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$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!

$SIVE looks like both a chokepoint and a bottleneck for CPO next year. Keep seeing information published from nontechnical people who miss any nuances. Here’s the reason why: 1. CW lasers are bottlenecked signaled by $LITE earnings. Laser fabs are heavily allocated to EML likely from former $NVDA contracts. -> Sumitomo/Furukawa = bottleneck -> Win Semi = bottleneck $SIVE does fab-lite, so are they a bottleneck? Yes, $SIVE sits in the laser bottleneck since control output supply of CW lasers from Win Semi and other fabs from allocation way early on (CEO stated they working with more capacity from other players as well). Perfect example is Kioxia/Sandisk. $SNDK controls NAND output, so they’re a bottleneck because they control final pricing. Demand exceeding supply from Ayar, Jabil, other pluggable vendors + Nvidia NVLink CPO ecosystem… final laser supply owned by $SIVE makes Sivers a bottleneck. $SIVE is also likely primary/sole source for Jabil, Gen-1 Ayar, $MRVL Celestial, and other hyperscaler asic/merchant CPO routes. So no way to get around it (can’t hot-swap single channel cw lasers with Sivers) 2. $SIVE is a chokepoint over CPO. $NVDA use $COHR, $LITE (which likely sources external cw capacity from Japanese competitors) $AVGO is likely vertically integrated as well. However: the entire ecosystem around it from ASIC programs (Marvell, AlChip, etc) and merchant programs (Ayar, Lightmatter, Lightelligence) Are all likely designed around $SIVE. Ayar for example, likely tried to multi-source with $MTSI / $LITE back in 2022 but their lasers probably couldn’t match the level of Sivers specification with arrays (removed Lumentum / Macom from their supply chain site recently) If there’s no alternative at least for the initial generations (obviously they’re working to multi-source). That makes $SIVE a structural chokepoint to go through for lasers. Even if you look at the 1.6T LRO $JBL designed, they achieved a “drastic moat” with performance built around $SIVE likely sole source. $SIVE is also the foundry level reference laser design for $GFS, which your hyperscalers use like $AMD (likely using Sivers + maybe Ayar for gen1): If every major player, who hasn’t achieved vertical integration (Nvidia/Broadcom) is using Sivers for CPO… That makes them a chokepoint. Just look at the entire CPO $NVDA NVLink ecosystem partners: every single one are all likely using Sivers. And they all use $GFS as well (where Sivers is default reference). So $SIVE is both a chokepoint and bottleneck when CPO really scales up H2 2027, over one of the biggest architectural shifts of all time (near $0 -> $81B or $91B TAM in the next 1 1/2 years from GS research note) This is why I say $SIVE looks like it could be the next $75B $LITE over the next couple years. All of this should play out next year. And it’s still trading less than a company with $50M in purchase agreements that buys Sivers lasers to repackage them.

宇树上市日，英伟达发布人形机器人🤖 黄仁勋 6 月 1 日在 GTC Taipei 2026 大会上宣布推出 NVIDIA Isaac GR00T 参考人形机器人，搭载 Jetson Thor 车载计算模块，算力达 2070 FP4 TFLOPS，配备 14 核 Arm CPU 与 128GB 内存。 机器人本体基于宇树 Unitree H2 Plus，拥有 31 个自由度，双手采用 Sharpa 五指方案共 22 个自由度，并集成 GR00T 1.7 软件模型，支持数据生成与策略验证。 计划于 2026 年 10 月开始供货。

@RyanU_1F42B This is 77% growth, from literally the start of the CPO supercycle H1 2026. I’d expect that number to just keep on compounding exponentially as we approach H2 2027, which is the true inflection of scale up CPO.

