In this interview from theCUBE + NYSE Wired: AI Factories, Faris Sbahi, chief executive officer and co-founder of Normal Computing, joins Craig Churchill, chief business officer of Normal Computing, to talk with theCUBE's John Furrier about how AI-powered chip design is reshaping the economics of next-generation AI infrastructure. Sbahi announces a $50M accelerator round led by Samsung, with strategic investors including Micron and Celesta, to scale Normal's mission of making custom silicon radically easier to design and deploy. He introduces thermodynamic computing — a new paradigm combining in-memory processing to resolve memory bandwidth bottlenecks with non-deterministic hardware matched to the approximate nature of intensive AI workloads like video generation. With inference now representing roughly two-thirds of all AI compute and a projected 49-gigawatt global power shortfall on the horizon, Sbahi frames tokens per dollar per watt as the defining efficiency metric of the AI era.
The conversation also explores Normal Computing's Electronic Design Automation (EDA) platform, which targets a chip-design discipline that has gone largely undisrupted for 40 years. Churchill points to a stark industry benchmark: first-pass silicon tape-out success rates have fallen to a historic low of 14%, and Normal's AI methodology — including an auto formalization technique that converts thousands of pages of engineering prose into machine-readable formal language — is designed to address that crisis directly. Sbahi illustrates the velocity of agentic workflows with a striking internal example: an engineer on paternity leave deployed agents for 43 consecutive days that collectively wrote 580,000 lines of production-quality code. From tackling a global shortage of roughly one million silicon engineers to maintaining a strategic advantage in EDA amid intensifying geopolitical competition, Sbahi and Churchill outline why the winners of the next infrastructure cycle will be defined not by the software they run, but by the systems they build.
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Faris Sbahi & Craig Churchill, Normal Computing
In this interview from theCUBE + NYSE Wired: AI Factories – Data Centers of the Future event, Glean co-founder and CEO Arvind Jain joins theCUBE’s John Furrier to unpack what’s really working in enterprise AI today and what comes next. Jain explains why knowledge access remains the first successful AI use case at scale and how Glean’s enterprise search brings AI into everyday work. He details the past year’s lessons with AI agents – from the need for guardrails, security, evaluation and monitoring to democratizing agent building so business owners (not just data scientists) can create production-grade agents.
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In this interview from theCUBE + NYSE Wired: AI Factories, Faris Sbahi, chief executive officer and co-founder of Normal Computing, joins Craig Churchill, chief business officer of Normal Computing, to talk with theCUBE's John Furrier about how AI-powered chip design is reshaping the economics of next-generation AI infrastructure. Sbahi announces a $50M accelerator round led by Samsung, with strategic investors including Micron and Celesta, to scale Normal's mission of making custom silicon radically easier to design and deploy. He introduces thermodynamic co...Read more
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What does Normal do for semiconductor companies, and what performance metric and efficiency improvements is it targeting?add
What is your perspective on the recent trend of faster chip tape-outs—driven by entrepreneurs and large players like NVIDIA—and the resulting pressure to boost U.S. productivity given historically low first‑pass silicon success rates, and can AI help address this?add
What methodological innovations are being pursued on the ASIC side, and what is "thermodynamic computing" — how does it work and how does it help with AI workloads such as video generation?add
How do you expect the data center and related infrastructure market to evolve over the next two to three years, and what will be the main constraints?add
How does a solution fit into the energy envelope and network/latency constraints when scaling GPU infrastructure — i.e., what is the "secret sauce" for enabling more GPUs given projected power shortfalls?add
What does the business development and timetable look like for the transition to agent-driven AI and AI-native silicon/software systems?add
What is auto formalization, and how does it translate human prose and block diagrams into formal language that purpose-built LLMs can use to improve chip design and verification?add
