エピソード

  • On China's open source AI trajectory

    On China's open source AI trajectory
    2025/09/09
    Hello everyone! I’m coming back online after two weeks of vacation. Thankfully it coincided with some of the slowest weeks of the year in the AI space. I’m excited to get back to writing and (soon) share projects that’ll wrap up in the last months of the year.It seemed like a good time to remind people of the full set of housekeeping for Interconnects. * Many people love the audio version of the essays (read by me, not AI). You can get them in your podcast player here. Paid subscribers can add private podcast feeds under “manage your subscription” where voiceover is available for paywalled posts.* The Interconnects Discord for paid subscribers continues to get better, and is potentially the leading paid perk amid the fragmentation of Twitter etc.* We’re going to be rolling out more perks for group subscriptions and experimental products this fall. Stay tuned, or get in touch if group discounts are super exciting for your company. For the time being, I’m planning trips and meetups across a few conferences in October. I’ll be speaking at The Curve (Oct. 3-5, Berkeley), COLM (Oct. 7-10, Montreal, interest form), and the PyTorch Conference (Oct. 21-24, SF) on open models, Olmo, and the ATOM Project, so stay tuned for meetups and community opportunities. On to the post!China is maneuvering to double down on its open AI ecosystem. Depending on how the U.S. and its allies change culture and mobilize investment, this could make the dominance of Chinese AI models this summer, from Qwen, Kimi, Z.ai, and DeepSeek, looks like foreshadowing rather than the maximum gap in open models between the U.S. and China. Until the DeepSeek moment, AI was likely a fringe issue to the PRC Government. The central government will set guidelines, rules, budgets, and focus areas that will be distributed and enforced across the decentralized government power structures. AI wasn’t a political focus and the strategy of open-source was likely set by companies looking to close the gap with leading American competitors and achieve maximum market share in the minimum time. I hear all the time that most companies in the U.S. want to start with open models for IT and philosophical reasons, even when spinning up access to a new API model is almost effortless, and it’s likely this bias could be even higher internationally where spending on technology services is historically lower.Most American startups are starting with Chinese models. I’ve been saying this for a while, but a more official reference for this comes from a recent quote from an a16z partner, Martin Casado, another vocal advocate of investment in open models in America. He was quoted in The Economist with regards to his venture portfolio companies:“I’d say 80% chance [they are] using a Chinese open-source model.”The crucial question for the next few years in the geopolitical evolution of AI is whether China will double down on this open-source strategy or change course. The difficulty with monitoring this position is that it could look like nothing is happening and China maintains its outputs, even when the processes for creating them are far different. Holding a position is still a decision.It’s feasible in the next decade that AI applications and open models are approached with the same vigor that China built public infrastructure over the last few decades (Yes, I’m reading Dan Wang’s new book Breakneck). It could become a new area that local officials compete in to prove their worth to the nation — I’m not sure even true China experts could make confident predictions here. A large source of uncertainty is whether the sort of top-down, PRC edicts can result in effective AI models and digital systems, where government officials succeeded in the past with physical infrastructure.At the same time as obvious pro-AI messaging, Chinese officials have warned of “disorderly competition” in the AI space, which is an indirect signal that could keep model providers releasing their models openly. Open models reduce duplicative costs of training, help the entire ecosystem monitor best practices, and force business models that aren’t reliant on simple race-to-the-bottom inference markets. Open model submarkets are emerging for every corner of the AI ecosystem, such as video generation or robotic action models, (see our coverage of open models, Artifacts Logs) with a dramatic evolution from research ideas to mature, stable models in the last 12-18 months.China improving the open model ecosystem looks like the forced adoption of Chinese AI chips, further specialization of companies’ open models to evolving niches, and expanded influence on fundamental AI research shared internationally. All of these directions have early signs of occurring.If the PRC Government wanted to exert certain types of control on the AI ecosystem — they could. This Doug Guthrie excerpt from Apple in China tells the story from the perspective of international companies. ...
