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.