Eliyan raised $145 million at a $1 billion valuation to address AI chip data bottlenecks by improving data delivery to GPUs. The company's funding round was part of a broader push to ease the flow of data into AI accelerators, where bottlenecks can limit how well the chips are used.
The investment reflects a growing recognition that raw computing power isn't enough. AI systems need better pipelines to feed data to processors fast enough to keep them working at capacity. When data can't reach GPUs quickly, expensive hardware sits idle.
Government Steps In
The U.S. Department of Commerce plans to award GlobalFoundries $300 million to develop faster AI chip links, including work on co-packaged optics aimed at boosting data transfer and energy efficiency. GlobalFoundries' effort centers on interconnects that can move data faster between chips and improve how efficiently those systems use power.
The federal investment signals that Washington sees chip infrastructure as a national priority, not just a private-sector problem. Faster interconnects could reduce energy consumption in data centers while improving performance — a combination that matters both economically and environmentally as AI systems scale.
AI Designing AI Chips
Nvidia partner ChipAgents raised $60 million to accelerate chip design using AI agents that can make decisions and execute tasks with minimal human oversight. The company's pitch is that AI can take on more of the design workflow itself, speeding development and reducing the amount of human intervention needed.
That's a notable shift. Chip design has traditionally required teams of engineers working through iterative cycles. If AI can handle more of that process autonomously, it could compress timelines and lower costs — though it also raises questions about quality control and the displacement of skilled labor.
Three Fronts, One Supply Chain
Together, the three developments show private capital and government funding moving on separate fronts in the AI chip supply chain: data delivery to GPUs, faster links between chips, and AI-driven automation in chip design.
Each addresses a different constraint. Eliyan's work tackles the problem of getting data to processors. GlobalFoundries focuses on moving data between chips once it's there. ChipAgents aims to speed up how chips are designed in the first place. None of these solves the full problem alone, but together they reflect where the industry sees its next set of challenges.
The involvement of federal dollars in one piece — GlobalFoundries' interconnect work — suggests the government views chip infrastructure as critical to maintaining technological leadership. That's consistent with broader U.S. efforts to rebuild domestic semiconductor capacity and reduce reliance on foreign supply chains.
Why This Matters:
The AI boom has created enormous demand for chips, but chips alone aren't the bottleneck anymore. Data delivery, energy efficiency, and design speed are now just as critical. These investments show the industry trying to solve those problems before they become limiting factors. The federal funding for GlobalFoundries also underscores a shift in U.S. industrial policy: the government is no longer leaving chip infrastructure entirely to the market. That reflects both economic strategy and national security concerns about maintaining an edge in AI technology. If these efforts succeed, they could extend the current AI expansion. If they don't, even the most powerful chips won't run at full capacity.