, Airbnb, and Tesla using subversive tactics early on 🔸 A framework for scanning your market for opportunities 🔸 The importance of finding your “zero-day marketing exploit” 🔸 How to apply these tactics ethically without actually being evil 🔸 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: 🏆 Hex — Helping teams ask and answer data questions by working together: 🏆 Vanta — Automate compliance. Simplify security: Some key takeaways: 1. Most startups focus too much on product features and not enough on distribution and go-to-market strategy. To succeed, you need to find an “unfair advantage” in how you capture attention and turn it into profitable demand. This is what Alistair and Emily call “just evil enough.” 2. Stop relying on generic growth hacks. Instead, adopt a mindset focused on “zero-day” marketing exploits—innovative, subversive tactics that are only now possible. Look for unconventional methods to change the game in your favor. 3. Encourage your team to embrace “disagreeable” thinking, where you challenge the status quo rather than following the herd. For example, aim for your ideas to elicit at least 50% disapproval—this can be a great indicator that you’re pushing boundaries and generating true interest. If everyone loves your idea, it might be too safe. 4. Alistair outlines 11 key tactics to grab people’s attention: a. Turning bugs into features (e.g. Salesforce positioning their limited feature set as simplicity) b. Buyer upgrade (e.g. selling to insurance companies instead of city councils for bridge inspections) c. Access (e.g. Bumble’s founder leveraging her sorority connections for initial growth) d. Bait and switch (e.g. Tupperware using dinner parties to sell products in postwar America) e. Combination (e.g. Kraft combining powdered cheese with macaroni or 1-800 Mattress including mattress removal with a mattress purchase) f. Arbitrage (e.g. early social media growth hacks using API data) g. Aggregation (e.g. Busbud aggregating bus schedules to become the default destination) h. Reframing (e.g. Tom’s of Maine positioning toothpaste as natural/unfluoridated) i. Regulation (e.g. organ donor opt-out vs. opt-in policies in Germany vs. Austria) j. Misappropriation (e.g. Netflix using the postal service as a high-latency network) k. Sliding the window (e.g. normalizing previously taboo topics or behaviors) 5. To apply these tactics: a. Spend time understanding your industry’s system and status quo b. Temporarily think like a “supervillain” to brainstorm novel approaches c. Use techniques like pre-mortems, counterfactuals, and embracing absurdity d. Look for ways to change your value chain or industry dynamics
1:08

Alistair Croll (Alistair Croll) is the co-author of the best-selling book Lean Analytics, and a longtime product manager, entrepreneur, and startup advisor. He also profoundly changed my life over a decade ago by convincing me to start a company, funding it, and helping us exit to Airbnb. More recently, Alistar has been running events such as O’Reilly’s Strata, Cloud Connect, FWD50, and Startupfest, and is about to release a book with co-author Emily Ross that I'm very excited about: Just Evil Enough: The Subversive Marketing Handbook. In our conversation, we discuss: 🔸 The role of subversive marketing strategies in most startups’ growth 🔸 11 specific subversive tactics 🔸 Examples of companies like Netflix, Airbnb, and Tesla using subversive tactics early on 🔸 A framework for scanning your market for opportunities 🔸 The importance of finding your “zero-day marketing exploit” 🔸 How to apply these tactics ethically without actually being evil 🔸 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: 🏆 Hex — Helping teams ask and answer data questions by working together: 🏆 Vanta — Automate compliance. Simplify security: Some key takeaways: 1. Most startups focus too much on product features and not enough on distribution and go-to-market strategy. To succeed, you need to find an “unfair advantage” in how you capture attention and turn it into profitable demand. This is what Alistair and Emily call “just evil enough.” 2. Stop relying on generic growth hacks. Instead, adopt a mindset focused on “zero-day” marketing exploits—innovative, subversive tactics that are only now possible. Look for unconventional methods to change the game in your favor. 3. Encourage your team to embrace “disagreeable” thinking, where you challenge the status quo rather than following the herd. For example, aim for your ideas to elicit at least 50% disapproval—this can be a great indicator that you’re pushing boundaries and generating true interest. If everyone loves your idea, it might be too safe. 4. Alistair outlines 11 key tactics to grab people’s attention: a. Turning bugs into features (e.g. Salesforce positioning their limited feature set as simplicity) b. Buyer upgrade (e.g. selling to insurance companies instead of city councils for bridge inspections) c. Access (e.g. Bumble’s founder leveraging her sorority connections for initial growth) d. Bait and switch (e.g. Tupperware using dinner parties to sell products in postwar America) e. Combination (e.g. Kraft combining powdered cheese with macaroni or 1-800 Mattress including mattress removal with a mattress purchase) f. Arbitrage (e.g. early social media growth hacks using API data) g. Aggregation (e.g. Busbud aggregating bus schedules to become the default destination) h. Reframing (e.g. Tom’s of Maine positioning toothpaste as natural/unfluoridated) i. Regulation (e.g. organ donor opt-out vs. opt-in policies in Germany vs. Austria) j. Misappropriation (e.g. Netflix using the postal service as a high-latency network) k. Sliding the window (e.g. normalizing previously taboo topics or behaviors) 5. To apply these tactics: a. Spend time understanding your industry’s system and status quo b. Temporarily think like a “supervillain” to brainstorm novel approaches c. Use techniques like pre-mortems, counterfactuals, and embracing absurdity d. Look for ways to change your value chain or industry dynamics

Lenny Rachitsky

39,965 Aufrufe • vor 1 Jahr

HERMES AGENT SUPPORTS 7 TYPES OF AI AGENTS. EACH ONE TAKES LESS THAN 90 SECONDS TO SET UP. MOST PEOPLE ONLY BUILD THE FIRST ONE. HERE ARE ALL SEVEN AND WHEN TO USE EACH. 1. BASIC AGENT WITH TOOLS your agent with access to terminal, browser, file system, web search, and calendar. it plans and executes tasks on its own. this is what you get on day one. "find flights to Lisbon under $400" "check my calendar and flag conflicts" "search the web for competitor pricing" set in Desktop app / Dashboard: Tools → enable what you need. when to use: single tasks that need tool access. 2. AGENT WITH MCP SERVERS connect your agent to external services. Notion, Google Drive, GitHub, Slack, databases, APIs, any MCP-compatible service. the agent doesn't scrape these services. it interacts through structured APIs. reads your Notion pages. creates GitHub issues. queries your database. sends Slack messages. set in Desktop app / Dashboard: MCP → Add Server. when to use: your workflow lives across multiple platforms. 3. SEQUENTIAL AGENTS (pipeline) one agent finishes. passes output to the next. assembly line for AI. agent 1: scans inbox for leads. agent 2: qualifies leads against criteria. agent 3: drafts outreach emails. in Hermes: cron jobs with wakeAgent gates. agent 1 writes output to a file. agent 2 wakes only when that file has new data. agent 3 wakes when agent 2 is done. each agent = a separate profile with its own model. when to use: multi-step workflows where each step depends on the previous one finishing. 4. PARALLEL EXECUTION AGENTS multiple agents working at the same time. results merge when all finish. "research these 5 competitors in parallel" in Hermes: delegate_task with batch mode. up to 3 sub-agents running in parallel by default. each gets its own clean context. only summaries return to the parent. delegation: model: "deepseek/deepseek-v4" children run cheap. parent synthesizes. when to use: independent tasks that don't depend on each other. research, data gathering, analysis. 5. AGENTS WITH ROUTERS conditions that send tasks down different paths based on the input. "if sales email → SDR profile. if support ticket → support profile. if calendar invite → EA profile." in Hermes: Kanban decompose. the decomposer reads profile descriptions and routes each task to the best-fit agent. or: Chief of Staff profile that triages and assigns to other profiles. when to use: incoming work that needs different specialists based on type. 6. HUMAN IN THE LOOP the agent does the work. asks for your approval before executing. "I drafted this email. approve before I send?" "this command will delete 3 files. proceed?" in Hermes: approvals.mode: manual (default). every dangerous action needs your confirmation. 60-second timeout. fails closed. or smart mode: LLM assesses risk. safe actions auto-approved. dangerous ones ask you. uncertain ones escalate. when to use: tasks where a mistake has real consequences. emails, deployments, financial transactions, public posts. 7. DYNAMIC SUB-AGENT SPAWNING your main agent realizes it needs help and spawns specialized sub-agents on the fly. "build this feature" → parent delegates: → sub-agent 1: research the API docs → sub-agent 2: write the code → sub-agent 3: write the tests in Hermes: delegate_task with role: orchestrator. raise max_spawn_depth for nested delegation. delegation: max_spawn_depth: 2 orchestrator_enabled: true depth 2 with concurrency 3 = up to 9 parallel workers. each level multiplies the spend. raise depth only when you need multi-level trees. when to use: complex tasks where the agent discovers what help it needs during execution. THE PROGRESSION: start with 1 (tools) and 6 (approvals). add 2 (MCP) when you need external services. add 4 (parallel) when tasks take too long one at a time. add 3 (sequential) when you build multi-step pipelines. add 5 (routing) when you run multiple profiles. add 7 (dynamic) when single-agent reasoning falls short. seven types. each under 90 seconds to configure. the value compounds as you stack them. comment AGENTS and I'll send you 3 ready-to-build agent setups that combine these types into real workflows.
1:15

HERMES AGENT SUPPORTS 7 TYPES OF AI AGENTS. EACH ONE TAKES LESS THAN 90 SECONDS TO SET UP. MOST PEOPLE ONLY BUILD THE FIRST ONE. HERE ARE ALL SEVEN AND WHEN TO USE EACH. 1. BASIC AGENT WITH TOOLS your agent with access to terminal, browser, file system, web search, and calendar. it plans and executes tasks on its own. this is what you get on day one. "find flights to Lisbon under $400" "check my calendar and flag conflicts" "search the web for competitor pricing" set in Desktop app / Dashboard: Tools → enable what you need. when to use: single tasks that need tool access. 2. AGENT WITH MCP SERVERS connect your agent to external services. Notion, Google Drive, GitHub, Slack, databases, APIs, any MCP-compatible service. the agent doesn't scrape these services. it interacts through structured APIs. reads your Notion pages. creates GitHub issues. queries your database. sends Slack messages. set in Desktop app / Dashboard: MCP → Add Server. when to use: your workflow lives across multiple platforms. 3. SEQUENTIAL AGENTS (pipeline) one agent finishes. passes output to the next. assembly line for AI. agent 1: scans inbox for leads. agent 2: qualifies leads against criteria. agent 3: drafts outreach emails. in Hermes: cron jobs with wakeAgent gates. agent 1 writes output to a file. agent 2 wakes only when that file has new data. agent 3 wakes when agent 2 is done. each agent = a separate profile with its own model. when to use: multi-step workflows where each step depends on the previous one finishing. 4. PARALLEL EXECUTION AGENTS multiple agents working at the same time. results merge when all finish. "research these 5 competitors in parallel" in Hermes: delegate_task with batch mode. up to 3 sub-agents running in parallel by default. each gets its own clean context. only summaries return to the parent. delegation: model: "deepseek/deepseek-v4" children run cheap. parent synthesizes. when to use: independent tasks that don't depend on each other. research, data gathering, analysis. 5. AGENTS WITH ROUTERS conditions that send tasks down different paths based on the input. "if sales email → SDR profile. if support ticket → support profile. if calendar invite → EA profile." in Hermes: Kanban decompose. the decomposer reads profile descriptions and routes each task to the best-fit agent. or: Chief of Staff profile that triages and assigns to other profiles. when to use: incoming work that needs different specialists based on type. 6. HUMAN IN THE LOOP the agent does the work. asks for your approval before executing. "I drafted this email. approve before I send?" "this command will delete 3 files. proceed?" in Hermes: approvals.mode: manual (default). every dangerous action needs your confirmation. 60-second timeout. fails closed. or smart mode: LLM assesses risk. safe actions auto-approved. dangerous ones ask you. uncertain ones escalate. when to use: tasks where a mistake has real consequences. emails, deployments, financial transactions, public posts. 7. DYNAMIC SUB-AGENT SPAWNING your main agent realizes it needs help and spawns specialized sub-agents on the fly. "build this feature" → parent delegates: → sub-agent 1: research the API docs → sub-agent 2: write the code → sub-agent 3: write the tests in Hermes: delegate_task with role: orchestrator. raise max_spawn_depth for nested delegation. delegation: max_spawn_depth: 2 orchestrator_enabled: true depth 2 with concurrency 3 = up to 9 parallel workers. each level multiplies the spend. raise depth only when you need multi-level trees. when to use: complex tasks where the agent discovers what help it needs during execution. THE PROGRESSION: start with 1 (tools) and 6 (approvals). add 2 (MCP) when you need external services. add 4 (parallel) when tasks take too long one at a time. add 3 (sequential) when you build multi-step pipelines. add 5 (routing) when you run multiple profiles. add 7 (dynamic) when single-agent reasoning falls short. seven types. each under 90 seconds to configure. the value compounds as you stack them. comment AGENTS and I'll send you 3 ready-to-build agent setups that combine these types into real workflows.

