The hottest AI Chips Substack posts right now

And their main takeaways
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Top Technology Topics

Export Controls and HBM

ChinaTalk • 340 implied HN points • 10 Dec 24
🕹 Technology Semiconductors AI Chips Export Controls Global Trade
  1. Export controls on high-bandwidth memory (HBM) are making it harder for China to develop its AI technology. This could slow down China's progress in creating advanced AI chips.
  2. HBM is super important for AI because it helps process data faster and more efficiently. Most AI chips, like those from Nvidia, need HBM to work well.
  3. Chinese companies are currently behind in HBM production and advanced packaging technology. Without catching up in these areas, their AI chip industry might struggle in the future.

Wafer Wars: Deciphering Latest Restrictions On AI And Semiconductor Manufacturing

SemiAnalysis • 4040 implied HN points • 24 Oct 23
🕹 Technology AI Chips Export Controls
  1. The restrictions on AI semiconductors are strict and close most loopholes to prevent workarounds.
  2. New controls on wafer fabrication equipment were implemented, but still have some gaps allowing significant tool shipments to China.
  3. China's semiconductor investment surge, despite sanctions, shows sustained growth and potential retaliation strategies.

The Next Wave Of AI Computing

Startup Pirate by Alex Alexakis • 235 implied HN points • 10 Mar 23
🕹 Technology Edge Computing AI Chips Cloud Computing Machine Learning
  1. Artificial intelligence has come a long way since Alan Turing, with AI chips being a key component for advanced computations.
  2. Edge computing moves computing power closer to where data is generated, enabling faster responses for AI applications like self-driving cars.
  3. Axelera AI is focusing on AI chips for edge computing and advancing technology for applications like computer vision in the physical world.

Nvidia's Margins May Be Safe

Tapa’s Substack • 59 implied HN points • 15 Mar 24
🕹 Technology AI Chips Networking Cost Analysis Performance
  1. Nvidia's GPUs are so good that even if competitors offered their chips for free, it's still not enough to beat Nvidia's performance. Overall, focusing only on GPU prices misses the bigger picture of total ownership costs.
  2. Networking costs make up a large part of the expenses when using AI chip clusters. Even if you find a cheaper GPU, the added networking costs can make it more expensive overall.
  3. AI chip startups need to consider the entire system's costs, not just the price of the chips themselves. If they don't, they may struggle to compete with Nvidia's established products.

Does NVIDIA Have a Future in China? Can Huawei Take Jensen On?

ChinaTalk • 163 implied HN points • 15 Feb 24
🕹 Technology AI Chips
  1. Chinese tech firms are diversifying their AI chip suppliers due to export controls on American chips, leading to opportunities for Chinese companies like Huawei.
  2. Nvidia faces challenges in China due to limitations on their AI chip supply, impacting their market share and customer relationships.
  3. The growth of Chinese new energy vehicles is driving demand for automotive-grade chips, with Nvidia holding a significant market share, but facing competition from domestic manufacturers and potential regulatory challenges.
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Softbank's Masayoshi Son Wants to Build an Nvidia Competitor too

Machine Economy Press • 2 implied HN points • 19 Feb 24
🕹 Technology AI Chips Venture Capital Semiconductor industry Artificial Intelligence
  1. Softbank's Masayoshi Son plans to build an AI chip company to compete with Nvidia and other giants in the industry.
  2. The project named 'Project Izanagi' aims to leverage Arm design and raise a staggering $100 billion, with $30 billion coming from Softbank and potential $70 billion from Middle Eastern institutions.
  3. Venture capitalists like Son see the potential for AI chips to drive artificial general intelligence development, with a goal of surpassing human intelligence.

Investment Review #1 - AI Portfolio Allocations for Q4 2023

e/alpha • 0 implied HN points • 05 Jan 24
💰 Finance Investment Portfolio AI Chips GPU LLMs
  1. The AI portfolio performance for Q4 2023 was impressive, outperforming the S&P 500 with an IRR of 95%.
  2. Investing in AI chips continues to be a promising choice, but there are concerns about the speed of commercialization and potential pitfalls.
  3. The future of LLMs (Large Language Models) is uncertain, but GPU investments are expected to stay strong until more clarity emerges.

Large models are pushing tech companies to rethink chip design; OpenAI unveils Sora generative video model; the world's first AI-powered Valentine's Day; ex-Salesforce CEO launches new startup

Computerspeak by Alexandru Voica • 0 implied HN points • 16 Feb 24
🕹 Technology AI Semiconductors Tech Companies AI Chips
  1. Large models like OpenAI's GPT series are reshaping the AI landscape by requiring vast computational resources and driving a buying frenzy among tech companies for AI chips.
  2. Designing AI chips involves significant costs spanning from R&D to testing, and challenges exist in producing low-volume chips due to economies of scale, NRE costs, and supply chain constraints.
  3. Advancements in semiconductor technology, including innovations like chiplets and AI-assisted design, offer potential ways to reduce costs and scale AI hardware production to meet the growing demand.