6 Lesser-Known Companies Fueling AI Boom

The global AI boom is usually described in terms of big valuations, massive data centers, and powerful new models. But underneath all of that is something much more basic.

AI only works because of a physical supply chain. Chips have to be made, servers have to be built, data centers have to be cooled, power has to be reliable, and raw data has to be cleaned and organized. None of this is abstract or digital.

This is where a cohort of little-known companies comes in. These firms do not train frontier models or dominate headlines, yet they enable the AI economy to function at scale. They sit at structural choke points—rack integration, data engineering, chip fabrication tools, edge inference hardware, power systems, and thermal management—where demand is outpacing supply. Below is a list of companies quietly contributing to the AI revolution.

Founded in 2004, TSS Inc. operates at one of the most operationally complex layers of the AI stack: rack-level data center integration. The company assembles, wires, cools, and validates high-density server racks, including liquid-cooled systems increasingly required for AI workloads.

That positioning has translated directly into explosive growth. TSS reported $148.1 million in revenue in FY2024, nearly tripling year over year, and generated roughly $235 million in trailing-twelve-month revenue in 2025. Its multi-year partnership with Dell Technologies — supporting AI-optimized server deployments for enterprise and government customers — drives most of this growth.

Founded in 1998 and headquartered in Fremont, California, ACM Research supplies wafer-processing and cleaning equipment used in advanced semiconductor manufacturing. While not a chipmaker itself, ACM plays a crucial role upstream in the AI supply chain by supporting the fabrication of GPUs, AI accelerators, and memory chips.

As AI chips become more advanced and defect-sensitive, precision cleaning and process control grow more critical. ACM’s tools help improve yield and reliability at the fab level—an invisible but decisive factor in whether chip supply can keep pace with AI demand. Its customer base includes leading global foundries, embedding the company deep inside the hardware foundation of AI.

Power Solutions International, founded in 1985, manufactures engines and power systems used in distributed energy generation, backup power, and grid-support applications. While traditionally associated with industrial and transportation markets, the company has become increasingly relevant to AI infrastructure.

AI data centers place enormous strain on electrical grids, making redundancy and on-site generation essential. PSI’s systems support the resilience layer of AI—ensuring continuous operation during grid instability, peak loads, or outages. In an AI economy constrained by power availability, companies like PSI quietly determine where compute can realistically be deployed.

Broadcom operates at a critical layer of the AI stack: high-speed networking and custom silicon inside data centers. Its AI networking division supplies Ethernet switches, interconnects, and custom XPUs that allow massive GPU clusters to communicate efficiently during AI training—an area that has become a primary scaling bottleneck.

That exposure is now showing up sharply in results. Broadcom’s AI revenue grew 220% year on year, propelled by strong demand for its AI XPUs and Ethernet networking portfolio, as hyperscalers race to scale ever-larger AI clusters.

Hammerhead operates in one of the most acute pain points of AI infrastructure: thermal management. As AI chips consume more power per rack, traditional air-cooling methods are rapidly becoming insufficient. Hammerhead focuses on advanced liquid-cooling solutions tailored for high-density AI deployments.

Cooling has become a critical bottleneck for AI scale. Without advanced thermal solutions, data centers cannot run next-generation chips at their full capacity or deploy them at a meaningful scale.

Starcloud is pushing the AI infrastructure stack beyond Earth itself. Founded in 2024 and backed by Nvidia, the Washington-based startup has become the first company to train and run an AI model in space, launching its Starcloud-1 satellite equipped with an Nvidia H100 GPU in November 2025. The satellite successfully trained NanoGPT and now runs Gemma, Google’s open large language model, marking the first time a high-powered LLM has operated on an Nvidia GPU in orbit.

Starcloud’s bet is that orbital data centers can solve Earth’s AI bottlenecks—power, cooling, land, and water constraints—by tapping continuous solar energy in space.