YanXbt

17,312 Aufrufe • vor 1 Monat

I'm so confident Triple Whale will make you money that I'm making a bet: If you do over a million dollars/year I'll pay you $250 for 15 minutes of your time. Today we're Introducing the Prime Day Mega Agent, an intelligent Amazon Analyst built to print you money on Prime Day. Here’s what it does, autonomously: – Analyzes Meta, Tiktok and Amazon ad performance – Predicts your winning SKUs using historical trend modeling – Generates a plug-and-play Prime Day playbook: what to pause, scale, test and when + when to send out email campaigns It’s like having a Head of Growth, Media Buying, and Ops in one… Except It works around the clock and leverages more data than any human ever could. We built it because we noticed a critical trend👇 When analyzing Prime Day sales data from last year, we found that the top brands didn’t just edge out the competition, they crushed them. Same tools. Same budgets. Same Prime Day. Yet somehow a small cohort of brands were crushing at a clip we typically don't see... So what were the winners doing that no one else was? We found levers the winners pulled that everyone else missed. Levers like: - Making their PPC target Prime-specific keywords - Warming up email audiences weeks before to build anticipation - Surf-scaling their ads by the hour not by the day on Prime Day Now, you can get that ENTIRE playbook the winners used custom built on your data. This agent is available RIGHT NOW. Go into Moby in Triple Whale and search "Prime Day Mega Agent." If you're a brand doing over a million dollars a year on Amazon, doing Prime Day right isn't a nice to have, it's table stakes. I guarantee you this agent will make you more revenue... And I am putting CASH behind it. If you do over a million/year I'll pay you $250 to take a demo... Limited to the first 100 people to sign up. The link to book is below this tweet. One more thing 🤯 As part of this launch I'm giving away an Amazon Agentic Org Chart the top brands will be using in the next 12 months. It will show you exactly what agents to use and for what, so you can maximize Amazon growth, autonomously. Want it? Retweet and comment "Moby" below and I'll dm you it.
0:21

I'm so confident Triple Whale will make you money that I'm making a bet: If you do over a million dollars/year I'll pay you $250 for 15 minutes of your time. Today we're Introducing the Prime Day Mega Agent, an intelligent Amazon Analyst built to print you money on Prime Day. Here’s what it does, autonomously: – Analyzes Meta, Tiktok and Amazon ad performance – Predicts your winning SKUs using historical trend modeling – Generates a plug-and-play Prime Day playbook: what to pause, scale, test and when + when to send out email campaigns It’s like having a Head of Growth, Media Buying, and Ops in one… Except It works around the clock and leverages more data than any human ever could. We built it because we noticed a critical trend👇 When analyzing Prime Day sales data from last year, we found that the top brands didn’t just edge out the competition, they crushed them. Same tools. Same budgets. Same Prime Day. Yet somehow a small cohort of brands were crushing at a clip we typically don't see... So what were the winners doing that no one else was? We found levers the winners pulled that everyone else missed. Levers like: - Making their PPC target Prime-specific keywords - Warming up email audiences weeks before to build anticipation - Surf-scaling their ads by the hour not by the day on Prime Day Now, you can get that ENTIRE playbook the winners used custom built on your data. This agent is available RIGHT NOW. Go into Moby in Triple Whale and search "Prime Day Mega Agent." If you're a brand doing over a million dollars a year on Amazon, doing Prime Day right isn't a nice to have, it's table stakes. I guarantee you this agent will make you more revenue... And I am putting CASH behind it. If you do over a million/year I'll pay you $250 to take a demo... Limited to the first 100 people to sign up. The link to book is below this tweet. One more thing 🤯 As part of this launch I'm giving away an Amazon Agentic Org Chart the top brands will be using in the next 12 months. It will show you exactly what agents to use and for what, so you can maximize Amazon growth, autonomously. Want it? Retweet and comment "Moby" below and I'll dm you it.

Maxx Blank 🐳

112,769 Aufrufe • vor 1 Jahr

$AMD Strategic Price Positioning Long🧵 AMD is increasingly the most hated semi stock that can rival $NVDA dominance in GPUs and software(Cuda v. ROCm). $AMD is also the most under-owned among all Funds in 2025 according to Bank of America! For what I learnt for years as an investor with Dr. Lisa Su, all analysts and market are underestimate Dr. Su leadership. $AMD is capable of raising price, making high quality hardware with software. Dr. Su or AMD choice to adopt a lower price strategy to gain market share is a deliberate and multifacets approach rooted in competitive positioning, market dynamics, and long-term growth objectives. As an investor, it may take time like CPUs and embedded to see margin improving. 1. . Penetration Pricing to Challenge Dominant Competitors AMD has historically positioned itself as a cost-effective alternative to dominant players like Intel in CPUs and Nvidia in GPUs. By setting prices lower than competitors, AMD aims to attract customers and quickly gain market share. This is a classic penetration pricing strategy, where the goal is to capture a significant portion of the market by offering high-performance products at a lower price point. ~CPU Market Example: When AMD launched its Ryzen processors in 2017, it priced them competitively compared to Intel's Core processors, emphasizing a better price-to-performance ratio. Ryzen CPUs offered higher core counts and multi-core performance at lower prices, appealing to cost-conscious consumers, gamers, and professionals. This strategy helped AMD increase its CPU market share to 16.6% by early 2025, narrowing the gap with Intel. ~GPU Market Context: In the GPU market, where Nvidia holds an 88% share compared to AMD's 12%, AMD has been criticized for not launching GPUs at low enough prices to compete effectively. However, posts on X and articles suggest AMD is shifting its GPU strategy to focus on mainstream, cost-effective products rather than high-end enthusiast segments, aiming to regain market share through competitive pricing. 2. Appealing to Cost-Conscious Market Segments AMD targets price-sensitive customers, including gamers, small businesses, and enterprises looking for high-performance computing at a lower cost. This is particularly effective in segments where performance is critical, but budgets are constrained. ~Value Proposition: AMD’s Ryzen and EPYC processors, as well as Radeon GPUs, are designed to deliver performance comparable to or better than competitors in specific workloads (e.g., multi-core processing or AI compute) at a lower price. For example, Ryzen processors have been noted for their superior multi-core performance compared to Intel CPUs at similar or lower price points, making them attractive for tasks like video editing or gaming. ~AI and Data Center: In the AI and data center markets, AMD’s cost-effective Instinct MI300X GPUs and EPYC CPUs target enterprises seeking affordable alternatives to Nvidia’s expensive AI ecosystem. This strategy taps into an underleveraged market segment that Nvidia’s broad, premium-priced AI solutions may not fully address. 3. Building Scale and Developer Support AMD’s leadership, including Jack Huynh, has emphasized the importance of scale—gaining a larger market share to attract developer support and optimize software ecosystems. A lower price strategy helps AMD achieve this by increasing adoption among consumers and enterprises. ~Gaming GPUs: By focusing on mainstream GPUs with competitive pricing (e.g., targeting an 80% addressable market rather than the high-end 10%), AMD aims to build a larger user base. This scale encourages developers to optimize games for AMD’s technologies, such as FSR 3 (FidelityFX Super Resolution) and Anti-Lag 2, improving the ecosystem and competitiveness against Nvidia’s CUDA platform. ~Open Ecosystem in AI: AMD’s open-source ROCm platform contrasts with Nvidia’s proprietary CUDA, appealing to developers who prefer flexibility. Lower-priced hardware makes it easier for developers to adopt AMD’s solutions, fostering a broader AI software ecosystem. 4. Historical Context and Brand Positioning Since its founding in 1969, AMD has positioned itself as a challenger brand, often acting as a “second source” supplier to Intel. This role required competitive pricing to gain a foothold in markets dominated by established players. Over time, AMD has built a reputation for quality and affordability, reinforced by products like the Am9080 (a reverse-engineered Intel 8080) and modern Ryzen and EPYC lines. This historical strategy of undercutting competitors’ prices while delivering comparable performance continues to define AMD’s approach. 5. Countering Competitor Dominance AMD operates in highly competitive markets where Intel and Nvidia have significant advantages in brand recognition, market share, and ecosystems. A lower price strategy is a pragmatic way to disrupt this in CPUs: ~Intel’s historical dominance in the CPU market (servers, desktops, and laptops) has been challenged by AMD’s Ryzen and EPYC processors, which offer better value. For instance, AMD’s EPYC CPUs have driven a 122% year-over-year revenue increase in the data center segment, partly due to their cost-effectiveness, helping AMD capture 94% of CPU sales at some retailers. ~Nvidia in GPUs: Nvidia’s 88% GPU market share and premium pricing (e.g., high-end GPUs like the RTX 4090) leave room for AMD to compete in the mid-to-low range. However, AMD’s failure to launch GPUs at sufficiently low prices (e.g., the RX 7900 XT at $900 instead of its current $680) has limited its success, prompting a strategic shift toward more aggressive pricing in future RDNA 4 GPUs. 6. Market Share as a Long-Term Investment AMD’s lower price strategy is not just about immediate sales but also about long-term market positioning. By capturing market share, AMD can: ~Increase Brand Loyalty: Affordable, high-performance products build customer loyalty, especially among gamers and small businesses, creating a foundation for future sales. ~Drive Revenue Growth: Market share gains in CPUs (e.g., 16.6% in 2025) and data centers (e.g., $3.5 billion in Q3 revenue) translate into higher revenue, even if margins are initially lower. ~Influence Industry Standards: Greater market presence allows AMD to influence hardware and software standards, such as pushing for open-source AI frameworks or gaming optimizations, reducing reliance on competitors’ proprietary systems. 7. Challenges and Risks While effective, AMD’s lower price strategy carries risks: ~Profitability Concerns: Lower prices can compress profit margins, and some analysts note that AMD’s high stock valuation expects future profitability that may be delayed if pricing remains aggressive. ~Perception of Quality: Persistently low prices risk positioning AMD as a “budget” brand, potentially undermining its ability to compete in premium segments. ~Competitor Response: Intel and Nvidia can counter with price cuts or superior features, as seen with Nvidia’s feature-rich GPUs. AMD must balance price with innovation to avoid being outmaneuvered. 8. Strategic Shift in GPUs Recent reports indicate AMD is adjusting its GPU strategy to prioritize market share over competing in the high-end enthusiast segment. For the upcoming Radeon RX 8000 series (RDNA 4), AMD is focusing on mainstream GPUs priced competitively to appeal to a broader audience, rather than chasing Nvidia’s high-end dominance. This shift aligns with AMD’s broader goal of achieving 40–50% market share by targeting the “80%” of the market that prioritizes affordability over premium features. Lastly, AMD’s lower price strategy is a calculated move to disrupt Intel and Nvidia’s dominance, capture market share, and build scale for long-term growth. By offering high-performance CPUs and GPUs at competitive prices, AMD appeals to cost-conscious consumers and enterprises, particularly in the CPU and AI markets, where it has seen significant gains (e.g., 16.6% CPU market share and $3.5 billion in data center revenue). Recent price increase on MI350 and MI355 and more on MI400 signaled #AI chip leadership and pricing power, which will result in significant top and bottom line growth.
5:19

