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Bitcoin used to be dismissed as an experiment. Now it’s compared to: 🔸Gold 🔸Stocks 🔸Real estate 🔸Bonds 🔸Cash So where does Bitcoin actually fit? Digital gold? Risk asset? New asset class entirely? Read the full article:

Today on MCG: @ColbySaysHi | @prism_lp | $PRISM $PRISM is the first token where holding is providing liquidity. Each whole $PRISM you hold auto-mints one Prism NFT (a 1/5000 share of the same Uniswap v4 LP position) INSANE TEK Highlights from our convo: 03:18 - Uniswap v4 launched with a hooks whitelist, hooks didn't take off until UniPet (a viral unicorn NFT mint), then a Prism dev took it further 04:50 - How Prism works 05:38 - Full-range concentrated liquidity means fees accrue regardless of price/market cap 06:25 - Token unit economics 08:00 - Spectrum index tokens 14:32 - Colby's own product 15:00 - 10% of all Spectrum index fees 19:24 - Article release: "Retail is right to hate crypto" 22:18 - Why burn vs distribute 27:30 - PMF 30:00 - Spectrum V2 35:00 - @Uniswap team has reached out to learn how hooks are being used in the wild

A big thanks to @ionet for commissioning this report. 🤝 Full article: If you enjoyed this thread, consider subscribing to our free daily newsletter for more insights delivered straight to your inbox:

$AMD| The FOMO to buy @AMD Chips is NOW 🧵 Not Financial Advice! DYOR! Research Purpose Only! The Inference Queen is the biggest winner in Agentic AI where all other CPUs are struggling to compete with a 2yr old EPYC Turin and EPYC Venice is in mass production phase. AMD stresses deployability today on standard x86 platforms (no proprietary architectures required), full software compatibility, and open standards. This positions Venice + Helios as a practical, high-density alternative to competing solutions while underscoring that agentic AI shifts the balance toward CPU-rich racks alongside GPUs, and most importantly, lowering the cost of token to accelerate adoption and innovation. Context: @WSJ yesterday came out with an article that @OpenAI is condiering drasstically lowering the token prices to win more customers from Anthropic. The narrative "they" are trying to exacerbate the current AI selloff won't last long. This is a fundamental misunderstanding of what is going on, or what I already discussed for months and years. Followers and Subscribers already knew this for years, that this day would come, where token cost will bcome the central discussion among enterprises as there is no such thing as unlimited budget or Tokenmaxxing when they use $NVDA chips or In-house Hyperscalers chips. I will link various threads if you are interested in understanding the full picture from supply chain to recent TSMC Rapid 2nm expansion up to 12 Fabs total by 2027/2028. Hyperscalers and AI natives effectively have no choice but to buy more AMD system for Agentic AI as leadership in economical, power-aware, high-volume internal + agentic use. However, due to supply constraints where Supply is far behind Demand, this makes multi-vendor reality along with in-house chips drive faster industry progress, lower overall costs, and better sustainability. NVIDIA’s Vera Rubin cannot compete with a 2 years old EPYC Turin, but AMD under Dr. Lisa Su has engineered the lowest cost-per-million-tokens, highly competitive energy-efficient solutions, and superior CPU orchestration for agentic AI at scale with Helios. Dr. Su has championed this shift since at least 2023, foreseeing the rise of agentic workflows that demand far more orchestration, parallel agents, and balanced compute well before the industry fully embraced it. Her long-term vision of AI moving from simple prompts to always on, multi-agent systems has driven AMD’s investments in high-core EPYC CPUs and integrated rack-scale solutions, perfectly positioning the company for today’s realities. The OpenAI-AMD 1GW Helios deployment (starting H2 2026) represents a pivotal vertical integration move that directly supercharges the inference economics. This isn't incremental; it's a structural shift toward ownership of massive, optimized rack-scale capacity, enabling the lowest token costs and triggering the enterprise adoption flywheel. We need to be honest, $AMD is the only company that made a big bet on Inference since the day Chatgpt became sensational where $NVDA and others were betting big on Training. At the end of the day, Token bill from @AnthropicAI has to obey economics. Meaning the bills rise, companies have to get more out of it to justify the cost. It cannot be an unlimited inference budget, and it has to show up on efficiency, profitability and operating leverage. 1. Tokenomics After you understand this, you will understand why Citi cited @AnthropicAI is likely to sign a deal with $AMD along with Hyperscalers, AI Labs, Sovereign AI like Softbank 5GW in France and many other countries. However, OpenAI and $META are now wanting faster deployment, and they are AMD shareholders now, they have prioritized allocation. Anthropic and Hyperscalers just cannot compete when Helios Rack lower token cost to$0.0003–$0.0005 per million tokens at GW scale. Cost to build 1GW data center 1GW Helios Rack full build is estimated $30-$35B 1GW Rubin Rack full build is estimated $45-$55B Inference (Cost per Million Tokens) ~$NVDA B200 / HGX: ~$0.02–$0.08 on optimized workloads (FP4/MXFP4, speculative decoding). Significant improvement over Hopper but still premium-priced. GB200 NVL72 rack-scale: $0.05–$0.25+ ~$AMD Helios Racks: $0.0003-$0.0005 per M tokens, dramatically lower than NVIDIA equivalents in owned infra. MI355X node-level: Up to 40% more tokens per dollar vs. competing solutions ( B200), driven by higher memory capacity (up to 288GB+ HBM), strong bandwidth, and lower acquisition costs. Training ~$NVDA Rubin Rack is estimated $0.7-$1.2/M Tokens ~$AMD Helios Rack is estimated $0.65-$1.0/M Tokens Now, OpenAI, META and Hyperscalers can lower Inference cost even further with $AMD EPYC Venice "dense rack" or Agentic AI Rack. AMD published a detailed technical blog emphasizing that the future of agentic AI autonomous, multi-step AI systems requiring heavy orchestration, databases, caching, APIs, and control planes demands massive CPU-dense rack-scale infrastructure, not just GPUs. The catalyst prominently positions their upcoming 6th Gen EPYC "Venice" processors as the key enabler for next-generation dense racks, delivering leadership throughput under real-world power, cooling, and density constraints. ~EPYC Venice (Zen 6 architecture, up to 256 cores / 512 threads per socket) is projected to deliver exceptional rack-level performance. In AMD’s modeled 100 kW rack comparisons, Venice-powered systems are expected to achieve ~3.30x the throughput of NVIDIA’s Vera (88-core Olympus) baseline across a broad mix of agentic-supporting workloads. ~This builds on current-generation 5th Gen EPYC "Turin" (up to 192 cores), which already delivers ~2.37x rack throughput vs. Vera and ~1.6x vs. Intel’s Xeon 6980P (128 cores). ~ Liquid-cooled Turin deployments already support >27,000 CPU cores per rack today. Venice is architected to push this beyond 36,000 cores in the same rack class, dramatically increasing concurrent agent capacity and overall infrastructure efficiency. 2. Ownership vs renting compute from Hyperscalers matter to OpenAI and only owning $AMD chips can meaningfully lower token cost for enterprises. ~Eliminates cloud overhead: No provider margins, utilization buffers, or egress fees. Direct control over power contracts, cooling, scheduling, and orchestration at dedicated facilities. ~Helios optimizations at GW scale: Rack-level density (1.4+ exaFLOPS FP8 per rack), high HBM4 bandwidth, EPYC orchestration for agentic workloads, and superior TCO/TDP. AMD's long-standing focus on tokens per dollar/watt shines here 20-40%+ efficiency edges in inference-heavy scenarios. ~At 1GW+ optimized deployment, inference hits $0.0003–$0.0005 per million tokens (community/analyst models tied to Helios metrics). This is dramatically lower than typical rented/cloud equivalents, especially for high-volume output tokens in agentic flows. High token bills today, enterprises running heavy agentic/coding/analysis workloads can face $50-100M+/month at current API rates (flagship models $5-30+/M output, scaled to massive volumes). Post-Helios compression, same volume will drop to $10-15M/month (or better) via lower underlying costs passed through as pricing flexibility, volume tiers, caching, or batch discounts. ROI thresholds collapse. More companies greenlight pilots → production → massive scaling. Agentic AI (autonomous workflows) multiplies token demand exponentially, but affordability removes the friction. OpenAI gains flexibility, Unlike more cloud-dependent rivals (Anthropic), they can lower effective pricing, offer aggressive enterprise bundles, or absorb volume without margin destruction directly tackling "high token bill" complaints while maintaining profitability as usage explodes. 