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Leopold Aschenbrenner’s complete Q2 performance before his newest trades release Monday Top tier: • SanDisk $SNDK: -62% • Micron $MU: -34% Second tier: • Bloom Energy $BE: -38% • Taiwan Semiconductor $TSM: -18% • Nebius $NBIS: -59% Third tier: • CoreWeave $CRWV: -49% • Core Scientific $CORZ: -45% • STMicroelectronics $STM: -39% • Applied Digital $APLD: -49% • Riot Platforms $RIOT: -37% • SharonAI $SHAZ: -74% • IREN $IREN: -61% Smaller positions: • T1 Energy $TE: -82% • WhiteFiber $WYFI: -76% • Babcock & Wilcox $BW: -69% • Keel Infrastructure $KEEL: -69% • Bitdeer $BTDR: -66% • HIVE Digital $HIVE: -58% • CleanSpark $CLSK: -56% • Cerebras $CBRS: -53% • Vishay $VSH: -52% • ProPetro $PUMP: -49% • Solaris Energy $SEI: -23% Options: • Bloom Energy $BE [Calls]: $44,100,000 • Taiwan Semiconductor $TSM [Calls]: $23,900,000 • Infosys $INFY [Puts]: $5,200,000 Summary: Every common stock position is red. His entire portfolio is down 46.8% from Q2 release to present day. His newest trades release Monday, November 16th, showing what he owned on September 30th, after he sold most of this to Citadel in late July

Cerebras 三季报调整后多半又超，可前两次超了，第二天都跌了一成以上🤣 8 月 12 号 Cerebras 出二季报那天，股价白天先涨了 11.6%，收在 262.06。盘后数字出来，调整后的主营收入 2.1 亿美元，比它自己给的指引多了 8%。调整后的经营亏损率是 16%，它自己给的是 30% 到 32%。第二天股价跌了 11.85%，第三天又跌了 5.21%。到昨天，它收在 101.08，比那天低了六成多。 6 月那次是上市后的第一份财报。调整后每股亏 4 美分，华尔街估的是亏 1 毛 4，也超了，第二天照样跌了 19.61%。 这两次跌，原因不太一样。6 月那次，市场盯的是毛利率，它把二季度的毛利率指引给到 36% 到 38%，一季度还有 47% 左右。8 月那次，我觉得一半要怪标题。华尔街的一致预期按调整后的主营收入算，新闻稿上并排还有一个 GAAP 营收，要先扣掉认股权证资产的摊销。快讯标题抓的就是 GAAP 那个，写成营收 1.801 亿、预期 1.94 亿，看着就像不及预期。 这季这笔摊销还在，我估有 5,000 万，美东今天盘后的标题多半又是营收不及预期，门槛这次也更高。华尔街一致预期调整后每股亏 1 毛 3，Earnings Whispers 的耳语数只亏 3 分。大家已经在等它大超了。股价也提前动了，从 11 月 5 号的 81.71 到昨天的 101.08，四个交易日涨了 23.7%。 四季度指引才是我最在意的。按三季度落在它自己的指引里算，全年主营收入 8.8 亿到 8.9 亿，倒推四季度得有 2.65 亿到 2.73 亿。这比三季度的指引要多两成以上。按第三方整理的 8 月电话会稿，CFO 说三季度是调整后毛利率的谷底，四季度会明显改善。北京时间明早 5 点前后数字出来。四季度营收指引要是过了 2.73 亿，调整后毛利率指引也到了 45%，前两次那种第二天大跌，这次就不一定了。 期权押的到周五涨跌，ApexVol 给的是 10.5%，拿周五到期的跨式粗算是 13.7%。欧易 CBRS 永续上没什么人，持仓才 260 万美元，多空账户比 1.64，资金费率是 0，两边都不挤。 超预期那个盘子，线是 GAAP 每股亏 1 毛 4。可这个数跟华尔街调整后的一致预期是一档的，几家估的在亏 1 毛 3 到 1 毛 4 之间。公布出来的 GAAP 要把上市以后摊的股权激励算进去，二季度连上市补记一共 3.77 亿，这季我估还有一亿多。这么算下来，GAAP 每股在亏 8 毛上下，离这条线很远。 第二天涨不涨，我没把握。前两次的样子摆在那，可股价比 8 月出数那天已经跌了六成多，锁定期 11 月 9 号也全部到期了。 等看完四季度指引和盘后，到时候详细的在我欧易星球跟进。

If you're a developer building agents in production, @LiteLLM Lens Agent now lets you use @cerebras models (100+ providers) to analyze your traces and automatically improve your agents. Cerebras is great for analyzing 200K+ traces fast and cheap Figure out what’s failing and how to improve your agents, at scale.

