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Furina ⚔ Aether Big shoutout to @rubyred_VA for voicing Furina! Aether model by @dab_neko Additional SFX library by @that1Lewddude Catbox: Higher res + bonus variant at my bio link! #furina #genshin

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737,131 Aufrufe • vor 2 Jahren •via X (Twitter)

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did u know Alveus Sanctuary has an email list and every email we send out is handmade/handwritten by me??? we only send out emails for big updates/announcements and I promise not to spam u!!!! sign up for handmade emails at the link in my bio <3

did u know Alveus Sanctuary has an email list and every email we send out is handmade/handwritten by me??? we only send out emails for big updates/announcements and I promise not to spam u!!!! sign up for handmade emails at the link in my bio <3

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My startup, Positron AI just raised $51.6M Series A to rebuild the infrastructure powering AI inference and bring Superintelligence to everyone. Led by Valor Equity Partners (meek) , Atreides Management (Atreides Management, LP) , and DFJ Growth (DFJ Growth) —the same teams behind SpaceX, Tesla, xAI, and other companies pushing compute to its physical and economic limits. Why inference? Because as AI moves from research into production, the real bottleneck isn’t training—it’s deploying transformer models at scale. GPUs were built for flexibility, not predictable workloads. That mismatch means wasted bandwidth, poor density, and massive power costs. So at Positron, we built silicon specifically optimized for inference: •>90% memory bandwidth utilization (GPUs typically ~30%) •Multi-model hosting per card (higher density, lower power) •Zero code changes — drop in compatability with the HuggingFace Transformers ecosystem. U.S.-made silicon, stable supply chain, geopolitically resilient We’re already shipping and running in production environments today. Here’s what we’ve built, and link to our WSJ coverage and press release in the follow up reply ↓

My startup, Positron AI just raised $51.6M Series A to rebuild the infrastructure powering AI inference and bring Superintelligence to everyone. Led by Valor Equity Partners (meek) , Atreides Management (Atreides Management, LP) , and DFJ Growth (DFJ Growth) —the same teams behind SpaceX, Tesla, xAI, and other companies pushing compute to its physical and economic limits. Why inference? Because as AI moves from research into production, the real bottleneck isn’t training—it’s deploying transformer models at scale. GPUs were built for flexibility, not predictable workloads. That mismatch means wasted bandwidth, poor density, and massive power costs. So at Positron, we built silicon specifically optimized for inference: •>90% memory bandwidth utilization (GPUs typically ~30%) •Multi-model hosting per card (higher density, lower power) •Zero code changes — drop in compatability with the HuggingFace Transformers ecosystem. U.S.-made silicon, stable supply chain, geopolitically resilient We’re already shipping and running in production environments today. Here’s what we’ve built, and link to our WSJ coverage and press release in the follow up reply ↓

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🥳Excited to share our latest work: "Diff-A-Riff"! 🥁 A Latent Diffusion Model that generates instrumental accompaniments for any musical input, specifically tailored for music producers! It's faster, lighter, and produces superior audio quality. Control via text/audio references. 48kHz sample rate, (pseudo) stereo, ~3Gb memory, takes 6 seconds to generate 90 seconds of music. Trained on a single GPU. 📜 🎶 🎸 "Diff-A-Riff" adapts to any musical input, following the artist's unique style. 🎛️ Optional controls via text prompts, audio references, interpolation slider, pseudo-stereo width and loop intensity. 🎚️ It produces state-of-the-art audio quality indistinguishable from real data by human raters and operates at unprecedented speed. 🧠 "Diff-A-Riff" is smaller and more efficient than previous models thanks to its Consistency Autoencoder, making it accessible and practical for various applications. Big shoutout to my outer space colleagues: Javier Nistal, the Machine in "machine learning" 🚄, Marco Pasini, the neural net whisperer 🤫, Cyran Aouameur, the troubleshootah 🛠️, Maarten Grachten, aka MaartenGPT 🤖. #Teamwork #AI #MusicTech #Innovation Javier Nistal Marco Pasini Cyran Aouameur SonyCSL(Paris)_Music Team

