Phil Trubey(@PTrubey):All-In Summit: Naveen Rao, CEO @unconvai Unconventional is one of half a dozen unicorns and startups that aim to completely replace conventional silicon, backprop algos, transformers, basically everything that powers our existing very powerful AI. They have a local connectionist training algorithm & hardware implementation (they showed off a picture and some specs from their just manufactured chip). Instead of encoding neuron weights in SRAM or HBM, it stores them in an analog dynamic environment of coupled oscillators. Meaning the compute and memory are part of the same substrate. What you get from this is the elimination of the Von Neumann bottleneck which costs a lot of energy. They have promised 1000x power efficiency over Nvidia performance within 2 years. The just manufactured chip is a proof of concept. No actual benchmarks were given (other than energy per image generation at an crappy image generation benchmark), so we don’t know its accuracy, chip density (both compute and memory), ability to train and inference LLMs, etc. So, some promise and advancement, but the jury is still very much out until we see some benchmarks. I tell people we’re still in the first inning with AI. Another analogy is that we’re still in Fortran era with goto statements. Our huge AI industry scaled way before we even started optimizing. Lots of disruptions are still expected, I just don’t know when and from where they’ll hit … yet.

2026.09.15 16:33

All-In Summit: Naveen Rao, CEO @unconvai Unconventional is one of half a dozen unicorns and startups that aim to completely replace conventional silicon, backprop algos, transformers, basically everything that powers our existing very powerful AI. They have a local connectionist training algorithm & hardware implementation (they showed off a picture and some specs from their just manufactured chip). Instead of encoding neuron weights in SRAM or HBM, it stores them in an analog dynamic environment of coupled oscillators. Meaning the compute and memory are part of the same substrate. What you get from this is the elimination of the Von Neumann bottleneck which costs a lot of energy. They have promised 1000x power efficiency over Nvidia performance within 2 years. The just manufactured chip is a proof of concept. No actual benchmarks were given (other than energy per image generation at an crappy image generation benchmark), so we don’t know its accuracy, chip density (both compute and memory), ability to train and inference LLMs, etc. So, some promise and advancement, but the jury is still very much out until we see some benchmarks. I tell people we’re still in the first inning with AI. Another analogy is that we’re still in Fortran era with goto statements. Our huge AI industry scaled way before we even started optimizing. Lots of disruptions are still expected, I just don’t know when and from where they’ll hit … yet.

Phil Trubey@PTrubey

2026.04.29 13:50

Recently four companies (@IneffableLabs , @amilabs, @thinkymachines, @unconvAI) raised over $4.5B to make AI chips several orders of magnitude better than NVIDIA … and they all raised the money with no clear roadmap of how they are going to accomplish this. For all their computation power, NVIDIA chips, TPUs, Trainium, Groq, all suffer from being massively power inefficient. They use way more power than they could if we had a different AI paradigm. The human brain is often cited as a counter example, running on a mere 20 watts of power. So these companies are all attacking this power problem from many different directions: New training algorithms using non-linear dynamics. Analog electronics instead of digital. Spiking neural nets. Compute in memory. Yann LeCun’s JEPA architecture. Multi-modal intelligence. Using Alpha Zero’s approach of not using any human training data to create a super learning algorithm. There’s no guarantee any of these companies will succeed. But if one of them does, it’ll massively disrupt the AI industry. It’s no surprise that NVIDIA and Google invested in some of these companies, they are hedging their entire revenue stream on the possibility of such disruption. Elon’s investment in Terafab, but more specifically on his R&D fab, is also a hedge against some breakthrough (the R&D fab is specifically going to investigate “new physics” for chipmaking). It won’t happen quickly even if a breakthrough happens tomorrow. It takes time to make new semiconductor devices, prove them out in a manufacturing lab, and ramp up production. We will get years of warning. The AI industry’s course is set for the next five years at least: massive data centers and a race to secure new massive energy infrastructure. But it’s nice to know there’s a lot of venture money working on something that's potentially massively more efficient.