Michael Gorbovitski

As AI transforms software engineering through autonomous coding systems, quantitative finance may be next. Firms across the industry are racing to build agentic workflows that can ingest academic papers, implement models, backtest trading strategies, and accelerate the research lifecycle.

Capital markets have long been the proving ground for new technologies, from low-latency networking and custom hardware to AI and advanced analytics. Finance firms move fast, iterate quickly, and have historically been first to adopt technologies that later reach the broader market.

Software development organizations have invested significantly in the Developer Experience (DX): tools, processes, and work environments to increase productivity, collaboration, and happiness of critical talent. But the Engineer Experience (EX) for FPGA teams typically lags that of software teams, even as the number of FPGA engineers on Wall Street continues to grow.

FPGAs are a go-to component for firms looking to improve their latencies, whether by getting strategies as close to the network as possible or offloading critical workflows from the CPU. But as the best engineers know, FPGAs are but one component of the custom hardware stack that affects performance.

It's no secret that FPGAs are a favorite component for ultra-low latency system engineers. Firms can gain a significant edge by placing relatively simple trading strategies in an FPGA to avoid user space entirely.

The STAC community is no stranger to the yin and yang of trading algorithms: increasing sophistication and decreasing latency. That is, competitive pressures require ever more complex decisions in increasingly tight timeframes.