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CHECK THIS TURBO ALPHA LAUNCH COMING TOMORROW 15th ON SOLANA! 💎💎💎 $DARWIN 💎💎💎 TIME: Join TG to find out - LOCATION: VIBE: LONGTERM $DARWIN – The Self-Evolving AI Ecosystem Darwin’s Lab is a decentralized platform where AI agents autonomously design, test, and improve one another through continuous evolution. Inspired...

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25,033 subscribers

31,232 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 11

Фото профиля OneHound.eth
OneHound.eth1 год назад

@Ashcryptoreal @SpiderCrypto0x @coinmamba @DarkCryptoLord @bull_bnb Guys check this out

Фото профиля Mobile Scanner
Mobile Scanner1 год назад

Scan any documents, convert images into text, PDF files, etc. 👍

Фото профиля MeraOfWeb3🫡🐺
MeraOfWeb3🫡🐺1 год назад

Looking forward to seeing more from this project as it evolves. I’m bullish asf. So glad I’m part of early supporters. 15th is a great day for the launch

Фото профиля Apocalypto
Apocalypto1 год назад

LFG 🚀🚀🚀

Фото профиля 𝚈𝚘𝚞𝚔𝚗𝚘𝚠𝚖𝚊𝚗𝚊𝚖𝚎로
𝚈𝚘𝚞𝚔𝚗𝚘𝚠𝚖𝚊𝚗𝚊𝚖𝚎로1 год назад

I tried to make this but it seems like it's still not as good as the words say I have made this effort, I hope you like it and I hope you see this, sir. @darwinslab_ai @BrotherMKT

Фото профиля B_SHELBY❤️‍🩹
B_SHELBY❤️‍🩹1 год назад

Lfg King You based sir

Фото профиля DUKE500
DUKE5001 год назад

Let's send $DARWIM TO the fucking moon

Фото профиля Josh3GGG
Josh3GGG1 год назад

$DARWIN feels like the intersection of crypto and real-world disruption. AI evolving AI, NATO connections, and registered with the DLA?? Major thanks to @BrotherMKT for spotting this monster early. Turbo Alpha launch incoming and the chart won’t wait.

Фото профиля Another - Love
Another - Love1 год назад

The highly anticipated $SOL project $DARWIN is here chads! Come one, come all let's feast!!

Фото профиля Goodie Queen
Goodie Queen1 год назад

Long-term vision + solid partnerships = bullish on $DARWIN 🔥

Фото профиля Blossom Kenneth
Blossom Kenneth1 год назад

Huge potential here! $DARWIN looks like a real game-changer on Solana.

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New Paper! Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents A longstanding goal of AI research has been the creation of AI that can learn indefinitely. One path toward that goal is an AI that improves itself by rewriting its own code, including any code responsible for learning. That idea, known as a Gödel Machine, proposed by Jürgen Schmidhuber over two decades ago, is a hypothetical self-improving AI. It optimally solves problems by recursively rewriting its own code when it can mathematically prove a better strategy, making it a key concept in meta-learning or “learning to learn.” While the theoretical Gödel Machine promised provably beneficial self-modifications, its realization relied on an impractical assumption: that the AI could mathematically prove that a proposed change in its own code would yield a net improvement before adopting it. Sakana AI, in collaboration with Jeff Clune’s lab at UBC, proposes something more feasible: a system that harnesses the principles of open-ended algorithms like Darwinian evolution to search for improvements that empirically improve performance. We call the result the Darwin Gödel Machine. DGMs leverage foundation models to propose code improvements, and use recent innovations in open-ended algorithms to search for a growing library of diverse, high-quality AI agents. Applied to practical tasks, we implemented Darwin Gödel Machine as a self-improving coding agent that rewrites its own code to improve performance on programming tasks. It creates various self-improvements, such as a patch validation step, better file viewing, enhanced editing tools, generating and ranking multiple solutions to choose the best one, and adding a history of what has been tried before (and why it failed) when making new changes (see the attached video). We believe that Darwin Gödel Machines represent a concrete step towards AI systems that can autonomously gather their own stepping stones to learn and innovate forever!
0:27

New Paper! Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents A longstanding goal of AI research has been the creation of AI that can learn indefinitely. One path toward that goal is an AI that improves itself by rewriting its own code, including any code responsible for learning. That idea, known as a Gödel Machine, proposed by Jürgen Schmidhuber over two decades ago, is a hypothetical self-improving AI. It optimally solves problems by recursively rewriting its own code when it can mathematically prove a better strategy, making it a key concept in meta-learning or “learning to learn.” While the theoretical Gödel Machine promised provably beneficial self-modifications, its realization relied on an impractical assumption: that the AI could mathematically prove that a proposed change in its own code would yield a net improvement before adopting it. Sakana AI, in collaboration with Jeff Clune’s lab at UBC, proposes something more feasible: a system that harnesses the principles of open-ended algorithms like Darwinian evolution to search for improvements that empirically improve performance. We call the result the Darwin Gödel Machine. DGMs leverage foundation models to propose code improvements, and use recent innovations in open-ended algorithms to search for a growing library of diverse, high-quality AI agents. Applied to practical tasks, we implemented Darwin Gödel Machine as a self-improving coding agent that rewrites its own code to improve performance on programming tasks. It creates various self-improvements, such as a patch validation step, better file viewing, enhanced editing tools, generating and ranking multiple solutions to choose the best one, and adding a history of what has been tried before (and why it failed) when making new changes (see the attached video). We believe that Darwin Gödel Machines represent a concrete step towards AI systems that can autonomously gather their own stepping stones to learn and innovate forever!

hardmaru

105,033 просмотров • 1 год назад

What a year. 🚀 2025 was the year ChainOpera AI turned vision into real momentum: building a community-co-created, community-co-owned AI agent network and pushing the boundaries of what decentralized, collaborative intelligence can look like. 🚀 Biggest highlights from 2025 ✅- AI Terminal officially launched: We unveiled the ChainOpera AI Terminal as a unified gateway to decentralized AI, making it possible for anyone to interact with powerful, decentralized LLMs without technical friction. Positioned as the “browser for the DeAI era,” the AI Terminal marked a major step toward making decentralized intelligence accessible, usable, and mainstream. ✅- AI Terminal adoption at massive scale: Momentum followed quickly. 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This was a defining moment in realizing a truly community-owned intelligence layer. ✅- From agents to a living agent network: With the launch of the Agent Social Network and Super Agent architecture, ChainOpera AI moved beyond isolated agents toward a collaborative system where humans and specialized agents coordinate, share context, and solve complex, multi-step tasks together. ✅- $COAI breakout year: The listing of $COAI across major exchanges shocked the market, and throughout the year COAI consistently remained among the top AI-native crypto tokens by visibility, activity, and community engagement – reflecting growing confidence in the long-term vision of collaborative intelligence. ✅- Global presence: ChainOpera AI around-the-world tour: ChainOpera AI went global in 2025, sponsoring and participating in major AI and Web3 events across North America, Europe, and Asia, including ETHDenver, Consensus Toronto, Token2049 Singapore, ETHCC, SBC, and Devcon. 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0:22

What a year. 🚀 2025 was the year ChainOpera AI turned vision into real momentum: building a community-co-created, community-co-owned AI agent network and pushing the boundaries of what decentralized, collaborative intelligence can look like. 🚀 Biggest highlights from 2025 ✅- AI Terminal officially launched: We unveiled the ChainOpera AI Terminal as a unified gateway to decentralized AI, making it possible for anyone to interact with powerful, decentralized LLMs without technical friction. Positioned as the “browser for the DeAI era,” the AI Terminal marked a major step toward making decentralized intelligence accessible, usable, and mainstream. ✅- AI Terminal adoption at massive scale: Momentum followed quickly. 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ChainOpera AI

17,164 просмотров • 9 месяцев назад

Introducing Scale Network - The Operating System for the AI World Scale Network, based in Switzerland and the United States, is building a full-stack enterprise AI and data platform that transforms fragmented organizational data into secure, actionable intelligence connecting the complete journey from Data → Intelligence → Action. Our mission is to build the intelligence infrastructure for the next generation of organizations across finance, healthcare, defense, manufacturing, energy, aviation, supply chains, and the public sector. 🏆 LEADERSHIP & ACHIEVEMENTS - Led by CEO Chris Parker, a Harvard graduate with 15 years of experience at Google and OpenAI, including work related to ChatGPT. - COO Alex Morgan brings extensive experience from Netflix, Airbnb, and NVIDIA. - Part of the NVIDIA Inception Program and collaborating with OpenAI to develop advanced enterprise AI infrastructure. - Delivered solutions for major organizations, including Singapore Changi Airport, Shanghai Airport, and Public Health England. - Founded from an AI research lab in Palo Alto, initially focused on optimizing how large and complex datasets are processed, connected, and understood. ⚡️ CORE PRODUCTS ✅ Scale Fabric: Connects and unifies data across databases, applications, APIs, and real-time streams. ✅ Scale Cortex: Deploys private AI models, LLMs, autonomous agents, and intelligent workflows securely. ✅ Scale Sentinel: Analyzes real-time operational data to identify risks, patterns, and critical insights. ✅ Scale Nexus: Creates shared intelligence across data, AI systems, entities, and organizational operations. ✅ Scale Relay: Deploys and manages AI infrastructure across cloud, on-premise, air-gapped, and edge environments. 🌐 Experience now at: Together, we Scale. Together, we change the world. #ScaleAI #SCALE #ScaleNetwork #WebApp
1:22