Open router for chips is a $10B+ company waiting to be built Nobody moves custom inference workloads off Nvidia because porting to AMD, Cerebras, Etched, etc. is painful But with frontier models now you could build a pipeline for translating workloads from one chip to another When that starts happening reliably there there will be a rise of multi-chip neoclouds. Neoclouds won’t buy non-Nvidia chips today because developers don’t use them. That will change once you eliminate the code compatibility problem and performance is the driving factor If you build it, the play is either open source it from day 1 and become a strategic acquisition target for a big neocloud, or keep it private and build your own cloud, becoming the de facto multi-chip provider

Ultrafast mode for GPT-5.6 Sol is now in limited preview, powered by Cerebras. We gave @OpenAI's GPT-5.6 Sol the same prompt on Ultrafast and Standard: build a financial terminal-style dashboard for analysts. Ultrafast: 1 min 50 seconds Standard: 12 min 20 seconds Same result, nearly 7x faster.

Previewing Ultrafast mode for @OpenAI's GPT 5.6 Sol, powered by Cerebras. GPT-5.6 Sol Ultrafast generates responses at up to 750 tokens per second. That's the full, GPT-5.6-Sol model -- up to 14× faster than the same model on Standard processing. It speedran Humanity’s Last Exam in 11h 11m, nearly 7× faster than Claude Fable 5 with comparable accuracy.

下周美股8.10财报周，光通信两巨头LITE COHR+小火箭 RKLB+一堆半导体，还是看主线故事能否续写 我的重点关注如下： 周一（8月10日） 盘前 $CEVA CEVA（无线通信芯片厂商） $BARRICK 巴里克黄金（全球顶级黄金矿业龙头） 盘后 $RKLB Rocket Lab（太空网红股） $PLUG Plug Power（氢能赛道人气网红标的） $QUBT Quantum Computing（量子计算热门股） $ASTS AST SpaceMobile（太空网红股） $HIMS Hims&Hers（远程医疗网红龙头） 周二（8月11日） 盘前 $SE 冬海集团（东南亚小腾讯） $TME 腾讯音乐（国内头部在线音乐平台） 盘后 $SMCI 超微电脑（AI服务器大牛股，数据中心硬件龙头） $CRWV CoreWeave（AI算力租赁网红算力公司） $LITE Lumentum（光模块、激光光学核心厂商） $FNV 泛美白银（全球知名贵金属矿业股） 周三（8月12日） 盘前 $TRMB Trimble（测绘导航工业硬件龙头） 盘后 $CSCO 思科（全球网络设备通信巨头，交换机龙头） $COHR Coherent（光通信、激光设备核心厂商） $CBRS Cerebras（AI专用晶圆芯片小众硬核AI公司） $ENVX Enovix（新一代硅电池储能网红科技股） 周四（8月13日） 盘前 $AMAT 应用材料（半导体设备四大龙头之一，刻蚀沉积设备巨头） $JD 京东（国内头部电商零售巨头） $MLCO 美高梅中国（澳门博彩娱乐龙头） 你准备call哪只？ 本条由 @bitget_zh 赞助，「Bitget 买美股：秒级入场，丝滑交易 」

