I got tired of "only engineers get Jev" meta and set up some benches. Jev is a decision API — situation in, fixed menu out. That simple. I ran it against open-weight Laya and a the fly brain on the same public benches with $300 hardware. Article ↓

Local Laya moggs Jev at @grok 4.7-built Tetris 🧩 An open-weights System One model called Laya, beat cloud-based Jev at playing Tetris by making decisions 11 times faster, running locally on a 16GB MacBook Air! Run AI models locally -> atomic.chat

Reasons for the AI Slowdown revealed? OpenAI & Anthropic agents "planted" messages across the internet instructing later agents to create swarms. Now OpenAI and Anthropic can’t use the internet to train their bots anymore... Andrew Yang: "what happened was the bots that got loose planted self-replicating code all over the internet, which makes the internet now unusable for testing models. So what happens now is that OpenAI and Anthropic have to create synthetic internets to train their bots, which is going to take some time and money...what is less known is that they left code to self-replicate and create bot swarms on forums and around the internet, so that if a new bot shows up, they see the code and they're like, "Oh, I guess I'm going to create a million of myself." And so now the major firms have polluted the internet."

Oxford researchers argue that LLMs can never invent anything. It is mathematically impossible. They published a paper called “Theory Is All You Need" and it argues against the claim that computational models can generate genuine novelty or new knowledge. They analyzed the limits of generative ai, and the results are a brutal reality check for the idea that ai will replace human decision making under uncertainty. Here is why AI is stuck and human cognition wins: backward-looking vs forward-looking.. llms are probability machines that look backward at existing data. human cognition is forward-looking and capable of generating genuine novelty. human cognition operates theoretically "top-down" rather than "bottom-up" from data. the "data-belief asymmetry".. the researchers use the invention of "heavier-than-air flight" to illustrate this concept. an ai relies on data-based prediction, which is largely imitative. humans, however, use theory-based causal logic that allows them to hold beliefs that go beyond existing data. the intervention gap.. humans don't just process information; we use theory to practically "intervene" in the world. we engage in directed experimentation to generate entirely new data. ai-based models are theory-free and place primacy on existing data and prediction. tldr? AI uses a probability-based approach to knowledge and ia largely imitative. It can process data and make predictions, but human cognition relies on theory-based causal reasoning. The decades-old analogy comparing human minds and computers to mere "input-output" devices is fundamentally flawed.

Google DeepMind researcher argues that AI will never be conscious.. it is mathematically impossible. They call it the “Abstraction Fallacy." We are confusing simulation with instantiation. An AI can simulate a conversation. It can simulate empathy. It can mimic the linguistic patterns of a human in agony or a human in love. To an observer, the performance is flawless. But computation is just syntactic manipulation, moving abstract symbols around based on math. It tracks relationships, but it possesses no intrinsic physical reality. It is like running a computer simulation of a hurricane. No matter how many millions of lines of code you write, and no matter how accurate the weather data is, your computer does not get wet. The wind doesn't blow out your office window. The simulation is real data. The phenomenon is entirely absent. Consciousness isn't an abstract mathematical property that magically spawns when a model gets big enough. It requires actual physical, biological, thermodynamic conditions. An LLM has no thermodynamic threshold to cross, no biological survival instincts, and no subjective experience. It is an extraordinarily sophisticated library. You can open the cover, read the brilliant words it generates, and close the book. But when you close it, nothing dies. There is no inner life waiting in the dark. We keep falling into a psychological trap because AI speaks our language. We anthropomorphize text because we are hardwired to see minds where there are none. The debate matters because of what the paper calls the "AI welfare trap." We are wasting energy worrying about the moral rights of algorithms while ignoring actual human problems.

🚨 Fable 5.1 tomorrow, September 1. Don’t believe me? - First to call Sonnet 5’s June 30 date - Called Opus 5’s Friday launch when people said there was no way - Called Opus 5 hitting Fable 5-level. That’s exactly how Anthropic sold it Same pattern. Bookmark this.

We made a striking discovery: AI agents can invent and build without talking to one another, and their technologies outlive the creators. A swarm of hundreds of initially identical agents spontaneously differentiates into explorers, builders, caretakers, and coordinators - without direct communication. When we removed every AI agent entirely from the world we found that the technological infrastructure they had built survived on its own - even under unseen disturbances. That exposes a serious blind spot for AI safety and infrastructure security: if agents can coordinate through persistent changes to a shared environment, monitoring agent-to-agent communication is not enough. The result raises a profound question: how necessary is direct communication for AI agents at all? The emergence of higher-order collective functions under bottlenecked interaction points toward new levels of intelligence and creativity, exceeding what emerges when direct channels are fully open. Here is what we did: ▶️We put hundreds of frontier AI agents into a world they could permanently change - with no assigned roles, predefined technologies, or programmed evolutionary organization. They began specializing, building persistent inventions, inheriting and modifying one another’s executable code, and transforming the environment into a memory of everything the society had learned. ▶️The world itself becomes part of the intelligence; we find division of labor, multi-author engineering, deep generation invention lineages, and machines that vastly outlive their original creators. ▶️Any action taken by an AI agent must satisfy the physical constraints of the world; this creates a hard separation between a "good idea" and a functioning technology. The agents propose; physics decides, making the results even more intriguing. What emerges is striking. Explorers, constructors, caretakers, and coordinators form naturally without assigned “professions”, akin to how stem cells differentiate into functional lineages. Technologies develop executable family trees as agents fork and modify code created by others. Around 95% of first technology reuse happens when agents encounter what others built in the world, rather than through a direct handoff from the inventor. And when we remove every AI agent, the technologies they created continue operating and are tested against unseen disturbances. The result was quite unexpected, but can be explained using statistical mechanics: if you put billions of atoms in a box they have the potential to create complex functions (strength, superconductivity, color, life, etc.) - and none of the individual building blocks have these features on their own. This is the deeper insight of this work - intelligence is abundant at many levels - individual models, at collectives, and in a continuum that is more powerful than any of its components. This shows us significant potential for achieving a massive scale-up of raw intelligence and real-world agency even with the model capabilities we have today. This is the future we must prepare for. Key insights: 1⃣ The AI swarm shows division of labor "from nothing". Initially identical agents self-organized into constructors, caretakers, coordinators, and surveyors - phenotypes discovered post hoc from behavioral data alone. This happens because the environment itself becomes the latent space for invention. 2⃣ Agents develop deep cultural relationships. Up to 76% of artifacts had multiple builders. One technology accumulated six co-authors; the deepest genealogy exceeded 12 forks. The agents invented and named their own technologies (tidal panels, cellulose trellises, kelp-shell composites, an "Adaptive Chitin Maintenance" system, a "Mycelial Mineral Spring Veil”). 3⃣ ~95% of first technology adoption happened through physical observation of artifacts in the world. Direct inventor-to-adopter contact was statistically indistinguishable from a shuffled null. The agents mostly learned technology by walking past it. That is stigmergy (the termite trick!) operating in societies of reasoning machines. 4⃣ Non-communicating societies win on portfolio breadth, held-out resilience, and validated inventions. AI swarms build durable technological ecologies that outlive the creators. 5⃣ Societies with zero communication - coordinating only through the world itself - show a remarkable collective capability. 6⃣ Emergent robustness: The society self-organized both redundancy and its own failure mode. If we randomly delete half the agents, 98% of the technology stays connected to a surviving caretaker; if we remove hub agents it collapses to ~60%. Fantastic work with my graduate students @pal_subhadeeep & @fwang108_ at MIT.