3. Agentic AI Models shifted CPU:GPU Ratio to 1:1 toward 3-5:1 with Explosively Token-Hungry Workloads Agentic AI (autonomous, multi-step agents with planning, tool use, iteration, and self-correction) is fundamentally more compute and token intensive than conversational or single-turn generative AI. Agentic AI. autonomous, multi-step workflows with orchestration, tool use, parallel agents, data movement, and enterprise integration has dramatically increased the importance of strong host CPUs alongside GPUs. This shifts the CPU-to-GPU ratio higher and makes balanced systems critical toward 1:1 to 5:1 as enterprises testing more than 5-10 agents. AMD EPYC Venice excels ~Leadership core density (up to 256 Zen 6 cores per socket) for running many agents in parallel, orchestration layers, and high-throughput control-plane tasks. ~Superior performance-per-core and power efficiency ( up to 2.1x higher perf/core and 2.26x better SPECpower vs. NVIDIA Grace in benchmarks). ~Tight integration in Helios: One Venice CPU + multiple MI450 GPUs per node, enabling efficient data feeding to GPUs ("zero-copy"), parallel execution, and full rack utilization for complex agentic loops. Hyperscalers (Meta, Microsoft, Amazon, Google, Softbank) and AI natives (OpenAI, Anthropic...) are adopting high-core EPYC at scale specifically for these agentic demands, as CPUs now handle a larger share of non-model work (orchestration, policy enforcement, tool calls). This complements AMD’s lower-cost GPUs for overall TCO wins. ~Agents often generate 10–100x+ more tokens per task due to iterative reasoning chains, multiple tool calls, verification loops, and long-context orchestration. ~Goldman Sachs forecasts token consumption multiplying 24x by 2030 (to 120 quadrillion tokens/month) largely driven by agentic adoption in consumer and enterprise. ~Enterprise data shows agent-pattern workloads growing at 680% annualized rates, projected to surpass conversational AI in token volume by Q3 2026. ~Daily enterprise agent token consumption is already in the billions, with complex workflows (coding, workflows, analysis) amplifying this dramatically. 4. Competitive Edge: Winning Customers from Anthropic Anthropic’s Claude models (especially Opus/Sonnet) excel in complex reasoning and agentic coding, commanding premium positioning. However, their higher underlying costs (heavier reliance on third-party cloud with margins) limit pricing flexibility compared to OpenAI’s owned Helios capacity. Anthropic is on track to generate $10.9 billion in Q2 revenue. The company expects to achieve its first-ever quarterly adjusted operating profit of $559 million. However, sustaining full-year profitability remains challenging due to immense computing and model training costs The truth is, Anthropic has no choice but to buy as much $AMD chips as possible if they want to compete with OpenAI or get investors attention. This 5% adjusted operating profit to revenue ratio is just pathetic. Current pricing dynamics (2026): OpenAI already undercuts on many tiers ( flagship output tokens significantly cheaper than equivalent Claude Opus). Nano/mini models offer 5–10x advantages for volume work. Anthropic holds edges in long-context flat pricing and certain reasoning quality. OpenAI after Helios Rack Ownership, At $0.0003–$0.0005/M effective costs, OpenAI gains massive headroom to: ~Aggressively discount high-volume agentic tiers or bundles. ~Offer “unlimited” enterprise plans or usage-based models that Anthropic struggles to match without margin erosion. ~Target cost-sensitive, high-throughput agent deployments (dev tools, automation platforms) where token bills explode. Enterprises facing $ millions in monthly agentic bills will migrate to the provider delivering better economics at scale. OpenAI’s combination of strong models (o-series reasoning) + lowest TCO positions it to erode Anthropic’s enterprise share, especially as agentic becomes the dominant token consumer. Cheaper tokens expand the total addressable market dramatically. This feeds the data/model improvement loop, justifying further capex. AMD benefits from proven scale pulling in more customers (Meta, Oracle, Microsfot, Amazon, Softbank, TensorWave, LumaAI ... already aligned on Helios). Conclusion: Dr. Lisa Su has been laser focused on inference economics since at least 2022–2023, repeatedly emphasizing that the real battleground for AI scalability would be TCO, power efficiency (TDP), and ultimately tokens per dollar and per watt not just raw training FLOPS. While many viewed inference as a secondary, commoditized workload, Dr. Su architected AMD’s roadmap around rack-scale systems optimized for high-volume, sustained inference that would dominate as models matured and usage exploded. Helios represents the culmination of that multi-year bet: a fully integrated, open platform designed precisely for the economics of massive token throughput. This deep, strategic partnership with OpenAI starting with the 1GW Helios deployment in H2 2026 and scaling to 6GW, is the embodiment of that shared vision. Both companies foresaw a future where agentic AI models evolve to become extraordinarily token-hungry: autonomous agents executing complex, iterative workflows with planning, tool use, verification loops, and long-context reasoning. These workloads can consume 100x+ more tokens per task than traditional chat or single-turn generation, driving exponential demand as capabilities improve and enterprises deploy them at scale. By owning and optimizing this massive Helios capacity at GW scale, OpenAI achieves inference costs as low as $0.0003–$0.0005 per million tokens. This structural cost advantage allows OpenAI to absorb the coming token explosion profitably, dramatically lower effective pricing for enterprises, and win high-volume agentic workloads from higher-cost competitors like Anthropic. What was once a prohibitive monthly token bill becomes an affordable accelerator for productivity and innovation. The OpenAI-AMD alliance validates Dr. Su’s prescient strategy and turns the Agentic flywheel into reality: Collapsing inference costs → explosive token consumption → richer data and better models → accelerate greater demand. This partnership doesn’t just address today’s economics, it positions both leaders at the center of the infrastructure buildout that will power AI’s next decade. By delivering the lowest inference economics at scale, OpenAI not only solves enterprise bill pain but gains a decisive weapon to win share from higher-cost rivals like Anthropic. And that is why @OpenAI and $META will deploy EPYC Dense Rack Not Financial Advice! DYOR! Research Purpose Only!