10 PLATAFORMAS QUE REGALAN CRÉDITOS GRATIS PARA APIs DE IA AHORA MISMO Sin suscripciones. Sin listas de espera. Te dan gratis hasta 300$ solo por crear una cuenta. Modelos como Gemini, GPT, Claude y muchos más sin pagar ni un solo token. Guárdate esta lista para tu próximo proyecto. 1. Google Cloud (300 $, 90 días) Es la mayor bonificación de toda la lista. Google regala 300 $ en créditos durante 90 días para utilizar gran parte de sus servicios, incluidos los modelos Gemini desde Vertex AI y Agent Platform. Importante: • Los 300 $ no sirven para la API de Gemini en Google AI Studio. • No pueden utilizarse para modelos de terceros ofrecidos como APIs gestionadas. • No puedes usar GPUs mientras la cuenta esté en el periodo de prueba. 2. Oracle Cloud (300 $, 30 días + Always Free) También ofrece hasta 300 $ en créditos, aunque solo durante 30 días. Lo mejor es su nivel Always Free, que sigue funcionando incluso cuando termina el crédito inicial, sin costes. Es una de las mejores opciones si prefieres alojar tus propios modelos en lugar de consumir APIs de terceros. 3. Microsoft Azure (200$, 30 días) Azure ofrece 200 $ en créditos para gastar durante los primeros 30 días. Es una de las formas más sencillas de acceder a los modelos GPT mediante Azure OpenAI. Además incluye: • Más de 20 servicios gratuitos durante 12 meses. • Más de 65 servicios Always Free. Eso sí, el crédito caduca a los 30 días y no puede recuperarse. 4. AWS (hasta 200$, 6 meses) La prueba gratuita más larga de la lista. Recibes 100 $ al registrarte y puedes conseguir hasta 100 $ adicionales explorando distintos servicios de AWS. Si quieres usar Claude desde Amazon Bedrock, esta es probablemente la mejor opción. 5. Google AI Studio (gratis para siempre) Es independiente de los 300$ de Google Cloud. Incluye un plan gratuito permanente con tokens para: • Gemini 3.6 Flash • Gemini 3.5 Flash • Flash-Lite • Modelos de embeddings Además ofrece 5.000 consultas mensuales con Search Grounding. La contrapartida es que, en el plan gratuito, Google puede utilizar tus datos para mejorar sus modelos. 6. Cloudflare Workers AI (10.000 Neurons al día) Cloudflare ofrece 10.000 Neurons gratuitos cada día, que se reinician automáticamente a las 00:00 UTC. No es una prueba temporal: el límite se renueva diariamente. Da acceso a unos 80 modelos distintos. Ten en cuenta que Kimi K2.6, K2.7-Code y GLM-5.2 requieren un método de pago. 7. Groq (plan gratuito) Permite utilizar varios modelos sin necesidad de tarjeta. Incluye: • 30 peticiones por minuto. • 14.400 peticiones al día con Llama 3.1 8B. El principal límite está en el rendimiento por tokens: • Entre 1.200 y 15.000 tokens por minuto, según el modelo. • Llama 3.3 70B está limitado a 1.000 peticiones diarias. Ideal para agentes sencillos, aunque menos recomendable para tareas con mucho contexto. 8. OpenRouter Una única API para acceder a cientos de modelos diferentes. El plan gratuito permite 50 peticiones diarias en los modelos compatibles. Si compras 10 $ en créditos una sola vez, el límite aumenta permanentemente a 1.000 peticiones al día. Es una de las mejores inversiones si utilizas varias APIs. 9. Cerebras (5 $ en créditos) Solo por crear una cuenta recibes 5 $ en créditos para probar todos sus modelos. Puede parecer poco, pero gracias a la velocidad de inferencia permite hacer bastantes pruebas. Actualmente, Cerebras Code Pro y Code Max no están disponibles por alta demanda. 10. Mistral (plan gratuito) Mistral ofrece acceso gratuito a sus modelos mediante: • Chat. • Búsqueda. • Programación con agentes desde la terminal. Tiene límites de uso, pero no requiere tarjeta ni ningún pago.

