Ali Esfahani

CFO | Investor | Advisor | Board Member

[Ali Esfahani @aesfahani]

https://t.co/3TqCvM6PqR

[Ali Esfahani @aesfahani]

Great conversation with @furrier and @theCUBE! Thank you for hosting me

[Unconventional AI @unconvAI]

In January, we gave ourselves 2 to 3 years to figure out the science behind building a new compute substrate to cut the power consumption of AI systems by 1,000x. We got there in 9 months, by answering these three questions:

Can you compute with dynamics?
Yes. The brain doesn't have floating point operations or instruction sets. It forms neural pathways when you learn and traverses them when you perform a task. Our systems work the same way: training sets the weights, a token perturbs the system, and the final state is the answer.

Is it power efficient?
Yes. Our first chip taped out on June 1st and is back in the lab. The power numbers are orders of magnitude below traditional inference systems.

Does it scale?
Yes, and this was the big one. A fully connected system needs trillions of wires, and nobody can physically build that. We found that cutting connections to roughly 2 to 3% improves output quality and learning and allows you to scale.

Watch @theCUBE where our CFO @aesfahani and @furrier discuss the research that brought us to the tape-out: https://t.co/2UhDbzYRCy

[Naveen Rao @NaveenGRao]

My talk from @theallinpod conference. You can learn a bit more about what we're up to @unconvAI !

[Naveen Rao @NaveenGRao]

We have A LOT more in the pipeline including new physical primitives to drive greater efficiency and scale. But we are continuing are continuing to do new things…below is a sequence generated in the pixel space learned by the causal dynamics (running on chip). We taught the system to count :)

[Naveen Rao @NaveenGRao]

I made some public disclosures about our hardware at the @theallinpod conf this week and wanted to share the progress here. Earlier this summer we got hardware back! The execution was madness…we went from no team in Jan to a tape out in 5 months. AI enabled MUCH tighter loops of research and our execution speed shows the results.

This is the first large-scale demonstration of a causal, physical dynamical system to do real compute. 🚀

We released the Un-0 model (see link) in June and it runs on this physical system; the images below come from the actual hardware. What’s more, this chip requires <900 nJ to generate an image; this is many orders of magnitude less energy than conventional machines.

https://t.co/zYU0ezXJUq

[Naveen Rao @NaveenGRao]

🦾🧠

[Ali Esfahani @aesfahani]

Let's go! This is one of the most interesting discoveries we have made in past few months.

[Unconventional AI @unconvAI]

We removed 93.25% of the connections in our Un-0 image model, fully expecting to pay for it in quality. But it got better.

FID 7.15 on ImageNet 64x64, roughly 1.9 ahead of the dense baseline at matched size. Same family of model, a fraction of the couplings, a better score.
Here is why that is not as strange as it sounds. Un-0 is a coupled oscillator model, and in the dense version every oscillator talks to every other one. That sounds like a strength, but it means the whole system can fall into catastrophic synchronization: everything locks into step, gradients go flat, and learning stalls.

Sparser connectivity leaves room for coherent and incoherent activity to coexist. The dynamics stay alive, and the model keeps learning.
Connectivity turns out to be a control knob, not a dial you turn up until it stops.

Learn more here: https://t.co/N4lIri8UQQ

[Unconventional AI @unconvAI]

Building a new memory means a twenty-year grind to drive the error rate toward zero. AI just made that grind optional.

Because neural networks are remarkably tolerant of noise, we no longer have to chase punishing reliability targets. Training can settle into a good solution even when the hardware underneath is imperfect (which is exactly what the loss landscape below is showing).
That single shift lets us stop optimizing for zero errors and start optimizing for what matters now: more bits per cell, higher density, lower energy. The universal memory never showed up, but AI may be the workload that tells each technology exactly which job it was born to do.

Read the full breakdown by Giacomo Pedretti (MTS, AI Hardware Architectures) and Srenik Mehta (VP of Engineering): https://t.co/ukj4GnTC7b

[Unconventional AI @unconvAI]

What if your AI chip didn't calculate the answer, but physically settled into it?

That's the premise behind dynamical system hardware, and there's never been a standard way to program it. In an upcoming International Symposium on Computer Architecture (@ISCAConfOrg) 2026 paper, Unconventional AI MTS Prof. Ang Li and co-authors introduce DS-ISA: a minimalist 9-instruction architecture that bridges digital processors and continuous-time analog physics.

It's a proof-of-concept, but it's the abstraction layer the field needs to build real compilers and software stacks for dynamics-based AI hardware.

Full deep dive: https://t.co/WxfjrlgURm

[Ali Esfahani @aesfahani]

The short version:
→ Demand for engineers will multiply, not shrink. The scarce skill just shifts from typing syntax to orchestrating systems.
→ Winning services firms won't sell hours, they will productize their work.
→ Per-seat SaaS is in trouble. Pricing will move from dollars per seat to dollars per action.

[Ali Esfahani @aesfahani]

https://t.co/3TqCvM6PqR

[Ali Esfahani @aesfahani]

The fear is everywhere. Building software is getting cheaper, AI writes more of the code every day. So what does this mean for the software companies and what does it mean for my job?

[Unconventional AI @unconvAI]

We’re at the beginning of the unconventional AI journey, but we are releasing artifacts to the community to enable us all to build together. We’ve open-sourced the Un-0 weights, training scripts, and ablation code.

If you build physics-based models, plug them into our scaffold and share your success!

Read the blog: https://t.co/docyekiKcp

Visit our GitHub: https://t.co/i4bNxdnmjr

[Unconventional AI @unconvAI]

Introducing our first model, Un-0!

We trained an image generator powered by a backbone of coupled oscillators in place of a more traditional conventional neural network.

[Michael Carbin @mcarbin]

Did you know this wiggly thing can be a computer?

We think of computation as symbolic: add, subtract, multiply....

…but…you can also compute directly with voltage, current, charge, waves, materials (octopus arms…look it up)...or wiggles.

Today, we’re (@unconvAI ) sharing Un-0: our first step toward scaling this idea to modern AI workloads.

[Naveen Rao @NaveenGRao]

🧠 Today we introduce Un-0 from @unconvAI : the first large-scale generative model build on physics as a compute primitive. This represents a “hello world” moment for physics-based models. We use the inherent time-varying behavior of physical systems to do compute for us. The result is a new way to build a computer that can be VASTLY more power efficient. 🧵
https://t.co/zYU0ezXJUq

[Unconventional AI @unconvAI]

Most companies use AI tools, but we are not a 'conventional company.'

We're building Unconventional AI for this new age as an AI-native organization.

In this latest blog, our [un]CFO, Ali Esfahani, shares how he has used AI in his first five months across finance, research, recruiting, legal, and other functions to help build a new kind of company.

The result isn't just greater efficiency. It's a fundamentally different way of operating.

Read more: https://t.co/wmm3RltqtL

[Ali Esfahani @aesfahani]

@LindseyGrahamSC @LindseyGrahamSC thank you Senator Graham. I may just move from SF to SC so I can vote for you!

[Ali Esfahani @aesfahani]

@TuckerCNews Who is watching TV? I hear they are also banning horses on highways 😂

[Ali Esfahani @aesfahani]

I think team sports are pushed in schools because it allows several kids to participate in the same activity while reducing overhead. So one or two coaches for 20 kids playing soccer .. same with basketball track and field etc. The facility costs are also lower for most of these sports … so automatically they are the best sports to teach kids at school. Tennis, Golf etc are harder to teach as they have high overhead and it will cost the schools a lot of money. All things equal I think people would choose for their kids to learn sports that matter later in life. I agree with whomever made this comments :)