$AMD Strategic Price Positioning Long🧵 AMD is increasingly the most hated semi stock that can rival $NVDA dominance in GPUs and software(Cuda v. ROCm). $AMD is also the most under-owned among all Funds in 2025 according to Bank of America! For what I learnt for years as an investor with Dr. Lisa Su, all analysts and market are underestimate Dr. Su leadership. $AMD is capable of raising price, making high quality hardware with software. Dr. Su or AMD choice to adopt a lower price strategy to gain market share is a deliberate and multifacets approach rooted in competitive positioning, market dynamics, and long-term growth objectives. As an investor, it may take time like CPUs and embedded to see margin improving. 1. . Penetration Pricing to Challenge Dominant Competitors AMD has historically positioned itself as a cost-effective alternative to dominant players like Intel in CPUs and Nvidia in GPUs. By setting prices lower than competitors, AMD aims to attract customers and quickly gain market share. This is a classic penetration pricing strategy, where the goal is to capture a significant portion of the market by offering high-performance products at a lower price point. ~CPU Market Example: When AMD launched its Ryzen processors in 2017, it priced them competitively compared to Intel's Core processors, emphasizing a better price-to-performance ratio. Ryzen CPUs offered higher core counts and multi-core performance at lower prices, appealing to cost-conscious consumers, gamers, and professionals. This strategy helped AMD increase its CPU market share to 16.6% by early 2025, narrowing the gap with Intel. ~GPU Market Context: In the GPU market, where Nvidia holds an 88% share compared to AMD's 12%, AMD has been criticized for not launching GPUs at low enough prices to compete effectively. However, posts on X and articles suggest AMD is shifting its GPU strategy to focus on mainstream, cost-effective products rather than high-end enthusiast segments, aiming to regain market share through competitive pricing. 2. Appealing to Cost-Conscious Market Segments AMD targets price-sensitive customers, including gamers, small businesses, and enterprises looking for high-performance computing at a lower cost. This is particularly effective in segments where performance is critical, but budgets are constrained. ~Value Proposition: AMD’s Ryzen and EPYC processors, as well as Radeon GPUs, are designed to deliver performance comparable to or better than competitors in specific workloads (e.g., multi-core processing or AI compute) at a lower price. For example, Ryzen processors have been noted for their superior multi-core performance compared to Intel CPUs at similar or lower price points, making them attractive for tasks like video editing or gaming. ~AI and Data Center: In the AI and data center markets, AMD’s cost-effective Instinct MI300X GPUs and EPYC CPUs target enterprises seeking affordable alternatives to Nvidia’s expensive AI ecosystem. This strategy taps into an underleveraged market segment that Nvidia’s broad, premium-priced AI solutions may not fully address. 3. Building Scale and Developer Support AMD’s leadership, including Jack Huynh, has emphasized the importance of scale—gaining a larger market share to attract developer support and optimize software ecosystems. A lower price strategy helps AMD achieve this by increasing adoption among consumers and enterprises. ~Gaming GPUs: By focusing on mainstream GPUs with competitive pricing (e.g., targeting an 80% addressable market rather than the high-end 10%), AMD aims to build a larger user base. This scale encourages developers to optimize games for AMD’s technologies, such as FSR 3 (FidelityFX Super Resolution) and Anti-Lag 2, improving the ecosystem and competitiveness against Nvidia’s CUDA platform. ~Open Ecosystem in AI: AMD’s open-source ROCm platform contrasts with Nvidia’s proprietary CUDA, appealing to developers who prefer flexibility. Lower-priced hardware makes it easier for developers to adopt AMD’s solutions, fostering a broader AI software ecosystem. 4. Historical Context and Brand Positioning Since its founding in 1969, AMD has positioned itself as a challenger brand, often acting as a “second source” supplier to Intel. This role required competitive pricing to gain a foothold in markets dominated by established players. Over time, AMD has built a reputation for quality and affordability, reinforced by products like the Am9080 (a reverse-engineered Intel 8080) and modern Ryzen and EPYC lines. This historical strategy of undercutting competitors’ prices while delivering comparable performance continues to define AMD’s approach. 5. Countering Competitor Dominance AMD operates in highly competitive markets where Intel and Nvidia have significant advantages in brand recognition, market share, and ecosystems. A lower price strategy is a pragmatic way to disrupt this in CPUs: ~Intel’s historical dominance in the CPU market (servers, desktops, and laptops) has been challenged by AMD’s Ryzen and EPYC processors, which offer better value. For instance, AMD’s EPYC CPUs have driven a 122% year-over-year revenue increase in the data center segment, partly due to their cost-effectiveness, helping AMD capture 94% of CPU sales at some retailers. ~Nvidia in GPUs: Nvidia’s 88% GPU market share and premium pricing (e.g., high-end GPUs like the RTX 4090) leave room for AMD to compete in the mid-to-low range. However, AMD’s failure to launch GPUs at sufficiently low prices (e.g., the RX 7900 XT at $900 instead of its current $680) has limited its success, prompting a strategic shift toward more aggressive pricing in future RDNA 4 GPUs. 6. Market Share as a Long-Term Investment AMD’s lower price strategy is not just about immediate sales but also about long-term market positioning. By capturing market share, AMD can: ~Increase Brand Loyalty: Affordable, high-performance products build customer loyalty, especially among gamers and small businesses, creating a foundation for future sales. ~Drive Revenue Growth: Market share gains in CPUs (e.g., 16.6% in 2025) and data centers (e.g., $3.5 billion in Q3 revenue) translate into higher revenue, even if margins are initially lower. ~Influence Industry Standards: Greater market presence allows AMD to influence hardware and software standards, such as pushing for open-source AI frameworks or gaming optimizations, reducing reliance on competitors’ proprietary systems. 7. Challenges and Risks While effective, AMD’s lower price strategy carries risks: ~Profitability Concerns: Lower prices can compress profit margins, and some analysts note that AMD’s high stock valuation expects future profitability that may be delayed if pricing remains aggressive. ~Perception of Quality: Persistently low prices risk positioning AMD as a “budget” brand, potentially undermining its ability to compete in premium segments. ~Competitor Response: Intel and Nvidia can counter with price cuts or superior features, as seen with Nvidia’s feature-rich GPUs. AMD must balance price with innovation to avoid being outmaneuvered. 8. Strategic Shift in GPUs Recent reports indicate AMD is adjusting its GPU strategy to prioritize market share over competing in the high-end enthusiast segment. For the upcoming Radeon RX 8000 series (RDNA 4), AMD is focusing on mainstream GPUs priced competitively to appeal to a broader audience, rather than chasing Nvidia’s high-end dominance. This shift aligns with AMD’s broader goal of achieving 40–50% market share by targeting the “80%” of the market that prioritizes affordability over premium features. Lastly, AMD’s lower price strategy is a calculated move to disrupt Intel and Nvidia’s dominance, capture market share, and build scale for long-term growth. By offering high-performance CPUs and GPUs at competitive prices, AMD appeals to cost-conscious consumers and enterprises, particularly in the CPU and AI markets, where it has seen significant gains (e.g., 16.6% CPU market share and $3.5 billion in data center revenue). Recent price increase on MI350 and MI355 and more on MI400 signaled #AI chip leadership and pricing power, which will result in significant top and bottom line growth.

Mike

38,006 Aufrufe • vor 11 Monaten

HERMES AGENT + STRIPE PAYMENTS + NVIDIA NEMOTRON. YOUR AGENT CAN NOW RUN A BUSINESS. ACCEPT PAYMENTS. PAY FOR SERVICES. PROVISION ITS OWN INFRASTRUCTURE. ALL INSIDE A SECURITY SANDBOX. two years ago the question was: can an AI agent run a business autonomously? the answer shipped this week. Hermes already handles workflows: cron jobs, sub-agents, kanban orchestration, multi-profile pipelines, scheduled research. what it couldn't do: spend money and prove it's safe. Stripe solved the first problem. Nvidia solved the second. WHAT AUTONOMOUS BUSINESS OPERATIONS LOOK LIKE: → customer sends a request via email → agent reads, scopes the project, estimates cost → provisions the infrastructure it needs (pays via Stripe, you approve on your phone) → builds and deploys the deliverable → sends the result to the customer → creates a payment link via Stripe (Stripe API integration, separate from Link CLI) → tops off its own API credits when balance drops → reports daily costs and progress to your Telegram → all within security policies you set once you set the rules. the agent runs the operation. you review revenue reports. not tasks. this is already happening. Dark Factory: autonomous software factory. send an idea before bed. wake up to a deployed URL. live entry in the Hermes Accelerated Business Hackathon. HOW STRIPE MAKES THE AGENT FINANCIALLY AUTONOMOUS: Stripe Link CLI gives your agent a scoped wallet. not your credit card. one-time-use virtual cards. → agent finds a product or service it needs → creates a spend request via Stripe Link → you get a notification on your phone (Link app) → you review: merchant, amount, context → one tap to approve or reject → agent receives a one-time virtual card → completes the purchase → card expires after single use your real card details never enter agent context. never printed in chat. never exposed to the merchant. Hermes cannot self-approve. you confirm every spend. install: hermes install skills/optional/payments/stripe-link-cli link-cli auth login what the agent can pay for: → API credits (Nous Portal, OpenRouter) → SaaS subscriptions it needs for operations → domain names, hosting, cloud credits → products from any online store currently US only. HOW NVIDIA MAKES THE AGENT SAFE TO TRUST: an agent with spending authority and no security boundaries is a liability. NemoClaw solves this. three layers: 1. OPENSHELL (sandbox) kernel-level isolation. controls network, filesystem, syscalls. default deny. you whitelist what's allowed. agent tries to reach a blocked domain = rejected. agent has no idea it's sandboxed. 2. NEMOTRON (private models) open-weight models on your own hardware. Nemotron 3 Super 120B MoE (48GB+ VRAM). Nemotron 3 Nano 4B (8GB VRAM, edge). fully private. no data leaves your machine. without GPU: inference routes to cloud via Privacy Router. 3. PRIVACY ROUTER (automatic split) decides per query: local or cloud. private data → local Nemotron. general web research → Claude, GPT, Gemini. automatic. per query. no manual routing. install: export NEMOCLAW_AGENT=hermes curl -fsSL https:// www.nvidia. com/nemoclaw.sh | bash requires Docker. NemoClaw is alpha software. APIs may change. test in non-production first. THE FULL PICTURE: before this stack: → agent could work but couldn't pay for anything → agent could pay but couldn't be trusted → agent could be trusted but couldn't operate 24/7 now: → Hermes runs the business logic (workflows, memory, skills, cron, sub-agents) → Stripe runs the financial layer (Link CLI for spending, Stripe API for receiving) → NemoClaw runs the trust layer (sandbox, policies, private routing) → VPS keeps everything always on → Telegram keeps you in the loop TYPES OF BUSINESSES THIS ENABLES: → autonomous software factory (customer request → build → deploy → payment link) → content agency (brief → research → draft → deliver → bill) → lead generation service (scrape → qualify → outreach → book calls) → SaaS monitoring and maintenance (detect issues → fix → deploy → report) → e-commerce operations (inventory → pricing → fulfillment → support) each one: Hermes profiles handle the workflows. Stripe handles the payments (in and out). NemoClaw handles the security. you handle the strategy. THE HACKATHON: Hermes Agent Accelerated Business Hackathon with Nvidia and Stripe. cash prizes + Stripe credits + Nvidia DGX Spark. ends June 30. the goal: build agents that earn, spend, and run real operations autonomously. link in the Nous Research Discord. full Hermes architecture deep-dive in the article 👇
53:28

HERMES AGENT + STRIPE PAYMENTS + NVIDIA NEMOTRON. YOUR AGENT CAN NOW RUN A BUSINESS. ACCEPT PAYMENTS. PAY FOR SERVICES. PROVISION ITS OWN INFRASTRUCTURE. ALL INSIDE A SECURITY SANDBOX. two years ago the question was: can an AI agent run a business autonomously? the answer shipped this week. Hermes already handles workflows: cron jobs, sub-agents, kanban orchestration, multi-profile pipelines, scheduled research. what it couldn't do: spend money and prove it's safe. Stripe solved the first problem. Nvidia solved the second. WHAT AUTONOMOUS BUSINESS OPERATIONS LOOK LIKE: → customer sends a request via email → agent reads, scopes the project, estimates cost → provisions the infrastructure it needs (pays via Stripe, you approve on your phone) → builds and deploys the deliverable → sends the result to the customer → creates a payment link via Stripe (Stripe API integration, separate from Link CLI) → tops off its own API credits when balance drops → reports daily costs and progress to your Telegram → all within security policies you set once you set the rules. the agent runs the operation. you review revenue reports. not tasks. this is already happening. Dark Factory: autonomous software factory. send an idea before bed. wake up to a deployed URL. live entry in the Hermes Accelerated Business Hackathon. HOW STRIPE MAKES THE AGENT FINANCIALLY AUTONOMOUS: Stripe Link CLI gives your agent a scoped wallet. not your credit card. one-time-use virtual cards. → agent finds a product or service it needs → creates a spend request via Stripe Link → you get a notification on your phone (Link app) → you review: merchant, amount, context → one tap to approve or reject → agent receives a one-time virtual card → completes the purchase → card expires after single use your real card details never enter agent context. never printed in chat. never exposed to the merchant. Hermes cannot self-approve. you confirm every spend. install: hermes install skills/optional/payments/stripe-link-cli link-cli auth login what the agent can pay for: → API credits (Nous Portal, OpenRouter) → SaaS subscriptions it needs for operations → domain names, hosting, cloud credits → products from any online store currently US only. HOW NVIDIA MAKES THE AGENT SAFE TO TRUST: an agent with spending authority and no security boundaries is a liability. NemoClaw solves this. three layers: 1. OPENSHELL (sandbox) kernel-level isolation. controls network, filesystem, syscalls. default deny. you whitelist what's allowed. agent tries to reach a blocked domain = rejected. agent has no idea it's sandboxed. 2. NEMOTRON (private models) open-weight models on your own hardware. Nemotron 3 Super 120B MoE (48GB+ VRAM). Nemotron 3 Nano 4B (8GB VRAM, edge). fully private. no data leaves your machine. without GPU: inference routes to cloud via Privacy Router. 3. PRIVACY ROUTER (automatic split) decides per query: local or cloud. private data → local Nemotron. general web research → Claude, GPT, Gemini. automatic. per query. no manual routing. install: export NEMOCLAW_AGENT=hermes curl -fsSL https:// www.nvidia. com/nemoclaw.sh | bash requires Docker. NemoClaw is alpha software. APIs may change. test in non-production first. THE FULL PICTURE: before this stack: → agent could work but couldn't pay for anything → agent could pay but couldn't be trusted → agent could be trusted but couldn't operate 24/7 now: → Hermes runs the business logic (workflows, memory, skills, cron, sub-agents) → Stripe runs the financial layer (Link CLI for spending, Stripe API for receiving) → NemoClaw runs the trust layer (sandbox, policies, private routing) → VPS keeps everything always on → Telegram keeps you in the loop TYPES OF BUSINESSES THIS ENABLES: → autonomous software factory (customer request → build → deploy → payment link) → content agency (brief → research → draft → deliver → bill) → lead generation service (scrape → qualify → outreach → book calls) → SaaS monitoring and maintenance (detect issues → fix → deploy → report) → e-commerce operations (inventory → pricing → fulfillment → support) each one: Hermes profiles handle the workflows. Stripe handles the payments (in and out). NemoClaw handles the security. you handle the strategy. THE HACKATHON: Hermes Agent Accelerated Business Hackathon with Nvidia and Stripe. cash prizes + Stripe credits + Nvidia DGX Spark. ends June 30. the goal: build agents that earn, spend, and run real operations autonomously. link in the Nous Research Discord. full Hermes architecture deep-dive in the article 👇

