𝗙𝗿𝗼𝗺 𝗚𝗣𝗨 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝘀 𝘁𝗼 𝗔𝗜 𝗙𝗮𝗰𝘁𝗼𝗿𝗶𝗲𝘀: 𝗪𝗵𝗮𝘁'𝘀 𝗥𝗲𝗮𝗹𝗹𝘆 𝗛𝗼𝗹𝗱𝗶𝗻𝗴 𝗕𝗮𝗰𝗸 𝗔𝗜 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲? 𝖠𝗌 𝖾𝗇𝗍𝖾𝗋𝗉𝗋𝗂𝗌𝖾𝗌 𝖺𝖼𝖼𝖾𝗅𝖾𝗋𝖺𝗍𝖾 𝗍𝗁𝖾𝗂𝗋 𝖠𝖨 𝖺𝗆𝖻𝗂𝗍𝗂𝗈𝗇𝗌, 𝗌𝖾𝖼𝗎𝗋𝗂𝗇𝗀 𝖦𝖯𝖴𝗌 𝗂𝗌 𝗃𝗎𝗌𝗍 𝗈𝗇𝖾 𝗉𝗂𝖾𝖼𝖾 𝗈𝖿 𝗍𝗁𝖾 𝗉𝗎𝗓𝗓𝗅𝖾. 𝖳𝗁𝖾 𝖻𝗂𝗀𝗀𝖾𝗋 𝖼𝗁𝖺𝗅𝗅𝖾𝗇𝗀𝖾 𝗅𝗂𝖾𝗌 𝗂𝗇 𝖻𝗎𝗂𝗅𝖽𝗂𝗇𝗀 𝗍𝗁𝖾 𝗂𝗇𝖿𝗋𝖺𝗌𝗍𝗋𝗎𝖼𝗍𝗎𝗋𝖾 𝗇𝖾𝖾𝖽𝖾𝖽 𝗍𝗈 𝗉𝗈𝗐𝖾𝗋, 𝖼𝗈𝗈𝗅, 𝖼𝗈𝗇𝗇𝖾𝖼𝗍 𝖺𝗇𝖽 𝗌𝖼𝖺𝗅𝖾 𝖠𝖨 𝗐𝗈𝗋𝗄𝗅𝗈𝖺𝖽𝗌. 𝖠𝗍 𝗍𝗁𝖾 𝖩𝖺𝗉𝖺𝗇 𝖢𝗅𝗈𝗎𝖽 & 𝖣𝖺𝗍𝖺𝖼𝖾𝗇𝗍𝖾𝗋 𝖢𝗈𝗇𝗏𝖾𝗇𝗍𝗂𝗈𝗇 𝟤𝟢𝟤𝟨, 𝗈𝗎𝗋 𝖱𝖾𝗌𝖾𝖺𝗋𝖼𝗁 𝖣𝗂𝗋𝖾𝖼𝗍𝗈𝗋 Marc Einstein (@MarcEinsteinAI) 𝗐𝗂𝗅𝗅 𝗍𝖺𝗄𝖾 𝗍𝗁𝖾 𝗌𝗍𝖺𝗀𝖾 𝗍𝗈 𝖾𝗑𝗉𝗅𝗈𝗋𝖾 𝗍𝗁𝖾 𝖿𝗂𝗏𝖾 𝖼𝗋𝗂𝗍𝗂𝖼𝖺𝗅 𝗂𝗇𝖿𝗋𝖺𝗌𝗍𝗋𝗎𝖼𝗍𝗎𝗋𝖾 𝖻𝗈𝗍𝗍𝗅𝖾𝗇𝖾𝖼𝗄𝗌 𝖾𝗇𝗍𝖾𝗋𝗉𝗋𝗂𝗌𝖾𝗌 𝗈𝖿𝗍𝖾𝗇 𝗎𝗇𝖽𝖾𝗋𝖾𝗌𝗍𝗂𝗆𝖺𝗍𝖾: • 𝖯𝗈𝗐𝖾𝗋 𝖺𝗏𝖺𝗂𝗅𝖺𝖻𝗂𝗅𝗂𝗍𝗒 𝖺𝗇𝖽 𝗌𝗂𝗍𝖾 𝗋𝖾𝖺𝖽𝗂𝗇𝖾𝗌𝗌 • 𝖱𝖺𝖼𝗄 𝖽𝖾𝗌𝗂𝗀𝗇 𝖺𝗇𝖽 𝖺𝖽𝗏𝖺𝗇𝖼𝖾𝖽 𝖼𝗈𝗈𝗅𝗂𝗇𝗀 • 𝖲𝖾𝗆𝗂𝖼𝗈𝗇𝖽𝗎𝖼𝗍𝗈𝗋 𝖺𝗇𝖽 𝗆𝖾𝗆𝗈𝗋𝗒 𝗌𝗎𝗉𝗉𝗅𝗒 • 𝖧𝗂𝗀𝗁-𝗌𝗉𝖾𝖾𝖽 𝗂𝗇𝗍𝖾𝗋𝖼𝗈𝗇𝗇𝖾𝖼𝗍𝗌 • 𝖢𝖺𝗉𝗂𝗍𝖺𝗅 𝗂𝗇𝗏𝖾𝗌𝗍𝗆𝖾𝗇𝗍 𝖨𝗇 𝗁𝗂𝗌 𝗌𝖾𝗌𝗌𝗂𝗈𝗇, "𝗙𝗿𝗼𝗺 𝗚𝗣𝗨 𝗖𝗹𝘂𝘀𝘁𝗲𝗿 𝘁𝗼 𝗔𝗜 𝗙𝗮𝗰𝘁𝗼𝗿𝘆: 𝗧𝗵𝗲 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗕𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 