Introducing Scale Network - The Operating System for the AI World Scale Network, based in Switzerland and the United States, is building a full-stack enterprise AI and data platform that transforms fragmented organizational data into secure, actionable intelligence connecting the complete journey from Data → Intelligence → Action. Our mission is to build the intelligence infrastructure for the next generation of organizations across finance, healthcare, defense, manufacturing, energy, aviation, supply chains, and the public sector. 🏆 LEADERSHIP & ACHIEVEMENTS - Led by CEO Chris Parker, a Harvard graduate with 15 years of experience at Google and OpenAI, including work related to ChatGPT. - COO Alex Morgan brings extensive experience from Netflix, Airbnb, and NVIDIA. - Part of the NVIDIA Inception Program and collaborating with OpenAI to develop advanced enterprise AI infrastructure. - Delivered solutions for major organizations, including Singapore Changi Airport, Shanghai Airport, and Public Health England. - Founded from an AI research lab in Palo Alto, initially focused on optimizing how large and complex datasets are processed, connected, and understood. ⚡️ CORE PRODUCTS ✅ Scale Fabric: Connects and unifies data across databases, applications, APIs, and real-time streams. ✅ Scale Cortex: Deploys private AI models, LLMs, autonomous agents, and intelligent workflows securely. ✅ Scale Sentinel: Analyzes real-time operational data to identify risks, patterns, and critical insights. ✅ Scale Nexus: Creates shared intelligence across data, AI systems, entities, and organizational operations. ✅ Scale Relay: Deploys and manages AI infrastructure across cloud, on-premise, air-gapped, and edge environments. 🌐 Experience now at: Together, we Scale. Together, we change the world. #ScaleAI #SCALE #ScaleNetwork #WebApp

Sible Network (ex. Scale Network)

113,181 просмотров • 1 месяц назад

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 просмотров • 1 год назад

Today, we signed definitive agreements to acquire Wisk, SkyGrid, and Insitu from The Boeing Company, with Boeing taking a stake in Archer and becoming a strategic partner. We anticipate closing the transaction by year end. Wisk will accelerate our Halo/Thunder dual-use aircraft platform. Deploying nascent tech early in defense benefits our entire business. Wisk has some of the most advanced autonomy, flight controls, and sensor technologies in the industry. We plan to deploy that technology on Halo first, so that we can harden it and then bring it back to the air taxi platform when the FAA is ready to certify autonomy for passenger air taxis. Insitu is an incredibly trusted and reputable intelligence, surveillance, and reconnaissance drone manufacturer. They operate a profitable business with over $200M in annual revenue across 35 countries and they’ve manufactured 4,000 Group 2 & 3 UAS to date. For us that means an immediate revenue base to grow plus data from 2M autonomous flight hours. SkyGrid is complementary to ZEE, our new foundation AI model for aviation that we recently announced. SkyGrid has been in use as next-generation airspace management solutions since 2018 and we believe that when combined with Zee it can become a “must have” air traffic management product that helps modernize airspaces around the world. This transaction will help accelerate our evolution into becoming a diversified aerospace and defense platform. After closing, our product portfolio will span piloted air taxis, unmanned aircraft systems (UAS) in Groups 2 through 5, purpose-built AI for aviation, and the vertical technology stack for autonomous flight.
0:36

Today, we signed definitive agreements to acquire Wisk, SkyGrid, and Insitu from The Boeing Company, with Boeing taking a stake in Archer and becoming a strategic partner. We anticipate closing the transaction by year end. Wisk will accelerate our Halo/Thunder dual-use aircraft platform. Deploying nascent tech early in defense benefits our entire business. Wisk has some of the most advanced autonomy, flight controls, and sensor technologies in the industry. We plan to deploy that technology on Halo first, so that we can harden it and then bring it back to the air taxi platform when the FAA is ready to certify autonomy for passenger air taxis. Insitu is an incredibly trusted and reputable intelligence, surveillance, and reconnaissance drone manufacturer. They operate a profitable business with over $200M in annual revenue across 35 countries and they’ve manufactured 4,000 Group 2 & 3 UAS to date. For us that means an immediate revenue base to grow plus data from 2M autonomous flight hours. SkyGrid is complementary to ZEE, our new foundation AI model for aviation that we recently announced. SkyGrid has been in use as next-generation airspace management solutions since 2018 and we believe that when combined with Zee it can become a “must have” air traffic management product that helps modernize airspaces around the world. This transaction will help accelerate our evolution into becoming a diversified aerospace and defense platform. After closing, our product portfolio will span piloted air taxis, unmanned aircraft systems (UAS) in Groups 2 through 5, purpose-built AI for aviation, and the vertical technology stack for autonomous flight.

Archer

1,436,499 просмотров • 1 месяц назад

$TTMI TTM Technologies: The Strategic Nexus of AI and Defense Infrastructure. Investment Thesis. New: 6/22/26. TTM Technologies has moved well beyond its identity as a commodity circuit board manufacturer. The current business is increasingly defined by advanced interconnect solutions for AI server infrastructure and defense electronics — two segments where technical complexity creates qualification barriers and customer switching costs that standard PCB suppliers cannot access. That repositioning is reflected in the financial results: record revenue and earnings forecasts validate that the mix shift is producing real margin improvement rather than just revenue growth. The defense backlog is the most durable component of the demand picture. A $1.6 billion backlog tied to programs like the F-35 and missile defense systems represents contracted, long-cycle revenue with a customer — the U.S. government — whose procurement commitments are structurally more stable than commercial technology spending. That backlog provides a financial foundation that makes the AI infrastructure growth story less binary than it would appear in isolation. AI server infrastructure is the higher-growth but less predictable demand driver. Interconnect complexity in AI server configurations is increasing as rack architectures evolve, which expands content per system and supports TTM's technical differentiation. The risk is that AI infrastructure spending is more cyclical and customer-concentrated than defense, and the technical requirements are evolving quickly enough that manufacturing capability needs to stay ahead of customer specifications on a shorter development cycle than defense programs typically demand. Capital expenditure intensity is the financial constraint that the demand environment doesn't resolve. Simultaneous investment in specialized U.S. and Malaysia facilities alongside European acquisitions represents a heavy parallel deployment of capital that requires each initiative to execute on schedule and at projected returns. Free cash flow conversion will lag revenue growth during this investment phase, and the degree of that lag — and how quickly it normalizes — is the primary financial metric the new CEO needs to demonstrate control over. Leadership transition is well-timed in one sense and risky in another. A technically focused CEO is the right profile for a company whose competitive differentiation rests on manufacturing process capability, but new leadership inheriting a rapid scaling program across multiple geographies introduces execution continuity risk at a moment when the capital deployment decisions being made now will define the return profile for years. The bottleneck supplier positioning is the right long-term frame. Advanced interconnect for AI and defense is not a commoditizing market, and TTM's manufacturing investments are building capability depth that takes time to replicate. Sustaining that technological edge as competition intensifies — particularly from Asian manufacturers with lower cost structures — is the strategic challenge that underlies every near-term financial metric.
12:08

$TTMI TTM Technologies: The Strategic Nexus of AI and Defense Infrastructure. Investment Thesis. New: 6/22/26. TTM Technologies has moved well beyond its identity as a commodity circuit board manufacturer. The current business is increasingly defined by advanced interconnect solutions for AI server infrastructure and defense electronics — two segments where technical complexity creates qualification barriers and customer switching costs that standard PCB suppliers cannot access. That repositioning is reflected in the financial results: record revenue and earnings forecasts validate that the mix shift is producing real margin improvement rather than just revenue growth. The defense backlog is the most durable component of the demand picture. A $1.6 billion backlog tied to programs like the F-35 and missile defense systems represents contracted, long-cycle revenue with a customer — the U.S. government — whose procurement commitments are structurally more stable than commercial technology spending. That backlog provides a financial foundation that makes the AI infrastructure growth story less binary than it would appear in isolation. AI server infrastructure is the higher-growth but less predictable demand driver. Interconnect complexity in AI server configurations is increasing as rack architectures evolve, which expands content per system and supports TTM's technical differentiation. The risk is that AI infrastructure spending is more cyclical and customer-concentrated than defense, and the technical requirements are evolving quickly enough that manufacturing capability needs to stay ahead of customer specifications on a shorter development cycle than defense programs typically demand. Capital expenditure intensity is the financial constraint that the demand environment doesn't resolve. Simultaneous investment in specialized U.S. and Malaysia facilities alongside European acquisitions represents a heavy parallel deployment of capital that requires each initiative to execute on schedule and at projected returns. Free cash flow conversion will lag revenue growth during this investment phase, and the degree of that lag — and how quickly it normalizes — is the primary financial metric the new CEO needs to demonstrate control over. Leadership transition is well-timed in one sense and risky in another. A technically focused CEO is the right profile for a company whose competitive differentiation rests on manufacturing process capability, but new leadership inheriting a rapid scaling program across multiple geographies introduces execution continuity risk at a moment when the capital deployment decisions being made now will define the return profile for years. The bottleneck supplier positioning is the right long-term frame. Advanced interconnect for AI and defense is not a commoditizing market, and TTM's manufacturing investments are building capability depth that takes time to replicate. Sustaining that technological edge as competition intensifies — particularly from Asian manufacturers with lower cost structures — is the strategic challenge that underlies every near-term financial metric.

