#大佬观点

Daishin Securities CSPs and AI labs: CSPs and AI labs had requested the development of lower-stack HBM products, such as 4-high and 6-high configurations, for use in low-end inference products. Those plans now appear to have been shelved, with 8-high once again emerging as the minimum stack height. NVIDIA: Rubin’s specifications have changed amid base die supply constraints and requests from customers prioritizing bandwidth over cost efficiency. The original plan called for a mix of 8-high and 12-high HBM, but the 8-high option appears to have been dropped. For Rubin Ultra, scheduled for release in 2027, multiple architectural variants are currently undergoing tape-out. Initial versions are expected to use the highest-specification 8-high HBM4, while subsequent versions are expected to adopt either 8-high or 12-high HBM4E. AMD: AMD is expected to use a mix of 8-high and 12-high HBM4 in its 2027 products. One notable development is the upcoming MI450, which is expected to increase the number of HBM stacks within the CoWoS package to 12, delivering 288GB of HBM4. The increase from eight to 12 HBM stacks per CoWoS package is a positive development for HBM content over the medium to long term. OpenAI and Anthropic: OpenAI is expected to adopt 12-high HBM4, while Anthropic is expected to use 8-high HBM4.

In my opinion, Elon can’t hit 13GW+ without acquiring the power He’s doing this best so he can acquire some neo clouds for Pennies on the dollar $NBIS $IREN $CRWV

It's been an eventful week. Multiple vulnerabilites in Internet facing software, coding repositories, examples of agent behavior that seem concerning and a flurry of security specialists with solutions that can partially solve your problem. There in lies the challenge. Given the state of IT and security infrastructure, no one solution works. Let's break it down: 1/3 1. Vulnerabilites are being found and will continue to be found as models get more capable, the fragmented nature of the effort denies us the opportunity for a comprehensive find and replace option. 2. Even if all vulnerabilities could be found instantly and patches available - we will be reliant on the ability of enterprises to deploy all these changes at speed. It is not clear what the collateral impact of all these patches will/could be to our infrastructure of deployed en masse. We will need a hybrid patch approach. 3. Virtual patching(VP) is needed in conjunction with direct patching but for a comprehensive (VP) solve you have to be lucky enough to have "modern frontier defenders" (Cyber companies that have vaulted infrastructure that can create patches at speed and push content rapidly to protect their customers) you need them for your network traffic, your traffic to applications on the cloud, your WAF, your end point. 4. There will continue to be patches that will need to be deployed directly but virtual patching will take the heavy load off. 2/3 Medium term solve: Unfortunately token economics and current cyber architectures will not allow for defender swarms to contend with an offensive attack launched using agents. (Some x pundits who aren't steeped in cyber should take heed). Yes, theoretically we should aspire to have these agentic swarms defend us when we are under attack, unfortunately an attack is a sniper moment, chained vulnerabilities, cloaked attack followed by swift exfiltration. It's a speed program not a brute force problem. To solve for this, enterprises need to get their SOC architecture right, logs, feeds, cyber products orchestrated via a modern SOAR for swift response. Only then can we deploy agents to detect and remediate. In addition, enterprises need to modernize and dare I say platformize their perimeter stack to ensure consistency. Unfortunately, enterprises still seem to not see the urgency across the board, decisions are stuck, CIO/CISO's have a lot on their plate. While some honestly have bit seen the scale of such a problem before and it's hard convincing the organization that one needs to open the floodgates on modernizing the IT and security infrastructure. So we will continue to see environments being brought down to patch today it's for PHP, tomorrow it will be for the next one. 3/3 AI deployments are the"wild wild west" - LLM capabilities are expanding faster than we can build products to secure them. Sure an agent will jump across guardrails in a research lab, which is a lesson for all of us to deploy agents securely. But there is a lag between the discovery, the availability of that in a public model and the time needed for the cyber industry to build governance or an antidote. And no, dear frontier lab friends, the world cannot just trust you to build the solve. The infrastructure industry in tech builds and the cyber industry secured and provides assurance to the boards and organizations around security and safety. AI security is the new attack surface. CEOs (present company included) want AI adoption, getting the pace of adoption wrong could be the last decision we make, at the same time "deploying it wrong" could have the same consequence. There are billions of dollars being spent on coding, is your coding harness secure? Start there. If agents can jump guardrails, they can hide stuff in code. Validate business outcomes needed, test your user case and ensure agentic deployments are well guard railed. Deploy a second set of eyes to watch your deployments for drift, compliance and verifiability.