𝗨𝗻𝗱𝗲𝗿𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗲," 𝖬𝖺𝗋𝖼 𝗐𝗂𝗅𝗅 𝖽𝗂𝗌𝖼𝗎𝗌𝗌 𝗐𝗁𝗒 𝗍𝗁𝖾 𝗇𝖾𝗑𝗍 𝗉𝗁𝖺𝗌𝖾 𝗈𝖿 𝖠𝖨 𝖾𝗑𝗉𝖺𝗇𝗌𝗂𝗈𝗇 𝖽𝖾𝗆𝖺𝗇𝖽𝗌 𝖺 𝗆𝗎𝖼𝗁 𝖻𝗋𝗈𝖺𝖽𝖾𝗋 𝗂𝗇𝖿𝗋𝖺𝗌𝗍𝗋𝗎𝖼𝗍𝗎𝗋𝖾 𝗌𝗍𝗋𝖺𝗍𝖾𝗀𝗒. 𝗗𝗮𝘁𝗲: 𝖭𝗈𝗏𝖾𝗆𝖻𝖾𝗋 𝟣𝟥, 𝟤𝟢𝟤𝟨 𝗣𝗹𝗮𝗰𝗲: 𝖧𝗂𝗅𝗍𝗈𝗇 𝖳𝗈𝗄𝗒𝗈, 𝖳𝗈𝗄𝗒𝗈, 𝖩𝖺𝗉𝖺𝗇 𝖯𝗅𝖺𝗇𝗇𝗂𝗇𝗀 𝗍𝗈 𝖺𝗍𝗍𝖾𝗇𝖽? 𝖢𝗈𝗇𝗇𝖾𝖼𝗍 𝗐𝗂𝗍𝗁 𝖬𝖺𝗋𝖼 𝖺𝗍 𝗍𝗁𝖾 𝖾𝗏𝖾𝗇𝗍 𝗍𝗈 𝖽𝗂𝗌𝖼𝗎𝗌𝗌 𝗍𝗁𝖾 𝖾𝗏𝗈𝗅𝗏𝗂𝗇𝗀 𝖠𝖨 𝗂𝗇𝖿𝗋𝖺𝗌𝗍𝗋𝗎𝖼𝗍𝗎𝗋𝖾 𝗅𝖺𝗇𝖽𝗌𝖼𝖺𝗉𝖾 𝖺𝗇𝖽 𝗐𝗁𝖺𝗍 𝗂𝗍 𝗍𝖺𝗄𝖾𝗌 𝗍𝗈 𝗌𝖼𝖺𝗅𝖾 𝖠𝖨 𝖿𝖺𝖼𝗍𝗈𝗋𝗂𝖾𝗌. 𝗚𝗲𝘁 𝗶𝗻 𝘁𝗼𝘂𝗰𝗵 𝘄𝗶𝘁𝗵 𝘂𝘀 𝗮𝘁: info@counterpointresearch.com 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗲𝘃𝗲𝗻𝘁 𝗵𝗲𝗿𝗲:  𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿 𝗵𝗼𝘄 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗰𝗲𝗻𝘁𝗲𝗿 𝗖𝗣𝗨 𝗹𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 𝗶𝘀 𝗲𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝗮𝗺𝗶𝗱 𝗴𝗿𝗼𝘄𝗶𝗻𝗴 𝗔𝗜 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗱𝗲𝗺𝗮𝗻𝗱: #AIInfrastructure# #DataCenters# #CloudComputing# #AIFactories# #Semiconductors# #AI#