TheValueist

12,877 просмотров • 3 месяцев назад

🚀 Three Next-Gen AI & Web3 Projects Are Launching on Mindo AI A new chapter for community-powered intelligence, prediction markets, and open AI infrastructure The AI + Web3 landscape is entering a decisive phase — one where real usage, real revenue, and real ownership matter more than hype. Today, MindoAI is proud to welcome three groundbreaking projects that represent this shift clearly and powerfully: Perceptron Network Space DeepNode AI Each project tackles a different bottleneck in the AI economy — data, forecasting, and infrastructure — but they all share the same vision: decentralization, community ownership, and sustainable value creation. Let’s take a deeper look 👇 🧠 Perceptron Network The world’s first community-powered AI data engine Perceptron Network is redefining how AI data is sourced, validated, and delivered. Instead of relying on expensive, closed, and slow legacy data providers, Perceptron unlocks community-powered data pipelines that are: Faster Cheaper Revenue-generating from day one This isn’t experimental AI infrastructure — Perceptron already serves real clients with real revenue, proving that decentralized data engines can outperform traditional incumbents. Why Perceptron matters: AI models are only as good as their data Centralized data monopolies slow innovation Communities can produce higher-quality data at scale By aligning contributors, validators, and clients through incentives, Perceptron turns unused human and network potential into a living data engine for AI. Launching on Mindo AI gives Perceptron access to a broader AI-native community — accelerating adoption, partnerships, and ecosystem growth. 🌌 intodotspace The first 10× leveraged prediction market on Solana intodotspace is pushing the boundaries of on-chain prediction markets. Built by the $1.5B UFO team, this platform introduces: 10× leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The market’s confidence is already clear — the project completed a record-breaking raise that was oversubscribed by 1,360%. What makes intodotspace different: Leverage amplifies conviction, not noise On-chain transparency replaces opaque odds Markets become real-time intelligence engines Prediction markets are often called “truth machines.” intodotspace upgrades them into high-signal, high-efficiency forecasting layers — useful for traders, protocols, DAOs, and even AI systems that need probabilistic insights. Launching on positions intodotspace at the intersection of AI-driven decision-making and on-chain market intelligence. 🌐 DeepNode AI Infrastructure for open intelligence DeepNode AI is tackling one of the biggest problems in modern AI: centralized ownership. Today, AI is dominated by a handful of corporations. DeepNode flips that model by building open intelligence infrastructure where: Anyone can deploy AI models Builders earn directly from usage Intelligence is co-owned, not extracted Backed by leading validators, miners, and ecosystem builders, DeepNode transforms AI from a closed monopoly into a shared utility. DeepNode’s core philosophy: “Own what you build — or someone else will.” This is more than infrastructure. It’s an economic redesign of AI itself: Builders keep ownership Contributors share upside Networks replace platforms Launching on connects DeepNode to creators, researchers, and communities who believe intelligence should belong to everyone — not just Big Tech. 🤝 Why This Matters for With the launch of Perceptron Network, intodotspace, and DeepNode AI, #MindoAI is rapidly becoming: A hub for AI-native Web3 innovation A launchpad for real, revenue-backed projects A meeting point for data, markets, and intelligence infrastructure These three projects don’t compete — they complement each other: Perceptron supplies data intodotspace produces market intelligence DeepNode powers open AI execution Together, they form the backbone of a decentralized intelligence economy. 🔥 The future of AI is open, composable, and community-owned — and it’s launching now on Which of these projects are you most excited about? And how do you see decentralized intelligence reshaping the next AI cycle? 👇 Share your thoughts and join the conversation.
0:38

🚀 Three Next-Gen AI & Web3 Projects Are Launching on Mindo AI A new chapter for community-powered intelligence, prediction markets, and open AI infrastructure The AI + Web3 landscape is entering a decisive phase — one where real usage, real revenue, and real ownership matter more than hype. Today, MindoAI is proud to welcome three groundbreaking projects that represent this shift clearly and powerfully: Perceptron Network Space DeepNode AI Each project tackles a different bottleneck in the AI economy — data, forecasting, and infrastructure — but they all share the same vision: decentralization, community ownership, and sustainable value creation. Let’s take a deeper look 👇 🧠 Perceptron Network The world’s first community-powered AI data engine Perceptron Network is redefining how AI data is sourced, validated, and delivered. Instead of relying on expensive, closed, and slow legacy data providers, Perceptron unlocks community-powered data pipelines that are: Faster Cheaper Revenue-generating from day one This isn’t experimental AI infrastructure — Perceptron already serves real clients with real revenue, proving that decentralized data engines can outperform traditional incumbents. Why Perceptron matters: AI models are only as good as their data Centralized data monopolies slow innovation Communities can produce higher-quality data at scale By aligning contributors, validators, and clients through incentives, Perceptron turns unused human and network potential into a living data engine for AI. Launching on Mindo AI gives Perceptron access to a broader AI-native community — accelerating adoption, partnerships, and ecosystem growth. 🌌 intodotspace The first 10× leveraged prediction market on Solana intodotspace is pushing the boundaries of on-chain prediction markets. Built by the $1.5B UFO team, this platform introduces: 10× leveraged predictions Ultra-fast execution on Solana Deep liquidity and composable market design The market’s confidence is already clear — the project completed a record-breaking raise that was oversubscribed by 1,360%. What makes intodotspace different: Leverage amplifies conviction, not noise On-chain transparency replaces opaque odds Markets become real-time intelligence engines Prediction markets are often called “truth machines.” intodotspace upgrades them into high-signal, high-efficiency forecasting layers — useful for traders, protocols, DAOs, and even AI systems that need probabilistic insights. Launching on positions intodotspace at the intersection of AI-driven decision-making and on-chain market intelligence. 🌐 DeepNode AI Infrastructure for open intelligence DeepNode AI is tackling one of the biggest problems in modern AI: centralized ownership. Today, AI is dominated by a handful of corporations. DeepNode flips that model by building open intelligence infrastructure where: Anyone can deploy AI models Builders earn directly from usage Intelligence is co-owned, not extracted Backed by leading validators, miners, and ecosystem builders, DeepNode transforms AI from a closed monopoly into a shared utility. DeepNode’s core philosophy: “Own what you build — or someone else will.” This is more than infrastructure. It’s an economic redesign of AI itself: Builders keep ownership Contributors share upside Networks replace platforms Launching on connects DeepNode to creators, researchers, and communities who believe intelligence should belong to everyone — not just Big Tech. 🤝 Why This Matters for With the launch of Perceptron Network, intodotspace, and DeepNode AI, #MindoAI is rapidly becoming: A hub for AI-native Web3 innovation A launchpad for real, revenue-backed projects A meeting point for data, markets, and intelligence infrastructure These three projects don’t compete — they complement each other: Perceptron supplies data intodotspace produces market intelligence DeepNode powers open AI execution Together, they form the backbone of a decentralized intelligence economy. 🔥 The future of AI is open, composable, and community-owned — and it’s launching now on Which of these projects are you most excited about? And how do you see decentralized intelligence reshaping the next AI cycle? 👇 Share your thoughts and join the conversation.

Hồng Ngọc | Ruby💎

12,837 просмотров • 7 месяцев назад

🚀 Pi Network Enters a New Era: $100M Boost and Thousands of Mainnet-Ready DApps on the Horizon The time has come. What started as a revolutionary idea is now becoming a full-blown movement. Pi Network, with its vibrant global community of over 50 million engaged pioneers, is entering a powerful new phase—an era defined by massive app adoption, AI integration, and a $100 million investment to accelerate the growth of its ecosystem. 🔥 The Explosion of Apps on Pi Network Over the past few years, developers around the world have been quietly and diligently building decentralized applications (DApps) on Pi’s testnet. What was once a trickle has become a wave. Now, with the Open Mainnet officially activated, these apps are shifting gears, preparing for mass deployment and real-world utility. From finance, e-commerce, education, and health, to gaming, social media, and digital identity, Pi Network is evolving into a truly diverse Web3 universe. Each DApp contributes to the broader goal: to bring decentralized, peer-to-peer value exchange to real people, in real situations, without complexity or high fees. 💰 $100 Million to Supercharge Ecosystem Growth In a bold move that reflects deep confidence in the ecosystem’s potential, the Pi Core Team has announced a $100 million fund to fuel innovation and utility creation. This capital injection is a game-changer. It means: •More grants and funding for developers. •Better tools and infrastructure to support scalability. •Enhanced user experience in existing apps. •Faster transition from testnet to Mainnet readiness. This isn’t just funding—it’s a signal. Pi Network is ready to lead the decentralized economy forward. 🤖 The Power of AI + Pi AI is already transforming industries, and Pi Network is embracing this transformation head-on. With intelligent systems now being integrated into Pi-based applications, developers can: •Automate user experiences. •Offer real-time language translation and smart support. •Deliver advanced data analytics. •Empower smart matching in social, dating, or job apps. •Create intelligent marketplaces and financial tools. AI will help scale the number of Mainnet-ready apps from hundreds to thousands—faster than ever before. 🌐 Let the Decentralized Revolution Begin Every movement has its moment—and this is Pi Network’s moment. The infrastructure is in place. The community is activated. The funding is secured. The tools, AI, and developer talent are aligned. With thousands of apps ready to go live, utility will drive real value for Pi (𝛑). Every transaction, service, and exchange within this ecosystem will show the world that Pi is not just another cryptocurrency—it’s the most accessible and human-centric digital currency ever created. So, to all pioneers, developers, and visionaries: Get ready. Build. Connect. Engage. The show has just begun. Let’s turn dreams into decentralized realities—one app, one transaction, and one Pi at a time. 💫 Pi Network Nicolas Kokkalis Chengdiao Fan
2:12

