.@sundarpichai joined @eladgil and me in the Cheeky Pint pub. I was excited to get into Google in 2026: how AGI-pilled Google is, compute bottlenecks, fast AI products, $180b capex, the intelligence overhang at enterprises, and deciding capital allocation at a company overflowing with ideas.

We’re excited to introduce Inherent, a lab designed from scratch to build AI agents that discover new knowledge. The coming era of machine-driven scientific inquiry demands a new kind of research institution and a new kind of AI. To achieve our mission, we live within the experiment, recursively self-improving the entire research organisation. We investigate questions including: - What does ‘AI taste’ look like in the sciences, and how can we build an institution that embraces this new aesthetic of discovery? - What new kinds of human-machine teaming will make the most of AI that can truly innovate? - How can we build recursive self-improvement at the collective level that continually increases human agency over outcomes? We have just closed a $50m seed round led by @IndexVentures and @radicalvcfund, with participation from other outstanding investors including NVentures (@nvidia's venture capital arm), @buildexante, Metaplanet, Macroscopic, @MythosVentures, Charlie Songhurst, @chalfs, @jluan, @dwarkesh_sp, @Thom_Wolf, @j_foerst and @maxjaderberg. We are advised by @matthewclifford. Inherent is a Public Benefit Corporation headquartered in London.

Introducing DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation pub.sakana.ai/diffusionblock… What if we didn’t have to hold an entire neural network in memory to train it? Standard neural net training optimizes all parameters jointly. As a result, the memory required during training grows linearly with the depth of the network. In our #ICLR2026 paper, we propose DiffusionBlocks, a principled framework to train networks one block at a time, drastically reducing memory requirements while matching end-to-end performance. With DiffusionBlocks, we split the network into blocks and train them one at a time, so you only need memory for a single block. How? We explicitly assign each block a role: to move the representation a little closer to the target than the block before it did. That role turns out to be precisely what a diffusion model does, step by step. Each block only needs to optimize its own objective and can be trained independently. We validated this across five different architectures: • ViT • DiT • Masked diffusion • Autoregressive transformers • Recurrent-depth transformers In each case, performance is competitive with end-to-end training while using a fraction of the memory. This perspective also extends naturally to recurrent-depth (Looped) transformers, which apply the same network iteratively and normally require expensive backpropagation through time (BPTT). Viewed through DiffusionBlocks, we can replace those multiple iterations with a single forward pass during training. Read our paper and code, to learn more. Paper: arxiv.org/abs/2506.14202 GitHub: github.com/SakanaAI/Diffusio… 🐟

The AGI will be a RNN

An analysis tracking E.coli in fluctuating nutrient conditions maps out a reaction-network architecture that integrates environmental history to tune future growth, showing how even the simplest organisms implement complex computational strategies. 🔗 go.aps.org/4tyo7GL