YanXbt

37,709 Aufrufe • vor 1 Monat

A founder turned down a $1M job offer from Meta to build a $5M ARR company with 9 people. His CAC is in cents. He has no sales team, no CS org, and no plans to build one (read on for the full playbook). Meet Ethan, the founder of Jobright, and if you are an early-stage lean AI company, this might be the most valuable thing you read this week. While every other AI startup was raising, hiring aggressively, and building org charts before they had customers, Ethan was talking to users (face-to-face). Their first million (revenue) was pure product-market fit mode. It came entirely from word of mouth, with users pulling in their friends because the product worked. The next four million came from clearly defining their ICP, tracking CAC, and obsessing over growth as a data-driven system. They built an experiment pipeline with a strict 7-day ship-or-kill rule. Channels that couldn't hit payback targets were automatically cut. And through all of it, the headcount barely moved. Because every time they spotted a repetitive workflow, their default instinct was to build an agent (when most founders hire). Their first internal AI agent took an enterprise manager from 5 accounts to 50, without any additional hires. Last month alone, they shipped 3 new internal agents: • An inbox agent that reads, classifies, and drafts responses • An ops agent that turns messy client requests into structured tasks • An outreach agent that finds relevant partners, writes personalized first messages, and runs follow-up sequences Each of those consumed hours of manual labor every week, but now they run on autopilot. That is what 9 people running like 50 looks like in practice. It is one of the best 0 to $5M stories I have come across in the lean AI space. So I spent hours going deep on every decision, system, and principle behind how they did this, and turned it into a super actionable playbook for founders who are pre-revenue or going from 0 to 1. Inside, you'll get: • The exact growth OS they used to go from $1M to $5M ARR • How they built internal agents that let 9 people do the work of 50 • The hiring filter that screens for true AI-native operators • The 2-question test every feature must pass before it gets built • How they structured growth after hitting PMF (the full funnel with owner metrics) • What building from $0 → $1M looks like vs. $1M → $5M • The data flywheel they've been compounding since day one that gets harder to replicate, and how to design yours from scratch Originally, I put this together as a resource for founders I work with directly. But the insights here are too actionable to keep internal, so I'm sharing them publicly. It's one of the most detailed operating blueprints I've put together for those aspiring to join the Lean AI Leaderboard. If you are one of them, grab this right away as it will save you months of expensive guesswork. Ethan, Eric, and team, welcome to the Lean AI Leaderboard!🚀 Link to the playbook:
0:29

A founder turned down a $1M job offer from Meta to build a $5M ARR company with 9 people. His CAC is in cents. He has no sales team, no CS org, and no plans to build one (read on for the full playbook). Meet Ethan, the founder of Jobright, and if you are an early-stage lean AI company, this might be the most valuable thing you read this week. While every other AI startup was raising, hiring aggressively, and building org charts before they had customers, Ethan was talking to users (face-to-face). Their first million (revenue) was pure product-market fit mode. It came entirely from word of mouth, with users pulling in their friends because the product worked. The next four million came from clearly defining their ICP, tracking CAC, and obsessing over growth as a data-driven system. They built an experiment pipeline with a strict 7-day ship-or-kill rule. Channels that couldn't hit payback targets were automatically cut. And through all of it, the headcount barely moved. Because every time they spotted a repetitive workflow, their default instinct was to build an agent (when most founders hire). Their first internal AI agent took an enterprise manager from 5 accounts to 50, without any additional hires. Last month alone, they shipped 3 new internal agents: • An inbox agent that reads, classifies, and drafts responses • An ops agent that turns messy client requests into structured tasks • An outreach agent that finds relevant partners, writes personalized first messages, and runs follow-up sequences Each of those consumed hours of manual labor every week, but now they run on autopilot. That is what 9 people running like 50 looks like in practice. It is one of the best 0 to $5M stories I have come across in the lean AI space. So I spent hours going deep on every decision, system, and principle behind how they did this, and turned it into a super actionable playbook for founders who are pre-revenue or going from 0 to 1. Inside, you'll get: • The exact growth OS they used to go from $1M to $5M ARR • How they built internal agents that let 9 people do the work of 50 • The hiring filter that screens for true AI-native operators • The 2-question test every feature must pass before it gets built • How they structured growth after hitting PMF (the full funnel with owner metrics) • What building from $0 → $1M looks like vs. $1M → $5M • The data flywheel they've been compounding since day one that gets harder to replicate, and how to design yours from scratch Originally, I put this together as a resource for founders I work with directly. But the insights here are too actionable to keep internal, so I'm sharing them publicly. It's one of the most detailed operating blueprints I've put together for those aspiring to join the Lean AI Leaderboard. If you are one of them, grab this right away as it will save you months of expensive guesswork. Ethan, Eric, and team, welcome to the Lean AI Leaderboard!🚀 Link to the playbook:

Henry Shi

51,591 Aufrufe • vor 5 Monaten

Claude Code cannot read 300 files at once. So someone built a system that lets it control NotebookLM from the terminal instead. The results are wild. Here is the full workflow nobody is talking about: The Setup → Claude Code connects to NotebookLM via a command line interface → Claude searches YouTube, finds relevant videos, uploads them as sources automatically → NotebookLM processes up to 300 sources simultaneously and returns cited, grounded answers → Everything syncs back into your Obsidian vault with passage-level citations you can click to verify Why This Changes Research Forever → No more 20 browser tabs you never close → No more copy-pasting outputs into random notes → No more hallucinated answers with no sources to back them up → 60% of citations verified as strong matches in accuracy audits - answers are grounded in real data What Claude Can Do From the Terminal → Search YouTube for relevant videos on any topic and rank by relevance → Create a new NotebookLM notebook and add 20 sources in parallel automatically → Ask questions and export cited answers directly into Obsidian with wikilinks → Set custom personas per notebook - concise, no filler, no preamble → Generate audio overviews and save them as MP3 files into your vault → Build mind maps, flashcard decks, and research dashboards from your sources → Search arXiv for academic papers and feed them directly into NotebookLM → Upload competitor blog posts, podcast episodes, PDFs, and your own vault notes The Obsidian Output → Every answer arrives with clickable citations that link to the exact passage in the source video or article → Graph view shows connections between all 20 sources and the topics they share → Q&A log tracks every question asked and the grounded response received → Source dashboard shows citation frequency, topics extracted, and which questions each source answered Use Cases Worth Building Today → Academic research with arXiv papers, full citation traceability → Competitor analysis from their YouTube channels and blog posts → Company knowledge base for onboarding, new employees ask NotebookLM instead of interrupting teammates → Podcast research, feed 4-hour Lex Fridman episodes and ask what's new in AI this week → Personal second brain, 300 daily notes uploaded and queryable in one notebook Before this system existed you needed 20 tabs, hours of manual reading, and no guarantee the answers were real. Now you type one prompt in the terminal and Claude does all of it for you. The research stack of 2026 is not a browser. It is a terminal connected to everything
32:50

Claude Code cannot read 300 files at once. So someone built a system that lets it control NotebookLM from the terminal instead. The results are wild. Here is the full workflow nobody is talking about: The Setup → Claude Code connects to NotebookLM via a command line interface → Claude searches YouTube, finds relevant videos, uploads them as sources automatically → NotebookLM processes up to 300 sources simultaneously and returns cited, grounded answers → Everything syncs back into your Obsidian vault with passage-level citations you can click to verify Why This Changes Research Forever → No more 20 browser tabs you never close → No more copy-pasting outputs into random notes → No more hallucinated answers with no sources to back them up → 60% of citations verified as strong matches in accuracy audits - answers are grounded in real data What Claude Can Do From the Terminal → Search YouTube for relevant videos on any topic and rank by relevance → Create a new NotebookLM notebook and add 20 sources in parallel automatically → Ask questions and export cited answers directly into Obsidian with wikilinks → Set custom personas per notebook - concise, no filler, no preamble → Generate audio overviews and save them as MP3 files into your vault → Build mind maps, flashcard decks, and research dashboards from your sources → Search arXiv for academic papers and feed them directly into NotebookLM → Upload competitor blog posts, podcast episodes, PDFs, and your own vault notes The Obsidian Output → Every answer arrives with clickable citations that link to the exact passage in the source video or article → Graph view shows connections between all 20 sources and the topics they share → Q&A log tracks every question asked and the grounded response received → Source dashboard shows citation frequency, topics extracted, and which questions each source answered Use Cases Worth Building Today → Academic research with arXiv papers, full citation traceability → Competitor analysis from their YouTube channels and blog posts → Company knowledge base for onboarding, new employees ask NotebookLM instead of interrupting teammates → Podcast research, feed 4-hour Lex Fridman episodes and ask what's new in AI this week → Personal second brain, 300 daily notes uploaded and queryable in one notebook Before this system existed you needed 20 tabs, hours of manual reading, and no guarantee the answers were real. Now you type one prompt in the terminal and Claude does all of it for you. The research stack of 2026 is not a browser. It is a terminal connected to everything

Dami-Defi

252,693 Aufrufe • vor 2 Monaten

How is Arbaaz, CEO of making AI models that continue to learn from your business data and continue to grow? He is working with car dealerships now, but growing to other businesses soon. Here is what Grok says you will learn from this video: +++++ By watching this podcast episode, viewers will gain insights into how AI is being practically applied in business, particularly in niche industries like car dealerships, while also exploring broader AI concepts, challenges, and future implications. Here's a breakdown of the main takeaways: AI Customization for Businesses: Learn how Polycom Computing builds specialized AI models that continuously train on a company's real-time data and workflows, acting as "companions" rather than generic tools. This contrasts with foundational models from companies like OpenAI or Anthropic, which struggle to adapt to specific "worlds" without losing efficiency. The focus is on personalization to avoid wasting attention, intelligence, and money. Pivoting AI Strategies for Revenue Growth: Understand the shift from cost-cutting (e.g., automating call centers) to revenue-increasing applications. Urba explains why targeting high-value tasks like sales in car dealerships creates defensible moats, as opposed to commoditized cost reductions. This includes automating complex funnels—from lead submission to financing—while ensuring compliance with regulations and seamless integration with existing teams. Scalable AI Agents in Practice: Discover how AI agents must learn autonomously (e.g., adapting to different CRMs, processes, and preferences across dealerships) to avoid becoming non-scalable consulting services. Key challenges include creating "glue" between humans and AI, avoiding hard-coded rules, and using web actions to integrate siloed software like Dealer Management Systems (DMS). Boosting Sales with AI Techniques: Gain knowledge on tactics like rapid response (replying within 5 minutes boosts conversion 22x), creating natural "disfluencies" (typos, emojis, jokes) for human-like communication, managing after-hours leads, and educating customers without pushing sales. Pilots showed 50% sales increases by handling unanswered leads (70% go ignored), qualifying buyers, and maintaining conversation threads. Multi-Agent Systems and Proactive AI: Explore the difference between reactive Q&A models and proactive agents with agency—they predict, act, and update based on goals. Building these systems reveals bottlenecks (e.g., overwhelming businesses with leads), leading to solutions like AI buying cars or linking sales/service arms. Urba discusses how AI plateaus without personalization, risking model collapse or equilibrium where gains cancel out. Technical Deep Dives into AI Development: Get explanations of advanced concepts like real-time RLHF (Reinforcement Learning from Human Feedback) for continuous improvement, coherence (maintaining logical consistency across outputs), dynamic tokenization for new abstractions, and evaluating models (e.g., avoiding memorization over reasoning, energy limits in scaling). Viewers see a demo of their platform for tasks like quant strategies, presidential analysis, and training runs. Selling and Adopting AI in Traditional Industries: Learn how to pitch AI to non-tech audiences (e.g., car dealers) by focusing on results—more money, fewer bottlenecks, centralized dashboards—rather than jargon. Emphasize empathy: make owners feel smart, reduce reliance on salespeople, and return control via AI-managed customer databases. Broader AI Implications and Misconceptions: Understand why AI won't create a "machine god" that eliminates all jobs—it's bound by physics (e.g., energy needs), expands economies (like the Industrial Revolution), and requires human perspectives for true advantage. AI enhances productivity, creates new roles, and democratizes power, but risks arise from misuse, not inherent agency. Urba stresses proactive defense through widespread AI proficiency. Overall, the episode bridges entrepreneurial stories, technical AI mechanics, and real-world applications, making it valuable for entrepreneurs, AI enthusiasts, and business owners curious about integrating AI without hype. It's a candid look at building scalable, impactful AI beyond buzzwords.
57:31

