crazy world crazy era
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.
Bing W retweeted
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.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
Live from OpenAI DevDay 2026: Keynote youtube.com/live/Fls_onRviPM… via @YouTube
everyone is doing now
Bing W retweeted
Hao Zhang 离开 Meta,加入 Discovery Loop。
他在 Meta 超级智能实验室待了多久?一个夏天。
今年他从 UCSD 学术休假进了 Meta 的 TBD Lab,
做 Muse 图像和视频的推理。离职帖里他写:
"有一个版本的我,本可以在这儿待上很久很久。
新的使命在等着。"
新的使命是 Discovery Loop——Jeff Dean 八月刚办的公司。创始人四个:Jeff Dean、Sanjay Ghemawat、Quoc Le、Oriol Vinyals。Jeff Dean 为这个离开了待了近 27 年的 Google。
Radical 和 Khosla 联合领投。据 BI 报道,现在在谈的估值
是 500 亿美元: 公司成立才一个多月。
他们要做的事:让 AI 自己提出实验、自己跑、自己学、
自己迭代,成千上万个实验并行。第一步是把机器学习研究
而 Hao Zhang 这几年做的东西,一件件都成了别人的默认选项:
vLLM 是现在跑大模型推理的标准引擎,Chatbot Arena 是
全世界给模型排名的地方,Vicuna 是开源对话模型的起点。
他自己把这条线写成:
Vicuna → Chatbot Arena → vLLM → DistServe → LMGame → FastVideo
今年他还拿了 Sloan Fellow、NSF CAREER、EuroSys 时间检验奖。
要让 AI 替人做 ML 研究,总得先有人把 ML 研究的地基修完。
更多信息, 请看: analysis.dinq.me
Career update: this Wednesday was my last day at @Meta Superintelligence Lab.
Earlier this year, I took academic leave from @UCSanDiego and joined Meta TBD Lab. It ended up being one of the best summers I've had in years -- honestly, I couldn't have asked for a better place to spend it.
TBD is a very special team. I got to work with some of the strongest engineers and minds, on problems that impact billions of people! ❤️
A huge part of that is @yinghai. He is one of the best infra leaders: deeply technical, ambitious, and is incredibly good at setting up an environment so the team can just build. And also @_tim_brooks, one of the greatest mind that starts the entire videogen industry 👍!
I'm particularly proud to have helped push Muse image and Muse video inference and watch it scale so rapidly. And on the product side, it has been amazing to watch Meta ships @Muse -- IMO one of the most impressive consumer agents out there -- and see it grows this fast. 🎉🎉
I will miss this team a lot, and there was a version of me that could happily have stayed here for a very, very long time. 😥
Best summer spent at @Meta superintelligence lab 🥹.
A new mission awaits. 🫡
Bing W retweeted
excuse me I must correct the record on this slander
🤖 Made with AI
You guys think @alexandr_wang is underdressed but he's literally flexing on you like crazy lol
There are levels to this
Readers added context they thought people might want to know
None of these clothing items exists. Hermes doesn’t make hunter t-shirts, Loro Piana doesn’t make zipper sweatpants, and Ferragamo doesn’t make high-end crocs. us.loropiana.com/en/ ferragamo.com/shop/us/en/men…
Bing W retweeted
you wouldn’t believe the kind of shit muse is getting invited to these days
🤖 Made with AI
very interesting
When I look back at the earliest @Muse mocks, the very first thing that was designed was the “status” pill at the top of the screen. That one tiny piece of UI survived every subsequent revision, many months on.
It remains one of my favorite parts of the design. Its purpose is to simply and clearly answer the question: what is my agent doing right now? And do that across every surface of the app.
Representing status in this way is more of a messaging pattern and was kind of controversial early on. Most AI apps had coalesced around “chain of thought” as the way to represent ongoing work, which almost always appeared at the bottom of the thread itself. I had a petulant aversion to this and it didn’t work anyway, given the initial app was already three tabs (feed, chat, library). A persistent status display was necessary.
The initial version didn’t have the avatar and was only capable of ~6 deterministic states. Even in that basic form it was incredibly useful during testing – just showing connection status was critical in those early days.
Excited to share some more stories about the design of Muse. I haven’t tweeted in a few years as I’ve just been working away, but I think this will be a fun story.
Bing W retweeted
Here is a walkthrough of Muse! Excited to share more about the personal agent we’ve been working on.
muse.ai
Lining up two spreadsheets usually means: copy, paste, VLOOKUP, repeat. 🥹😮💨
We didn't. We just told Zoom Sheets what we needed — even when something broke along the way.
Watch how it played out 👇
#ZoomSheets
Bing W retweeted
Sam Altman (CEO of OpenAI):
"You don't need to write prompts anymore. You need loops and graphs that write them for you."
In 78 minutes he explains how to use LLMs better than almost anyone does
Prompts → Agents → Loops → Graphs
most people are still rewriting the same message and calling it work
he stopped writing messages and built the thing that writes them instead
same model, same tokens, completely different week
watch it today
the full graph engineering guide is below, save it while it is still early ↓






