AI factory deployment at a prepared site: from six months down to 14 days. NVIDIA DSX POD brings Vera Rubin AI factories in containers, integrated and validated before delivery. Announced at GTC Berlin:

AI Conferences and Investor Days, Friday, October 9, 2026 TODAY No tracked events today. TOMORROW No tracked events tomorrow. REMAINING THIS WEEK Nothing remaining this week. LATER OCP GLOBAL SUMMIT 2026 Mon Oct 12 to Thu Oct 15, San Jose McEnery Convention Center. Theme: "Scaling Innovation for the AI Era". Sold out; registration closed with no onsite signup. No public livestream found; keynote and session recordings expected on the Open Compute Project YouTube channel after the event: Keynotes Mon Oct 12 (PT): AMD Chair/CEO Lisa Su, 4:15pm, open full-stack infrastructure for agentic AI; Broadcom's Asad Khamisy (SVP, Core Switching), 5:30pm, "Scaling AI Infrastructure Through Open Innovation"; NVIDIA's Ian Buck (VP Hyperscale/HPC), 5:55pm, "AI Factories at Scale"; Meta keynote (speaker and time unconfirmed). Themes to watch: rack-scale AI factory architectures (Vera Rubin NVL72, MGX, DSX); Ethernet vs InfiniBand for AI scale-up (Broadcom Tomahawk 6, Thor Ultra 800G, OCP ESUN vs NVIDIA Spectrum-X); power delivery and thermals (800V DC racks, liquid cooling); optical interconnect (CPO, 448G signaling). 42 tracked company sessions (Oct 12-13; times ET): AMD, NVIDIA, Broadcom, Meta, Alphabet, Arm, Marvell, Astera Labs, Credo, Lattice, SK hynix, Samsung, MediaTek, Intel, Cisco, HPE, Dell, Microsoft, Oracle, Super Micro, Vertiv, Eaton, Nokia, TE Connectivity, Celestica, Flex, Jabil, Sanmina, Wiwynn, Pegatron, Mitac, Foxconn Industrial Internet, Inventec, Keysight. Also: Lattice/AMI keynote Tue Oct 13, 10:00am PT, "The Open Control Plane: From AI Racks to Autonomous Infrastructure" (Ford Tamer, Sanjoy Maity). APPLIED MATERIALS INVESTOR DAY (INVESTOR BREAKFAST AT SEMICON WEST) Tue Oct 13, 8:00-10:30am PT. Webcast: replay same day. Setup: executive leadership team presents during SEMICON West. The number: the 2027 WFE (wafer fab equipment) growth outlook. Why it matters: AMAT is the largest WFE vendor; its outlook frames the 2027 capex cycle and AI-driven fab capacity plans. Watch: a firm 2027 WFE growth figure; commentary on AI-driven capacity adds. WORKDAY FINANCIAL ANALYST DAY Tue Oct 13, 2:00pm PT, Las Vegas (during Workday Rising). Webcast: replay 90+ days. Setup: analyst day alongside the annual customer conference. The number: Agentic AI ARR ~$600M (+200% YoY). Why it matters: the multi-year AI monetization framework sets expectations for converting the installed base to AI products. Debates: seat-based vs consumption (FlexCredits) pricing durability. Watch: FlexCredits disclosure; FY28 subscription growth vs the ~11% framework. ACCENTURE INVESTOR AND ANALYST CONFERENCE Wed Oct 14, 8:30-11:30am ET, virtual. Setup: shares +23% on the Q4 print (~$84.5B FY bookings), faded ~6% since. The number: refreshed long-term growth and margin targets. Why it matters: bellwether for enterprise IT services demand and AI-led consulting. Debates: fixed-price mix; granularity of AI disclosure. Watch: long-term targets; AI bookings disclosure. SAILPOINT INVESTOR DAY Wed Oct 14, 12:00pm ET, virtual. Webcast: replay 1 year. Setup: recap of product introductions from Navigate (annual identity security conference, Austin, Oct 5-8). The number: not a guidance event; product-led. Why it matters: identity security is consolidating; SailPoint's AI positioning against Microsoft and Palo Alto Networks. Watch: genuinely new capability vs repackaging; AWS/NVIDIA pipeline commentary. SEMTECH DATA CENTER TEACH-IN (ANALYST DAY) Thu Oct 15, 9:00am PT, San Jose. CEO Hong Hou, Imran Sherazi (Signal Integrity GM), CFO Mark Lin. Webcast: Setup: stock at 52-week highs; past ACC timing wobbles have been punished. The number: 1.6T/800G and ACC (active copper cable) ramp timing. Why it matters: key read on optical DSP and copper interconnect demand in AI clusters. Watch: ACC ramp milestones; long-term financial objectives. THINKEQUITY CONFERENCE Thu Oct 15, Mandarin Oriental, New York. Single-day small-cap broker conference. AI-relevant presenters include Roadzen (RDZN, 9:30am ET) and Mobilicom (drone track, 1:30pm ET). WEEK OF OCT 19 AND BEYOND • Qnity Electronics (Mon Oct 19, ICPT planarization/CMP conference, Gangseo): semiconductor materials read. • Zoom Communications Investor Day (Tue Oct 20, 4:00pm, timezone unconfirmed). • Tempus AI (Fri Oct 23, presenting at ESMO 2026): AI healthcare name at a medical oncology conference. • Celestica Investor Day (Tue Oct 27): AI data center EMS; key read on AI server build demand. • Guidewire Investor Day (Tue Oct 27, 7:30pm, timezone unconfirmed): insurance software. • Oracle Investor Day (Wed Oct 28): OCI and AI infrastructure. Also tracked: semicap names at the 18th Annual CEO Investor Summit (Tue Oct 13, California): ACM Research, Cohu, Photronics; Kulicke & Soffa (CEO Summit, San Francisco); BE Semiconductor (SEMICON West); Zhen Ding (20th QIC CEO Week, Hong Kong). Wolfspeed at the Wolfe Research quant/macro conference (Wed Oct 14, New York). Unusual Machines at the Maxim Growth Summit (Mon Oct 12, New York).

