LLMs @ Microsoft Superintelligence. ex research scientist at Augment Code. PhD from UT Austin.

Joined April 2016
Jiayi Wei 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.
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Nowadays I can easily get 2X done when I can just talk to Codex with voice input. Then the bottleneck shifts to the speed of my thought. 🦦🚀
Grateful to have worked on the research and model training for these transcription models with an incredible team. 🚀 As a heavy Codex user, I rely on transcription throughout my day—from coding at my desk to talking to Codex in the car with music playing. What makes it truly useful isn’t just that it works reliably in real-world noisy environments, but that it’s context-aware: understanding domain-specific terminology, colleague names, mixed-language conversations, and my accents. That’s the kind of experience that makes AI feel like it can genuinely fit into everyday life.
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Jiayi Wei retweeted
Fifteen years ago, @Coursera and online courses changed education. It worked better than almost anyone expected, expanding access by opening up where you can learn. But how you learn remains largely the same as it has for centuries: it is still one-size-fits-all, taught the same way to each person who shows up. We now have an opportunity to change how learning happens. With advances in AI, we can now build a custom learning guide for each person. We will turn learning from one‑to‑many to one‑to‑one. I'm starting LearnVector to invent this next generation of learning. We are starting with a $100M investment from Coursera, and plan to collaborate closely with Coursera and Udemy. Good learning needs much more than just a chatbot. Research shows that chatbots without guardrails harm learning. They help complete tasks and enable students to do better on homework. But cognitive offloading to a chatbot results in them being less skilled. And, you cannot always trust what a chatbot tells you. In contrast, LearnVector will plan a path with you, adapt to how you learn, and patiently stay with you until you’ve mastered new skills. One thing has not changed in all this time. People want learning they can trust: material that is accurate, relevant, and worth the effort you put into it. Anything less wastes the most valuable thing a learner has: time. Coursera has a trusted library of materials from authoritative sources. LearnVector plans to work with Coursera to bring this trustworthy learning to everyone. I'm grateful to Greg Hart and the entire Coursera team for supporting LearnVector. I look forward to working with our talented team to change how we learn, and accelerate human development. learnvector.ai
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Thrilled to share this launch! 🚀 I had the privilege of leading the post-training efforts for ChatGPT’s new dictation model. It truly took an incredible team to make this launch happen. So proud of what we accomplished together. More to come!
This week's ChatGPT new feature drop - June 26: 1/ New dictation model – more accurate at transcribing languages and accents and in particular Japanese, Korean, Chinese, Urdu, Vietnamese 2/ Refreshed 'composer' on the web – in particular a new Plus (+) menu and type @ to quickly connect to services like Gmail, add images or search the web. 3/ New 5.5 instant model - Improvements shipped yesterday; should feel smarter generally 4/ Polish to our sidebar on mobile – we simplified it, refreshed icons and typography, and it now opens above the conversation without fully covering it The team is shipping weekly improvements to Chat, and we listen to all the feedback so keep sharing!
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I feel incredibly honored to have contributed to this work alongside the most talented and hardworking team I’ve ever worked with. @MicrosoftAI Building and climbing an LLM from scratch was full of unknowns, but there are also many magical moments when things finally worked. Excited to share what we learned and give back to the community! microsoft.ai/wp-content/uplo…
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Jiayi Wei retweeted
Excited to share as many details on what we @MicrosoftAI have been working on. Building a LLM from scratch is an awesome journey with pain and suffering battling unknowns but also many cool moments to see it (somehow) works out every stage! microsoft.ai/wp-content/uplo…
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Jiayi Wei retweeted
WOW microsoft new "MAI Thinking 1" model comes with a 109 page tech report that looks REALLY detailed, this is amazing
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Super excited to announce seven new world-class MAI models today. They represent what we consider a new era in AI designed to keep you in control and on the frontier. First is our text foundation model, MAI-Thinking-1, exceptionally strong on reasoning and SWE tasks. - It’s a 35B active parameter MoE with a 256K context window. Independent human raters on Surge prefer it for overall quality in blind side-by-sides versus Sonnet 4.6, and it’s achieved 97% on AIME 2025, the key measure of its general-purpose reasoning abilities. - It's at 53% on SWE Bench Pro, placing it right alongside Opus 4.6 on one of the toughest coding benchmarks. - And since we co-designed our models with our own silicon, MAI-Thinking-1 is optimized on our MAIA 200 chip. Benchmarking head-to-head against the GB200, we see 30% better performance per dollar as well as a 1.4x performance-per-watt gain when running our MAI models on the MAIA 200 end-to-end. Next