Solving AI's Energy Crisis
The smart thermostat for data centers
Nature-inspired neural processors that optimize data center cooling in real-time, reducing energy waste by 40% while maximizing AI performance
Problem
AI is driving unprecedented growth
AI is reshaping our world, with applications like ChatGPT handling over 1 billion queries daily. Each query consumes 10x more energy than a standard Google search.
Energy Crisis
AI workloads will consume over 90TWh of electricity by 2026 - equivalent to powering 8 million American homes.
A single AI model training can use enough energy to power 64,000 homes for a month.
Cooling Inefficiency
40% of data center energy is consumed by cooling systems that rely on outdated, rigid control methods. This creates a paradox: the systems meant to reduce energy consumption end up contributing significantly to the problem they're trying to solve.
Solution
Nature's blueprint for efficient computing
Traditional cooling systems face a paradox: they need powerful computers to optimize energy usage, but these computers themselves generate heat and consume power. The industry needs a fundamentally different approach.
While traditional cooling systems rely on rigid rules and delayed responses, our brain-inspired approach mimics nature's 20-watt supercomputer. By adopting the brain's architecture, we control megawatt-scale cooling systems while using mere watts of power.
Team
Bridging high-performance computing & neural innovation
Our founding team combines deep expertise in high-performance computing, neuromorphic architecture, and energy systems - the exact intersection needed to solve AI's energy crisis.
Param Nayar
CEO
Energy & Infrastructure: HPC Engineer & M&A @ bp
Startup Success: 1x Founder (Acquired)
Leadership: Chief of Staff @ Sema
Education: BS Electrical Engineering, UT Austin
Jonathan Naoukin
CTO
Murat Isik
Chief Scientist
Advisory Board
Pioneers in advanced computing & neural architecture
Our advisors represent decades of pioneering work in neuromorphic computing, GPU architecture, and FPGA design - providing strategic guidance across every layer of our technology stack.
Dr. Newton Howard
Neuromorphic SME
A distinguished brain and cognitive scientist who bridges neuroscience and computing. As the founder of the MIT Mind Machine Project and former director of Oxford's Computational Neuroscience Laboratory, he brings crucial insights into brain-inspired computing architectures and their practical applications.
Dr. Shihao Song
GPU & Neuromorphic SME
A leading GPU architect at NVIDIA and affiliate professor at Drexel University, Dr. Song specializes in the intersection of hardware architecture and neural networks. His expertise in GPU design and neuromorphic computing systems is instrumental in shaping our technical roadmap.
Dr. Jonathan Greene
FPGA SME
A pioneering FPGA architect and UC Berkeley visiting scholar with over 5,000 research citations. As the owner of Cambios Computing, his extensive experience in computer-aided design and FPGA architecture provides invaluable guidance for our hardware implementation strategy.
Contact
Get in touch
Reach us anytime via support@type1compute.com
We will respond within 24 hours.
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