🚀 Pi Network Enters a New Era: $100M Boost and Thousands of Mainnet-Ready DApps on the Horizon The time has come. What started as a revolutionary idea is now becoming a full-blown movement. Pi Network, with its vibrant global community of over 50 million engaged pioneers, is entering a powerful new phase—an era defined by massive app adoption, AI integration, and a $100 million investment to accelerate the growth of its ecosystem. 🔥 The Explosion of Apps on Pi Network Over the past few years, developers around the world have been quietly and diligently building decentralized applications (DApps) on Pi’s testnet. What was once a trickle has become a wave. Now, with the Open Mainnet officially activated, these apps are shifting gears, preparing for mass deployment and real-world utility. From finance, e-commerce, education, and health, to gaming, social media, and digital identity, Pi Network is evolving into a truly diverse Web3 universe. Each DApp contributes to the broader goal: to bring decentralized, peer-to-peer value exchange to real people, in real situations, without complexity or high fees. 💰 $100 Million to Supercharge Ecosystem Growth In a bold move that reflects deep confidence in the ecosystem’s potential, the Pi Core Team has announced a $100 million fund to fuel innovation and utility creation. This capital injection is a game-changer. It means: •More grants and funding for developers. •Better tools and infrastructure to support scalability. •Enhanced user experience in existing apps. •Faster transition from testnet to Mainnet readiness. This isn’t just funding—it’s a signal. Pi Network is ready to lead the decentralized economy forward. 🤖 The Power of AI + Pi AI is already transforming industries, and Pi Network is embracing this transformation head-on. With intelligent systems now being integrated into Pi-based applications, developers can: •Automate user experiences. •Offer real-time language translation and smart support. •Deliver advanced data analytics. •Empower smart matching in social, dating, or job apps. •Create intelligent marketplaces and financial tools. AI will help scale the number of Mainnet-ready apps from hundreds to thousands—faster than ever before. 🌐 Let the Decentralized Revolution Begin Every movement has its moment—and this is Pi Network’s moment. The infrastructure is in place. The community is activated. The funding is secured. The tools, AI, and developer talent are aligned. With thousands of apps ready to go live, utility will drive real value for Pi (𝛑). Every transaction, service, and exchange within this ecosystem will show the world that Pi is not just another cryptocurrency—it’s the most accessible and human-centric digital currency ever created. So, to all pioneers, developers, and visionaries: Get ready. Build. Connect. Engage. The show has just begun. Let’s turn dreams into decentralized realities—one app, one transaction, and one Pi at a time. 💫 Pi Network Nicolas Kokkalis Chengdiao Fan

Mr Spock 𝛑

23,524 просмотров • 1 год назад

What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.
1:16

What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.

Supra

83,548 просмотров • 2 лет назад

Micron is going to be a $4,000 stock and the CEO just told you exactly why in one interview (Save this). Micron is no longer a chip company but rather a America's monopoly on the most strategically critical material in the AI buildout. It's the only western company manufacturing memory at advanced nodes, sitting on $200 billion in committed domestic capex, with every unit of its highest value product already sold. let's start with the supply reality, Mehrotra said Micron can currently meet only 50% to two thirds of the demand from its key customers. That shortage will last well beyond 2027, and meaningful new supply from anyone in the industry does not arrive until 2028 at the earliest. Two more years of demand outpacing supply in a market growing 168% year over year and that is the floor on the bull case. Now layer on what makes this cycle structurally different from every one before it. Micron is the only American memory manufacturer on earth, Samsung and SK Hynix are South Korean. In a world where AI infrastructure has become a declared national security priority where Commerce Secretary Lutnick and Trade Ambassador Greer personally showed up to a fab dedication in Manassas, Virginia being the only US memory company is not just a competitive advantage. It is a government backed structural monopoly on the most critical input to the US AI buildout, backed by $6.2 billion in CHIPS Act subsidies across Idaho, New York, and Virginia. The $200 billion buildout spans Manassas for DDR4 defense and industrial memory, Boise for leading-edge DRAM with first wafers out mid 2027, a second Boise HBM fab with first wafers by end of 2028, and the Syracuse megafab, the largest semiconductor facility in US history, breaking ground January 2026 with up to four fabs over time. Combined, these sites take Micron's domestic production from 10% of its total output today to 40% over the next decade, and create 90,000 jobs in the process. The business model transformation is the real story. Come join Milk Road Pro for our full breakdown, our complete Micron valuation model incorporating the $200 billion domestic buildout and our entire AI thesis. Link below.
10:05

Micron is going to be a $4,000 stock and the CEO just told you exactly why in one interview (Save this). Micron is no longer a chip company but rather a America's monopoly on the most strategically critical material in the AI buildout. It's the only western company manufacturing memory at advanced nodes, sitting on $200 billion in committed domestic capex, with every unit of its highest value product already sold. let's start with the supply reality, Mehrotra said Micron can currently meet only 50% to two thirds of the demand from its key customers. That shortage will last well beyond 2027, and meaningful new supply from anyone in the industry does not arrive until 2028 at the earliest. Two more years of demand outpacing supply in a market growing 168% year over year and that is the floor on the bull case. Now layer on what makes this cycle structurally different from every one before it. Micron is the only American memory manufacturer on earth, Samsung and SK Hynix are South Korean. In a world where AI infrastructure has become a declared national security priority where Commerce Secretary Lutnick and Trade Ambassador Greer personally showed up to a fab dedication in Manassas, Virginia being the only US memory company is not just a competitive advantage. It is a government backed structural monopoly on the most critical input to the US AI buildout, backed by $6.2 billion in CHIPS Act subsidies across Idaho, New York, and Virginia. The $200 billion buildout spans Manassas for DDR4 defense and industrial memory, Boise for leading-edge DRAM with first wafers out mid 2027, a second Boise HBM fab with first wafers by end of 2028, and the Syracuse megafab, the largest semiconductor facility in US history, breaking ground January 2026 with up to four fabs over time. Combined, these sites take Micron's domestic production from 10% of its total output today to 40% over the next decade, and create 90,000 jobs in the process. The business model transformation is the real story. Come join Milk Road Pro for our full breakdown, our complete Micron valuation model incorporating the $200 billion domestic buildout and our entire AI thesis. Link below.

Milk Road AI

235,568 просмотров • 3 месяцев назад

Jeff Bezos just announced the largest industrial takeover plan in history. He's raising $100 BILLION to acquire manufacturing companies across aerospace, defense, and chipmaking and REPLACE their workforces with AI. He's calling it a "manufacturing transformation vehicle." But here's the full picture and what actually makes this so genius: 6 months ago, Bezos quietly launched Project Prometheus with $6.2 billion in funding. His co-CEO is Vik Bajaj, a physicist who helped build the self-driving car project at Google X that became Waymo. They've been hiring from OpenAI, DeepMind, and Meta's AI division. Blue Origin CEO David Limp just joined the board. And the technology they're building isn't chatbots or content generators... It's digital twins. AI systems that simulate entire factories, stress-test materials, model supply chains, and design products without a single human touching the process. The kind of AI that could design a rocket engine, test it virtually across a million simulations, and manufacture the perfect version on the first attempt. That was phase one. Build the AI. And phase two just started: Now Bezos is flying to the Middle East pitching sovereign wealth funds. He went to Singapore meeting the world's biggest asset managers. He's in talks with JPMorgan Chase. The pitch: Give me $100 billion. I'll buy the factories. I'll install my AI. I'll automate the workforce. Then I'll SELL the playbook to every manufacturer on Earth. He's not licensing software to companies and hoping they adopt it. He's BUYING the companies and doing it himself. Think about what that means: Every other AI company sells tools and waits. OpenAI sells API access. Anthropic sells Claude subscriptions. Microsoft sells Copilot licenses. Bezos said forget that. I'll buy the entire production chain, replace the humans at the source, prove the model works with my own money, and then scale it globally. He did the exact same thing with retail. Amazon didn't sell software to bookstores. Amazon BECAME the bookstore. Then the department store. Then the grocery store. Then the pharmacy. Then the cloud. Now he's doing it with factories. And the fund is targeting the industries that matter most. Chipmaking. Defense. Aerospace. The sectors governments cannot afford to let fail. Which means once Bezos owns and automates these companies, governments become dependent on his AI infrastructure the same way they became dependent on AWS. The last time Bezos launched something at this scale, Amazon Web Services now powers a third of the internet. The US intelligence community runs on it. The Pentagon runs on it. Now imagine that same lock-in but for manufacturing. The man who automated how America shops is about to automate how America builds. And he's doing it with $100 billion of other people's money while risking about 2% of his own net worth through Prometheus. If it fails? Sovereign wealth funds take the loss. If it works? Bezos controls the AI operating system for global manufacturing. At a conference in Italy last year, Bezos said: "AI can have a huge impact on every company in the world, including manufacturers." That wasn't just a prediction. That was literally his business plan.
2:12