AI compute might be turning into a whole new asset class! @CernBasher makes an interesting point: the AI buildout is getting way too expensive for tech companies to fund everything themselves. That’s where Wall Street comes in. Pension funds, insurers and big investment firms could finance the AI factories, while companies pay to use the compute they produce. Basically, finance AI infrastructure like we already finance airports, power plants and real estate. If that model takes off, it could change how Tesla, SpaceX and the rest of the AI industry fund the next massive wave of compute. $TSLA $SPCX

The AI era needs a new CPU. Meet NVIDIA Vera, 80% faster agentic task completion than x86. Built for AI factories. Built for what's next. The CPU for agents has arrived. 🔗

Europe is building its AI future, and we’re all in. From AI factories and AI agents to robotics and healthcare, Jensen took the stage at #NVIDIAGTC# Berlin to show how AI is taking shape across the continent. Here's a look back at the highlights:

How can AI factories get more from the power they have while helping keep the grid flexible? NVIDIA DSX MaxLPS optimizes power within an AI factory's existing budget. At @nscale, it boosted aggregate inference throughput by 49% without adding provisioned power. NVIDIA DSX Flex lets that same facility respond to the grid. In a simulated grid-demand event, DSX Flex and MaxLPS together cut GPU power consumption by 25% for 30 minutes. See how Nscale put dynamic power management to work at its Iceland AI factory ➡️

NVIDIA INTRODUCES DSX POD AI FACTORIES TO ACCELERATE EUROPE’S INFRASTRUCTURE DEPLOYMENT; DSX POD UNITS REDUCE DEPLOYMENT TIME TO AS LITTLE AS 14 DAYS; EUROPEAN ECOSYSTEM BUILDS WORLD’S MOST EFFICIENT AI FACTORIES WITH NVIDIA DSX

Nvidia-backed Reflection AI has launched Beam, a 501B-parameter open-weight AI model built for coding, reasoning and AI-agent workloads. Beam is aimed at competing with leading open-weight models from China. Beam is a text-only mixture-of-experts model with: • 501B total parameters, 23B active • 1M-token context window • Pretrained on 23.8T tokens • Built for coding, reasoning, tool use and agentic workloads Reflection says Beam performs comparably with Z .ai’s GLM-5.2 on advanced reasoning benchmarks while using 3-4x less inference compute. The company’s benchmark claims have not yet been independently verified. Beam’s reinforcement-learning run used 10,500 NVIDIA $NVDA GB300 GPUs for four weeks and generated more than 100M rollouts. Its base model was pretrained on 6,144 GB300 GPUs in under four weeks. Reflection is targeting enterprises, developers and sovereign governments, with a broader strategy around customized “AI factories” that can connect organizations’ proprietary data to locally controlled AI systems. The NVIDIA-backed startup has also signed more than $7B worth of compute deals with SpaceX and Nebius $NBIS to secure access to GB300 infrastructure through 2029. Reflection plans to release Beam’s weights under an Apache 2.0 license later this month, along with its technical report, model card and tools for running, evaluating and fine-tuning the model.

We built over 50 AI agents and tools for clients. Clients were happy and we were making money, but we shut it all down anyways. We stopped building individual systems. We started building AI Factories for each client. An AI Factory is the system that builds the systems. > The input is client AI use cases prioritized by impact > The output is custom agents and tools (I'm oversimplifying, but that's the gist of it) But now we can't scale any further unless we shut it all down again. We have to stop building AI Factories for each client. We need to build the AI Factory-Factory. Meta, I know, but here me out: The AI Factory-Factory builds a factory for each client. > The input is client problems, growth levers, and business goals > The output is a custom AI Factory that can handle every opportunity (I'm oversimplifying again, but you get it) Now we can deliver absurd value at unprecedented speed, even by AI-native standards. We're kinda disrupting ourselves though, so next we need to start pricing for outcomes because we've completely decoupled time and value.