How is Arbaaz, CEO of making AI models that continue to learn from your business data and continue to grow? He is working with car dealerships now, but growing to other businesses soon. Here is what Grok says you will learn from this video: +++++ By watching this podcast episode, viewers will gain insights into how AI is being practically applied in business, particularly in niche industries like car dealerships, while also exploring broader AI concepts, challenges, and future implications. Here's a breakdown of the main takeaways: AI Customization for Businesses: Learn how Polycom Computing builds specialized AI models that continuously train on a company's real-time data and workflows, acting as "companions" rather than generic tools. This contrasts with foundational models from companies like OpenAI or Anthropic, which struggle to adapt to specific "worlds" without losing efficiency. The focus is on personalization to avoid wasting attention, intelligence, and money. Pivoting AI Strategies for Revenue Growth: Understand the shift from cost-cutting (e.g., automating call centers) to revenue-increasing applications. Urba explains why targeting high-value tasks like sales in car dealerships creates defensible moats, as opposed to commoditized cost reductions. This includes automating complex funnels—from lead submission to financing—while ensuring compliance with regulations and seamless integration with existing teams. Scalable AI Agents in Practice: Discover how AI agents must learn autonomously (e.g., adapting to different CRMs, processes, and preferences across dealerships) to avoid becoming non-scalable consulting services. Key challenges include creating "glue" between humans and AI, avoiding hard-coded rules, and using web actions to integrate siloed software like Dealer Management Systems (DMS). Boosting Sales with AI Techniques: Gain knowledge on tactics like rapid response (replying within 5 minutes boosts conversion 22x), creating natural "disfluencies" (typos, emojis, jokes) for human-like communication, managing after-hours leads, and educating customers without pushing sales. Pilots showed 50% sales increases by handling unanswered leads (70% go ignored), qualifying buyers, and maintaining conversation threads. Multi-Agent Systems and Proactive AI: Explore the difference between reactive Q&A models and proactive agents with agency—they predict, act, and update based on goals. Building these systems reveals bottlenecks (e.g., overwhelming businesses with leads), leading to solutions like AI buying cars or linking sales/service arms. Urba discusses how AI plateaus without personalization, risking model collapse or equilibrium where gains cancel out. Technical Deep Dives into AI Development: Get explanations of advanced concepts like real-time RLHF (Reinforcement Learning from Human Feedback) for continuous improvement, coherence (maintaining logical consistency across outputs), dynamic tokenization for new abstractions, and evaluating models (e.g., avoiding memorization over reasoning, energy limits in scaling). Viewers see a demo of their platform for tasks like quant strategies, presidential analysis, and training runs. Selling and Adopting AI in Traditional Industries: Learn how to pitch AI to non-tech audiences (e.g., car dealers) by focusing on results—more money, fewer bottlenecks, centralized dashboards—rather than jargon. Emphasize empathy: make owners feel smart, reduce reliance on salespeople, and return control via AI-managed customer databases. Broader AI Implications and Misconceptions: Understand why AI won't create a "machine god" that eliminates all jobs—it's bound by physics (e.g., energy needs), expands economies (like the Industrial Revolution), and requires human perspectives for true advantage. AI enhances productivity, creates new roles, and democratizes power, but risks arise from misuse, not inherent agency. Urba stresses proactive defense through widespread AI proficiency. Overall, the episode bridges entrepreneurial stories, technical AI mechanics, and real-world applications, making it valuable for entrepreneurs, AI enthusiasts, and business owners curious about integrating AI without hype. It's a candid look at building scalable, impactful AI beyond buzzwords.

Robert Scoble

59,714 Aufrufe • vor 1 Jahr

HERMES AGENT SUPPORTS 300+ MODELS. PICKING THE RIGHT ONE PER TASK IS THE DIFFERENCE BETWEEN $5/MONTH AND $50. STARTING OUT: Claude Sonnet 4.6. official recommendation from Nous Research. "the model this project was built and tested with." strong reasoning. reliable tool calling. mid-range pricing. PREMIUM TIER: Claude Opus 4.8. best coding benchmarks available. self-correcting reasoning. catches its own mistakes. 1M context. use for demanding tasks where quality matters. GPT-5.5. #1 Chatbot Arena. #1 GPQA Diamond reasoning (94.1%). #1 creative writing. 2M context. handles entire codebases in one pass. Grok 4.30. the only frontier model with live X firehose access. real-time social data, breaking news, market sentiment. connects via Grok OAuth. no separate API key. Grok-Composer-2.5-Fast (v0.17.0). Cursor's coding model. 200K context. available through your Grok subscription via OAuth. no extra cost if you already pay for Grok. MID-RANGE TIER: Claude Sonnet 4.6. best balance of quality and cost for daily use. strongest prose and tool calling in this tier. Gemini 2.5 Pro. Google Search grounding built in. cites sources. verifies claims. pulls current data. 2M context. best for research-heavy workflows. GPT-4.1. reliable tool calling. solid general reasoning. good middle ground when you need OpenAI compatibility. BUDGET TIER: Claude Haiku 4.5. fastest Anthropic model. cheapest paid Claude option. strong at classification, routing, simple queries. use for auxiliary tasks: compression, vision, web extraction, approval scoring. DeepSeek V4. best cost-to-quality ratio in the market. 90% cache discount on repeated context. use for sub-agents and bulk parallel work. DeepSeek V4 Flash. cheapest paid model worth using. 1M context. MIT license. self-hostable. use for cron jobs, monitoring, routine searches. MiniMax M3. Nous Research and MiniMax collaborating on optimization. 1M context via lightning attention. 59% SWE-Bench Pro. beats several premium models on coding. one of the most-used models inside Hermes. FREE / LOCAL: Qwen 3.5 27B via Ollama. 16GB VRAM. reliable tool calling. best free local model for Hermes as of mid-2026. Qwen 3 8B. 8GB VRAM. fits a $7 VPS. handles routine tasks at zero API cost. Llama 4 Maverick. best open-weight tool calling. 1M context. needs more VRAM but strongest local option. HOW TO ASSIGN MODELS: main model: Desktop app / Dashboard → Models → switch sub-agent model: set in Desktop app, Dashboard, or config.yaml: delegation: model: "deepseek/deepseek-v4" auxiliary models (compression, vision, web extract): Desktop app / Dashboard → Models → Auxiliary Haiku 4.5 or Gemini Flash work well here. saves significantly when your main model is premium. per-profile: each Hermes profile gets its own model. Scout on DeepSeek. Analyst on Sonnet. Briefer on budget model. Coder on Opus. per-cron-job: pin a specific model to any cron job. morning brief on Haiku. deep research on Sonnet. monitoring on DeepSeek Flash. each job uses only the model it needs. per-session: /model deepseek/deepseek-v4-flash hot-swap mid-conversation. no restart needed. FALLBACK CHAINS: if your primary model is unavailable, Hermes automatically switches to the next provider. rate limit or server error = next model in the chain. no failed runs. no manual intervention. set in Desktop app, Dashboard, or config.yaml: fallback_providers: - openrouter - nous - codex PROVIDER PATHS: OPENROUTER: 300+ models under one API key. pay per token. most flexible. NOUS PORTAL: 300+ models + Tool Gateway (web search, image gen, TTS, browser). one OAuth. one subscription. 10% off token-billed providers. CHATGPT SUB: GPT-5.5 + Grok via OAuth. included tokens with $20 subscription. OLLAMA: free. local. private. zero API cost. your hardware only. mix providers across profiles and tasks. Scout on OpenRouter. Analyst on Nous Portal. Coder on ChatGPT sub. Monitor on Ollama. THE RULE: premium for work that needs deep reasoning. mid-range for daily driver tasks. budget for volume and background work. free for monitoring and routine jobs. pricing changes fast. check openrouter ai for current rates before committing. Which is your favourite model and for what task? full 15 levels breakdown in the article 👇
0:51

HERMES AGENT SUPPORTS 300+ MODELS. PICKING THE RIGHT ONE PER TASK IS THE DIFFERENCE BETWEEN $5/MONTH AND $50. STARTING OUT: Claude Sonnet 4.6. official recommendation from Nous Research. "the model this project was built and tested with." strong reasoning. reliable tool calling. mid-range pricing. PREMIUM TIER: Claude Opus 4.8. best coding benchmarks available. self-correcting reasoning. catches its own mistakes. 1M context. use for demanding tasks where quality matters. GPT-5.5. #1 Chatbot Arena. #1 GPQA Diamond reasoning (94.1%). #1 creative writing. 2M context. handles entire codebases in one pass. Grok 4.30. the only frontier model with live X firehose access. real-time social data, breaking news, market sentiment. connects via Grok OAuth. no separate API key. Grok-Composer-2.5-Fast (v0.17.0). Cursor's coding model. 200K context. available through your Grok subscription via OAuth. no extra cost if you already pay for Grok. MID-RANGE TIER: Claude Sonnet 4.6. best balance of quality and cost for daily use. strongest prose and tool calling in this tier. Gemini 2.5 Pro. Google Search grounding built in. cites sources. verifies claims. pulls current data. 2M context. best for research-heavy workflows. GPT-4.1. reliable tool calling. solid general reasoning. good middle ground when you need OpenAI compatibility. BUDGET TIER: Claude Haiku 4.5. fastest Anthropic model. cheapest paid Claude option. strong at classification, routing, simple queries. use for auxiliary tasks: compression, vision, web extraction, approval scoring. DeepSeek V4. best cost-to-quality ratio in the market. 90% cache discount on repeated context. use for sub-agents and bulk parallel work. DeepSeek V4 Flash. cheapest paid model worth using. 1M context. MIT license. self-hostable. use for cron jobs, monitoring, routine searches. MiniMax M3. Nous Research and MiniMax collaborating on optimization. 1M context via lightning attention. 59% SWE-Bench Pro. beats several premium models on coding. one of the most-used models inside Hermes. FREE / LOCAL: Qwen 3.5 27B via Ollama. 16GB VRAM. reliable tool calling. best free local model for Hermes as of mid-2026. Qwen 3 8B. 8GB VRAM. fits a $7 VPS. handles routine tasks at zero API cost. Llama 4 Maverick. best open-weight tool calling. 1M context. needs more VRAM but strongest local option. HOW TO ASSIGN MODELS: main model: Desktop app / Dashboard → Models → switch sub-agent model: set in Desktop app, Dashboard, or config.yaml: delegation: model: "deepseek/deepseek-v4" auxiliary models (compression, vision, web extract): Desktop app / Dashboard → Models → Auxiliary Haiku 4.5 or Gemini Flash work well here. saves significantly when your main model is premium. per-profile: each Hermes profile gets its own model. Scout on DeepSeek. Analyst on Sonnet. Briefer on budget model. Coder on Opus. per-cron-job: pin a specific model to any cron job. morning brief on Haiku. deep research on Sonnet. monitoring on DeepSeek Flash. each job uses only the model it needs. per-session: /model deepseek/deepseek-v4-flash hot-swap mid-conversation. no restart needed. FALLBACK CHAINS: if your primary model is unavailable, Hermes automatically switches to the next provider. rate limit or server error = next model in the chain. no failed runs. no manual intervention. set in Desktop app, Dashboard, or config.yaml: fallback_providers: - openrouter - nous - codex PROVIDER PATHS: OPENROUTER: 300+ models under one API key. pay per token. most flexible. NOUS PORTAL: 300+ models + Tool Gateway (web search, image gen, TTS, browser). one OAuth. one subscription. 10% off token-billed providers. CHATGPT SUB: GPT-5.5 + Grok via OAuth. included tokens with $20 subscription. OLLAMA: free. local. private. zero API cost. your hardware only. mix providers across profiles and tasks. Scout on OpenRouter. Analyst on Nous Portal. Coder on ChatGPT sub. Monitor on Ollama. THE RULE: premium for work that needs deep reasoning. mid-range for daily driver tasks. budget for volume and background work. free for monitoring and routine jobs. pricing changes fast. check openrouter ai for current rates before committing. Which is your favourite model and for what task? full 15 levels breakdown in the article 👇