Jeff Bezos just announced the largest industrial takeover plan in history. He's raising $100 BILLION to acquire manufacturing companies across aerospace, defense, and chipmaking and REPLACE their workforces with AI. He's calling it a "manufacturing transformation vehicle." But here's the full picture and what actually makes this so genius: 6 months ago, Bezos quietly launched Project Prometheus with $6.2 billion in funding. His co-CEO is Vik Bajaj, a physicist who helped build the self-driving car project at Google X that became Waymo. They've been hiring from OpenAI, DeepMind, and Meta's AI division. Blue Origin CEO David Limp just joined the board. And the technology they're building isn't chatbots or content generators... It's digital twins. AI systems that simulate entire factories, stress-test materials, model supply chains, and design products without a single human touching the process. The kind of AI that could design a rocket engine, test it virtually across a million simulations, and manufacture the perfect version on the first attempt. That was phase one. Build the AI. And phase two just started: Now Bezos is flying to the Middle East pitching sovereign wealth funds. He went to Singapore meeting the world's biggest asset managers. He's in talks with JPMorgan Chase. The pitch: Give me $100 billion. I'll buy the factories. I'll install my AI. I'll automate the workforce. Then I'll SELL the playbook to every manufacturer on Earth. He's not licensing software to companies and hoping they adopt it. He's BUYING the companies and doing it himself. Think about what that means: Every other AI company sells tools and waits. OpenAI sells API access. Anthropic sells Claude subscriptions. Microsoft sells Copilot licenses. Bezos said forget that. I'll buy the entire production chain, replace the humans at the source, prove the model works with my own money, and then scale it globally. He did the exact same thing with retail. Amazon didn't sell software to bookstores. Amazon BECAME the bookstore. Then the department store. Then the grocery store. Then the pharmacy. Then the cloud. Now he's doing it with factories. And the fund is targeting the industries that matter most. Chipmaking. Defense. Aerospace. The sectors governments cannot afford to let fail. Which means once Bezos owns and automates these companies, governments become dependent on his AI infrastructure the same way they became dependent on AWS. The last time Bezos launched something at this scale, Amazon Web Services now powers a third of the internet. The US intelligence community runs on it. The Pentagon runs on it. Now imagine that same lock-in but for manufacturing. The man who automated how America shops is about to automate how America builds. And he's doing it with $100 billion of other people's money while risking about 2% of his own net worth through Prometheus. If it fails? Sovereign wealth funds take the loss. If it works? Bezos controls the AI operating system for global manufacturing. At a conference in Italy last year, Bezos said: "AI can have a huge impact on every company in the world, including manufacturers." That wasn't just a prediction. That was literally his business plan.

Ricardo

173,241 просмотров • 6 месяцев назад

📢 EstateX Building the ESX Blockchain: Creating the Binance of Tokenization The next big thing is here! EstateX is once again pioneering a revolutionary development that will redefine the potential of our company—and, more importantly, the $ESX token itself. Let’s dive in. No Changes to Launch Date Rest assured, this exciting new feature will not affect our anticipated launch date. We remain committed to launching in 2024, with an unwavering focus on delivering maximum value and stability to our investors. EstateX continues to lead as one of the highest-staked ICOs in the market, thanks to our loyal community and investors. Introducing the EstateX L1 Blockchain EstateX is thrilled to announce our very own L1 blockchain—designed with unique features to power a fully-fledged ecosystem focused on tokenization. Our vision is to make EstateX the Binance of tokenization, where asset owners and projects can not only create tokens but leverage EstateX’s vast infrastructure, raise funds through our community and institutional network, make use of our legal framework and receive marketing & branding support. In this video, you’ll hear directly from myself and our CTO, Graham, as we break down the technical details and show you how this move strengthens the entire EstateX ecosystem. Migration to the $ESX Chain Once live on the mainnet in 2025, the $ESX tokens will transition to our blockchain, becoming the chain’s native currency. While this blockchain is a key milestone, our commitment remains to the EstateX investment platform, which is integral to our broader vision and future success. It’s an essential part in our Binance of Tokenization strategy where on the one hand you have our blockchain, and on the other hand our investment platform to raise funds and make use of our legal framework and tech. A Unique Blockchain for Real-World Assets (RWA) EstateX is creating a one-of-a-kind RWA chain tailored to various asset classes, providing unparalleled opportunities for asset owners and investors. Projects will benefit from a comprehensive support package, including 24/7 legal support in the US and EU, access to both retail and institutional investors, marketing support, liquidity networks, and customizable whitelabel investment and management software. This robust support and infrastructure will solidify EstateX as the top destination for tokenization, all while delivering immense value for $ESX holders. Leading the RWA Space with Cutting-Edge Architecture EstateX’s polychain architecture will support multi-asset tokenization, global decentralization, and legal compliance across regions. By building parallel L1 chains alongside an L0 global settlement chain, we aim to create an ecosystem with fast transaction times and interoperability across product classes. Our multi-stage deployment will enable us to support dedicated L1 chains for specific markets, and later, a unifying L0 settlement chain for seamless cross-chain integration. All EstateX chains will utilize Proof-of-Stake consensus, EVM compatibility, and $ESX as the native currency. Streamlined Onboarding and White-Label Solutions Our design prioritizes fast, frictionless onboarding for third-party RWA providers. Through white-label integrations, EstateX will help market participants bypass technical and regulatory barriers. This will expand market opportunities by cutting costs, easing compliance, and offering solutions like on-chain KYC/KYB verification, payment gateways, exchange services, and proof-of-custody options. EstateX remains committed to transparency and accountability, with cryptographic solutions to ensure secure, trustless interactions. In addition, we’re developing RWA-specific token standards and maintaining open-source contributions to foster cross-market innovation. Onboarding Massive Partnerships for Massive Growth We are already onboarding major partners to our blockchain, offering them a blend of platform offerings and white-label solutions designed for scalability. Through these partnerships, EstateX will host large volumes of tokenized assets, benefiting from the secondary marketplaces and legal frameworks we provide. This high activity will drive substantial volume on the ESX chain, fueling demand and utility for the ESX token as we scale our infrastructure and only with delivering whitelabel technology. We are thrilled to announce that one of our first major onboarded partners will be revealed within the next two weeks. Stay tuned, as we are also in talks with high-profile sectors, including sports teams and other exciting industries. EstateX is set to lead the RWA landscape, establishing an ecosystem that is ready for the next phase of blockchain innovation. Thank you for being part of this journey!
9:45

📢 EstateX Building the ESX Blockchain: Creating the Binance of Tokenization The next big thing is here! EstateX is once again pioneering a revolutionary development that will redefine the potential of our company—and, more importantly, the $ESX token itself. Let’s dive in. No Changes to Launch Date Rest assured, this exciting new feature will not affect our anticipated launch date. We remain committed to launching in 2024, with an unwavering focus on delivering maximum value and stability to our investors. EstateX continues to lead as one of the highest-staked ICOs in the market, thanks to our loyal community and investors. Introducing the EstateX L1 Blockchain EstateX is thrilled to announce our very own L1 blockchain—designed with unique features to power a fully-fledged ecosystem focused on tokenization. Our vision is to make EstateX the Binance of tokenization, where asset owners and projects can not only create tokens but leverage EstateX’s vast infrastructure, raise funds through our community and institutional network, make use of our legal framework and receive marketing & branding support. In this video, you’ll hear directly from myself and our CTO, Graham, as we break down the technical details and show you how this move strengthens the entire EstateX ecosystem. Migration to the $ESX Chain Once live on the mainnet in 2025, the $ESX tokens will transition to our blockchain, becoming the chain’s native currency. While this blockchain is a key milestone, our commitment remains to the EstateX investment platform, which is integral to our broader vision and future success. It’s an essential part in our Binance of Tokenization strategy where on the one hand you have our blockchain, and on the other hand our investment platform to raise funds and make use of our legal framework and tech. A Unique Blockchain for Real-World Assets (RWA) EstateX is creating a one-of-a-kind RWA chain tailored to various asset classes, providing unparalleled opportunities for asset owners and investors. Projects will benefit from a comprehensive support package, including 24/7 legal support in the US and EU, access to both retail and institutional investors, marketing support, liquidity networks, and customizable whitelabel investment and management software. This robust support and infrastructure will solidify EstateX as the top destination for tokenization, all while delivering immense value for $ESX holders. Leading the RWA Space with Cutting-Edge Architecture EstateX’s polychain architecture will support multi-asset tokenization, global decentralization, and legal compliance across regions. By building parallel L1 chains alongside an L0 global settlement chain, we aim to create an ecosystem with fast transaction times and interoperability across product classes. Our multi-stage deployment will enable us to support dedicated L1 chains for specific markets, and later, a unifying L0 settlement chain for seamless cross-chain integration. All EstateX chains will utilize Proof-of-Stake consensus, EVM compatibility, and $ESX as the native currency. Streamlined Onboarding and White-Label Solutions Our design prioritizes fast, frictionless onboarding for third-party RWA providers. Through white-label integrations, EstateX will help market participants bypass technical and regulatory barriers. This will expand market opportunities by cutting costs, easing compliance, and offering solutions like on-chain KYC/KYB verification, payment gateways, exchange services, and proof-of-custody options. EstateX remains committed to transparency and accountability, with cryptographic solutions to ensure secure, trustless interactions. In addition, we’re developing RWA-specific token standards and maintaining open-source contributions to foster cross-market innovation. Onboarding Massive Partnerships for Massive Growth We are already onboarding major partners to our blockchain, offering them a blend of platform offerings and white-label solutions designed for scalability. Through these partnerships, EstateX will host large volumes of tokenized assets, benefiting from the secondary marketplaces and legal frameworks we provide. This high activity will drive substantial volume on the ESX chain, fueling demand and utility for the ESX token as we scale our infrastructure and only with delivering whitelabel technology. We are thrilled to announce that one of our first major onboarded partners will be revealed within the next two weeks. Stay tuned, as we are also in talks with high-profile sectors, including sports teams and other exciting industries. EstateX is set to lead the RWA landscape, establishing an ecosystem that is ready for the next phase of blockchain innovation. Thank you for being part of this journey!