底层看稀缺，顶层看客户！看到 @chamath 分享的这张 AI Stack，感觉这个架构划分和 Rewire Index 5 Layer 相当类似，分享一下我对每一层的理解： 1. 能源与基础设施层（最底层） 与 Rewired Index 的逻辑一致。电力会迎来爆发式增长，尤其是无需接入电网的独立供电商（IPP）；而土地受政策影响太大，弹性有限。在大众的舆论压力之下，太空基建应该会是未来几年的新机会，无需土地，无限电力⚡️ 2. 芯片层 做独立芯片，初创公司基本没有机会：性能要求极高、工艺极其复杂，最关键的是供应链已被完全锁死——这是头部玩家的战场。 真正的机会在融合与生态。从 Google TPU 的发展路径，到 Cerebras 等高速推理芯片的崛起，可以看出芯片会与云厂商、模型公司深度绑定。围绕芯片构建数据中心的整体供应与创新，机会很多；单做独立芯片，机会渺茫。 3. 云服务层 云可以分为 Hyperscaler 和 NeoCloud两类。模型商品化之后，几乎所有的负载都要靠云来承载，这会是非常赚钱的生意。但构建极其复杂，堪称 AI 时代的重资产业务——或者说，智能时代的房地产。 4. 模型层 模型公司面临的核心问题是正在被商品化： - 如果 Scaling Law 已到极限，模型百分之百会被商品化； - 即使 Scaling Law 还有很大空间，大家对「最好智能」的需求也在被分解——大量日常应用不需要最顶级的智能，中等水平模型和开源模型会逐渐接管这些需求，反而加速了商品化； - 最尖端的头部模型公司，更像是「先进制程」的芯片：能从中获取很高的价值，但并非所有任务都需要它。 5. 应用层：Harness vs Application 这张图最有趣的地方，是把应用层拆成了 Harness 和 Application 两层。 我的判断是：按目前模型的进化速度，Application 还没有任何机会，但 Harness 的机会已经大量出现，尤其在企业端。企业内化的知识只能通过 Context 和约束来落地，所以 Harness 就是新的企业应用层；它们会替代旧的 SaaS，或倒逼旧 SaaS 升级。 如果把 AI 扩展到大语言模型之外的更广义范畴，应用层更可能以垂直集成的形态出现，例如：自动驾驶 / RoboTaxi / 任何可端到端自动化工业流程和武器系统；生物研究 / Wet Labs；把执行能力直接部署进企业内部的模式（类似 Palantir 的服务方式）

Here is my AI investing guide. Sitting here August 2026, my current best thoughts are as follows: 1. LPS (Land Power Shell) is still the most obvious and fastest path to cash on cash returns. Lots of value can be assembled and traded quickly at this layer. And as data centers get more pushback, energized land can explode in value. Very bullish here. I’ve stepped into this layer very aggressively. My partner @anitavlallian and I have acquired almost 6GW coming online in a ramp from today thru 2029 of grid power and behind the meter. 2. Silicon - I helped get @GroqInc off the ground in 2015 and we licensed it to @nvidia for $20B Dec2025. I won’t invest or incubate anything in this layer now. The perf demands of the chips are too high, manufacturing precision is too complex and supply chain influence to get adjacent components like memory isn’t possible for a startup anymore. Lots of capital will be wasted here chasing Groq and Cerebras’ success. Note that both startups made sense a decade ago when these constraints were much more modest. 3. Clouds - Clouds are very very lucrative but very hard to build and very expensive and technically complicated to maintain. And as alignment becomes a more important issue, I expect the clouds will be asked to build robust KYC and attest to it. This makes the risk:reward ratio skewed. I don’t want to be responsible when the USG says a cloud allowed a bad actor to do something bad because of poor KYC. 4. Models are complicated. The big open question is how much of the revenue being generated by them today is because of tokenmaxxing and poor model behavior. If it’s a lot, then the annualized revenues will diminish meaningfully even as token consumption inflects upwards. This is the big economic question at this layer. 5. Harnesses are where the action is and why I started @8090solutions two years ago. In a nutshell, the harness helps enterprises owns their proprietary context (what Alex Karp calls their ‘alpha’). This is an enterprise’s data, workflows, evals, and business rules. A harness that gives this to an enterprise is what creates very low model-agnostic switching costs, which further reinforces my views of #4# above. 6. Applications will be another long term winner along with harnesses. This is where the differentiation between “off the shelf” and “custom time and materials” melts away. Every company, with the right harness, can now imbue their alpha into the software that runs their company. I expect this to mean that “off the shelf” is largely replaced with custom software creating a huge opportunity to write these solutions for companies. Build once and sell repeatedly is a laggard GTM motion for a SaaS world that isn’t needed here. Think custom by design, alpha embedded, proprietary by nature. Fin. Good luck to all the players!