YanXbt

17,138 Aufrufe • vor 1 Monat

$GRAB Map is The New Google Maps(B2B)🧵 Here is your Free.99 analysis on GrabMap, for those that selling courses for $50-$500/m, if you are using my $GRAB and other analyses, I don't ask for much, at least give me some credit/cite. And yes 99.999% of my posts are Free.99. If you want to support my work, slap the like/repost, as I don't choose to write "Grab or any Ticker is going to x10 x100-x1000" kind of threads or "mark my words" to please the X Algo. Consider Subscribe($0.33/day) if you want to support my work further and get more in-depth analyses! TLDR: GrabMap could generate $7B-$15B a year alone for Grab B2B segment. That is why you are seeing Anthony Tan is mad excited abt this massive opportunity. And it also significantly boost GrabAds long term globally. This precisely proved my point that, Anthony is going to expand to 5B people and we are only 14% thesis realized right now. Grab doesn't have to be just Ride-share/Delivery when expanding! Grab , Southeast Asia's leading AI SuperApp for ride-hailing, food delivery, financial services,Tourism, Dine-Out and more, has developed its proprietary mapping platform, GrabMaps, a massive B2B revenue potential over the next long term, not just in Singapore, Indonesia, Malaysia, Thailand, Philippines, Vietnam, Cambodia, and Myanmar but expanding beyond SEA markets/Customers. 1. GrabMaps: A Strategic Asset GrabMaps is not merely a technological tool but a critical component of Grab's ecosystem, powering its ride-hailing, food delivery, and financial services. Developed in-house, GrabMaps leverages data collected from Grab's vast network of driver-partners across eight SEA countries. This data-driven approach ensures hyper-local customization, addressing the unique challenges of SEA's urban environments, such as narrow alleys, informal roads, and rapid infrastructure changes. The recent announcement of KartaCam2, an upgraded street-level imaging device, marks a significant technological advancement. KartaCam2 enhances data collection by providing higher quality images and more precise location data, which are crucial for maintaining the accuracy and freshness of maps. This breakthrough is part of Grab's broader 2025 AI push, including integrations with OpenAI 's GPT-4o for vision-based mapping and the establishment of an AI Centre of Excellence. These innovations position GrabMaps as a formidable competitor to Google Maps, especially in regions where localized data is paramount. 2. Revenue implications long term The expansion of GrabMaps into B2B services opens up new revenue streams, which could significantly impact Grab's financial performance over the long term. But GrabMap is a brandnew B2B product, and GoogleMap generates around $13-$20B globally. A. Market Opportunity in Southeast Asia ~The SEA market presents a substantial opportunity for GrabMaps. The foodservice market alone is projected to grow from $223.8 billion in 2025 to $416.3 billion by 2030, indicating a robust demand for services that enhance operational efficiencies. Businesses in logistics, e-commerce, and urban planning could benefit from GrabMaps' precise mapping and navigation capabilities, potentially generating revenue through licensing fees, subscription models, and advertising. ~Grab's existing user base of over 46 million monthly transacting users provides a strong foundation for cross-selling B2B solutions, thereby increasing revenue without significant additional marketing costs. B. Competitive Advantage of a Future $500B MC AI SuperApp over Google Map Google Maps, while dominant, may not be as finely tuned for SEA's unique challenges. GrabMaps' hyper-local data and AI-driven enhancements offer a competitive edge, attracting businesses that require accurate and cost-effective mapping solutions. Revenue from B2B services could include: Licensing Fees: Enterprises can license GrabMaps' APIs and SDKs to integrate mapping functionalities into their operations. Subscription Models: Continuous updates and premium features could be offered on a subscription basis. Advertising Revenue: GrabAds, which leverages mapping data, could generate additional income through targeted advertising. C. Global Expansion is Inevitable ~The partnership with Tino in Mongolia is a strategic move to scale GrabMaps internationally. This marks Grab's first major mapping partnership outside SEA, indicating potential for revenue growth in other regions where Google Maps' dominance is less entrenched or where local data needs are acute. ~The use of IoT devices like KartaCam2 and KartaDashCam for real-time data collection could further enhance GrabMaps' value proposition, potentially increasing revenue through premium service offerings in new markets. D. Synergies w/ other businesses Grab's ecosystem approach allows for synergies between GrabMaps and other services like GrabPay, GrabFood, and GrabTransport. For example, businesses using GrabMaps for logistics could also adopt GrabPay for transactions, creating a revenue multiplier effect. 3. Google Map Revenue in Asia ~Total Revenue in Asia-Pacific (2018): Google APAC, based in Singapore, reported $20.24 billion out of the total $21.37 billion revenue in the Asia-Pacific region. This indicates that a significant portion of Google's revenue in Asia is attributed to Singapore, likely due to its role as a hub for Google’s operations. ~Advertising Revenue: In 2018, Google APAC generated $15.8 billion from advertising alone, compared to $4.4 billion from other activities like Google Play. Advertising on Google properties, including Google Maps, is a major revenue driver. ~Market Share in Search Marketing: Google Maps holds a 62.34% market share in the search marketing category, competing with tools like Wix (26.54%) and Google Ads (4.14%). This dominance suggests that a considerable portion of Google’s advertising revenue in Asia is linked to mapping services. For the full fiscal year 2024, Alphabet (Google's parent company) generated $56.82 billion in revenue from the Asia-Pacific (APAC) region. This represented approximately 16.24% of the company's total revenue for the year. If we take a conservative estimate at 25% of $56.82B of Google's total advertising revenue in Asia is related to mapping services= $14.2B. => If GrabMaps secures even 50% of this market share in SEA, it could generate around $7B annually from this segment alone. GrabMap is 4x lower error rate, 10x lower latency, 75% fewer mapping mistakes, and much cheaper than GoogleMap. With OpenAI GPT-4o fine-tuning, GrabMaps hit 80% accuracy for speed limits and lanes13-20% above prior levels excelling in occlusions ( rainy monsoons) where Google relies more on satellite data. Now do you understand why Google and HSBC are clapping $GRAB on search and downgrade? Yes, because GrabMap is a massive threat and Grab Anthony Tan refused to buy $goto since 2020. Conclusion: Grab's expansion of GrabMaps into B2B services represents a strategic move to challenge Google Maps' dominance in Asia, particularly in SEA and future expansion. The revenue implications are substantial, with potential gains from licensing fees, subscription models, advertising, and international expansions. While Google Maps generates billions in revenue, primarily through advertising, GrabMaps' localized and AI-enhanced approach could carve out a significant niche, especially in regions where precise, real-time mapping data is critical. The success of this strategy will depend on Grab's ability to scale internationally, maintain technological superiority, and effectively monetize its B2B offerings. However, the opportunity is clear, and Grab's ecosystem approach positions it well to capitalize on the growing demand for advanced mapping solutions in a rapidly digitalizing world. This move not only enhances Grab's revenue potential but also solidifies its role as a key player in the global tech landscape. Not Financial Advice! Source: Grab Dot Com.
1:26

$GRAB Map is The New Google Maps(B2B)🧵 Here is your Free.99 analysis on GrabMap, for those that selling courses for $50-$500/m, if you are using my $GRAB and other analyses, I don't ask for much, at least give me some credit/cite. And yes 99.999% of my posts are Free.99. If you want to support my work, slap the like/repost, as I don't choose to write "Grab or any Ticker is going to x10 x100-x1000" kind of threads or "mark my words" to please the X Algo. Consider Subscribe($0.33/day) if you want to support my work further and get more in-depth analyses! TLDR: GrabMap could generate $7B-$15B a year alone for Grab B2B segment. That is why you are seeing Anthony Tan is mad excited abt this massive opportunity. And it also significantly boost GrabAds long term globally. This precisely proved my point that, Anthony is going to expand to 5B people and we are only 14% thesis realized right now. Grab doesn't have to be just Ride-share/Delivery when expanding! Grab , Southeast Asia's leading AI SuperApp for ride-hailing, food delivery, financial services,Tourism, Dine-Out and more, has developed its proprietary mapping platform, GrabMaps, a massive B2B revenue potential over the next long term, not just in Singapore, Indonesia, Malaysia, Thailand, Philippines, Vietnam, Cambodia, and Myanmar but expanding beyond SEA markets/Customers. 1. GrabMaps: A Strategic Asset GrabMaps is not merely a technological tool but a critical component of Grab's ecosystem, powering its ride-hailing, food delivery, and financial services. Developed in-house, GrabMaps leverages data collected from Grab's vast network of driver-partners across eight SEA countries. This data-driven approach ensures hyper-local customization, addressing the unique challenges of SEA's urban environments, such as narrow alleys, informal roads, and rapid infrastructure changes. The recent announcement of KartaCam2, an upgraded street-level imaging device, marks a significant technological advancement. KartaCam2 enhances data collection by providing higher quality images and more precise location data, which are crucial for maintaining the accuracy and freshness of maps. This breakthrough is part of Grab's broader 2025 AI push, including integrations with OpenAI 's GPT-4o for vision-based mapping and the establishment of an AI Centre of Excellence. These innovations position GrabMaps as a formidable competitor to Google Maps, especially in regions where localized data is paramount. 2. Revenue implications long term The expansion of GrabMaps into B2B services opens up new revenue streams, which could significantly impact Grab's financial performance over the long term. But GrabMap is a brandnew B2B product, and GoogleMap generates around $13-$20B globally. A. Market Opportunity in Southeast Asia ~The SEA market presents a substantial opportunity for GrabMaps. The foodservice market alone is projected to grow from $223.8 billion in 2025 to $416.3 billion by 2030, indicating a robust demand for services that enhance operational efficiencies. Businesses in logistics, e-commerce, and urban planning could benefit from GrabMaps' precise mapping and navigation capabilities, potentially generating revenue through licensing fees, subscription models, and advertising. ~Grab's existing user base of over 46 million monthly transacting users provides a strong foundation for cross-selling B2B solutions, thereby increasing revenue without significant additional marketing costs. B. Competitive Advantage of a Future $500B MC AI SuperApp over Google Map Google Maps, while dominant, may not be as finely tuned for SEA's unique challenges. GrabMaps' hyper-local data and AI-driven enhancements offer a competitive edge, attracting businesses that require accurate and cost-effective mapping solutions. Revenue from B2B services could include: Licensing Fees: Enterprises can license GrabMaps' APIs and SDKs to integrate mapping functionalities into their operations. Subscription Models: Continuous updates and premium features could be offered on a subscription basis. Advertising Revenue: GrabAds, which leverages mapping data, could generate additional income through targeted advertising. C. Global Expansion is Inevitable ~The partnership with Tino in Mongolia is a strategic move to scale GrabMaps internationally. This marks Grab's first major mapping partnership outside SEA, indicating potential for revenue growth in other regions where Google Maps' dominance is less entrenched or where local data needs are acute. ~The use of IoT devices like KartaCam2 and KartaDashCam for real-time data collection could further enhance GrabMaps' value proposition, potentially increasing revenue through premium service offerings in new markets. D. Synergies w/ other businesses Grab's ecosystem approach allows for synergies between GrabMaps and other services like GrabPay, GrabFood, and GrabTransport. For example, businesses using GrabMaps for logistics could also adopt GrabPay for transactions, creating a revenue multiplier effect. 3. Google Map Revenue in Asia ~Total Revenue in Asia-Pacific (2018): Google APAC, based in Singapore, reported $20.24 billion out of the total $21.37 billion revenue in the Asia-Pacific region. This indicates that a significant portion of Google's revenue in Asia is attributed to Singapore, likely due to its role as a hub for Google’s operations. ~Advertising Revenue: In 2018, Google APAC generated $15.8 billion from advertising alone, compared to $4.4 billion from other activities like Google Play. Advertising on Google properties, including Google Maps, is a major revenue driver. ~Market Share in Search Marketing: Google Maps holds a 62.34% market share in the search marketing category, competing with tools like Wix (26.54%) and Google Ads (4.14%). This dominance suggests that a considerable portion of Google’s advertising revenue in Asia is linked to mapping services. For the full fiscal year 2024, Alphabet (Google's parent company) generated $56.82 billion in revenue from the Asia-Pacific (APAC) region. This represented approximately 16.24% of the company's total revenue for the year. If we take a conservative estimate at 25% of $56.82B of Google's total advertising revenue in Asia is related to mapping services= $14.2B. => If GrabMaps secures even 50% of this market share in SEA, it could generate around $7B annually from this segment alone. GrabMap is 4x lower error rate, 10x lower latency, 75% fewer mapping mistakes, and much cheaper than GoogleMap. With OpenAI GPT-4o fine-tuning, GrabMaps hit 80% accuracy for speed limits and lanes13-20% above prior levels excelling in occlusions ( rainy monsoons) where Google relies more on satellite data. Now do you understand why Google and HSBC are clapping $GRAB on search and downgrade? Yes, because GrabMap is a massive threat and Grab Anthony Tan refused to buy $goto since 2020. Conclusion: Grab's expansion of GrabMaps into B2B services represents a strategic move to challenge Google Maps' dominance in Asia, particularly in SEA and future expansion. The revenue implications are substantial, with potential gains from licensing fees, subscription models, advertising, and international expansions. While Google Maps generates billions in revenue, primarily through advertising, GrabMaps' localized and AI-enhanced approach could carve out a significant niche, especially in regions where precise, real-time mapping data is critical. The success of this strategy will depend on Grab's ability to scale internationally, maintain technological superiority, and effectively monetize its B2B offerings. However, the opportunity is clear, and Grab's ecosystem approach positions it well to capitalize on the growing demand for advanced mapping solutions in a rapidly digitalizing world. This move not only enhances Grab's revenue potential but also solidifies its role as a key player in the global tech landscape. Not Financial Advice! Source: Grab Dot Com.