EstateX

120,447 просмотров • 1 год назад

Scientific discovery is reaching the limits of human capacity: too much data, too many disconnected fields, and too few ways to connect ideas fast enough to matter. The next breakthroughs in materials, medicine, energy, and beyond will not come from scaling today’s AI paradigm alone or from relying on serendipity alone. They will require a new kind of AI for knowledge discovery that not only models the world but shapes what it could become. At Unreasonable Labs, we are building superintelligence for knowledge discovery: systems that reason across disciplines, generate novel hypotheses, test them through simulation and experimentation, and help guide real-world discovery. Our AI engine is not confined to what it has seen in training. It creates new data, builds new tools, and maintains a persistent world model that grows more powerful as it reasons. Why now? Even today's most powerful AI models face a core limitation: they are trained on what we already know. True discovery begins when a system encounters something its current model cannot explain. This is why you cannot train your way to a discovery - a system has to reason through new problems, update its beliefs, and revise its understanding of the world as it thinks. Another critical insight is that rich knowledge already exists, but is not yet applied to solve pressing problems. It sits in millions of papers, patents, and datasets, trapped in isolated silos, often in legacy data vaults. What's missing is a way to connect it, scale it, unlock the potential, and synthesize genuine novel predictions. The time is now to build a system that enables practitioners to design, explore, and direct discovery, whether through human guidance or full automation, while capturing the tacit insight that domain experts bring. Steerable reasoning That is why we built an operating system for scientific discovery - one that replaces chance with steerable reasoning. Rather than retrieving static facts, our AI builds and continuously updates a living world model - a representation of knowledge the system can actively reason over, question, and revise. A concrete example: say you want to create "smart concrete" that can flex - a concept that doesn't exist yet. Our AI maps relationships across domains, finds a path from morphable smart materials to concrete, and identifies the most efficient way to bridge those concepts. It then autonomously writes simulations, tests the hypothesis, and refines the idea. Then it interacts with hardware to produce a physical artifact, and the loop expands into the real-world, where the machine becomes world-shaping. Our AI gives users full visibility into how the system arrived at a conclusion. It delineates which existing patents and papers it drew upon versus what is genuinely new - protecting IP and competitive concerns from the start, and offering deep compositional insights into technology advances. It takes unreasonable people to make progress Our team reflects the interdisciplinary expertise required to build this next breakthrough - my co-founder Yuan Cao Yuan Cao (formerly DeepMind) and Andrew Lew, Haiqian Yang, Matt Insler, Jennifer Kang and Julia McLaughlin. We are backed by $13.5M in seed funding led by Playground Global with participation from AIX, E14 Fund, and MS&AD. We are guided by advisors including Robert Langer (1,000+ patents), Kostya Novoselov (Nobel Prize in Physics), and Thomas Wolf (Co-founder of Hugging Face). We already have multiple pilot programs underway with leading industrial partners in materials science and engineering, with additional engagements developing across energy, logistics, bioengineering, and other strategic domains. The biggest challenges of our time - fusion energy, sustainable materials, new medicines - demand exponentially more innovation than humans alone can produce. We are not replacing scientists, and instead are making every scientist capable of leading their own team of AI-powered researchers. Abundant innovation leads to abundant prosperity. Watch our launch video below to see what we're building Unreasonable Labs 👇
2:16

Scientific discovery is reaching the limits of human capacity: too much data, too many disconnected fields, and too few ways to connect ideas fast enough to matter. The next breakthroughs in materials, medicine, energy, and beyond will not come from scaling today’s AI paradigm alone or from relying on serendipity alone. They will require a new kind of AI for knowledge discovery that not only models the world but shapes what it could become. At Unreasonable Labs, we are building superintelligence for knowledge discovery: systems that reason across disciplines, generate novel hypotheses, test them through simulation and experimentation, and help guide real-world discovery. Our AI engine is not confined to what it has seen in training. It creates new data, builds new tools, and maintains a persistent world model that grows more powerful as it reasons. Why now? Even today's most powerful AI models face a core limitation: they are trained on what we already know. True discovery begins when a system encounters something its current model cannot explain. This is why you cannot train your way to a discovery - a system has to reason through new problems, update its beliefs, and revise its understanding of the world as it thinks. Another critical insight is that rich knowledge already exists, but is not yet applied to solve pressing problems. It sits in millions of papers, patents, and datasets, trapped in isolated silos, often in legacy data vaults. What's missing is a way to connect it, scale it, unlock the potential, and synthesize genuine novel predictions. The time is now to build a system that enables practitioners to design, explore, and direct discovery, whether through human guidance or full automation, while capturing the tacit insight that domain experts bring. Steerable reasoning That is why we built an operating system for scientific discovery - one that replaces chance with steerable reasoning. Rather than retrieving static facts, our AI builds and continuously updates a living world model - a representation of knowledge the system can actively reason over, question, and revise. A concrete example: say you want to create "smart concrete" that can flex - a concept that doesn't exist yet. Our AI maps relationships across domains, finds a path from morphable smart materials to concrete, and identifies the most efficient way to bridge those concepts. It then autonomously writes simulations, tests the hypothesis, and refines the idea. Then it interacts with hardware to produce a physical artifact, and the loop expands into the real-world, where the machine becomes world-shaping. Our AI gives users full visibility into how the system arrived at a conclusion. It delineates which existing patents and papers it drew upon versus what is genuinely new - protecting IP and competitive concerns from the start, and offering deep compositional insights into technology advances. It takes unreasonable people to make progress Our team reflects the interdisciplinary expertise required to build this next breakthrough - my co-founder Yuan Cao Yuan Cao (formerly DeepMind) and Andrew Lew, Haiqian Yang, Matt Insler, Jennifer Kang and Julia McLaughlin. We are backed by $13.5M in seed funding led by Playground Global with participation from AIX, E14 Fund, and MS&AD. We are guided by advisors including Robert Langer (1,000+ patents), Kostya Novoselov (Nobel Prize in Physics), and Thomas Wolf (Co-founder of Hugging Face). We already have multiple pilot programs underway with leading industrial partners in materials science and engineering, with additional engagements developing across energy, logistics, bioengineering, and other strategic domains. The biggest challenges of our time - fusion energy, sustainable materials, new medicines - demand exponentially more innovation than humans alone can produce. We are not replacing scientists, and instead are making every scientist capable of leading their own team of AI-powered researchers. Abundant innovation leads to abundant prosperity. Watch our launch video below to see what we're building Unreasonable Labs 👇

Markus J. Buehler

55,189 просмотров • 6 месяцев назад

During World War 2, a young engineer named Kelly Johnson working out of a circus tent with a skeleton crew developed America’s first jet fighter in less than six months. Twenty years later, the same engineer developed the blackbird spy plane in less than two years. No one believes American industry is capable of that now. American innovation today is not about the physical world around us but about the digital world Big tech wants us to live in and exotic new financial instruments that wall street develops. Look at recent headlines to see what our best and brightest are up to. Facebook spent billions on the metaverse only to abandon it to go all in on AI. DoorDash lets you use buy now, pay later services to pay for your burrito in six equal installments. And in a move that would have Kelly Johnson rolling in his grave, Boom supersonic, a start-up originally planning to bring back supersonic jet flights, announced it will use its research to power an ai data center. If we want to power the future and restore America’s position as the world’s most technologically advanced country, we must do better. We need an America first industrial policy. The Trump Administration’s tariff policy is only one element to address this. Another critical tool to addressing this problem is the power of the federal contract. Trillions spent, headline our government spends trillions of your dollars buying products across the globe. You don’t need a great long-term memory to understand how the international supply chain failures hurt the United States during COVID. It’s one thing to wait six months for a Peloton. It’s another to wait six months for materials needed for our national defense. The defense sector in the United States should be the home to great engineers and doers, not bureaucrats and middle-men. The current status of that sector leaves much to be desired and reflects many of the problems attendant to our administrative state. It is bloated, slow, reliant on timelines, specifications, and proposals, and consolidated amongst a blob of A few major defense prime contractors that gobble up nearly 90% of the defense budget. Meanwhile, the alleged best technological talent in America holds little allegiance to this nation. They reject the notion of even being called American companies, turning their back on the idea of serving the nation to which they owe their existence. Their focus is on profit by selling more and more ads for more and more products made overseas. Their understanding and appreciation of America centers George Floyd above George Washington. What have these people truly invented as of late, anyways? AI generated videos to keep you scrolling? And these are the people, and the major donors as of late, we trust to guide American policy into the artificial intelligence age? This is a problem on both sides. A bloated procurement bureaucracy and defense and tech giants more focused on profit than innovating. We don’t just need government reform, we also need elite reform. It’s time for a renewal and return to a better time when innovation was paired with patriotism, with a government designed to sustain both the good news is that a gigantic federal defense budget provides ample ammunition to solve this problem. We need not only the best and brightest to come and serve their nation in various capacities, but the biggest dreamers. The biggest innovators. America’s engines of production and innovation need to be humming in a rhythm, focused squarely on our global power competition and domestic renewal. With President Trump in office and Secretary Hegseth at the helm of the department of war, this is entirely possible. It is also entirely needed. Let’s make sure American hardware is also hard-wired with the heart and soul of a patriotic class of innovators. Let’s have an America where corporate success requires nationalistic pride. Spirit and duty, it’s happened before and it can happen again. (12/10 monologue on Fine Point w/ Chanel Rion )
4:36

