Investor Briefing

AI infrastructure is hitting a wall.
Brain-CA is what’s on the other side.

For investors evaluating AI infrastructure investment, Brain-CA Technologies is a fabless semiconductor company building a new foundational layer for AI infrastructure, one that solves the AI energy efficiency problem at the architectural level, not the software layer.

Three granted US patents. Eight peer-reviewed ISCA papers. A proof-of-concept chip in fabrication at TSMC. The foundation is built. The proof is public. The window is open.

3

Granted US patents
Device, system, and ripple pattern operations — all layers protected

8

Peer-reviewed ISCA papers
Presented at ISCA workshops 2024, 2025 & 2026

$5M

Seed round — closed
Foundation capital secured, fabrication underway

TSMC

Proof-of-concept chip in fabrication
First silicon — the critical transition from concept to hardware

The problem with incumbent AI infrastructure

The GPU was never designed for AI.
The costs of that mismatch are now visible.

GPUs were built for graphics rendering and repurposed for AI because they were fast and available. That repurposing created two structural costs — energy and data movement — that compound with every new model, every new query, and every new deployment. These costs cannot be optimized away. They are properties of the architecture itself.

The energy cost

Inference costs more energy than training — and it keeps growing

Cumulative AI inference energy now exceeds training energy — and the gap widens with every deployment. Data centers are turning down AI workloads due to power constraints. The cost per query does not decrease as models scale — it increases. This is not a utilization problem. It is a structural property of floating-point arithmetic at scale.

The movement cost

Data movement costs 3× more than the arithmetic itself

The Von Neumann separation of memory and compute means data must travel between them billions of times per inference. That movement costs three times more energy than the arithmetic it enables. No GPU optimization eliminates this — it is a property of the architecture. Every new GPU added to a cluster adds to this cost, not reduces it.

The energy wall is structural.
Brain-CA addresses it at the root.

Every other AI infrastructure company is optimizing the existing paradigm — making GPUs faster, chips more efficient, models more compressed. Those gains are real. But the movement cost and the arithmetic cost are still being paid on every inference. Brain-CA replaces the assumptions those costs are built on.

The result is not a better GPU. It is a different architecture — one that learns without arithmetic, computes without data movement, and scales without a power plant. The addressable market is every deployment environment where energy and compute constraints limit what AI can do today.

The opportunity

AI infrastructure is hitting an energy wall.
The companies that solve it at the foundational level will define the next decade.

The current generation of AI — large language models, foundation models, GPU-based inference — is running into a physical ceiling. AI energy efficiency has become a boardroom problem. The energy cost of running AI at scale is no longer theoretical. It is showing up in data center power contracts, GPU shortages, and inference costs that erode margins at scale.

AI infrastructure investment has so far flowed to incremental solutions: more efficient chips, better cooling, software optimizations. None of them address the root cause. The root cause is architectural — the Von Neumann separation of memory and compute, and the floating-point arithmetic that neural networks require at every inference.

Brain-CA addresses the root cause. Not by optimizing the existing architecture — by replacing the foundational assumptions it is built on. The result is an AI system that learns without arithmetic, computes without data movement, and scales without a power plant.

⚡ The Energy Crisis is Structural

AI energy efficiency gains are being outpaced by inference energy costs. The gap between what AI demands and what the grid can deliver is widening every year. This is not a software problem. It requires a hardware solution at the AI infrastructure level.

📍 The Timing is Right

Infrastructure transitions happen at inflection points — when the incumbent architecture visibly hits its limits. We are at that inflection point now. The companies that have the foundational IP when the market turns will capture an outsized share of what follows.

🔒 The Moat is Defensible

Brain-CA’s IP is layered from the device level to the system architecture level to international markets. Three granted patents. Eight peer-reviewed papers. A portfolio that is still growing. The foundation is protected.

🌐 The Market is Enormous

AI infrastructure investment — spanning hardware, data centers, and edge compute — is one of the largest capital allocation opportunities of the decade. Brain-CA is positioned at the foundational layer of that market, not as an application layer company competing on features.

What makes Brain-CA different

Not a better neural network. A different architecture entirely.

Every other AI infrastructure company is optimizing the existing paradigm. Brain-CA is building a different paradigm. The distinction matters for investors because it determines the nature of the moat.

01

Foundational IP — not incremental improvement

Brain-CA’s patents cover the atomic device level (the Estimator and Cincinnati Algorithm) and the system architecture level (the Learning Fabric). This is not an efficiency improvement on an existing design — it is a protected alternative to the entire neural network stack.

