Just a shower thought on $AMD vs $NVDA Right now Nvidia undoubtedly dominates in robotics (Isaac, Omniverse, GR00T, Jetson). They own the whole development stack robots get built/trained on. Once humanoids are scaling massively (when we're out of dev. phase) and it becomes a cost competition, this is typically where $AMD outperforms Nvidia. Not to mention, its embedded/adaptive silicon (Xilinx) is crucial to real-time robotics control, which $NVDA doesn't have. Ofc this is contingent on robotics becoming progressively more mainstream. Just wanted to toss this thought out there. TLDR: wouldn't bet against $NVDA or $AMD in the long-run, but when it comes to robotics scaling, $AMD should have a higher upside.

In a 2023 podcast, Dwarkesh asked me if HFT was zero-sum for society. I argued that extreme competition at the margins for nanosecond-level advantage might subsidize the creation of broadly beneficial new technology. How AI training/inference stacks have become a clear example of this Over the last ten years, quantitative trading firms built or invested in the fundamental components of distributed systems and high-performance computing. Many technologies now commonly associated with AI were created for HFT during this period or reached scale because of it. A non-exhaustive list of these technologies, from high-throughput networking to vectorized databases to precision timestamping: • Mellanox InfiniBand and ConnectX NICs: HFT colocation and cross-connect environments drove early large-scale deployment of RDMA-capable InfiniBand and the ConnectX series. These became the default high-bandwidth, low-latency fabric inside modern GPU pods for all-reduce, parameter servers, and expert parallelism. Mellanox was acquired by Nvidia in 2020. • Solarflare/OpenOnload (and later Xilinx/AMD equivalents): Kernel-bypass networking stacks and specialized low-latency Ethernet NICs let applications DMA packets straight into user space. These techniques were required for software-based trading systems to reach the microsecond range of tick-to-trade latency. Engineers for hyperscalers commonly use this technology today for distributed cloud training and inference networks. • RDMA and RoCE: Remote Direct Memory Access, refined under HFT firms’ nanosecond requirements, has become the transport that allows GPUs to exchange gradients and activations with microsecond-scale latency and minimal CPU involvement. • FPGA feed handlers and inline accelerators (Alveo, custom RTL on Xilinx/Intel): FPGAs are not the hardware solution of choice for AI, where GPUs and custom ASICs have dominated. However, HFT use of FPGAs for wire-speed market-data parsing, book building, and sub-microsecond decision logic created a pipeline of talent and verification processes that transferred into AI inference accelerators. • Vectorized/columnar analytics engines: Quant and HFT shops require extreme scan and aggregation performance over massive tick and order-book histories, using pioneering technologies like KDB/Q. Several firms I’ve worked at became large users and funders of ClickHouse, which has become essential to logging and offline data preparation for large model training. • Hardware timestamping and PTP: HFT firms’ need for exact packet ordering and latency measurement hardened the timing infrastructure that distributed AI training and multi-node inference rely on for correct collective operations and debugging. • Lock-free, zero-allocation, cache-aware software patterns: The performance culture and specific techniques developed for tick-to-trade loops became standard practice in high-performance CUDA kernels, inference servers, and communication libraries. These techniques include carefully tuned concurrent data structures, huge pages, CPU pinning, and avoidance of syscalls in the critical path. The natural followup question in 2023 was whether too much capital and talent had been or could be spent on HFT. The HFT industry is still relatively small, employing around 10,000 people and producing about as much yearly revenue as Nvidia’s and AMD’s R&D budgets. If an ancillary use case for HFT is a bounty program to fund the infrastructure of our next technological epoch, the costs still seem low.

Stories of High-Quality Development | How a top inbound-tourism hotspot in China is handling its massive influx of visitors People often ask this question: What has made Zhangjiajie a hotspot in China's inbound tourism? The mountains are our calling card, service is our hidden ace, and culture is our trump card. This is my answer after guiding more than 500 foreign tourists. The mountains and rivers are Zhangjiajie's real advantages. From the summit, with clouds and mist swirling around, Tianmen Mountain and the floating mountains appear occasionally in the haze. The breathtaking sight of thousands of peaks standing in alignment and one pillar propping up the sky is like a universal language. It strikes straight at the heart without the need for translation. Over the past two years, I have witnessed firsthand the real effort Zhangjiajie has put into inbound tourism. Foreign tourists get through entry quickly with just a passport. Trilingual signs in Chinese, English and Korean cover the scenic zones, and travelers get tax refunds the moment they make a purchase, making it truly possible to travel freely and shop with ease. "Arriving with empty suitcases and leaving with them full" has become a common sight among foreign visitors. More importantly, a chance conversation with a foreign tourist made me realize that what draws them here and keeps them lingering is not just the stunning natural scenery, but also the ancient and modern culture, and the everyday vibe of local life. From that point on, I began to give myself "extra lessons." I first understood things myself, then found materials and translated them clearly. When the Tujia ethnic group's intangible cultural heritage brocade, through my "interpretation," became more than just traditional dowry bedding, Xilankapu (traditional brocade woven by Tujia people) naturally took on an added layer of historical depth in Eastern aesthetics in the hands of foreign tourists. In Zhangjiajie, there are more than 2,000 foreign-language tour guides and interpreters like me. We regularly take part in industry training, quickly evolving from scenic-area guides into cultural ambassadors. And we are not just guides. From city entry procedures to sightseeing in the scenic zones, from navigation to shopping, everyone here is a mobile window to the outside world, genuinely showing the world the lovable image of China. (Narrator: Li Zejia, English-speaking Tour Guide, Zhangjiajie, Hunan Province)