Exclusive: China has begun mass producing domestically developed immersion deep-ultraviolet lithography machines, a technology crucial to advanced chipmaking, marking a key step forward in Beijing's drive to reduce its reliance on foreign technologies

Elon Musk: "In the next 6 to 12 months, we’ll be doing our first implants for vision, where even if somebody is completely blind, we can write directly to the visual cortex." "Long term, you would have very high resolution and be able to see multispectral wavelengths... you could see in infrared, ultraviolet, radar. It's like a superpower situation."

A framework to understand how value accrues across the AI stack. This is a blueprint for understanding what builds AI into its pragmatic parts: what each layer is, where it ends, and where value is accrued. So here’s how you can think about it: 1. Layer 1 - Infrastructure Before any AI model trains or any robot moves, an industrial foundation must exist. Land, energy grids, cooling systems, critical minerals, and fabrication facilities. Infrastructure is the constraint that all the other layers depend on. 2. Layer 2 - Chips Transistors that are etched onto silicon wafers using extreme ultraviolet light. This is what allows both physical and digital AI to take an input, process it, and return a predictive output. The more transistors that fit on a chip, the more computation it can perform. 3. Layer 3 - Data Both digital and physical models train on data. Digital models train on text, code, and images; physical models train on gravity, friction, depth, and sensor streams. The more accurate the data, the more accurate the output. 4. Layer 4 - Models A model is a system that learns from examples. Feed it enough examples of inputs paired with correct outputs, and it adjusts its internal structure until it can predict correct outputs on inputs it has never seen before. LLMs represent a specific class trained on text. They learn by processing billions of examples of human language, developing the ability to write, reason, summarize, and generate code. 5. Layer 5 - Execution This is what lets models take actions on behalf of users. The execution layer lets models pursue objectives through sequential action: observing the environment, reasoning about the next step, acting, and looping until the goal is reached. 6. Layer 6 - Application All of the AI Stack’s revenue originates at the application layer, then goes to the layers below. Every dollar paid for AI is paid for an outcome, a task completed, and an answer delivered. Nobody wants H100s for their own sake. They want H100s because someone, somewhere, wants to run an application. These are the different layers that make up the entire ecosystem of AI. We did a full study on the AI stack. If you want to read about it, head over to my Substack (

▶ SK Hynix flooded with unprecedented offers from Big Tech to secure chip supplies - SK Hynix is reportedly receiving aggressive proposals from global Big Tech firms to fund new production line investments and the purchase of expensive semiconductor manufacturing equipment. - These offers are highly unusual in the global memory industry, coming as demand for memory chips — essential for AI data centers, smartphones, and PCs — has surged beyond what chipmakers can supply. - According to multiple sources, SK Hynix's customers have proposed directly investing in dedicated memory production lines. - Other proposals involve customers funding the purchase of ASML's EUV (extreme ultraviolet) lithography equipment. EUV tools, which etch circuit patterns onto silicon wafers, cost several hundred million dollars per unit. - However, with ample cash on hand, SK Hynix is taking a cautious stance toward accepting customer financing. - The concern is that such arrangements could lock the company into specific customers and potentially require it to supply memory at lower prices in exchange for guaranteed long-term supply. - An SK Hynix official said, "Regardless of the form of the proposal, our currently available production capacity is essentially 'zero,'" adding that "we don't even have small volumes that could be allocated to specific customers." - The official also noted that investment proposals have been made targeting the Phase 1 production line of the large-scale fab currently under construction at the Yongin cluster. - This pattern departs from the memory industry's traditional boom-bust cycle, with the industry increasingly viewing the current AI-driven demand growth as long-term structural growth rather than a short-term cycle. - The development underscores the intensifying competition to secure memory chips amid the AI boom.

DAILY SITUATION RECAP: Nvidia launches the Open Secure AI Alliance in order to find and fix vulnerabilities using open-source AI, sort of like an open Project Glasswing. Founding partners include a mix of enterprise software companies (Databricks, Salesforce, IBM, SAP, Siemens, Snowflake), cybersecurity companies (Palo Alto Networks, Red Hat), open-source providers (Hugging Face, LangChain, OpenClaw, Nous, the Linux Foundation), AI labs (SpaceXAI, Thinking Machines, Cognition), and other major companies (Nvidia, Microsoft, Cisco, Palantir, Dell). Moonshot AI releases the Kimi K3 weights and technical report after eleven days since launch. Kimi K3 is a 2.8T parameter mixture-of-experts (MoE) model with 104B active parameters and a 1M token context window. Moonshot also open-sourced much of their infrastructure, including their attention kernels, agent environment platform, and MoE communication library. Just because you can download it in theory doesn’t mean you actually can — the model is far too big to be run on any consumer hardware. Nvidia invests $5B in Ilya Sutskever’s SSI. Sutskever, formerly co-founder and Chief Scientist of OpenAI, founded Safe Superintelligence in 2024 with the sole goal of building a safe superintelligence, with no other products along the way. It has since raised $3B at up to a $32B valuation (likely higher now). SSI is famously very secretive about its research, but Sutskever said it’s “focused on overlooked aspects of how the human brain functions”. The new funding, and access to Nvidia Vera Rubin GPUs, will allow SSI to 10x its compute. More companies sign on to Nvidia’s open source letter. The letter, posted by Jensen Huang on Friday, advocates for a robust American open-source ecosystem with minimal government regulation. New signatories include Google, SpaceXAI, OpenAI, AMD, Cisco, Palo Alto Networks, Nebius, Scale, Fireworks AI, Baseten, Cohere, Sakana AI, Periodic Labs, Core Automation, OpenClaw, and GitHub. Every major American frontier lab except for Anthropic has now signed. CXMT stock surges 466% on its first trading day. The company, formerly ChangXin Memory Technologies, is the largest memory manufacturer in China and the fourth-largest in the world (after SK Hynix, Samsung, and Micron), with a 9% global market share. It now has the second-highest market cap of any Chinese company after Tencent. CXMT doesn’t make the most leading-edge HBM for AI chips, but supplies DRAM to consumer tech manufacturers and data centers. Nvidia may guarantee $250-350B of financing for an OpenAI data center. SB Energy, a subsidiary of SoftBank, is developing a massive 10 GW data center on federal land in Ohio at a total cost of over $500B. The financing guarantee would allow SB Energy to borrow money at lower rates, and possibly allow OpenAI to spend more on Nvidia chips. China begins manufacturing DUV machines. Deep ultraviolet (DUV) lithography machines print intricate nanoscale patterns on silicon wafers, a critical step in chipmaking. The new machines, built by an unnamed state-backed company, will be shipped to local chipmakers including SMIC, Hua Hong Semiconductor, and CXMT. China is still behind on the most advanced extreme ultraviolet (EUV) lithography, which is solely produced by Dutch company ASML. ASML stock fell 6% on the news. Dario Amodei explains Anthropic’s position on open models: open-weight models without dangerous capabilities are a public good, and Anthropic has never supported a full ban. However, we should be worried about the CCP using them for repression, as well as cyber/bio/alignment risk. To that end, we should not sell chips to China, crack down on distillation, and require mandatory safety testing for all sufficiently capable open and closed models. China threatens to respond if the US sanctions their AI labs. The Chinese Ministry of Commerce said US accusations of distillation were “smears” and that China will “take all necessary measures” to defend its rights and interests against any action that substantively harms them. DeepSeek has suspended its recent funding round after comments from a private investor call with CEO Liang Wenfeng were leaked. Written by @theojaffee. Read more at our link in bio.