Our Vision
The Positronic Brain, Realized
Isaac Asimov gave us the dream. We're building the reality.
We stand at the threshold.
For decades, science fiction promised us machines that think. The Positronic Brain—first imagined in 1940—became the symbol: from the Daleks to Data, from I, Robot to the Bicentennial Man. Now, generative AI is making that vision a reality.
The bottleneck isn't intelligence—it's infrastructure.
Today's AI systems are starved for memory bandwidth, constrained by capacity, and limited by architectures never designed for this moment. Positron exists to solve this: purpose-built silicon that puts memory at the center, enabling the AI systems the future demands.
Market Insights
How We See the Market
Power availability is the bottleneck.
Energy is the New Constraint
Data centers are no longer constrained by what they can afford—they're constrained by what the grid can deliver. Every major AI deployment now faces the same bottleneck: power availability. The next generation of AI infrastructure must deliver dramatically more intelligence per watt, or the scaling laws that have driven progress will stall.
The hardware that wins will move data fastest.
Memory, Not Compute, is the Bottleneck
Modern AI workloads are memory-bound. Context windows are growing from thousands to millions of tokens. Models are scaling past trillions of parameters. Agentic workflows demand persistent state. Yet most AI accelerators achieve less than 30% of their theoretical memory bandwidth on real inference workloads. The hardware that wins will have the highest realized memory bandwidth and capacity.
Training and inference need different silicon.
Inference is a Different Problem
GPUs were designed for graphics, then adapted for training. Training is compute-bound and tolerates batching. Inference is memory-bound and latency-sensitive. These are fundamentally different problems that demand different architectures. Purpose-built inference hardware isn't a nice-to-have—it's where the step-function improvements come from.
Looking Ahead
How We See the Evolution of Inference
All software will run on inference.
Token Demand Will Be Insatiable
Every future computing workload will increasingly be mediated through neural network computation. From code generation to scientific discovery, from creative work to autonomous systems—inference will become the foundation of all software. Demand for tokens will grow to the absolute limits of available energy. The question isn't whether we'll need more inference capacity, but whether we can build it fast enough.
One model, many computational profiles.
Inference is Many Subworkloads
Inference isn't a single problem—it's a collection of fundamentally different subworkloads, each with distinct computational profiles. Prefill (prompt processing) and decode (token generation) have vastly different compute and memory requirements. Within those, attention layers and feed-forward networks demand different resources. As models evolve, new techniques like speculative decoding, mixture-of-experts, and sparse attention will continue to reshape these requirements. The hardware that wins will be the hardware that adapts.
No single chip will rule them all.
The Future is Heterogeneous
No single architecture will dominate inference. Different hardware will specialize in different subworkloads, and the most efficient deployments will orchestrate across them. Positron focuses on what we do best: the generative decode phases of AI and any workload with high demand for memory bandwidth and capacity. We're designed to work alongside GPUs and other accelerators that excel at compute-dense operations. The future of AI infrastructure is purpose-built silicon working in concert.
Our Principles
What We Believe
Memory-First Architecture
Most AI accelerators are designed around compute density, with there being a lot of theoretical but unattainable FLOPs, then memory is added as an afterthought. We invert this: Asimov is designed around memory bandwidth and capacity first, with compute balanced to match. The result is over 90% realized memory bandwidth utilization versus under 30% for GPUs—and dramatically better economics for the workloads that matter.
Reindustrializing America
We're building resilient supply chains rooted in the United States. Atlas is fully designed, fabricated, assembled, and tested in America—ensuring security, quality control, and supporting the reindustrialization of advanced manufacturing. The future of AI infrastructure shouldn't depend on fragile global supply chains.
LPDDR Over HBM
We chose commodity LPDDR memory over High Bandwidth Memory. HBM delivers impressive theoretical bandwidth, but at extreme cost and power. Our architecture achieves comparable realized bandwidth from LPDDR at a fraction of the cost and power—while delivering drastically more memory capacity per chip.
Iteration Speed as Strategy
We've designed our organization around rapid iteration—8 months from company founding to FPGA prototype; 7 months from prototpe to first shipped product; 7 months from that to shipping to a major cloud service provider. Development speed isn't a nice-to-have; it's existential.
Full-Stack Control
The greatest gains come from controlling everything—silicon, systems, and software. We don't assemble components; we architect complete solutions where every layer is optimized for the layers above and below it.
TCO and ROI Over Vanity Metrics
We optimize for what customers actually measure: end-to-end latency, tokens per dollar, joules per inference request. All of these boil down to Total Cost of Ownership (TCO) and Return on Investment (ROI), and we are laser focused on delivering those values to our customers over vanity metrics and designed by committee test suites.
The trajectory of AI depends on the infrastructure underneath it. We're building that infrastructure—purpose-built silicon and systems that make inference dramatically cheaper and more efficient. The most ambitious AI applications will run on hardware designed for them from first principles.