    続きを読む 一部表示
    14 分
  • Ranking the Chinese Open Model Builders

    Ranking the Chinese Open Model Builders
    2025/08/17
    The Chinese AI ecosystem has taken the AI world by storm this summer with an unrelenting pace of stellar open model releases. The flagship releases that got the most Western media coverage are the likes of Qwen 3, Kimi K2, or Zhipu GLM 4.5, but there is a long-tail of providers close behind in both quality and cadence of releases.In this post we rank the top 19 Chinese labs by the quality and quantity of contributions to the open AI ecosystem — this is not a list of raw ability, but outputs — all the way from the top of DeepSeek to the emerging open research labs. For a more detailed coverage of all the specific models, we recommend studying our Artifacts Log series, which chronicles all of the major open model releases every month. We plan to revisit this ranking and make note of major new players, so make sure to subscribe.At the frontierThese companies rival Western counterparts with the quality and frequency of their models.DeepSeekdeepseek.com | 🤗 deepseek-ai | X @DeepSeek_AIDeepSeek needs little introduction. Their V3 and R1 models, and their impact, are still likely the biggest AI stories of 2025 — open, Chinese models at the frontier of performance with permissive licenses and the exposed model chains of thought that enamored users around the world.With all the attention following the breakthrough releases, a bit more has been said about DeepSeek in terms of operations, ideology, and business model relative to the other labs. They are very innovative technically and have not devoted extensive resources to their consumer chatbot or API hosting (as judged by higher than industry-standard performance degradation).Over the last 18 months, DeepSeek was known for making “about one major release a month.” Since the updated releases of V3-0324 and R1-0528, many close observers have been surprised by their lack of contributions. This has let other players in the ecosystem close the gap, but in terms of impact and actual commercial usage, DeepSeek is still king.An important aspect of DeepSeek’s strategy is their focus on improving their core models at the frontier of performance. To complement this, they have experiments using their current generation to make fundamental research innovations, such as theorem proving or math models, which ultimately get used for the next iteration of models. This is similar to how Western labs operate. First, you test a new idea as an experiment internally, then you fold it into the “main product” that most of your users see.DeepSeekMath, for example, used DeepSeek-Coder-Base-v1.5 7B and introduced the now famous reinforcement learning algorithm Group Relative Policy Optimization (GRPO), which is one of the main drivers of R1. The exception to this (at least today) is Janus, their omni-modal series, which has not been used in their main line.Qwenqwenlm.ai | 🤗 Qwen | X @Alibaba_QwenTongyi Qianwen, the primary AI lab within Alibaba’s cloud division, is by far and away most known for their open language model series. They have been releasing many models across a range of sizes (quite similar to Llama 1 through 3) for years. Recently, their models from Qwen 2.5 and Qwen 3 have had accelerating market share among AI research and startup development.Qwen is closer to American Big Tech companies than to other Chinese AI labs in terms of releases: They are covering the entire stack, from VLMs to embedding models, coding models, image and video generation, and so on.They also cater to all possible customers (or rather every part of the open community) by releasing capable models of all sizes. Small dense models are important for academia to run experiments and for small/medium businesses to power their applications, so it comes to no surprise that Qwen-based models are exploding in popularity.On top of model releases for everyone, they also focused on supporting the (Western) community, releasing MLX and GGUF versions of their models for local usage or a CLI for their coding models, which includes a generous amount of free requests.Unlike some American companies, the core team seems to have stayed relatively small in terms of headcount, in line with other Chinese AI labs: Qwen3 has 177 contributors, whereas Llama 3 has thrice the amount, while Gemini 2.5 has over 3,000 people as part of the model. Close competitorsThese companies have recently arrived at the frontier of performance and we will see if they have the capability to consistently release great models at a pace matching Qwen or DeepSeek.Moonshot AI (Kimi)moonshot.cn | 🤗 moonshotai | X @Kimi_MoonshotMoonshot AI is one of the so-called “AI tigers”, a group of hot Chinese AI startups determined by Chinese media and investors. This group consists of Baichuan, Zhipu AI, Moonshot AI, MiniMax, StepFun, and 01.AI — most of which have attracted investments by tech funds and other tech grants. For example, Alibaba is seen as a big winner in the AI space by having their own models and by being a ...