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BREAKING: OpenAI just dropped GPT-6 ASTRA!!! 🚀✨ We’ve been testing it extensively at Every 📧 across coding, writing, and knowledge work. My take: it’s a big upgrade from 5.6-Sol, with some frustrating habits that keep it from matching Fable at the top end. Here’s your vibe check: - The best writing model I’ve tried. It’s fast, produces very little slop, and is easy to steer. It’s a good companion for actually working through the writing I do every day. (Not to mention, it one-shotted the first draft of its own vibe check today!) - The computer use is wild. It can go for hours at a time using complicated apps to get work done. It did the first cut of our Fable 5.1 vibe check video...kind of mindblowing - Impressive 3D games and visualizations. It can make beautiful 3D worlds from a single prompt. I one-shotted a historically accurate rendition of the Battle of Waterloo - It can overcomplicate things. (Especially at higher effort levels.) Ask for a simple interface and you get extra labels, buttons, and features everywhere. It has a habit of turning everything into a landing page. It just doesnt quite match Fable's ability to intuitively understand your prompt and do something delightful (without overcomplicating.) Net Result: If you already live in ChatGPT for Work or Codex and can afford it, it’s an easy upgrade from 5.6-Sol. The biggest proof of Astra's effectiveness at helping you do work is our vibe check. We found out it was launching at 3 AM this morning, and had a 4,000 word vibe check + video done by 2 PM. Not possible without this model. I’m reaching for Astra all day, but Fable 5.1 still gets my biggest tasks. On ambitious builds, Fable is better at understanding what I want and taking it further than I would have thought to ask. State of Play: Astra is launching to Enterprise customers today, and the rest of ChatGPT users over the coming days. Now, both OpenAI and Anthropic have a higher class of models that cost more to use. That changes who gets to use frontier AI and how. It's also a new vector of competition between them: Fable and Astra are priced at the same level. We'll see what that means for adoption in the coming days and weeks. read our full vibe check Every 📧 today:

Dan Shipper

386,999 Aufrufe • vor 12 Tagen

[VIDEO] The Humane AI pin just launched for $699. Is this pin going to be the new Apple iPhone? Idk, but here are 3 big takeaways from the launch: 1. OpenAI/Microsoft will eventually become infra for all tech Hard to beat them at this rate, they will become AWS for AI 2. AI is gonna make apps (as we know them) a lot less important Screenless interfaces powered by AI will be connected to apps on the back end and bundled into a subscription service My Humane subscription will come with Tidal and I will rarely/never need to interface with tidal again Many consumer products will go from B2C -> B2B 3. Product design is going to look a lot different in 5-10 years Maybe unrecognizable. The bottom line is how we are interacting with these devices are changing. This week we had the GPT store. Agents are the new apps. Now we have AI pins. The palm of your hand is your new screen. Wild times. -- Follow me GREG ISENBERG for more takes on where the world is going. Link in bio for more insights. Email going out today on how to find proven startup ideas.

[VIDEO] The Humane AI pin just launched for $699. Is this pin going to be the new Apple iPhone? Idk, but here are 3 big takeaways from the launch: 1. OpenAI/Microsoft will eventually become infra for all tech Hard to beat them at this rate, they will become AWS for AI 2. AI is gonna make apps (as we know them) a lot less important Screenless interfaces powered by AI will be connected to apps on the back end and bundled into a subscription service My Humane subscription will come with Tidal and I will rarely/never need to interface with tidal again Many consumer products will go from B2C -> B2B 3. Product design is going to look a lot different in 5-10 years Maybe unrecognizable. The bottom line is how we are interacting with these devices are changing. This week we had the GPT store. Agents are the new apps. Now we have AI pins. The palm of your hand is your new screen. Wild times. -- Follow me GREG ISENBERG for more takes on where the world is going. Link in bio for more insights. Email going out today on how to find proven startup ideas.