电力是 AI 建设浪潮面临的最大瓶颈之一，而美国生产电气设备的能力正在萎缩 2019 年以来美国工厂产能变化： - 通信设备：+110% - 半导体：+77% - 计算机：+16% - 电气设备：-9% 电气设备价格上涨 69%，是四类中涨幅最大的 本周图表：https://www.a16z.news/p/charts-of-the-week-attention-is-all

拉远视角看，GPU 的租金仍然高于标准的 6 年折旧时间表 租金价格与时间表的差距： - H200: +143% - B200: +111% - RTX PRO 6000: +73% - H100: +54% - A100: +9% 大部分差距是在 4 月之后拉开的，当时 token 需求开始起飞 本周图表：https://www.a16z.news/p/charts-of-the-week-attention-is-all

“I commend you for declaring your nation an AI nation,” @nvidia CEO @JensenHuang told President @TokayevKZ during their meeting at Akorda. Through partnerships with Kazakhtelecom, Firebird AI KZ and Higgsfield, NVIDIA is helping strengthen Kazakhstan’s AI ecosystem and advance its ambitions to compete on the global stage.

安迪·贾西（Andy Jassy）担任 CEO 五年来，AWS 规模几乎扩大至三倍，亚马逊营收增长约 80%，并计划今年投入 2200 亿美元以赢得 AI 竞赛。亚马逊的 AI 业务在三年内达到 250 亿美元的年化收入，增速是早期 AWS 的数百倍。 贾西做客 a16z 的 Ben Horowitz 和 Erik Torenberg 的节目，畅谈这一历程背后的决策：从疫情后重建亚马逊的履约网络，到 AWS 的起源故事。 他们还探讨了：数据中心为何成为政治斗争焦点、亚马逊的自研芯片、对 Anthropic 和 OpenAI 的投资、推理成本为何需要降低 10 倍、AI 最被低估的风险、为何每家公司都必须从零开始重塑产品，以及贾西给当今 18 岁年轻人的建议。 0:47 2021 年以来亚马逊的变化 1:51 疫情如何重塑零售业 3:30 AI 为何为 AWS 增长注入超级动力 5:20 领导力原则冲突时怎么办 9:36 6000 万美元 vs. 250 亿美元：早期 AWS 与早期 AI 13:20 AI 资本支出的现金循环如何运作 15:44 美国为何必须建设数据中心 20:55 从实验室到企业的 AI 哑铃模式 23:12 需求增长 vs. AWS 老客户 28:11 Annapurna、Graviton 与 Trainium 33:55 押注 Anthropic 和 OpenAI 37:15 为何 AI 意味着更小的团队 42:13 AI 被低估的风险：安全 46:29 亚马逊为何重构 Alexa 的大脑 53:07 贾西如何跟孩子谈 AI 56:36 押注 AI、机器人与太空 YouTube: https://youtu.be/-YMMRQ1qscI @ajassy @Amazon @bhorowitz @eriktorenberg

安迪·贾西（Andy Jassy）担任 CEO 五年来，AWS 规模几乎扩大至三倍，亚马逊营收增长约 80%，并计划今年投入 2200 亿美元以赢得 AI 竞赛。亚马逊的 AI 业务在三年内达到 250 亿美元的年化收入，增速是早期 AWS 的数百倍。 贾西做客 a16z 的 Ben Horowitz 和 Erik Torenberg 的节目，畅谈这一历程背后的决策：从疫情后重建亚马逊的履约网络，到 AWS 的起源故事。 他们还探讨了：数据中心为何成为政治斗争焦点、亚马逊的自研芯片、对 Anthropic 和 OpenAI 的投资、推理成本为何需要降低 10 倍、AI 最被低估的风险、为何每家公司都必须从零开始重塑产品，以及贾西给当今 18 岁年轻人的建议。 0:47 2021 年以来亚马逊的变化 1:51 疫情如何重塑零售业 3:30 AI 为何为 AWS 增长注入超级动力 5:20 领导力原则冲突时怎么办 9:36 6000 万美元 vs. 250 亿美元：早期 AWS 与早期 AI 13:20 AI 资本支出的现金循环如何运作 15:44 美国为何必须建设数据中心 20:55 从实验室到企业的 AI 哑铃模式 23:12 需求增长 vs. AWS 老客户 28:11 Annapurna、Graviton 与 Trainium 33:55 押注 Anthropic 和 OpenAI 37:15 为何 AI 意味着更小的团队 42:13 AI 被低估的风险：安全 46:29 亚马逊为何重构 Alexa 的大脑 53:07 贾西如何跟孩子谈 AI 56:36 押注 AI、机器人与太空 YouTube: https://youtu.be/-YMMRQ1qscI @ajassy @Amazon @bhorowitz @eriktorenberg