02

Peer-reviewed science — not a pitch deck

Eight papers published at ISCA — the International Symposium on Computer Architecture — across 2024, 2025, and 2026. The architecture has been independently reviewed and validated by the scientific community, not just claimed by the team building it.

03

Hardware in fabrication at TSMC — not just simulation

Brain-CA has moved from software simulation — where 100% accuracy was demonstrated with minimal training data — to physical chip fabrication at TSMC in Taiwan. As a fabless semiconductor company, this is the critical transition from validated concept to working silicon.

04

Scalable from edge to data center

The same architecture that runs on a medical implant scales to a data center without changing its fundamental design. The addressable market spans every deployment environment where energy and compute constraints limit what AI can do today.

05

Continuous learning — no retraining required

Because training and inference are the same loop in Brain-CA’s architecture, the system adapts in real time. This enables use cases that are structurally constrained with neural networks — AI that learns where the data lives, continuously, without cloud connectivity.

06

International IP protection

PCT filings in WO 2024 and WO 2025 extend patent protection across international markets. The moat is not limited to the US market — it covers the global AI infrastructure opportunity.

Traction & milestones

From algorithm to silicon — at TSMC

Brain-CA is a fabless semiconductor company that has moved through every foundational stage of deep tech development — IP, science, simulation, funding, and now physical fabrication. Each milestone has been validated independently before the next was pursued.

IP foundation

3 granted US patents — architecture protected at every layer

The Estimator, Cincinnati Algorithm, and Ripple Pattern Operations are all covered by granted patents. International PCT filings extend protection globally. The IP foundation was established before external funding was raised.

Scientific validation

8 papers published at ISCA 2024, 2025, and 2026

Peer-reviewed and presented at the International Symposium on Computer Architecture — the most competitive and prestigious venue in the field. Independent scientific validation of the core architecture across three consecutive years.

Simulation breakthrough

100% accuracy demonstrated with minimal training data

Brain-CA’s processor simulation achieved 100% accuracy — demonstrating that the architecture performs as designed and that simple, self-contained systems can outperform complex neural networks in both speed and adaptability.

Hardware fabrication

Proof-of-concept chip at TSMC — first silicon expected soon

Brain-CA’s proof-of-concept chip design is currently at TSMC in Taiwan for fabrication. This represents the critical transition from validated simulation to physical hardware — separating Brain-CA from research-stage competitors who have not yet reached silicon.

Capital

$5M seed round — closed

Foundation capital secured to advance hardware development, expand the engineering team, and move from fabrication to deployment-ready silicon. The seed round is closed. The next phase of development is underway.

Recognition

Featured in CB Insights Early-Stage Trends Report

Brain-CA was included in CB Insights’ Early-Stage Trends Report in February 2026 — independent recognition of the company’s position at the frontier of AI infrastructure innovation.

The team

Built by founders who saw this problem coming.

Brain-CA was founded in Cincinnati, Ohio and has been developing this architecture since 2023. The full team profile is available on our About page and in the investor deck.

Jerry Felix

Co-Founder

Co-founder of Brain-CA Technologies. Full biography and background available on the About page and in the investor deck.

Steve Brunker

Co-Founder

Co-founder of Brain-CA Technologies. Full biography and background available on the About page and in the investor deck.

IP & research at a Glance

A portfolio built to defend the foundational layer.

Brain-CA’s intellectual property covers every level of the architecture — from the atomic device to the system architecture to international markets. Additional applications are in preparation.

3

Granted US patents

2

PCT international filings

8

Peer-reviewed ISCA papers

+

Additional applications in preparation
  • Protected by 3 granted US patents
  • Presented at ISCA in 2024, 2025 & 2026

The window

Infrastructure transitions have a window. It is open now.

The history of computing is punctuated by infrastructure transitions — moments when the incumbent architecture visibly hits its limits and a new foundation emerges. Those transitions reward early investors asymmetrically. The window for foundational bets closes as the new architecture becomes obvious.

  • The energy wall is not coming — it is here. Data centers are already turning down AI workloads due to power constraints. The gap between what AI demands and what the grid can deliver is widening every year.

  • Hardware development has long lead times. The companies that have working silicon first will be years ahead of anyone starting the fabrication process today. Brain-CA is already at TSMC.

Talk to the team

Ready to understand what Brain-CA makes possible?

We work with investors who are thinking seriously about the next era of AI infrastructure. Whether you want to schedule a call, review the deck, or reach out directly — we’re ready to talk.

All investor conversations are confidential. For technical due diligence, our team is available to walk through the architecture, IP, and roadmap in detail.