GREG ISENBERG

2,310,801 Aufrufe • vor 2 Jahren

Larry Ellison, the man who built Oracle into a $500 billion enterprise software empire and he said something that every investor needs to hear (Save this). "By 2029, I can guarantee you, AI is not going to be the problem." The problem is going to be compute specifically, who has enough of it and who does not. Ellison described the current AI race in terms that strip away all the abstract commentary about models and capabilities and reduce it to the one thing that actually determines who wins: "Me and Elon begging Jensen for GPUs. Please take our money. We need you to take more of our money, please." Citigroup raised its forecast for AI infrastructure spending to $2.8 trillion through 2029, with hyperscalers already spending at a $490 billion annual rate by end of 2026 and the firm estimates global AI compute demand will require 55 gigawatts of new power capacity by 2030 at a cost of approximately $50 billion per gigawatt. Sam Altman publicly thanked Jensen Huang this past March for significantly increasing NVIDIA's capacity at AWS, the CEO of the most important AI lab in the world writing a thank you note to the chip supplier because compute is still the binding constraint on everything OpenAI wants to build. Ellison's point about getting there first is the part of this clip that deserves a second read. He named three specific races, self-driving, reading cancer biopsy slides, and synthesizing video and said that being first in each one is a big deal. The logic is that in winner take most AI markets, the first mover trains the best model, the best model attracts the most usage, the most usage generates the most data, and the most data trains the next best model, a compounding loop that the second-place finisher never catches up to. "The guys in this race are very smart and they understand they need to be best at something," Ellison said. What makes this clip so important right now is the timing. The AI GPU chip market is projected to grow at a 32.4% CAGR through 2029, reaching $145 billion in incremental spend, and NVIDIA's data center revenue is already running at a pace that would have seemed impossible three years ago. Every major hyperscaler, Microsoft, Amazon, Google, Oracle, Meta is no longer funding AI capex from operating cash flows alone, they are borrowing to keep up, because falling behind in compute now means ceding the winner-take-most race Ellison just described. At Milk Road, we have been positioned in NVIDIA, AVGO, AAOI, MU, and Bloom Energy and more. Come join Milk Road Pro and get the full picture on how we are playing every layer of the GPU demand supercycle that Larry Ellison just guaranteed will not slow down before the end of the decade, link below/bio.

Larry Ellison, the man who built Oracle into a $500 billion enterprise software empire and he said something that every investor needs to hear (Save this). "By 2029, I can guarantee you, AI is not going to be the problem." The problem is going to be compute specifically, who has enough of it and who does not. Ellison described the current AI race in terms that strip away all the abstract commentary about models and capabilities and reduce it to the one thing that actually determines who wins: "Me and Elon begging Jensen for GPUs. Please take our money. We need you to take more of our money, please." Citigroup raised its forecast for AI infrastructure spending to $2.8 trillion through 2029, with hyperscalers already spending at a $490 billion annual rate by end of 2026 and the firm estimates global AI compute demand will require 55 gigawatts of new power capacity by 2030 at a cost of approximately $50 billion per gigawatt. Sam Altman publicly thanked Jensen Huang this past March for significantly increasing NVIDIA's capacity at AWS, the CEO of the most important AI lab in the world writing a thank you note to the chip supplier because compute is still the binding constraint on everything OpenAI wants to build. Ellison's point about getting there first is the part of this clip that deserves a second read. He named three specific races, self-driving, reading cancer biopsy slides, and synthesizing video and said that being first in each one is a big deal. The logic is that in winner take most AI markets, the first mover trains the best model, the best model attracts the most usage, the most usage generates the most data, and the most data trains the next best model, a compounding loop that the second-place finisher never catches up to. "The guys in this race are very smart and they understand they need to be best at something," Ellison said. What makes this clip so important right now is the timing. The AI GPU chip market is projected to grow at a 32.4% CAGR through 2029, reaching $145 billion in incremental spend, and NVIDIA's data center revenue is already running at a pace that would have seemed impossible three years ago. Every major hyperscaler, Microsoft, Amazon, Google, Oracle, Meta is no longer funding AI capex from operating cash flows alone, they are borrowing to keep up, because falling behind in compute now means ceding the winner-take-most race Ellison just described. At Milk Road, we have been positioned in NVIDIA, AVGO, AAOI, MU, and Bloom Energy and more. Come join Milk Road Pro and get the full picture on how we are playing every layer of the GPU demand supercycle that Larry Ellison just guaranteed will not slow down before the end of the decade, link below/bio.