$SIVE Q1 2026 컨퍼런스콜 확인 후 결론. 나는 아직 매도할 이유를 찾지 못했다. 물론 Q1 숫자만 보면 좋다고 말하기 어렵다. 매출은 전년 대비 감소했고, AEBITDA도 적자였다. 미국 정부 셧다운, 방산 예산 승인 지연, FX 역풍으로 Q1/Q2 일부 매출이 H2 2026으로 밀렸다는 회사 설명도 확인됐다. 그래서 단기 실적만 보면 실망할 수 있다. 하지만 이번 발표에서 중요한 건 Q1 숫자 자체가 아니라, 2026~2027년 상업화 전환 경로가 더 구체화됐다는 점이라고 본다. 가장 중요한 숫자는 Opportunity Pipeline 799M USD. 2025년 말 453M USD에서 2026년 5월 799M USD까지 증가. 연초 대비 +77%. 물론 이건 확정 매출도 아니고, backlog도 아니고, 수주잔고도 아니다. 정확히는 Opportunity Pipeline이다. 즉 “확보한 매출”이 아니라 “잠재 기회”다. 그래도 SIVE 같은 소형 회사 입장에서는 회사 체급 자체를 바꿀 수 있는 규모의 잠재 기회라고 본다. 내가 이번 자료에서 좋게 본 포인트는 크게 3개다. 첫째, AI Datacenter Optics. Jabil과의 1.6T LRO pluggable transceiver 협력은 중요하다. Jabil은 FY2026 매출 전망 34B USD 규모의 글로벌 제조·공급망 기업이고, Sivers DFB laser를 사용한 1.6T LRO module 개발 계획이 공식 발표됐다. 이건 Sivers laser technology가 AI datacenter optical interconnect 시장에서 검증받는 중요한 단계라고 본다. 다만 아직 production order, volume ramp, revenue contribution은 발표되지 않았다. 여기서 고객 주문으로 전환되는지가 핵심이다. 둘째, SATCOM / Space / Defense. York Space의 ALL SPACE 인수는 Sivers 입장에서 긍정적인 이벤트로 보인다. Sivers는 York이 Space Development Agency와 강한 연관을 가진다고 설명했고, SATCOM 쪽에서 2027 ramp를 위한 new production orders imminent라는 표현도 사용했다. 이 문구는 긍정적이다. 하지만 아직 order won은 아니다. 현재 상태는 order expected에 가깝다. 셋째, DoD / CHIPS Act / Electronic Warfare. EW STAR 프로그램 관련 US CHIPS Act Year 2 funding 6.6M USD도 확보했다. Year 1의 5.6M USD보다 약 18% 높은 규모다. BAE Systems, MIT Lincoln Laboratory, Columbia University와 연결된 전자전 프로그램이라는 점도 중요하다. 단기 매출 크기보다 중요한 건, Sivers 기술이 미국 방산 생태계 안에서 계속 검증되고 있다는 점이라고 본다. 결국 내가 보는 SIVE의 핵심 구조는 이거다. Pipeline → Order Order → Product Revenue Product Revenue → Margin Margin → Cash Flow 아직 완성된 실적주는 아니다. 하지만 이제 완전히 막연한 스토리주라고 보기도 어렵다. Jabil 1.6T AI datacenter lasers SATCOM production order 기대 Tachyon 28GHz production PO 60GHz development partnership Tier-1 telco FWA Automotive / Industrial LiDAR ramp CHIPS Act Year 2 funding 이렇게 여러 전환 포인트가 동시에 보이기 시작했다. 내가 중요하게 보는 건 SIVE thesis가 단일 제품 하나에만 걸린 구조가 아니라는 점이다. AI optics SATCOM FWA LiDAR Defense 여러 옵션 중 몇 개만 실제 주문과 매출로 연결돼도 회사 체급이 달라질 수 있다. 물론 리스크는 분명하다. 799M USD pipeline은 계약이 아니다. Jabil 매출은 아직 없다. SATCOM 주문도 아직 공시되지 않았다. Convertible facility 때문에 희석 가능성도 남아 있다. Nasdaq New York dual listing도 아직 검토 단계일 뿐이다. 그래서 앞으로 봐야 할 것은 네 가지다. SATCOM 실제 production order Jabil 협력의 customer order 전환 H2 2026 방산 매출 회복 2027 product revenue ramp 이 네 가지가 숫자로 확인되면 SIVE thesis는 훨씬 강해진다. 반대로 지연이 반복되면 시장은 799M USD pipeline을 점점 할인해서 볼 것이다. 그래도 현재 확인된 사실 기준으로는, 나는 아직 $SIVE를 매도할 이유를 찾지 못했다. 단기 실적주는 아니다. AI optics / SATCOM / DoD 슈퍼사이클에 걸린 고위험·고수익 상업화 콜옵션에 가깝다. 내 기준에서 SIVE thesis는 아직 살아 있다. 개인 기록. 투자 조언 아님.

ZTE has partned with XLSMART to deliver Indonesia's 1st nationwide 5G blanket coverage in just 8 months: • 20,000+ 4G base stations upgraded • 7,000+ new 5G sites deployed • H2 2025's fastest 5G network (Ookla) • Seamless connectivity for 73M users Learn the full story 🎥

前阵子在面 H2 已经能感觉到面试官的 bar 在抬 不再问你做过什么项目 直接问你有什么是 AI 不能替你完成的判断。对那些原本就憋着想做 own 一条线的 P5/P6 反而是最快的一次跳级机会 因为竞争层级被 AI 抹掉了一层

Kye-hyun Kyung, Samsung Electronics Senior Advisor: "Memory prices to fall in H2 next year… Korea must cultivate deep-tech manufacturing" Kye-hyun Kyung, Senior Advisor and former head of Samsung Electronics' Device Solutions (DS) Division, forecast that memory semiconductor prices will decline starting in the second half of next year, and urged Korean industry to prepare in advance. Delivering the keynote at the 285th NAEK Forum, hosted by the National Academy of Engineering of Korea (NAEK) at L-Tower in Seocho-gu, Seoul on the 18th, Advisor Kyung said, "Chinese players are aggressively expanding production capacity (CAPA)," adding that "as memory supply surges, the market could shift starting in the second half of next year or the first half of 2028." Citing global market research firms, Kyung projected that memory prices will fall from H2 next year, when global memory CAPA is expected to surge to 6 million wafers per month. "If Big Tech's return on capex deteriorates, there is a possibility that investment could be scaled back," he said, also warning that memory demand itself could contract from 2028 onwards. While Korean industry, led by Samsung Electronics and SK Hynix, is currently enjoying unprecedented growth by capturing Big Tech's memory demand, the former head of Samsung's semiconductor business argued that Korea must prepare in advance for the post-boom period. Advisor Kyung pointed out, "Korea holds nearly 70% share of the DRAM market, but only 1.5% of the fabless market, and unlike Taiwan, Korea lacks a full-stack semiconductor ecosystem that includes fabless." He went on to advise that "Korea must leap forward as a deep-tech-based manufacturing nation." The point is that Korea should independently build advanced technology capabilities—not only in memory but also in fabless-based system semiconductors and sovereign AI—and actively apply them to its existing strength in manufacturing. He added, "It is difficult for Korea to compete simultaneously with the U.S. and China in both hardware and software," and that "it is important for Korea to do what it does well, and to that end, we must seriously consider how to deploy AI."

looks like at least part of my food sensitivity goes through the histamine pathways H1 (loratadine + fexofenadine) and H2 (pepcid) blockers both help lessen my reaction H1 blockers help with the inflammation I feel when I chew food H2 blockers help with inflammation in the gut after I swallow so I likely have a mild version of mast cell activation syndrome