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Grok Voice just ranked #1# on Vapi’s Humanness Index and it's scary good That is a huge deal This benchmark is simple: Listeners hear blind same-voice battles and pick which voice sounds more human Grok TTS scored 100 in humanness matching the human baseline That means Grok is reaching human-level voice quality on one of the most important benchmarks for real AI agents And Grok is doing this at $15 per 1M characters, while major competitors listed on the leaderboard are charging $60–$100 xAI is building world's best voice layers for real AI agents

"Capital should never stop working." The clearest framing of what we're building toward. Modular infrastructure, specialized layers, zero idle time. Worth reading in full. 👇

Qian Tang River Shows Natural Piano Tide! The water flow turns into keyboard keys and plays Liang Zhu 🎹, forming layers of wave patterns like black and white keyboard keys spread across the river surface. Accompanied by the piano version of “Liang Zhu”, the river water plays and sings from time immemorial. The ultimate romance of nature is only in the Qiantang River Tide! 钱塘江惊现天然钢琴潮！水流化作琴键弹起梁祝🎹，成形"钢琴潮"层层潮纹如同黑白琴键铺在江面，伴着钢琴版《梁祝》，江水自弹千古绝唱。 大自然的顶级浪漫，只在钱塘江潮！ # Qiantang River Wonderland # Piano Tide

$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!

The narrative that AI will wipe out enterprise SaaS overnight is one of the most misunderstood ideas circulating in markets right now, and the evidence does not support it (Save this). @DavidSacks made this case directly and the logic is worth working through carefully. Salesforce is a system of record debugged by millions of customer support tickets over twenty five years, stress tested across thousands of enterprise deployments and deeply embedded into revenue operations at the largest companies on earth. The idea that a CFO will replace that with probabilistically generated code from an AI assistant without compliance guarantees, integration depth, audit trails, and enterprise support infrastructure is not how these decisions actually get made. The market has been pricing in the existential version of this risk anyway and the results have been extreme. Over $1 trillion in SaaS market cap was erased in the first week of February 2026 alone. Global SaaS spending is still projected to grow from $318 billion in 2025 to $512 billion in 2028 which is not the trajectory of a category being killed. The operating reality is entirely disconnected from the stock price narrative. ServiceNow beat earnings nine consecutive quarters in a row and its stock crashed 11% on the same day. Salesforce raised its full year forecast to $41.5 billion on record results and the stock still fell. Sacks makes an important distinction between survivability risk and value capture risk. The survivability risk, enterprises ripping out Salesforce for AI generated software is largely overstated. The SaaS products genuinely at risk are narrow ones charging high prices for underused features with no proprietary data and low switching costs. The value capture risk is real and it is the more sophisticated threat. AI orchestration layers like Claude CoWork are being designed to sit above all of these tools pulling data from Salesforce, ServiceNow, and Snowflake simultaneously and owning the user's primary workspace in the process. If enterprise users move from living inside Salesforce to living inside an AI agent that calls into those systems on their behalf, the SaaS platforms do not disappear but rather become infrastructure. The expansion revenue, the premium pricing power and the next decade of value creation all migrate to whoever owns that orchestration layer.

一个中国 crypto trader，在 TikTok 上发了一段 neural network visualization 结果疑似不小心把系统正在 Polymarket 实时交易的画面露出来了 画面里全是蓝色连接线 hidden layers 纵向堆叠 neurons 在屏幕上不断触发 大多数人第一次看时，都忽略了中间一个很小的标签： “Bitcoin XVIII” 他把这条视频包装成一个普通 AI experiment 虚拟水族馆模拟 reinforcement learning “教神经网络学习生存行为。” 这是视频标题 但暂停在 0:16，细节就不对了 Profile： 模型似乎并不是在学习鱼的行为 hidden layer 里的标签，几乎和实时 Bitcoin prediction markets 对上了： price windows directional probabilities volatility ranges 这些信息被直接映射到 neural network 的 nodes 上，而所谓“模拟”还在后台继续运行 然后大家找到了这个 wallet 30 天 profit：$367,385 1,988 predictions 最大单笔 win：$183,000 几乎所有 active positions，都和 Bitcoin range markets 有关 entry price 集中在 94-98¢ 这正是自动化系统最喜欢 farm 的那类低波动 spreads： 赔率很高 空间很小 但可以持续重复 而且不需要人工一直盯着 1 小时内，评论区直接变成 detective board 有人把 TikTok 调到 0.25x 逐帧拼接 neural network 画面 然后把 hidden layer labels 和这个 Polymarket wallet 的 active positions 一一对比 时间点匹配得太精准 观众以为自己在看 AI visualization 但后台看起来更像是一个模型正在实时分类 market conditions，并根据 BTC 短线波动，把交易自动分配到不同 probability buckets 原 TikTok 只有 11,000 views。 但那条曝光 wallet 的 repost，一夜之间超过 600,000 views。 第二天早上，已经有人开始 clone 这个 interface，重建 network layout，并试图弄清楚： 为什么这个账户几乎所有 positions 都集中在 96-99¢，而且投入金额异常高。 最有意思的是： 原作者没有删除任何内容。 Wallet 也仍然 active。 问题是： 这类 Polymarket bot 的 edge，来自预测 BTC，还是来自把实时市场状态映射成可自动执行的概率分组？

Five layers. Seventeen minutes. One structure built to last for decades. This is Stage 4: Lamination. Heat, vacuum pressure, and precision engineering transform glass, EVA, and solar cells into a fully sealed solar module ready for the next phase of production. This is where individual components become energy infrastructure.

The glass is already being broken out on the new Throne Labs smart portable restrooms in Seattle, Washington These bathrooms were installed just 2 weeks ago and are now already unusable because the windows are broken out Cost to taxpayers is $116,250 per year, per bathroom Seattle installed them as part of a one-year pilot program with the Seattle Department of Transportation to support increased foot traffic ahead of the 2026 FIFA World Cup No one is surprised by this. The only surprise I have is it not always being completely covered in multiple layers of graffiti yet Democrat policies destroy cities

Johann Kerbrat on why Robinhood is building its own L2 on Ethereum At Consensus Hong Kong in February 2026, Robinhood launched a public testnet for Robinhood Chain, an Ethereum Layer 2 built with Arbitrum. Following six months of private testing, Robinhood plans to launch the mainnet in 2026 and will migrate tokenized US stocks and ETFs currently offered to EU customers on Arbitrum One to its own dedicated Arbitrum-powered L2 that still settles on Ethereum. Coinbase has adopted a similar strategy with its plans to take full control over Base’s tech stack and advance the L2 toward Stage 2 decentralization. Robinhood Head of Crypto Johann Kerbrat explains the rationale behind building an L2: “We can still get the security and liquidity of Ethereum, benefit from all of the work that the Arbitrum team has done, and on top of that, customize [the chain] every time we want to build something.” He continues: “If we want to give people access to all of the tools and elements of traditional finance, we also need flexibility [to comply with] what regulators are asking us to do. A world where you have full privacy and you can do whatever you want without KYC and trade securities is still a bit far for us. But what we are trying to find is a good middle ground. You will be on a permissionless chain. You will be able to interact with DeFi and do a lot of things with your stock tokens. We think it’s a great compromise.” Galaxy Head of Research Alex Thorn points out that you can embed a lot of compliance controls at the application and token layers on top of Ethereum. Source: @glxyresearch (May 2026)