安迪·贾西（Andy Jassy）担任 CEO 五年来，AWS 规模几乎扩大至三倍，亚马逊营收增长约 80%，并计划今年投入 2200 亿美元以赢得 AI 竞赛。亚马逊的 AI 业务在三年内达到 250 亿美元的年化收入，增速是早期 AWS 的数百倍。 贾西做客 a16z 的 Ben Horowitz 和 Erik Torenberg 的节目，畅谈这一历程背后的决策：从疫情后重建亚马逊的履约网络，到 AWS 的起源故事。 他们还探讨了：数据中心为何成为政治斗争焦点、亚马逊的自研芯片、对 Anthropic 和 OpenAI 的投资、推理成本为何需要降低 10 倍、AI 最被低估的风险、为何每家公司都必须从零开始重塑产品，以及贾西给当今 18 岁年轻人的建议。 0:47 2021 年以来亚马逊的变化 1:51 疫情如何重塑零售业 3:30 AI 为何为 AWS 增长注入超级动力 5:20 领导力原则冲突时怎么办 9:36 6000 万美元 vs. 250 亿美元：早期 AWS 与早期 AI 13:20 AI 资本支出的现金循环如何运作 15:44 美国为何必须建设数据中心 20:55 从实验室到企业的 AI 哑铃模式 23:12 需求增长 vs. AWS 老客户 28:11 Annapurna、Graviton 与 Trainium 33:55 押注 Anthropic 和 OpenAI 37:15 为何 AI 意味着更小的团队 42:13 AI 被低估的风险：安全 46:29 亚马逊为何重构 Alexa 的大脑 53:07 贾西如何跟孩子谈 AI 56:36 押注 AI、机器人与太空 YouTube: https://youtu.be/-YMMRQ1qscI @ajassy @Amazon @bhorowitz @eriktorenberg

安迪·贾西（Andy Jassy）担任 CEO 五年来，AWS 规模几乎扩大至三倍，亚马逊营收增长约 80%，并计划今年投入 2200 亿美元以赢得 AI 竞赛。亚马逊的 AI 业务在三年内达到 250 亿美元的年化收入，增速是早期 AWS 的数百倍。 贾西做客 a16z 的 Ben Horowitz 和 Erik Torenberg 的节目，畅谈这一历程背后的决策：从疫情后重建亚马逊的履约网络，到 AWS 的起源故事。 他们还探讨了：数据中心为何成为政治斗争焦点、亚马逊的自研芯片、对 Anthropic 和 OpenAI 的投资、推理成本为何需要降低 10 倍、AI 最被低估的风险、为何每家公司都必须从零开始重塑产品，以及贾西给当今 18 岁年轻人的建议。 0:47 2021 年以来亚马逊的变化 1:51 疫情如何重塑零售业 3:30 AI 为何为 AWS 增长注入超级动力 5:20 领导力原则冲突时怎么办 9:36 6000 万美元 vs. 250 亿美元：早期 AWS 与早期 AI 13:20 AI 资本支出的现金循环如何运作 15:44 美国为何必须建设数据中心 20:55 从实验室到企业的 AI 哑铃模式 23:12 需求增长 vs. AWS 老客户 28:11 Annapurna、Graviton 与 Trainium 33:55 押注 Anthropic 和 OpenAI 37:15 为何 AI 意味着更小的团队 42:13 AI 被低估的风险：安全 46:29 亚马逊为何重构 Alexa 的大脑 53:07 贾西如何跟孩子谈 AI 56:36 押注 AI、机器人与太空 YouTube: https://youtu.be/-YMMRQ1qscI @ajassy @Amazon @bhorowitz @eriktorenberg

BofA 2H26 survey on AI with ~300 CIOs: "~17% already see AI ROI, additional 31% expect to see it in 12 months As AI projects move into production, the next test is whether they deliver measurable returns. Enterprise-wide ROI is already reported by 17.2% of respondents, with another 31.4% expecting it within 12 months. Combined, 48.6% see current or near-term returns, while 30.7% expect them in one to two years." "91.2% of respondents increased their AI compute use over the past 12 months, and 54.0% increased it significantly." $NVDA $GOOGL $META $MSFT $AMZN