Unconventional AI

Most real-world systems are dynamic. So why do we still treat computation as static? Our latest blog explores computation through motion using gyroscopes, rods, springs, and ordinary differential equations to perform handwritten digit classification. A deep dive into: • dynamical systems as compute • differentiable ODE solvers • physics-inspired machine learning • emergent computation through interaction Read here: unconv.ai/blog/machine-l…

Tomorrow, May 15, is the final day to submit pre-proposals for the Unconventional Grant. Over the past several weeks, we’ve seen proposals spanning: • computation as dynamics • in-memory and in-physics compute • architectures that minimize data movement • new abstractions beyond linear algebra Many converge on the same intuition: meaningful efficiency gains in AI will not come from scaling existing approaches alone, but from fundamentally different ways of representing and computing. We are looking for technically grounded ideas that challenge assumptions across hardware, systems, and learning. We’re not looking for taller ladders to the moon. We’re looking for rockets. unconv.ai/blog/unconvent…

Getting to 1000x energy efficiency in AI isn’t about one breakthrough. It’s about solving two hard constraints: 1. Data movement dominates energy 2. Amdahl’s Law caps system-level gains Which means you have to rethink everything: models, hardware, and how they’re designed together. If this kind of problem excites you, you’ll enjoy our latest blog: unconv.ai/blog/how-to-im…

We are proud to announce that Unconventional AI has been named on the 2026 @CBinsights AI 100 list. Find us here: cbinsights.com/learn/ai-100-t…

Attending #ODSCAIEast today? Join our cofounder @mcarbin at 2:35 PM ET in the Keynote Room. “Nonlinear Dynamics as the Next Substrate for Intelligence” explores a fundamental idea: we may be building AI on the wrong substrate. The human brain runs on ~20 watts. Today’s AI systems require orders of magnitude more. That gap isn’t just engineering, it’s architectural. Michael will share why moving beyond digital computation toward nonlinear physical dynamics could unlock a radically more efficient path to intelligence.

Analog vs. digital. Which is actually better? As AI systems hit efficiency limits, it might be time to rethink the abstractions we’ve taken for granted and focus on using the right tool for the job. Explore the case for a mixed-signal future in our latest blog post “Analog is dead, long live analog.” unconv.ai/blog/analog-is…

We’re hiring people who are endlessly curious, comfortable with discomfort, and drawn to the edge of chaos. If this sounds like you, apply here: unconv.ai/careers

We’re introducing the Unconventional Grant. A new research grant program supporting bold, unconventional ideas in AI. We’re allocating $500,000 in total funding, awarding up to five $100,000 grants to researchers exploring new paradigms in efficient, scalable, and biologically inspired AI systems. We’re especially interested in ideas that challenge how AI systems are built today, from unconventional circuits and architectures to new approaches in neural networks and theory. Not incremental work. Not safe bets. Ideas that push the field forward. unconv.ai/grant/

@blip_tm If that's true, we'll do it ("90%" needs to be validated) We're not idealogical about analog at all. There are certain things that work only in analog and we're actively investigating that line.

this is all true, but you can achieve 90% of the benefits of dynamical systems acceleration using purely digital circuits and avoid all the manufacturability issues that come with analog design

Stop copying the past. Is modern AI just a "cargo cult" worshipping the GPU? For 60 years, hardware and software have lived in completely separate worlds. But AI is forcing us to tear down those walls. The industry is stuck optimizing old abstractions and linear algebra not because it’s the best way to build intelligence, but because it’s what we inherited. We’re flipping the script at Unconventional AI and introducing neural co-evolution. We are building hardware and neural networks together from day zero to bypass the limits of traditional computing and unlock mind-blowing 1000x efficiency gains. The era of siloed design is over. Ready to see the future of compute? Read the full breakdown in our latest blog post. unconv.ai/blog/neural-co…

In a recent episode of the @ThisWeeknAI, our CEO @NaveenGRao joined @jason for a roundtable discussion with @chaselochmiller (@CrusoeAI) and @ml_angelopoulos (@arena). Naveen discusses the long-term limits of scaling AI on today’s computing architectures and why new approaches to hardware and computation will be needed as the field continues to grow. The discussion also touches on infrastructure constraints, evaluation challenges, and how engineers can stand out as the industry matures. These are the kinds of questions we are exploring every day at Unconventional AI. Watch the full conversation below. youtu.be/9o13P7SMAaY?si…

10/10 What other fundamental questions should we be asking about dynamical systems and computation? Join us as we continue to explore the limits of programmability, steerability, and efficiency for tomorrow’s computing. unconv.ai/careers/

9/10 Success! When you plot the learned dynamics through time, the results demonstrate the steerability and programmability of these simple dynamical systems. Yes, this demo is whimsical, but it provides a clear answer: these systems are highly programmable. This answer hints toward a bright future for energy efficient computation by reimaging the very components.

1/10 Reimaging computing using dynamical systems raises a host of fundamental questions, among them: How programmable/steerable is a candidate system? This week, we ran an experiment to test the limits of programmability by asking: is a toy 4-oscillator system expressive enough to dynamically sweep out any arbitrary pattern in phase-difference space? After testing our “[un]” logo, we concluded these systems are highly steerable. Here’s how we did it.