is MAI-Image-2.5 and its Flash variant. Two super strong models now at #2 on the leaderboards, surpassing the score of Nano Banana 2 on image editing. Last for now is MAI-Code-1-Flash, our new inference efficient coding model, especially tuned for VS Code and GitHub Copilot CLI. - Code-1-Flash achieves 51% on SWE Bench Pro, despite having just 5B parameters, putting it closer to Haiku in size but cheaper in cost. All of this is the foundation for Microsoft Frontier Tuning. It lets you customize our models to create custom, company-specific agents that only you control. You can make our model, your model. Your data. Your agents. Your moat. Early adopters are already seeing a difference. When we tuned our models for McKinsey’s tasks, MAI delivered the highest win rate, outperforming GPT-5.5 on quality, while being 10x lower on cost. Also really excited to be collaborating with the amazing team at Mayo Clinic to jointly train a new frontier AI model for healthcare. Our announcements today mark another milestone on the road to humanist superintelligence. You can learn more and about our other new models in our latest blog: microsoft.ai/news/building-a…
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Jiayi Wei retweeted
Is your code retriever silently breaking functions with line-based chunking? We developed cAST, our new #EMNLP2025 work, for AST-based code chunking that preserves syntactic structure and semantic boundaries. Paper: arxiv.org/abs/2506.15655 Code: github.com/yilinjz/astchunk
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Jiayi Wei retweeted
> helped millions get into ai before it became mainstream > built coursera and changed online education forever > founded deeplearning. ai to keep teaching the world > leads major ai projects while staying humble and calm > no scandals no noise just steady work > still codes and still experiments > builds small tools and projects purely for curiosity > teaches only when he has real value to add > doesn’t chase hype or predictions > lives quietly learning and building at his own rhythm has andrew ng quietly figured out life better than everyone else?
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UTCS is hiring in all areas, including PL! Please DM me if you are on the job market this year and interested in joining our wonderful department :)
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Passionate about frontier AI models, classical symbolic reasoning, and safe/secure software? Consider applying for this position on AI-aided code analysis in my team at @GoogleDeepmind: job-boards.greenhouse.io/dee…. The job is London-based, and the application deadline is July 14.
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Revoking visas to Chinese PhD students is economically shortsighted and inhumane. Most Chinese PhD students stay in the U.S. after graduation (first image, stats from 2022). They're staying and building technology in the U.S., not taking it to China. Immigrant students create startup companies that employ Americans (second image, stats from 2018). I couldn't find stats for Chinese students specifically but anecdotally they are a significant force powering GenAI and LLM companies. These are the next $1B+ companies that are going to hire thousands of people even as other jobs are automated. There are no real "secrets" in academic GenAI. Everything is open-source. What is the security risk? It's a much higher risk to give DeepSeek and others an advantage in hiring the most talented young researchers in these areas by preventing them from coming to the U.S. Revoking visas for these students destroys American innovation for no benefit. It's also unfair and inhumane. In my experience, Chinese PhD students are hardworking young researchers interested in their fields of study and being part of the cutting edge of academic and industrial innovation. They aren't geopolitical pawns. Stop implementing policies that treat them that way.
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🚀 Claude Sonnet 4 is rolling out in Augment Code! @AnthropicAI's best model now powers every autocomplete, edit, and chat—no extra cost.
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I don’t think the next big leap in software productivity comes from “vibe coding.” It comes from removing the grind in real codebases.@AugmentCode’s new Remote Agent tackles flaky tests, stale docs & tedious refactors—with up to 10 autonomous agents. Learn more: augmentcode.com/
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🚀Introducing CRUST-Bench, a dataset for C-to-Rust transpilation for full codebases 🛠️ A dataset of 100 real-world C repositories across various domains, each paired with: 🦀 Handwritten safe Rust interfaces. 🧪 Rust test cases to validate correctness. 🧵[1/6]
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🧵We just released the #1 open-source agent on the SWE-bench Verified leaderboard by assembling the best of Claude Sonnet 3.7 and O1. Open-source repo here: github.com/augmentcode/augme… Here's how we achieved 65.4% success rate on the hardest coding benchmark in the industry: 🧠👇
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For formal methods folks looking for a new position: @VeridiseInc is hiring a formal methods researcher to work on verification/analysis tools targeting zero-knowledge applications. More details are here: veridise.com/careers/zk-rese…
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🧵 Ever spent hours updating your codebase after changing a single field? We built a feature that does it for you. Here's how Next Edit works:
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