Milk Road AI

486,176 Aufrufe • vor 3 Monaten

A new DeFi era needs a return to fundamental primitives. Announcing Soul Public Sale! DeFi is entering a new era, led by protocols that bring real innovation, but to live up to its full potential, it needs to re-embrace its founding ethos with broad distribution and fair allocation. Soul is part of this: a 0-to-1 DeFi primitive that reimagines and unites crypto lending, built to unlock a new category of opportunities for users and enabling new DeFi economies led by increased efficiency and new on-chain dynamics. The launch of our Testnet has been a significant initial milestone in our journey, and we are beyond grateful to see such a warm welcome from our early community members! With this being just the first step of a long path ahead, we are excited to announce another significant milestone we are gearing up towards. Introducing the $SO Token Fair Public Round on May 16th! As a fully decentralized protocol, our goal is not to lead a community, but to lead together with the community! We are big believers that a united goal to create a change in the world will bring the strongest foundations for long-term success. We are excited to share with you that Soul successfully raised $4M in a private round from valuable partners such as X Ventures, TPCº, and Runtime Vеrification alongside angels from Coinbase 🛡️, LayerZero, Flowdesk, and . This was an important step to support the early development of the protocol and bring the initial vision to life. However, we believe that the true value of Soul should be established transparently, through broad community participation, rather than being set by private market dynamics. To support this, we are introducing the Fair Public Round for the $SO Token. A process designed to ensure equal access, fair valuation, and eliminate preferential terms for early participants. // The problem Over the past few years, a familiar pattern has emerged across the crypto sector. Projects often raise substantial capital through private rounds, securing early valuations and preferential terms for a limited group of participants. When a public round is eventually offered, it typically comes at a significantly higher valuation, offering fewer opportunities and limited upside for retail investors. This approach creates a fundamental imbalance, restricting participation and disconnecting value creation from important groups who also contribute to a project's success: users, supporters, and builders. At Soul, we believe there is a better model, one that aligns incentives from the beginning and builds stronger foundations for long-term growth. By giving the community the opportunity to participate early and on fair terms, we enable those who contribute to Soul’s success to directly benefit from the value they help create. // A new ICO era emerges An era in which initial valuations are not decided around a table of early private participants, but by the open and public participation itself. In 2017–2018, we witnessed one of the most dynamic periods in crypto history: the ICO era. A time when anyone, anywhere in the world, could participate meaningfully in the early growth of projects they believed in. Access was open, participation was broad, and innovation was funded directly by the community. Over time, however, that spirit of openness faded. The rise of private rounds and exclusive allocations shifted the landscape toward more traditional fundraising models, limiting access and weakening the connection between projects and their communities. We believe it's time to restore that original spirit, to rebuild a model where openness, fairness, and community participation are at the core of crypto startup innovation. // Pushing for an ICO 2.0 Era At Soul, we are committed to building a foundation that prioritizes fair participation and long-term alignment. 25% of the total token supply will be allocated to the community through this Public Round, establishing the basis for strong, decentralized governance as the protocol launches. Soul’s valuation will be determined upon the conclusion of the Public Sale, based solely on the capital raised during the round. We are equally committed to avoiding the common pitfalls of low circulating supply and artificially high FDV. All tokens offered in the public round will be fully unlocked at TGE, allowing for a transparent, market-driven valuation from the outset. // $SO Utility The $SO token is an essential component of the Soul Protocol, designed to drive governance, gauge voting, yield boosting, and value sharing. Rather than existing as a speculative asset, $SO is structured to reward participation, align incentives across stakeholders, and strengthen long-term protocol sustainability. Its utility is directly tied to protocol adoption and engagement, ensuring that value creation flows to those who contribute meaningfully to the protocol. Soul rejects short-term incentive models such as liquidity mining, which often generate unsustainable growth. Instead, prioritizes product-driven revenue and long-term value accrual, distributing rewards to $SO stakers and active participants in proportion to their contributions. Governance & Emissions Control $SO is the governance token of the Soul Protocol, enabling holders to participate in critical decision-making processes. Through governance proposals and emission gauge voting, $SO holders directly influence the allocation of incentives across Soul’s integrated Money Markets, ensuring that ecosystem growth is aligned with stakeholder interests. Boosted Yields Through Staking Staking $SO enhances users' lending positions by granting access to additional yield opportunities on top of base APYs. This mechanism directly links token utility to core protocol activity, ensuring that demand for $SO is driven by real usage rather than speculative behavior. Revenue Sharing & Buyback Mechanism The protocol’s fee switch will direct a portion of collected revenue to purchase $SO tokens from the open market, which will then be redistributed to stakers through emissions and reward mechanisms. This model will directly link protocol revenue to tokenholder rewards, ensuring that $SO ownership is economically tied to the protocol’s activity and long-term growth. For full details on tokenomics, please visit our dedicated page:

A new DeFi era needs a return to fundamental primitives. Announcing Soul Public Sale! DeFi is entering a new era, led by protocols that bring real innovation, but to live up to its full potential, it needs to re-embrace its founding ethos with broad distribution and fair allocation. Soul is part of this: a 0-to-1 DeFi primitive that reimagines and unites crypto lending, built to unlock a new category of opportunities for users and enabling new DeFi economies led by increased efficiency and new on-chain dynamics. The launch of our Testnet has been a significant initial milestone in our journey, and we are beyond grateful to see such a warm welcome from our early community members! With this being just the first step of a long path ahead, we are excited to announce another significant milestone we are gearing up towards. Introducing the $SO Token Fair Public Round on May 16th! As a fully decentralized protocol, our goal is not to lead a community, but to lead together with the community! We are big believers that a united goal to create a change in the world will bring the strongest foundations for long-term success. We are excited to share with you that Soul successfully raised $4M in a private round from valuable partners such as X Ventures, TPCº, and Runtime Vеrification alongside angels from Coinbase 🛡️, LayerZero, Flowdesk, and . This was an important step to support the early development of the protocol and bring the initial vision to life. However, we believe that the true value of Soul should be established transparently, through broad community participation, rather than being set by private market dynamics. To support this, we are introducing the Fair Public Round for the $SO Token. A process designed to ensure equal access, fair valuation, and eliminate preferential terms for early participants. // The problem Over the past few years, a familiar pattern has emerged across the crypto sector. Projects often raise substantial capital through private rounds, securing early valuations and preferential terms for a limited group of participants. When a public round is eventually offered, it typically comes at a significantly higher valuation, offering fewer opportunities and limited upside for retail investors. This approach creates a fundamental imbalance, restricting participation and disconnecting value creation from important groups who also contribute to a project's success: users, supporters, and builders. At Soul, we believe there is a better model, one that aligns incentives from the beginning and builds stronger foundations for long-term growth. By giving the community the opportunity to participate early and on fair terms, we enable those who contribute to Soul’s success to directly benefit from the value they help create. // A new ICO era emerges An era in which initial valuations are not decided around a table of early private participants, but by the open and public participation itself. In 2017–2018, we witnessed one of the most dynamic periods in crypto history: the ICO era. A time when anyone, anywhere in the world, could participate meaningfully in the early growth of projects they believed in. Access was open, participation was broad, and innovation was funded directly by the community. Over time, however, that spirit of openness faded. The rise of private rounds and exclusive allocations shifted the landscape toward more traditional fundraising models, limiting access and weakening the connection between projects and their communities. We believe it's time to restore that original spirit, to rebuild a model where openness, fairness, and community participation are at the core of crypto startup innovation. // Pushing for an ICO 2.0 Era At Soul, we are committed to building a foundation that prioritizes fair participation and long-term alignment. 25% of the total token supply will be allocated to the community through this Public Round, establishing the basis for strong, decentralized governance as the protocol launches. Soul’s valuation will be determined upon the conclusion of the Public Sale, based solely on the capital raised during the round. We are equally committed to avoiding the common pitfalls of low circulating supply and artificially high FDV. All tokens offered in the public round will be fully unlocked at TGE, allowing for a transparent, market-driven valuation from the outset. // $SO Utility The $SO token is an essential component of the Soul Protocol, designed to drive governance, gauge voting, yield boosting, and value sharing. Rather than existing as a speculative asset, $SO is structured to reward participation, align incentives across stakeholders, and strengthen long-term protocol sustainability. Its utility is directly tied to protocol adoption and engagement, ensuring that value creation flows to those who contribute meaningfully to the protocol. Soul rejects short-term incentive models such as liquidity mining, which often generate unsustainable growth. Instead, prioritizes product-driven revenue and long-term value accrual, distributing rewards to $SO stakers and active participants in proportion to their contributions. Governance & Emissions Control $SO is the governance token of the Soul Protocol, enabling holders to participate in critical decision-making processes. Through governance proposals and emission gauge voting, $SO holders directly influence the allocation of incentives across Soul’s integrated Money Markets, ensuring that ecosystem growth is aligned with stakeholder interests. Boosted Yields Through Staking Staking $SO enhances users' lending positions by granting access to additional yield opportunities on top of base APYs. This mechanism directly links token utility to core protocol activity, ensuring that demand for $SO is driven by real usage rather than speculative behavior. Revenue Sharing & Buyback Mechanism The protocol’s fee switch will direct a portion of collected revenue to purchase $SO tokens from the open market, which will then be redistributed to stakers through emissions and reward mechanisms. This model will directly link protocol revenue to tokenholder rewards, ensuring that $SO ownership is economically tied to the protocol’s activity and long-term growth. For full details on tokenomics, please visit our dedicated page:

