Who Is Entering AI Infrastructure? An Open Ecosystem View of the Full Stack
For most of the open-source AI conversation, "infrastructure" meant access to GPUs. That framing is now outdated. A new wave of companies is entering every layer of the stack at once, and for an open ecosystem the question is no longer just "which model is best?" — it is "which layers can the community actually reach, inspect, and build on?"
Chips: a more plural silicon market
NVIDIA still leads training, but the field is widening fast: AMD Instinct, Intel Gaudi, and custom hyperscaler accelerators like Google TPU, Amazon Trainium, Microsoft Maia, and Meta MTIA. For open builders this plurality matters because portability across accelerators — not lock-in to one vendor's kernels — is what keeps an ecosystem healthy.
Networking, materials, power, grid, and cooling
Large clusters are stitched together with NVLink, InfiniBand, and RoCE Ethernet, increasingly aided by silicon photonics to cut data-movement energy. Beneath the chips sits a materials bottleneck — CoWoS advanced packaging, HBM stacks, and substrate supply — that quietly limits how many accelerators ship. Power is the hardest constraint of all: new datacenters contract directly with utilities for hundreds of megawatts and pursue dedicated gas, nuclear, and renewable capacity, while grid interconnection queues dictate site choice. And once racks pass roughly 100kW, air cooling fails, making direct-to-chip liquid and immersion cooling (Vertiv, LiquidStack, Submer) standard infrastructure rather than a luxury.
The software "harness"
Above the metal, a fast-growing software layer turns raw checkpoints into usable systems: inference engines (vLLM, TensorRT-LLM, SGLang), model gateways, AI IDEs and coding agents, orchestration, and evaluation harnesses. This harness is where open communities have the most leverage, because it is mostly software and therefore inspectable and forkable. A grounded multimodal assistant such as AI Chat lives here, combining web-crawl grounding with image, video, report, chart, song, and 3D generation in one flow — and from an open-ecosystem lens, the value is traceability: claims can be checked against evidence.
Fabs, foundries, and capacity deals
No chip exists without a fab. TSMC remains the center of gravity, with Samsung Foundry and Intel Foundry competing, while CHIPS Act incentives pull leading-edge capacity into Arizona, Japan, and Germany. The decisive moves are co-design deals — Google with Broadcom, Amazon's Annapurna Labs, and OpenAI's reported partnership with Broadcom and TSMC — that reserve scarce capacity years ahead. For open communities, capacity concentration is the real centralization risk, more than any single model release.
The new inference boards
The boldest entrants attack inference directly: Groq's deterministic low-latency LPU, Cerebras's wafer-scale engine that fits a whole model on one die, Etched's Sohu (the transformer architecture etched into the ASIC), and Taalas, which aims to compile a model straight into silicon. When architectures stabilize, this fixed-function hardware can dramatically lower cost per token — which, if accessible, could make local and community-run inference far more viable.
Final thought
An open AI ecosystem should track the whole stack, not just model weights. Power, packaging, foundry capacity, and the software harness all decide what the community can realistically deploy. Tools like AI-Chat are useful precisely because they expose a grounded, multimodal surface on top of this infrastructure — and openness is best measured by how many of these layers stay inspectable and interoperable.