During World War 2, a young engineer named Kelly Johnson working out of a circus tent with a skeleton crew developed America’s first jet fighter in less than six months. Twenty years later, the same engineer developed the blackbird spy plane in less than two years. No one believes American industry is capable of that now. American innovation today is not about the physical world around us but about the digital world Big tech wants us to live in and exotic new financial instruments that wall street develops. Look at recent headlines to see what our best and brightest are up to. Facebook spent billions on the metaverse only to abandon it to go all in on AI. DoorDash lets you use buy now, pay later services to pay for your burrito in six equal installments. And in a move that would have Kelly Johnson rolling in his grave, Boom supersonic, a start-up originally planning to bring back supersonic jet flights, announced it will use its research to power an ai data center. If we want to power the future and restore America’s position as the world’s most technologically advanced country, we must do better. We need an America first industrial policy. The Trump Administration’s tariff policy is only one element to address this. Another critical tool to addressing this problem is the power of the federal contract. Trillions spent, headline our government spends trillions of your dollars buying products across the globe. You don’t need a great long-term memory to understand how the international supply chain failures hurt the United States during COVID. It’s one thing to wait six months for a Peloton. It’s another to wait six months for materials needed for our national defense. The defense sector in the United States should be the home to great engineers and doers, not bureaucrats and middle-men. The current status of that sector leaves much to be desired and reflects many of the problems attendant to our administrative state. It is bloated, slow, reliant on timelines, specifications, and proposals, and consolidated amongst a blob of A few major defense prime contractors that gobble up nearly 90% of the defense budget. Meanwhile, the alleged best technological talent in America holds little allegiance to this nation. They reject the notion of even being called American companies, turning their back on the idea of serving the nation to which they owe their existence. Their focus is on profit by selling more and more ads for more and more products made overseas. Their understanding and appreciation of America centers George Floyd above George Washington. What have these people truly invented as of late, anyways? AI generated videos to keep you scrolling? And these are the people, and the major donors as of late, we trust to guide American policy into the artificial intelligence age? This is a problem on both sides. A bloated procurement bureaucracy and defense and tech giants more focused on profit than innovating. We don’t just need government reform, we also need elite reform. It’s time for a renewal and return to a better time when innovation was paired with patriotism, with a government designed to sustain both the good news is that a gigantic federal defense budget provides ample ammunition to solve this problem. We need not only the best and brightest to come and serve their nation in various capacities, but the biggest dreamers. The biggest innovators. America’s engines of production and innovation need to be humming in a rhythm, focused squarely on our global power competition and domestic renewal. With President Trump in office and Secretary Hegseth at the helm of the department of war, this is entirely possible. It is also entirely needed. Let’s make sure American hardware is also hard-wired with the heart and soul of a patriotic class of innovators. Let’s have an America where corporate success requires nationalistic pride. Spirit and duty, it’s happened before and it can happen again. (12/10 monologue on Fine Point w/ Chanel Rion )

Mike Howell

16,205 просмотров • 9 месяцев назад

Arizona Secures Critical American Biofactory Hard Tech Headlines: - REGENT, the American seaglider startup, has successfully demonstrated its autonomous seaglider at Silent Swarm. It is the first defense-specific wing-in-ground-effect craft in U.S. history. - Textron Inc. has secured a $230M+ deal with the U.S. Navy for two Ship-to-Shore Connectors, amphibious hovercraft designed to move troops, vehicles, and supplies from ships offshore directly onto beaches. - Precigenetics, the startup developing real-time living-cell imaging technology, is expanding to a new biofactory in Arizona, where it plans to manufacture critical biohardware in the U.S. - Smack Technologies has secured a $61M Series B led by Costanoa Ventures. Smack is developing a frontier AI lab for defense, enabling autonomy decisions to be made at the campaign level. The startup is using the funding to develop custom hardware to deploy its models on-prem. - EdgeRunner AI, in partnership with the U.S. Army’s AI2C, has introduced a new military-focused large language model that reduced error rates by 37% across Army-specific tasks and is designed to run locally at the tactical edge. - Grounded has raised a $5M seed round. The Detroit startup develops software-defined, modular commercial electric vehicles and is expanding production into a new 50,000-square-foot facility. - Eoptic, Inc., the advanced imaging firm, has secured a deal to supply multiple payloads for strategic space imaging programs, with first deliveries planned for early 2027. - Aerogen Systems has launched out of Y Combinator, developing self-cleaning infrastructure for semiconductor manufacturing that creates ultra-clean environments around wafers. - Saildrone has opened a European HQ in Copenhagen and is expanding USV production in Europe, including its next-generation Surveyor at Umoe Mandal’s facility in Norway. - Skanska and Stacy Witbeck secured a $2.43B contract to expand LA Metro’s rail infrastructure in the San Fernando Valley. - Einride is purchasing 500 Tesla Semi trucks, the largest Tesla Semi order in history. - The U.S. Army has awarded Elroy Air $46M to advance autonomous heavy-lift aircraft for moving cargo across contested environments. The VTOL can carry 500+ pounds of cargo, fly up to 450 miles, and operate without runways in austere environments. - Valinor Enterprises announced HarborPOD, designed to turn autonomous systems into casualty evacuation or resupply platforms. We break it down in today’s Hard Tech Headlines video.
1:45

Arizona Secures Critical American Biofactory Hard Tech Headlines: - REGENT, the American seaglider startup, has successfully demonstrated its autonomous seaglider at Silent Swarm. It is the first defense-specific wing-in-ground-effect craft in U.S. history. - Textron Inc. has secured a $230M+ deal with the U.S. Navy for two Ship-to-Shore Connectors, amphibious hovercraft designed to move troops, vehicles, and supplies from ships offshore directly onto beaches. - Precigenetics, the startup developing real-time living-cell imaging technology, is expanding to a new biofactory in Arizona, where it plans to manufacture critical biohardware in the U.S. - Smack Technologies has secured a $61M Series B led by Costanoa Ventures. Smack is developing a frontier AI lab for defense, enabling autonomy decisions to be made at the campaign level. The startup is using the funding to develop custom hardware to deploy its models on-prem. - EdgeRunner AI, in partnership with the U.S. Army’s AI2C, has introduced a new military-focused large language model that reduced error rates by 37% across Army-specific tasks and is designed to run locally at the tactical edge. - Grounded has raised a $5M seed round. The Detroit startup develops software-defined, modular commercial electric vehicles and is expanding production into a new 50,000-square-foot facility. - Eoptic, Inc., the advanced imaging firm, has secured a deal to supply multiple payloads for strategic space imaging programs, with first deliveries planned for early 2027. - Aerogen Systems has launched out of Y Combinator, developing self-cleaning infrastructure for semiconductor manufacturing that creates ultra-clean environments around wafers. - Saildrone has opened a European HQ in Copenhagen and is expanding USV production in Europe, including its next-generation Surveyor at Umoe Mandal’s facility in Norway. - Skanska and Stacy Witbeck secured a $2.43B contract to expand LA Metro’s rail infrastructure in the San Fernando Valley. - Einride is purchasing 500 Tesla Semi trucks, the largest Tesla Semi order in history. - The U.S. Army has awarded Elroy Air $46M to advance autonomous heavy-lift aircraft for moving cargo across contested environments. The VTOL can carry 500+ pounds of cargo, fly up to 450 miles, and operate without runways in austere environments. - Valinor Enterprises announced HarborPOD, designed to turn autonomous systems into casualty evacuation or resupply platforms. We break it down in today’s Hard Tech Headlines video.