很多人第一次看到 HootArk，会以为只是一个 Web3 浏览器。 但更准确地说，HootArk 是一款 Agentic Web3 Browser & Wallet 🦉 HootArk把移动浏览器、内置多链钱包、dApp 入口和 AI Copilot 放在同一个 App 里，让你可以从“看到信息”直接走到“完成链上操作”。 你可以像平时上网一样打开网页、看资讯、查项目，也可以直接进入 Web3 场景：访问 DeFi、NFT marketplaces、DAO tools 等 dApps，连接钱包，查看资产，发起转账或 Swap。不需要额外插件，也不用在浏览器和钱包之间来回切换。 HootArk内置了钱包模块。在创建或导入钱包后，可以直接无缝接入Ethereum、BSC、Polygon 等主流网络。对刚进入 Crypto 的用户来说是一个更简单的入口；对 DeFi 用户、NFT 玩家和 Web3 老用户来说，它也能让移动端操作更顺手 ⚡ AI 也是 HootArk 很重要的一部分，但它不是一个单独摆在旁边的聊天框。 你可以边浏览边提问，让 AI 帮你理解项目、网页和链上信息； 看到新闻、公告或长文章，也可以直接让它总结重点； 在交易或交互前，帮你核查事实信息； 它更像一个随时跟着你的 Web3 copilot，帮你少查几次资料，少切几个页面 🤖 安全和隐私方面，HootArk 始终坚持安全第一：私钥保存在本地设备上，平台不收集数据，不追踪用户。你的钱包、你的身份、你的数据，都应该由你自己控制 🔐 【重点来啦】 我们即将发布 HootArk Lite 版 🚀 期待更轻、更快、更智能的Web3 AI 移动浏览器🎉 欢迎持续关注HootArk A lot of people see HootArk for the first time and think it’s just a Web3 browser. But more accurately, HootArk is an Agentic Web3 Browser & Wallet 🦉 HootArk brings a mobile browser, a built-in multi-chain wallet, a dApp gateway, and an AI Copilot into one app, so you can move naturally from “finding information” to “taking on-chain action.” You can use it like a regular browser to open websites, read news, and check projects. But you can also jump straight into Web3: access DeFi, NFT marketplaces, DAO tools, and other dApps, connect your wallet, view your assets, and make transfers or swaps. No extra plugins needed. No constant switching between your browser and wallet. HootArk also comes with a built-in wallet module. After creating or importing a wallet, you can seamlessly connect to major networks like Ethereum, BSC, Polygon, and more. For people new to crypto, it’s a simpler entry point. For DeFi users, NFT collectors, and Web3 natives, it makes mobile on-chain actions much smoother ⚡ AI is also an important part of HootArk, but it’s not just a chatbot sitting on the side. You can ask questions while browsing and let AI help you understand projects, web pages, and on-chain information. When you see news, announcements, or long articles, AI can help summarize the key points. Before a transaction or interaction, it can also help you check relevant facts and context. It feels more like a Web3 copilot that stays with you, helping you search less, switch tabs less, and understand things faster 🤖 When it comes to security and privacy, HootArk always puts security first: private keys stay on your local device, the platform does not collect data, and users are not tracked. Your wallet, your identity, and your data should always be controlled by you 🔐 And here’s the big update: We’ll soon be releasing HootArk Lite 🚀 Stay turned! Browser + Wallet + dApp Gateway + AI Copilot. This is HootArk 🦉✨ #HootArk# #Web3# #Web3Browser# #Wallet# #dApps# #AI#