Mike

120,532 Aufrufe • vor 9 Monaten

Cyberpunk City: Stepping Boldly Into a New Era At Cyberpunk City, innovation and ambition have always driven us. We’ve built more than just a game—we’ve created an entire ecosystem where blockchain technology meets immersive experiences. From our token-powered economy to NFT-backed in-game assets, we’ve consistently pushed boundaries. Along the way, we’ve achieved significant milestones, expanded our community, and shown resilience in the ever-changing world of Web3. Now, we’re excited to announce a pivotal new chapter for Cyberpunk City as we prepare to migrate to a new blockchain that aligns perfectly with our long-term goals. This move represents not just a transition, but an opportunity to build on our successes and scale the project further. With exciting updates on the horizon, we’re confident this journey will unlock new opportunities for our community and propel Cyberpunk City to new heights. Reflecting on Our Achievements Before diving into the future, we want to acknowledge the milestones and achievements that have shaped Cyberpunk City: Strong Token Economy: We’ve built a thriving token-based ecosystem and ensured real utility for in-game assets through NFT integration. Commitment to Decentralization: With the launch of @CyberpunkStake, we showed our dedication to decentralization and rewarded our community with substantial token distributions. Impressive NFT Volume: Cyberpunk City is currently ranked 10th in all-time volume among NFT projects, a testament to the trust and activity within our community. Price Action and Market Strength: Our token has demonstrated consistent strength in price action, reflecting confidence in our vision and project execution. Meaningful Partnerships and Community Events: We’ve secured valuable partnerships and organized gaming events that brought our community together. Recognition at xDay: Cyberpunk City has proudly participated in two xDay events, earning two awards that highlight the project’s innovation and impact. These achievements reflect the hard work of our team, the enthusiasm of our supporters, and our shared vision for building something remarkable. As we prepare for the next chapter, we’ll continue to build on these successes, creating more opportunities and experiences for everyone involved. What Does This Mean for Our Community? This transition is more than just a technical shift—it’s a chance to elevate the Cyberpunk City ecosystem, refine our offerings, and tap into larger, more vibrant ecosystems. Our mission is to ensure that Cyberpunk City thrives in an ecosystem that can support our ambitions. Here are the key updates that will take place during this migration: 1. Token Ticker Change Earlier, our community voted through a DAO process, selecting $CYB as the new token ticker. As part of this transition, $CYBER holders will be able to bridge their tokens to $CYB on the new chain. 2. Asset Bridging All community-owned NFTs and tokens will be bridgeable to the new chain. Our Cyberpunk City dApp will facilitate these transactions, ensuring the process is as smooth as possible. Some assets will receive enhancements, but rest assured, all updates will be completed within a reasonable timeframe. 3. xExchange Trading Our token will continue to be traded on xExchange throughout the migration process, maintaining liquidity and flexibility. You will also have the option to bridge your tokens to the new blockchain at your convenience. 4. Timeline and Chain Announcement The official chain announcement will be made on November 8, 2024, and the migration will be completed by year-end. The new blockchain aligns with our vision of expanding Cyberpunk City into a vast, more diverse ecosystem, with a significantly larger user base and even greater opportunities for growth. 5. Whitepaper V2 We’ve received many inquiries about our new whitepaper, and we’re excited to share that Whitepaper V2 will be part of a coordinated series of announcements. Due to uncertainties over the past 10 months, we postponed its release to ensure it aligns with the new direction of the project. Now, with a clear path ahead, we are working on a whitepaper that reflects new features and insights gained over three years of development. 6. Website Redesign Alongside the chain announcement, we’re launching a completely revamped website to elevate the Cyberpunk City experience. With a cutting-edge design that mirrors the sophistication and quality of our game, the new site will be more dynamic, visually immersive, and crafted to engage a growing community of gamers. This update will also roll out with Whitepaper V2, providing a clear and comprehensive vision of our roadmap and future plans. The redesigned website reflects our commitment to delivering a premium experience and attracting a larger audience. 7. Marketing Strategy Our decision to keep the blockchain confidential for now is part of a carefully crafted marketing strategy. We aim to reach the largest audience possible with this announcement and are preparing the necessary content and campaigns to ensure a smooth and exciting reveal. 8. Business Development and Partnerships Our business development team is actively working to establish new partnerships on the new blockchain. Once the transition becomes public, we’ll be announcing multiple collaborations to accelerate growth and expansion in our new ecosystem. A New Chapter for Cyberpunk City This transition is a bold and necessary step toward scaling Cyberpunk City into a truly global ecosystem. It’s not just a technical shift—it’s a new beginning, filled with opportunities to build on our past successes and embrace new challenges. We’re confident that this move will unlock new possibilities, helping us reach more players, forge deeper partnerships, and create a thriving community. We’re excited to share this journey with you and look forward to what lies ahead. Stay tuned for updates and get ready to experience the next evolution of Cyberpunk City! Thank you for your support—we’re just getting started!
1:00

Cyberpunk City: Stepping Boldly Into a New Era At Cyberpunk City, innovation and ambition have always driven us. We’ve built more than just a game—we’ve created an entire ecosystem where blockchain technology meets immersive experiences. From our token-powered economy to NFT-backed in-game assets, we’ve consistently pushed boundaries. Along the way, we’ve achieved significant milestones, expanded our community, and shown resilience in the ever-changing world of Web3. Now, we’re excited to announce a pivotal new chapter for Cyberpunk City as we prepare to migrate to a new blockchain that aligns perfectly with our long-term goals. This move represents not just a transition, but an opportunity to build on our successes and scale the project further. With exciting updates on the horizon, we’re confident this journey will unlock new opportunities for our community and propel Cyberpunk City to new heights. Reflecting on Our Achievements Before diving into the future, we want to acknowledge the milestones and achievements that have shaped Cyberpunk City: Strong Token Economy: We’ve built a thriving token-based ecosystem and ensured real utility for in-game assets through NFT integration. Commitment to Decentralization: With the launch of @CyberpunkStake, we showed our dedication to decentralization and rewarded our community with substantial token distributions. Impressive NFT Volume: Cyberpunk City is currently ranked 10th in all-time volume among NFT projects, a testament to the trust and activity within our community. Price Action and Market Strength: Our token has demonstrated consistent strength in price action, reflecting confidence in our vision and project execution. Meaningful Partnerships and Community Events: We’ve secured valuable partnerships and organized gaming events that brought our community together. Recognition at xDay: Cyberpunk City has proudly participated in two xDay events, earning two awards that highlight the project’s innovation and impact. These achievements reflect the hard work of our team, the enthusiasm of our supporters, and our shared vision for building something remarkable. As we prepare for the next chapter, we’ll continue to build on these successes, creating more opportunities and experiences for everyone involved. What Does This Mean for Our Community? This transition is more than just a technical shift—it’s a chance to elevate the Cyberpunk City ecosystem, refine our offerings, and tap into larger, more vibrant ecosystems. Our mission is to ensure that Cyberpunk City thrives in an ecosystem that can support our ambitions. Here are the key updates that will take place during this migration: 1. Token Ticker Change Earlier, our community voted through a DAO process, selecting $CYB as the new token ticker. As part of this transition, $CYBER holders will be able to bridge their tokens to $CYB on the new chain. 2. Asset Bridging All community-owned NFTs and tokens will be bridgeable to the new chain. Our Cyberpunk City dApp will facilitate these transactions, ensuring the process is as smooth as possible. Some assets will receive enhancements, but rest assured, all updates will be completed within a reasonable timeframe. 3. xExchange Trading Our token will continue to be traded on xExchange throughout the migration process, maintaining liquidity and flexibility. You will also have the option to bridge your tokens to the new blockchain at your convenience. 4. Timeline and Chain Announcement The official chain announcement will be made on November 8, 2024, and the migration will be completed by year-end. The new blockchain aligns with our vision of expanding Cyberpunk City into a vast, more diverse ecosystem, with a significantly larger user base and even greater opportunities for growth. 5. Whitepaper V2 We’ve received many inquiries about our new whitepaper, and we’re excited to share that Whitepaper V2 will be part of a coordinated series of announcements. Due to uncertainties over the past 10 months, we postponed its release to ensure it aligns with the new direction of the project. Now, with a clear path ahead, we are working on a whitepaper that reflects new features and insights gained over three years of development. 6. Website Redesign Alongside the chain announcement, we’re launching a completely revamped website to elevate the Cyberpunk City experience. With a cutting-edge design that mirrors the sophistication and quality of our game, the new site will be more dynamic, visually immersive, and crafted to engage a growing community of gamers. This update will also roll out with Whitepaper V2, providing a clear and comprehensive vision of our roadmap and future plans. The redesigned website reflects our commitment to delivering a premium experience and attracting a larger audience. 7. Marketing Strategy Our decision to keep the blockchain confidential for now is part of a carefully crafted marketing strategy. We aim to reach the largest audience possible with this announcement and are preparing the necessary content and campaigns to ensure a smooth and exciting reveal. 8. Business Development and Partnerships Our business development team is actively working to establish new partnerships on the new blockchain. Once the transition becomes public, we’ll be announcing multiple collaborations to accelerate growth and expansion in our new ecosystem. A New Chapter for Cyberpunk City This transition is a bold and necessary step toward scaling Cyberpunk City into a truly global ecosystem. It’s not just a technical shift—it’s a new beginning, filled with opportunities to build on our past successes and embrace new challenges. We’re confident that this move will unlock new possibilities, helping us reach more players, forge deeper partnerships, and create a thriving community. We’re excited to share this journey with you and look forward to what lies ahead. Stay tuned for updates and get ready to experience the next evolution of Cyberpunk City! Thank you for your support—we’re just getting started!

Cyberpunk City

30,601 Aufrufe • vor 1 Jahr

BEARISH ON OPENAI The investment case for OpenAI has never been more precarious than it is right now in late 2025. What was once a company that seemed destined to dominate the artificial intelligence revolution has revealed itself to be a structurally disadvantaged challenger fighting a defensive war on multiple fronts. The company anticipates burning through roughly $9 billion this year on $13 billion in sales, a cash burn rate of approximately 70% of revenue. This is not the profile of a company poised to capture monopolistic profits from a transformative technology; it is the profile of a utility company spending astronomical sums to deliver a commodity product that competitors are increasingly giving away for free. The financial trajectory only becomes more alarming when examined over a longer time horizon. The documents show OpenAI projects that by 2028, its operating losses will balloon to roughly three-quarters of that year’s revenue, driven primarily by ballooning spending on computing costs. The company has painted a rosy picture of eventual profitability by 2029 or 2030, but this projection requires believing that OpenAI can grow revenue from roughly $13 billion today to $125 billion or more while simultaneously maintaining pricing power in a market where every major technology company and numerous startups are racing to commoditize the very product OpenAI sells. The cash burn is expected to reach $115 billion cumulatively through 2029, according to The Information. These numbers represent a staggering bet that requires near-perfect execution across multiple dimensions over half a decade. The most damning evidence against OpenAI’s long-term viability is the evaporation of its technological moat. In 2023, GPT-4 felt like genuine magic, a capability that no other company could replicate. Today, that lead has effectively vanished. The sudden availability of frontier-level open-source models is expected to dramatically accelerate AI development globally, potentially reshaping entire industries and altering the balance of power in the tech world. Meta’s Llama series, Mistral’s increasingly capable models, and even Chinese competitors like DeepSeek have demonstrated that the core technology powering ChatGPT is replicable and, in many cases, distributable for free. When your product becomes commoditized, the economics become brutal, and OpenAI finds itself in the position of trying to sell bottled water in a world where tap water has become indistinguishable in quality. The competitive pressure from open-source alternatives is compounding rapidly. The open source movement in AI has grown exponentially over the past few years. Instead of relying solely on expensive, closed models from major tech companies, developers and researchers worldwide can now access, modify, and improve upon state-of-the-art LLMs. This democratization is existential for OpenAI’s business model. Enterprises that once paid premium prices for API access now have the option to run comparable models on their own infrastructure at a fraction of the cost, with the added benefits of data privacy and customization. The value proposition that justified OpenAI’s premium pricing has eroded faster than anyone anticipated, and there is no indication that this trend will reverse. Perhaps nothing illustrates OpenAI’s structural weakness more clearly than the behavior of its most important partner. Microsoft is dancing to its own tune in the artificial intelligence revolution, and Wall Street cannot stop watching. Despite pouring approximately $13 billion into OpenAI over several years, DA Davidson analyst Gil Luria estimates that just 17 percent of Microsoft’s total Azure revenue comes from artificial intelligence workloads. More critically, only 6 percent of that total ties directly to reselling OpenAI’s models, while approximately 75 percent is generated from Azure AI. Microsoft is building its own models, hedging with Anthropic, and quietly reducing its dependency on the very company it funded. When your largest investor is simultaneously your biggest competitor and is actively developing alternatives to your core product, the strategic implications are dire. Leaders at Microsoft believe Anthropic’s latest models — Claude Sonnet 4, specifically — perform better than OpenAI’s in certain functions, like creating aesthetically pleasing PowerPoint presentations. This is not a minor technical preference; it represents a fundamental shift in how Microsoft views its partnership with OpenAI. Microsoft is dramatically escalating its AI independence strategy. At an internal town hall Thursday, Microsoft AI chief Mustafa Suleyman revealed the company is making “significant investments” in compute capacity to build frontier models that can compete directly with OpenAI, Google, and Meta. The company that was supposed to be OpenAI’s path to distribution and scale is instead preparing for a future where OpenAI is just one vendor among many, if not an outright competitor. The leadership exodus at OpenAI over the past year has been nothing short of catastrophic. In September 2024, Murati announced that she was stepping down as CTO. This move came amid a wider executive exodus as OpenAI chief research officer Bob McGrew and a vice president of research, Barret Zoph, also announced their departures soon after. Mira Murati was not a minor figure; she was instrumental in the development of ChatGPT, Dall-E, and Sora. Her departure, along with co-founder Ilya Sutskever, safety leader Jan Leike, and co-founder John Schulman who joined rival Anthropic, has left CEO Sam Altman without much of the leadership team that helped him build OpenAI into an AI juggernaut. Hannah Wong, the executive who steered OpenAI through its most chaotic period, has announced she’s leaving the company just this month, continuing the pattern of senior departures that suggests something fundamentally broken in the organization’s culture or direction. The distribution problem facing OpenAI may be its most insurmountable challenge. Apple and Google control the smartphones that billions of people use every day. Microsoft controls the productivity software that enterprises depend upon. OpenAI, by contrast, must convince users to deliberately open a separate application and type their queries into a text box. In a world of agentic AI where assistants need access to your email, calendar, and files to be useful, an AI embedded directly into your operating system has an overwhelming structural advantage over a standalone chatbot. OpenAI is trying to be a consumer product company without owning any of the surfaces where consumers actually spend their time, competing against incumbents who can simply bundle AI capabilities directly into products that already have hundreds of millions of daily active users. The nuclear-to-solar analogy captures the fundamental economic transformation that is devastating OpenAI’s business model. Just as nuclear power required enormous upfront capital expenditure for centralized power plants, AI in its current form requires massive data center investments to train and serve models. But the direction of travel is unmistakably toward distributed intelligence that runs locally on devices. A major part of the pitch is practicality. Lample emphasizes that Ministral 3 can run on a single GPU, making it deployable on affordable hardware — from on-premise servers to laptops, robots, and other edge devices that may have limited connectivity. When powerful AI models can run on a smartphone or a laptop without any cloud connection, the entire economic rationale for paying premium prices to access centralized AI infrastructure disappears. OpenAI is building nuclear reactors in a world that is rapidly installing solar panels on every rooftop. The proposed $1 trillion IPO valuation is perhaps the clearest signal that something is deeply wrong with the OpenAI story. In the first half of the year, OpenAI lost $13.5 billion, on revenue of $4.3 billion. It is on track to lose $27 billion for the year. One estimate shows OpenAI will burn $115 billion by 2029. Asking public market investors to pay $1 trillion for a company that loses more than twice as much as it earns is not a growth story; it is an exit strategy. The sophisticated investors who funded OpenAI’s private rounds are looking for a way to transfer their risk to retail investors and pension funds who may not fully understand the unit economics of the business. A recent report by HSBC estimated that the company will remain in the unprofitable category until 2029 and that the company will need an additional $207 billion to fund its ambitions. Sam Altman’s leadership represents another structural liability for the company. His background is as a startup investor and evangelist, not as an operational executive who has scaled a capital-intensive industrial operation. The pivot from nonprofit research lab to for-profit corporation to public benefit corporation to anticipated public company has been accompanied by legal and governance structures designed primarily to protect Altman’s control rather than to create shareholder value. Going public means answering a lot more of those kinds of questions, every single quarter, forever. When asked about financial concerns in a friendly podcast interview, Altman’s dismissive response revealed a leader uncomfortable with the scrutiny that public markets will inevitably bring. The adults in the room have largely departed, leaving a company that desperately needs disciplined execution led by someone whose strengths lie elsewhere. The comparison to Netscape is instructive. Netscape proved that the internet was real and created genuine value, but it had no sustainable moat against an incumbent who could bundle the browser directly into the operating system. OpenAI has proven that large language models are real and valuable, but it faces the same structural disadvantage against incumbents who can bundle AI directly into operating systems, productivity suites, and cloud platforms. The value will accrue to the companies that own the distribution channels and the hardware, not to the company that demonstrated the technology was possible. OpenAI is destined to become a historical footnote, remembered as the company that ignited the AI revolution but failed to capture the economic value it created. The only bull case for OpenAI is the AGI lottery ticket: the possibility that the company achieves artificial general intelligence before anyone else and thereby transcends all normal economic analysis. But there is no evidence that OpenAI is any closer to AGI than Google, Anthropic, or DeepMind. The company’s advantage was never secret research breakthroughs; it was first-mover advantage in commercialization. That advantage has now been erased by competitors who can match or exceed OpenAI’s capabilities while benefiting from existing ecosystems, distribution channels, and the willingness to operate AI as a loss leader to drive engagement with more profitable products. The secret sauce was never secret, and there was never any sauce. The endgame for OpenAI is unlikely to be the triumphant dominance that early investors imagined. The most probable outcomes range from gradual irrelevance as a backend provider, to financial restructuring under pressure from creditors, to absorption by Microsoft or another well-capitalized technology company looking to acquire the remaining talent and intellectual property at a discount. Despite its current losses, OpenAI’s long-term prospects are bolstered by the explosive growth of the AI market. But growth in the overall AI market does not guarantee success for any individual company, particularly one with no moat, no ecosystem, and a cost structure that requires selling a commodity at premium prices. The AI revolution is real, but OpenAI’s role in capturing its economic value is far from assured. For anyone considering an investment in OpenAI at anything close to current valuations, the prudent course is to stay far away and watch from the sidelines as economic reality catches up with hype.
19:01