Soul Labs

289,766 Aufrufe • vor 1 Jahr

My good friend, Stacy Langton, (Stacy Langton), who is featured in Dinesh D'Souza's film "Police State", is being sued by failed "Republican" Fairfax County School Board candidate, Harry Jackson, who created a fake X account with Stacy's high-school yearbook photo and a bio stating she's an "Only Fans" porn star. He stupidly posted screenshots of an unrelated topic on FB that showed he was logged in as the fake account. Stacy sued him for defamation, but due to the mounting legal fees, had no choice but to drop the lawsuit. In response, this loser, who had previously been arrested 3x for domestic abuse, has now counter-sued her for $3M. Stacy is a stay-at-home mother of 6 children, wife of a Navy officer, and was a major catalyst in Virginia Governor Glenn Youngkin's come-from-behind win against McAuliffe in 2021. She contacted his campaign just days before their final debate to let him know that when McAuliffe was VA Governor, he vetoed a bill that would've made it a requirement to notify parents of any sexually-explicit books in schools. When Youngkin brought it up at the debate, McAuliffe infamously responded, "I don't think parents should be telling schools what they should teach", and instantaneously ended his chances of winning, in spite of being ahead in the polls. The next day, the Youngkin campaign produced the ad below, which features a brief clip from Stacy's appearance at a Fairfax County School Board meeting, where she read graphic passages from two of the books she found in her children's school library. The school board ironically tried to silence her because there were "children present" at the meeting. We are often told to get engaged locally and Stacy did just that. Her reward is dirty tricks, defamation, and lawfare by this scumbag "Republican", who was endorsed by establishment "Republicans". This clown who is allegedly an FBI contractor even likes to brag about how he uses his intel connections to dig up dirt on his opponents. He is now trying to financially ruin Stacy's family. I know times are tough right now, but if there's any way you can help Stacy and her family fight this lawsuit with a small donation and/or your prayers, I know she would be extremely grateful. If the Uniparty can do this to her, they most certainly can do it to you. She fought for her children and for your children. She doesn't deserve what this loser is putting her through. Here's the link to her GiveSendGo campaign, where you can read more about what she's been dealing with (including plenty of "sauce" to back everything up): If you can help by sharing this post, I would greatly appreciate it. I am praying this will get out to as many people as possible, so that she can hopefully raise enough money to pay her legal fees and hopefully defeat him in court.

My good friend, Stacy Langton, (Stacy Langton), who is featured in Dinesh D'Souza's film "Police State", is being sued by failed "Republican" Fairfax County School Board candidate, Harry Jackson, who created a fake X account with Stacy's high-school yearbook photo and a bio stating she's an "Only Fans" porn star. He stupidly posted screenshots of an unrelated topic on FB that showed he was logged in as the fake account. Stacy sued him for defamation, but due to the mounting legal fees, had no choice but to drop the lawsuit. In response, this loser, who had previously been arrested 3x for domestic abuse, has now counter-sued her for $3M. Stacy is a stay-at-home mother of 6 children, wife of a Navy officer, and was a major catalyst in Virginia Governor Glenn Youngkin's come-from-behind win against McAuliffe in 2021. She contacted his campaign just days before their final debate to let him know that when McAuliffe was VA Governor, he vetoed a bill that would've made it a requirement to notify parents of any sexually-explicit books in schools. When Youngkin brought it up at the debate, McAuliffe infamously responded, "I don't think parents should be telling schools what they should teach", and instantaneously ended his chances of winning, in spite of being ahead in the polls. The next day, the Youngkin campaign produced the ad below, which features a brief clip from Stacy's appearance at a Fairfax County School Board meeting, where she read graphic passages from two of the books she found in her children's school library. The school board ironically tried to silence her because there were "children present" at the meeting. We are often told to get engaged locally and Stacy did just that. Her reward is dirty tricks, defamation, and lawfare by this scumbag "Republican", who was endorsed by establishment "Republicans". This clown who is allegedly an FBI contractor even likes to brag about how he uses his intel connections to dig up dirt on his opponents. He is now trying to financially ruin Stacy's family. I know times are tough right now, but if there's any way you can help Stacy and her family fight this lawsuit with a small donation and/or your prayers, I know she would be extremely grateful. If the Uniparty can do this to her, they most certainly can do it to you. She fought for her children and for your children. She doesn't deserve what this loser is putting her through. Here's the link to her GiveSendGo campaign, where you can read more about what she's been dealing with (including plenty of "sauce" to back everything up): If you can help by sharing this post, I would greatly appreciate it. I am praying this will get out to as many people as possible, so that she can hopefully raise enough money to pay her legal fees and hopefully defeat him in court.

Lisa Mei

89,207 Aufrufe • vor 2 Jahren

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026

$AMD $5 Trillion is Inevitable LT| Agentic AI🧵 Agentic AI is the new $5 Trillion TAM 🚨🚨🚨 This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battles—exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwan’s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026

Mike

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