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.

How it started How its going :)

Did you know your ₹100 in the bank isn’t sitting in a vault? It’s lent, spent, and re-deposited within hours. One printed ₹100 turns into ₹500 across the economy. Banks decide how much. India runs this slower than the rest of the world. 50% credit-to-GDP against a global 148%. On purpose. The instinct was built in 1969 and never left. It’s the shield that kept India out of 2008. It’s also the ceiling. You can’t fully have one without the other..

A startup in China packed roughly 1,000 Mac Mini M4 computers into a single data center to run AI workloads without paying ongoing cloud fees. The base Mac Mini M4 starts at $599 and draws just 10 to 30 watts under load, a fraction of the 300 to 500 watts a traditional GPU server pulls.

“The model alone is no longer the product" @AravSrinivas says the real product is now the harness around it: orchestration, tools, enterprise context, and cost performance. The post-frontier AI race is about systems, not just the smartest model

OpenAI just shipped the biggest release in their history. I got in before launch. GPT-5.6 family : Sol, Terra, Luna. A new super app that merges @ChatGPTapp and Codex. Three modes: Chat, Work, Code. I sat down with @wulfie_bain_ from @OpenAI and we built a full startup on camera. Pitch deck, working web app, computer-use agents finding and fixing bugs live, and an autonomous 16-hour overnight run. The idea that changes everything: stop writing prompts. Start writing loops. Give the agent a goal and a way to check its own work. That's how a model runs overnight and comes back finished. We also answer the real questions what you build that OpenAI won't kill, tokens vs engineers, and why India wins in this era. Coder or not watch this one.

Imagine a fable 5 quality model that’s 3-4x less expensive in less than 6 months. And an Opus 4.8 grade model that can run on a local device in less than 12 months. Greater than 50% chance that these events will happen. Worth keeping in mind when you make predictions about the future.

Perplexity CEO Aravind Srinivas on why the equation 1.01^365 = 37.78 is his team's core mantra:

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