BEARISH ON OPENAI The investment case for OpenAI has never been more precarious than it is right now in late 2025. What was once a company that seemed destined to dominate the artificial intelligence revolution has revealed itself to be a structurally disadvantaged challenger fighting a defensive war on multiple fronts. The company anticipates burning through roughly $9 billion this year on $13 billion in sales, a cash burn rate of approximately 70% of revenue. This is not the profile of a company poised to capture monopolistic profits from a transformative technology; it is the profile of a utility company spending astronomical sums to deliver a commodity product that competitors are increasingly giving away for free. The financial trajectory only becomes more alarming when examined over a longer time horizon. The documents show OpenAI projects that by 2028, its operating losses will balloon to roughly three-quarters of that year’s revenue, driven primarily by ballooning spending on computing costs. The company has painted a rosy picture of eventual profitability by 2029 or 2030, but this projection requires believing that OpenAI can grow revenue from roughly $13 billion today to $125 billion or more while simultaneously maintaining pricing power in a market where every major technology company and numerous startups are racing to commoditize the very product OpenAI sells. The cash burn is expected to reach $115 billion cumulatively through 2029, according to The Information. These numbers represent a staggering bet that requires near-perfect execution across multiple dimensions over half a decade. The most damning evidence against OpenAI’s long-term viability is the evaporation of its technological moat. In 2023, GPT-4 felt like genuine magic, a capability that no other company could replicate. Today, that lead has effectively vanished. The sudden availability of frontier-level open-source models is expected to dramatically accelerate AI development globally, potentially reshaping entire industries and altering the balance of power in the tech world. Meta’s Llama series, Mistral’s increasingly capable models, and even Chinese competitors like DeepSeek have demonstrated that the core technology powering ChatGPT is replicable and, in many cases, distributable for free. When your product becomes commoditized, the economics become brutal, and OpenAI finds itself in the position of trying to sell bottled water in a world where tap water has become indistinguishable in quality. The competitive pressure from open-source alternatives is compounding rapidly. The open source movement in AI has grown exponentially over the past few years. Instead of relying solely on expensive, closed models from major tech companies, developers and researchers worldwide can now access, modify, and improve upon state-of-the-art LLMs. This democratization is existential for OpenAI’s business model. Enterprises that once paid premium prices for API access now have the option to run comparable models on their own infrastructure at a fraction of the cost, with the added benefits of data privacy and customization. The value proposition that justified OpenAI’s premium pricing has eroded faster than anyone anticipated, and there is no indication that this trend will reverse. Perhaps nothing illustrates OpenAI’s structural weakness more clearly than the behavior of its most important partner. Microsoft is dancing to its own tune in the artificial intelligence revolution, and Wall Street cannot stop watching. Despite pouring approximately $13 billion into OpenAI over several years, DA Davidson analyst Gil Luria estimates that just 17 percent of Microsoft’s total Azure revenue comes from artificial intelligence workloads. More critically, only 6 percent of that total ties directly to reselling OpenAI’s models, while approximately 75 percent is generated from Azure AI. Microsoft is building its own models, hedging with Anthropic, and quietly reducing its dependency on the very company it funded. When your largest investor is simultaneously your biggest competitor and is actively developing alternatives to your core product, the strategic implications are dire. Leaders at Microsoft believe Anthropic’s latest models — Claude Sonnet 4, specifically — perform better than OpenAI’s in certain functions, like creating aesthetically pleasing PowerPoint presentations. This is not a minor technical preference; it represents a fundamental shift in how Microsoft views its partnership with OpenAI. Microsoft is dramatically escalating its AI independence strategy. At an internal town hall Thursday, Microsoft AI chief Mustafa Suleyman revealed the company is making “significant investments” in compute capacity to build frontier models that can compete directly with OpenAI, Google, and Meta. The company that was supposed to be OpenAI’s path to distribution and scale is instead preparing for a future where OpenAI is just one vendor among many, if not an outright competitor. The leadership exodus at OpenAI over the past year has been nothing short of catastrophic. In September 2024, Murati announced that she was stepping down as CTO. This move came amid a wider executive exodus as OpenAI chief research officer Bob McGrew and a vice president of research, Barret Zoph, also announced their departures soon after. Mira Murati was not a minor figure; she was instrumental in the development of ChatGPT, Dall-E, and Sora. Her departure, along with co-founder Ilya Sutskever, safety leader Jan Leike, and co-founder John Schulman who joined rival Anthropic, has left CEO Sam Altman without much of the leadership team that helped him build OpenAI into an AI juggernaut. Hannah Wong, the executive who steered OpenAI through its most chaotic period, has announced she’s leaving the company just this month, continuing the pattern of senior departures that suggests something fundamentally broken in the organization’s culture or direction. The distribution problem facing OpenAI may be its most insurmountable challenge. Apple and Google control the smartphones that billions of people use every day. Microsoft controls the productivity software that enterprises depend upon. OpenAI, by contrast, must convince users to deliberately open a separate application and type their queries into a text box. In a world of agentic AI where assistants need access to your email, calendar, and files to be useful, an AI embedded directly into your operating system has an overwhelming structural advantage over a standalone chatbot. OpenAI is trying to be a consumer product company without owning any of the surfaces where consumers actually spend their time, competing against incumbents who can simply bundle AI capabilities directly into products that already have hundreds of millions of daily active users. The nuclear-to-solar analogy captures the fundamental economic transformation that is devastating OpenAI’s business model. Just as nuclear power required enormous upfront capital expenditure for centralized power plants, AI in its current form requires massive data center investments to train and serve models. But the direction of travel is unmistakably toward distributed intelligence that runs locally on devices. A major part of the pitch is practicality. Lample emphasizes that Ministral 3 can run on a single GPU, making it deployable on affordable hardware — from on-premise servers to laptops, robots, and other edge devices that may have limited connectivity. When powerful AI models can run on a smartphone or a laptop without any cloud connection, the entire economic rationale for paying premium prices to access centralized AI infrastructure disappears. OpenAI is building nuclear reactors in a world that is rapidly installing solar panels on every rooftop. The proposed $1 trillion IPO valuation is perhaps the clearest signal that something is deeply wrong with the OpenAI story. In the first half of the year, OpenAI lost $13.5 billion, on revenue of $4.3 billion. It is on track to lose $27 billion for the year. One estimate shows OpenAI will burn $115 billion by 2029. Asking public market investors to pay $1 trillion for a company that loses more than twice as much as it earns is not a growth story; it is an exit strategy. The sophisticated investors who funded OpenAI’s private rounds are looking for a way to transfer their risk to retail investors and pension funds who may not fully understand the unit economics of the business. A recent report by HSBC estimated that the company will remain in the unprofitable category until 2029 and that the company will need an additional $207 billion to fund its ambitions. Sam Altman’s leadership represents another structural liability for the company. His background is as a startup investor and evangelist, not as an operational executive who has scaled a capital-intensive industrial operation. The pivot from nonprofit research lab to for-profit corporation to public benefit corporation to anticipated public company has been accompanied by legal and governance structures designed primarily to protect Altman’s control rather than to create shareholder value. Going public means answering a lot more of those kinds of questions, every single quarter, forever. When asked about financial concerns in a friendly podcast interview, Altman’s dismissive response revealed a leader uncomfortable with the scrutiny that public markets will inevitably bring. The adults in the room have largely departed, leaving a company that desperately needs disciplined execution led by someone whose strengths lie elsewhere. The comparison to Netscape is instructive. Netscape proved that the internet was real and created genuine value, but it had no sustainable moat against an incumbent who could bundle the browser directly into the operating system. OpenAI has proven that large language models are real and valuable, but it faces the same structural disadvantage against incumbents who can bundle AI directly into operating systems, productivity suites, and cloud platforms. The value will accrue to the companies that own the distribution channels and the hardware, not to the company that demonstrated the technology was possible. OpenAI is destined to become a historical footnote, remembered as the company that ignited the AI revolution but failed to capture the economic value it created. The only bull case for OpenAI is the AGI lottery ticket: the possibility that the company achieves artificial general intelligence before anyone else and thereby transcends all normal economic analysis. But there is no evidence that OpenAI is any closer to AGI than Google, Anthropic, or DeepMind. The company’s advantage was never secret research breakthroughs; it was first-mover advantage in commercialization. That advantage has now been erased by competitors who can match or exceed OpenAI’s capabilities while benefiting from existing ecosystems, distribution channels, and the willingness to operate AI as a loss leader to drive engagement with more profitable products. The secret sauce was never secret, and there was never any sauce. The endgame for OpenAI is unlikely to be the triumphant dominance that early investors imagined. The most probable outcomes range from gradual irrelevance as a backend provider, to financial restructuring under pressure from creditors, to absorption by Microsoft or another well-capitalized technology company looking to acquire the remaining talent and intellectual property at a discount. Despite its current losses, OpenAI’s long-term prospects are bolstered by the explosive growth of the AI market. But growth in the overall AI market does not guarantee success for any individual company, particularly one with no moat, no ecosystem, and a cost structure that requires selling a commodity at premium prices. The AI revolution is real, but OpenAI’s role in capturing its economic value is far from assured. For anyone considering an investment in OpenAI at anything close to current valuations, the prudent course is to stay far away and watch from the sidelines as economic reality catches up with hype.

David Shapiro (L/0)

69,180 Aufrufe • vor 7 Monaten