    続きを読む 一部表示
    13 分
  • Contra Dwarkesh on Continual Learning

    Contra Dwarkesh on Continual Learning
    2025/08/15
    Dwarkesh Patel’s now well-read post on why he is extending his AI timelines focuses on the idea of continual learning. If you ask me, what we have already is AGI, so the core question is: Is continual learning a bottleneck on AI progress?In this post, I argue that continual learning as he describes it actually doesn’t matter for the trajectory of AI progress that we are on. Continual learning will eventually be solved, but in the sort of way that a new type of AI will emerge from it, rather than continuing to refine what it means to host ever more powerful LLM-based systems. Continual learning is the ultimate algorithmic nerd snipe for AI researchers, when in reality all we need to do is keep scaling systems and we’ll get something indistinguishable from how humans do it, for free.To start, here’s the core of the Dwarkesh piece as a refresher for what he means by continual learning.Sometimes people say that even if all AI progress totally stopped, the systems of today would still be far more economically transformative than the internet. I disagree. I think the LLMs of today are magical. But the reason that the Fortune 500 aren’t using them to transform their workflows isn’t because the management is too stodgy. Rather, I think it’s genuinely hard to get normal humanlike labor out of LLMs. And this has to do with some fundamental capabilities these models lack.I like to think I’m “AI forward” here at the Dwarkesh Podcast. I’ve probably spent over a hundred hours trying to build little LLM tools for my post production setup. And the experience of trying to get them to be useful has extended my timelines. I’ll try to get the LLMs to rewrite autogenerated transcripts for readability the way a human would. Or I’ll try to get them to identify clips from the transcript to tweet out. Sometimes I’ll try to get them to co-write an essay with me, passage by passage. These are simple, self contained, short horizon, language in-language out tasks - the kinds of assignments that should be dead center in the LLMs’ repertoire. And they're 5/10 at them. Don’t get me wrong, that’s impressive.But the fundamental problem is that LLMs don’t get better over time the way a human would. The lack of continual learning is a huge huge problem. The LLM baseline at many tasks might be higher than an average human's. But there’s no way to give a model high level feedback. You’re stuck with the abilities you get out of the box. You can keep messing around with the system prompt. In practice this just doesn’t produce anything even close to the kind of learning and improvement that human employees experience.The core issue I have with this argument is the dream of making the LLMs we’re building today look more like humans. In many ways I’m surprised that Dwarkesh and other very AGI-focused AI researchers or commentators believe this — it’s the same root argument that AI critics use when they say AI models don’t reason. The goal to make AI more human is constraining the technological progress to a potentially impossible degree. Human intelligence has long been the inspiration for AI, but we have long surpassed it being the mirror we look to for inspiration. Now the industry is all in on the expensive path to make the best language models it possibly can. We’re no longer trying to build the bird, we’re trying to transition the Wright Brothers’ invention into the 737 in the shortest time frame possible.To put it succinctly. My argument very much rhymes with some of my past writing. Do language models reason like humans? No. Do language models reason? Yes. Will language model systems continually learn like humans? No.Will language model systems continually learn? Of course.Interconnects is a reader-supported publication. Consider becoming a subscriber.Dwarkesh writes “Rather, I think it’s genuinely hard to get normal humanlike labor out of LLMs.” This is because we’re still early on the buildout of the technology. Human labor takes an immense amount of context and quick thinking, both of which we’re starting to unlock with our language models. On top of this, human labor may not be what we want to create — we want to augment it. Using LLMs as drop in replacements for humans is not a requirement for AGI nor is what Dwarkesh describes a fundamental limitation on AI progress. Francois Chollet cleverly poked at this weakness in his recent conversation with Dwarkesh at an ARC-AGI event:Well, how do you define the difference between the ability to adapt to a new task and learning on the fly? It's, it sounds like the same thing to me.Language models can already pick up subtle context extremely fast. ChatGPT’s memory feature has gotten far better for me. When we’re using the far more powerful models we can expect in the next 18 months this’ll already start to appear magical. Language models are extremely apt at inferring context even without us giving it to them. Soon we...