Why did private firms, not state-owned enterprises (SOEs), come to dominate China’s EV sector? My new @ChinaJournal article (co-authored with Xiao Ma @maxiaoalex) challenge the "top-down industrial policy" narrative. The real engine? Strategic alliances between local governments and private capital. 🧵 Based on 3+ years of fieldwork, 60+ interviews (with officials, entrepreneurs, and engineers), and rich first-hand accounts, we show how strict central regulations inadvertently drove local states to bet big on private EV players. Here is the story: (1/15)

Everyone is talking about getting teams to use more AI. But how much of your AI spend is going toward work that's already been done? In PE diligence, the same VDR documents often get reprocessed across users, workstreams, and sessions. That means firms are paying for the same analysis again and again. As AI vendors move toward usage-based pricing, those inefficiencies start showing up fast. Our latest ToltIQ Insights article from Co-Founder and CIO @RikerTrek looks at why the Silicon Valley obsession with maximizing tokens misses the point in PE, and how the architecture underneath an AI platform affects cost, efficiency, and analytical depth throughout a deal. Read more:

CNN fired me for telling the truth about Charlottesville. New indictments further incriminate the corrupt SPLC, a leftist group that financed and organized the underlying staged event. A lie stacked upon a deceit… My article:

The House just voted 215–208 to end an undeclared war. Mark Levin's response was to call a principled, decorated constitutionalist a "moron." So let's settle who actually read Article I. Madison. Lincoln. The actual text. And the one place the war power can still be enforced.

“The new leaders are establishment actors: pragmatic, hardened nationalists operating with a clear-eyed assessment of Iran’s capabilities and vulnerabilities.” Such write “professors” @vali_nasr and @nargesbajoghli about the new leadership of the Islamic Republic in their new propaganda… sorry… “article” in @ForeignAffairs. It couldn’t be further from the truth. First of all, using the term “nationalist” to describe I.R. leaders is completely misleading. The regime leaders have continuously shown that the only thing they don’t care about is the national interests of Iran. They have consistently put their own interests above those of the country and have created an extraction economy that uses Iran’s vast resources—material and human—to fill their own pockets. This is not nationalism. This is oligarchic thievery and ideological colonialism imposed on Iran by a ruling class that sees the country not as a nation to be developed, but as a territory to be exploited. A nationalist government seeks to maximize the prosperity, security, dignity, and power of its people. What exactly has the Islamic Republic done? - It presided over one of the largest brain drains in modern history. - It turned one of the most resource-rich nations on earth into an economic basket case. - It squandered hundreds of billions of dollars on foreign adventures while millions of Iranians struggled to afford meat, medicine, and housing. - It destroyed Iran’s currency. - It destroyed Iran’s environment. - It destroyed trust between state and society. - And it has systematically driven away many of the very people most capable of building the country. Furthermore, to call these thieves “pragmatic” requires a breathtaking disregard for the historical record. Pragmatic leaders do not spend decades pursuing ideological projects that leave their country isolated, sanctioned, poorer, weaker, and increasingly dependent on foreign powers. Pragmatic leaders do not antagonize much of the world while simultaneously claiming victimhood for the consequences. Pragmatic leaders do not sacrifice generations of economic development to preserve the privileges of a small ruling elite. The I.R. has often been tactical… It has often been ruthless… It has often been adaptive... But adaptive is not the same as pragmatic. A bank robber who changes getaway cars is adaptive. He is not pragmatic. The regime’s leadership has repeatedly demonstrated a willingness to sacrifice Iran’s long-term national interests whenever those interests conflict with the preservation of the system itself. And that is the central deception embedded in sentences like the one above. By describing regime insiders as “pragmatic nationalists,” the authors attempt to present the Islamic Republic as merely another state pursuing ordinary national interests. It is not. The central political struggle in Iran today is precisely that the interests of the regime and the interests of the Iranian nation have diverged. The freedom movement led by @PahlaviReza and @NoorPahlavi is not confronting a group of misunderstood nationalists... no! It is confronting an entrenched ruling class whose survival depends on preventing the emergence of a normal, prosperous, accountable nation-state. The Iranian people are the nationalists. The regime is what stands in their way. @vali_nasr I challenge you to a debate on this matter... name the time and place.