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Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.

GPT-6 and Intelligent UI, now rolling out in ChatGPT for everyone. Intelligent UI in ChatGPT delivers fast, interactive answers that make everyday questions more visual, complex topics easier to grasp, and interactive tools for your task available on the spot.

that’s pretty cool! i also gave it a try with a different style direction and the result (after a few rounds of tweaking) is impressive

We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training. You can read the full post here: darioamodei.com/post/we-must…

Twelve machines. 2.4 terabytes of unified memory. One coordinated inference problem. Seven DGX Sparks: 896 GB, CUDA, brutal at compute. Five Mac Studios: 1.5 TB, Metal, brutal at bandwidth and unbeatable per watt. Plus three Mac minis, a 5080, some V100s. At 4-bit, that Studio memory holds a 1 to 2 trillion parameter model with room left over for a 900,000-token context. The weights fit. That was never the problem. The problem is prefill. Before a model with that context says one word, it has to read everything you gave it. On Apple Silicon that is roughly 400 tokens a second. A 900K-token load is over half an hour of silence. Decode is fine, 25 to 30 tokens a second all day, quiet, 300 watts. It’s the first word that costs you. The Sparks prefill four to five times faster and can’t hold the model. The Studios hold the model and can’t prefill. Everyone with mixed silicon owns both halves of the answer and no way to join them. So we joined them. NVIDIA prefills, Apple decodes, one request. Two engines that share no cache format, no framework, no vendor. Instead of transferring a cache neither can read, the prefill box computes the decoder’s finished cache using the decoder’s own weights and writes it into the decoder’s prefix store. About 10 KB per token crosses the wire, over plain 10 gigabit Ethernet through the two switches in the first picture. No RDMA, no Thunderbolt. Measured today on DeepSeek-V4-Flash, 284B, 241,000-token cold load: Mac Studio alone, 12 minutes to the first word. Two Sparks feeding it, 3 minutes. Same prompt again, 19 seconds. Decode identical. Answers identical. That ratio is what makes the goal real: prefill 900K on the Sparks for a trillion-parameter model living on five Studios. Tonight we took the prefill window from 262K to 524K. Not finished, and every number gets posted either way. Why this is a paradigm shift for us: our agent is persistent and has 54MB of memory files..and then we drop a transcript or a codebase on top of it mid-conversation. The wait was the product’s real cost. It isn’t anymore. All credit to everyone who contributed to these concepts before us. We distill knowledge from all the greats and give credit to all. Standing on the shoulders of GitHub wizards unapologetically without fear of failure or judgement. Local ai must win! github.com/chadhurley25075-p… #localai #heterogeneousinference #dgxspark #applesilicon