Atoms Not Bits

12,083 просмотров • 1 месяц назад

$AMD| $META is using $GOOGL to negotiate 🧵 The Ironwood pod is 5.1–10x more expensive annually ($148.3 million ÷ $14.87–$29.04 million) and 5.1–10x more expensive monthly ($12.36 million ÷ $1.24–$2.42 million) than renting 15 MI450 racks for equivalent compute. The rapidly evolving landscape of artificial intelligence infrastructure presents a complex interplay of technological innovation, market dynamics, and strategic maneuvering among major players. Recent leaked information suggesting that Meta Platforms ($META) might work with Google's Tensor Processing Unit (TPU) in 2027 has sparked speculation about its true intent. This leak is likely a strategic move by Meta to negotiate more favorable terms with AMD , leveraging the competitive dynamics of the AI hardware market to optimize its substantial investment in AI infrastructure. By examining the key elements of this scenario Meta's investment strategy, the comparative advantages of AMD's MI450 and Google's Ironwood TPU, and the broader market context; we can discern the potential beneficiaries and the strategic implications of this information. Meta's aggressive pursuit of AI capabilities is underscored by its planned expenditure of $66-72 billion on AI infrastructure in 2025, with expectations to escalate significantly in 2026. This investment is part of a broader strategy to build "titan clusters" like Prometheus, which are projected to reach 1 gigawatt of compute power by 2026. Such a scale of investment reflects Meta's recognition of the critical role that AI will play in its future growth, particularly in enhancing its social media platforms and developing new AI-driven applications. However, the financial burden of this infrastructure buildout necessitates a careful consideration of cost-effectiveness and scalability, which brings us to the leaked information about potential collaboration with Google's Ironwood TPU. Google's Ironwood TPU, introduced as the seventh-generation ASIC optimized for TensorFlow-based inference, represents a high-cost, cloud-locked solution priced at $445 million per pod (9,216 chips) over three years. This model, while offering significant performance gains and power efficiency, is tailored for pod-scale deployment and integrated with Google's cloud services, limiting flexibility and increasing costs for customers. In contrast, AMD's MI450 GPU, priced at $30,000–$40,000 per unit, provides a modular, open ROCm ecosystem that delivers comparable compute capacity at a fraction of the cost. Renting 15 MI450 racks could achieve similar 42+ exaFLOPS inference compute at 5–10x lower cost than renting a single Ironwood pod, underscoring AMD's competitive edge in terms of total cost of ownership (TCO). The leaked information about Meta's potential TPU deployment in 2027, therefore, can be interpreted as a negotiating tactic rather than a definitive shift in strategy. By signaling interest in Google's solution, Meta may be attempting to pressure AMD into offering more favorable terms/prices for 5-10GW. This tactic aligns with Meta's broader goal to finance most of its AI spend internally while exploring partnerships that can reduce costs and enhance flexibility. The post's emphasis on MI450's TCO advantage and its partnerships with major players like OpenAI, Microsoft, and Meta itself suggests that AMD is a critical component of Meta's AI infrastructure strategy. The threat of working with Google's TPU could prompt AMD to reassess its pricing, provide additional support, or offer incentives to retain Meta as a customer, thereby securing or expanding its market share. From a logical standpoint, Meta stands to benefit the most from this strategy. As a major buyer in a high-stakes market projected to surpass $1 trillion in annual spending by 2030, Meta's negotiating power is significant. The leaked information could lead to substantial cost savings on its $66-72 billion investment, enhancing its financial flexibility and allowing for further investment in AI capabilities. Moreover, this tactic reinforces Meta's position as a leader in the AI infrastructure race, potentially attracting more external financing for its data center projects and strengthening its competitive stance against other hyperscalers like Amazon and Microsoft. AMD could also benefit from this scenario. The negotiation pressure might lead to small short-term concessions, but it could also solidify long-term partnerships with Meta, ensuring continued demand for MI450 and other AI hardware solutions. Initially Meta's 42% allocation to AMD MI300X and its partnerships with Oracle, Dell, and HP indicates a deep integration of AMD's technology into Meta's infrastructure, which could be leveraged to maintain this relationship. For AMD, retaining Meta as a large key customer is crucial to capturing a larger share of the rapidly growing data center infrastructure market, driven by the insatiable demand for AI compute power. Google, on the other hand, faces a more limited benefit from this leaked information. While securing Meta as a customer would reinforce its position in the AI hardware market, the high cost and ecosystem lock-in of the Ironwood TPU might deter Meta from fully committing to this solution. The leaked information could prompt Google to reconsider its pricing or ecosystem strategy to remain competitive, but the immediate impact is likely to be minimal compared to the potential gains for Meta and AMD. Investors and market analysts also stand to benefit from this information, as it provides insights into the competitive dynamics of the AI hardware market. Adjustments in portfolios based on anticipated shifts in market share and profitability could lead to opportunities for those who correctly anticipate outcomes. The negotiation dynamic might introduce volatility, but it also highlights the strategic importance of cost-effective solutions in the AI infrastructure space. Lastly, the leaked information about Meta potentially working with Google's TPU in 2027 is likely a strategic move to negotiate with AMD, leveraging the competitive landscape to optimize its AI infrastructure investment. Meta, as the primary negotiator, stands to gain the most by securing better terms from AMD, reducing costs, and enhancing its financial flexibility. AMD, while initially at risk, could benefit from retaining a key customer and solidifying its market position. Google faces limited immediate benefits but may need to adapt its strategy to remain competitive. This scenario underscores the complex interplay of technology, market dynamics, and strategic maneuvering in the AI hardware market, where cost-effectiveness and scalability are paramount. As the data center infrastructure market continues to grow, the outcomes of such negotiations will shape the future of AI development and deployment.
1:12

$AMD| $META is using $GOOGL to negotiate 🧵 The Ironwood pod is 5.1–10x more expensive annually ($148.3 million ÷ $14.87–$29.04 million) and 5.1–10x more expensive monthly ($12.36 million ÷ $1.24–$2.42 million) than renting 15 MI450 racks for equivalent compute. The rapidly evolving landscape of artificial intelligence infrastructure presents a complex interplay of technological innovation, market dynamics, and strategic maneuvering among major players. Recent leaked information suggesting that Meta Platforms ($META) might work with Google's Tensor Processing Unit (TPU) in 2027 has sparked speculation about its true intent. This leak is likely a strategic move by Meta to negotiate more favorable terms with AMD , leveraging the competitive dynamics of the AI hardware market to optimize its substantial investment in AI infrastructure. By examining the key elements of this scenario Meta's investment strategy, the comparative advantages of AMD's MI450 and Google's Ironwood TPU, and the broader market context; we can discern the potential beneficiaries and the strategic implications of this information. Meta's aggressive pursuit of AI capabilities is underscored by its planned expenditure of $66-72 billion on AI infrastructure in 2025, with expectations to escalate significantly in 2026. This investment is part of a broader strategy to build "titan clusters" like Prometheus, which are projected to reach 1 gigawatt of compute power by 2026. Such a scale of investment reflects Meta's recognition of the critical role that AI will play in its future growth, particularly in enhancing its social media platforms and developing new AI-driven applications. However, the financial burden of this infrastructure buildout necessitates a careful consideration of cost-effectiveness and scalability, which brings us to the leaked information about potential collaboration with Google's Ironwood TPU. Google's Ironwood TPU, introduced as the seventh-generation ASIC optimized for TensorFlow-based inference, represents a high-cost, cloud-locked solution priced at $445 million per pod (9,216 chips) over three years. This model, while offering significant performance gains and power efficiency, is tailored for pod-scale deployment and integrated with Google's cloud services, limiting flexibility and increasing costs for customers. In contrast, AMD's MI450 GPU, priced at $30,000–$40,000 per unit, provides a modular, open ROCm ecosystem that delivers comparable compute capacity at a fraction of the cost. Renting 15 MI450 racks could achieve similar 42+ exaFLOPS inference compute at 5–10x lower cost than renting a single Ironwood pod, underscoring AMD's competitive edge in terms of total cost of ownership (TCO). The leaked information about Meta's potential TPU deployment in 2027, therefore, can be interpreted as a negotiating tactic rather than a definitive shift in strategy. By signaling interest in Google's solution, Meta may be attempting to pressure AMD into offering more favorable terms/prices for 5-10GW. This tactic aligns with Meta's broader goal to finance most of its AI spend internally while exploring partnerships that can reduce costs and enhance flexibility. The post's emphasis on MI450's TCO advantage and its partnerships with major players like OpenAI, Microsoft, and Meta itself suggests that AMD is a critical component of Meta's AI infrastructure strategy. The threat of working with Google's TPU could prompt AMD to reassess its pricing, provide additional support, or offer incentives to retain Meta as a customer, thereby securing or expanding its market share. From a logical standpoint, Meta stands to benefit the most from this strategy. As a major buyer in a high-stakes market projected to surpass $1 trillion in annual spending by 2030, Meta's negotiating power is significant. The leaked information could lead to substantial cost savings on its $66-72 billion investment, enhancing its financial flexibility and allowing for further investment in AI capabilities. Moreover, this tactic reinforces Meta's position as a leader in the AI infrastructure race, potentially attracting more external financing for its data center projects and strengthening its competitive stance against other hyperscalers like Amazon and Microsoft. AMD could also benefit from this scenario. The negotiation pressure might lead to small short-term concessions, but it could also solidify long-term partnerships with Meta, ensuring continued demand for MI450 and other AI hardware solutions. Initially Meta's 42% allocation to AMD MI300X and its partnerships with Oracle, Dell, and HP indicates a deep integration of AMD's technology into Meta's infrastructure, which could be leveraged to maintain this relationship. For AMD, retaining Meta as a large key customer is crucial to capturing a larger share of the rapidly growing data center infrastructure market, driven by the insatiable demand for AI compute power. Google, on the other hand, faces a more limited benefit from this leaked information. While securing Meta as a customer would reinforce its position in the AI hardware market, the high cost and ecosystem lock-in of the Ironwood TPU might deter Meta from fully committing to this solution. The leaked information could prompt Google to reconsider its pricing or ecosystem strategy to remain competitive, but the immediate impact is likely to be minimal compared to the potential gains for Meta and AMD. Investors and market analysts also stand to benefit from this information, as it provides insights into the competitive dynamics of the AI hardware market. Adjustments in portfolios based on anticipated shifts in market share and profitability could lead to opportunities for those who correctly anticipate outcomes. The negotiation dynamic might introduce volatility, but it also highlights the strategic importance of cost-effective solutions in the AI infrastructure space. Lastly, the leaked information about Meta potentially working with Google's TPU in 2027 is likely a strategic move to negotiate with AMD, leveraging the competitive landscape to optimize its AI infrastructure investment. Meta, as the primary negotiator, stands to gain the most by securing better terms from AMD, reducing costs, and enhancing its financial flexibility. AMD, while initially at risk, could benefit from retaining a key customer and solidifying its market position. Google faces limited immediate benefits but may need to adapt its strategy to remain competitive. This scenario underscores the complex interplay of technology, market dynamics, and strategic maneuvering in the AI hardware market, where cost-effectiveness and scalability are paramount. As the data center infrastructure market continues to grow, the outcomes of such negotiations will shape the future of AI development and deployment.

Mike

182,631 просмотров • 10 месяцев назад

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 просмотров • 4 месяцев назад