Instinct will be a $ 100b company in 10 years.

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After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution

Five first-place results in MLPerf® Inference v6.1. 603,023 tokens/sec on DeepSeek R1 at 72 GPUs and preview-category results on the Nebius @nvidia Vera Rubin NVL72. We were one of only two submitters with results on that hardware. Full results: nebius.com/blog/posts/mlperf… #MLPerf #MLCommons #Inference

We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next. Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d an open-source model to surpass GPT-6 Astra on our analysis benchmark. We call it Neon. This is real footage from our lab. We’re focusing first on hard problems in materials science, including superconductors, magnets, and semiconductor materials. Read our blog posts below.

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A training algorithm is also a bet on what computers should look like. That is how I read Sakana’s PC-ALM. The possibility that interests me is making learning practical on machines where training is currently too expensive. Sakana reports training 1,000-layer residual networks on MNIST, staying within roughly two percentage points of backpropagation. This tests whether useful learning signals can travel through a very deep network using local interactions. Sakana’s report The mechanism is elegant. In ordinary predictive coding, neighboring layers repeatedly adjust to reduce their prediction errors. In deep, narrow networks, the supervision signal can become too weak to guide early layers. PC-ALM gives each layer an accumulator that remembers its local errors during this process. That accumulated error feeds back into subsequent adjustments. Under the paper’s stability conditions, linear networks converge to the same gradients backprop would compute. Paper This separates two things that are easy to conflate: the information needed to improve a network, and the procedure used to calculate it. A familiar learning signal can emerge from a different physical process. That matters because hardware has preferences. Memory access, communication, precision and coordination all have costs. An algorithm that performs more arithmetic could still be useful if it substantially reduces something more expensive. There is already a concrete precedent. In 2022, researchers demonstrated a physical resistor network that learned through local circuit updates, without a central processor calculating those updates. These were small experiments, but the learning happened in the physical system itself. Experiment Imagine implementing repeated local adjustments directly in a circuit. The engineering question becomes how quickly and cheaply that circuit settles into a useful state. PC-ALM still has a substantial bill to pay. Its training procedure initializes with a forward pass, runs repeated local updates, then changes the weights. The iteration budget grows with depth, and the accumulators require extra memory. Algorithm and costs Shorter communication distances do not automatically mean lower total cost. Repeating a cheap operation enough times can make it expensive. But I also think there is a trap in demanding that every alternative first win on the hardware we have spent years optimizing for existing methods. We should evaluate the algorithm and its implementation together. That means measuring energy and elapsed time to reach useful accuracy, including communication, state storage and control overhead. Backprop deserves the strongest implementation we can give it in that comparison. My bet is that the first valuable application of this research direction will involve adaptation under a tight power budget. Think of a sensor learning to cope with changing conditions where it is deployed. There, the important outcome is how much useful adaptation the device can perform before exhausting its energy budget. That is a different target from adding more layers to an image classifier. It would require substantial further work, including learning over time without destroying earlier knowledge. Still, it gives this research a destination I find compelling. A device that can afford to keep learning can be useful in circumstances its designers did not fully anticipate. I would watch for the point where learning becomes cheap enough to leave switched on.