    続きを読む 一部表示
    10 分
  • GPT-5 and the arc of progress

    GPT-5 and the arc of progress
    2025/08/07
    If you want a video version of this, check out the last 20 minutes of the livestream reaction (edit, fixed link) I did with Will Brown of Prime Intellect and Swyx of Smol AI & Latent Space.GPT-5 was set up to fail on some of the narratives it was expected to satisfy. The two central themes it had to decide between were the AGI (or superintelligence) narrative that Sam Altman & co. have been using to fundraise and the fact that ChatGPT is one of the fastest-growing consumer technologies of all time. To fulfill both, GPT-5 needed to be AGI while also being cheap enough to serve as the most-used AI system in the world. Business and technological realities made it inevitable that GPT-5’s primary impact would be to solidify OpenAI’s market position, even if it raises a lot of eyebrows for the long-term trajectory of AI.The reactions online capture this as well. The OpenAI live streams have historically catered to AI insiders, but the product speaks entirely to a different audience. The people discussing this release on Twitter will be disappointed in a first reaction, but 99% of people using ChatGPT are going to be so happy about the upgrade. Confusingly enough, this includes many of the critics. GPT-5 is a good AI system. It’s right in line with best-in-class across pretty much every evaluation, while being cheap enough to serve the whole world. OpenAI is largely fixing its product offering with an announcement that was hyped to be one of the biggest AI news cycles of the year. AI news being loud is defined by narratives being different more-so than technology being better. OpenAI releasing an open model again will likely be pinpointed as just as important a day for the arc of AI as the GPT-5 release. In many ways GPT-5 was set up to fail and that is very off-putting for those expecting maximum AI progress in the near term.I’m not going to dwell on it, but oh boy, that was a messy release. GPT-5 being announced and rolled out like this is very odd. Countless plots were mislabeled, live demos had bugs, and the early rollout is doing some weird stuff. This reinforces how OpenAI was torn about the release and backed into a corner with their messaging. They knew they needed to improve the experience with strong competition in the industry, but releasing GPT-5 needed to make a splash after how long they’ve waited (and already parked the GPT 4.5 name).The core question we track in this post is: What does it mean for the next 6-18 months of AI progress if GPT-5 is just as good as all the best models out there, e.g., Claude Sonnet for coding or o3 for search, funneled into one, super cheap package? If AGI was a real goal, the main factor on progress would be raw performance. GPT-5 shows that AI is on a somewhat more traditional technological path, where there isn’t one key factor, it is a mix of performance, price, product, and everything in between. Interconnects is a reader-supported publication. Consider becoming a subscriber.GPT-5’s performanceThere are a few places that we can see that GPT-5 represents a solid step on the performance trend line, but nothing like a step change. First, on LMArena, GPT-5 is fantastic, sweeping the board to #1 on all categories. The last model to claim #1 in pretty much every category was Gemini 2.5 Pro — and that was the biggest step change in Elo since GPT-4 Turbo skyrocketed past the first Claude.Second, GPT-5 is the top model on the ArtificialAnalysis composite benchmark.These two, LMArena & ArtificialAnalysis, represent two coarse evaluations — community vibes and raw benchmarks. Both of these can be gamed, but are still correlated with real-world use. You can also see in OpenAI’s shared results how much the smaller versions improve on the likes of GPT-4.1 mini and o4-mini.In many ways, the march of progress on evals has felt slowed for a while because model releases are so frequent and each individual step is smaller. Lots of small steps make for big change. The overall trend line is still very positive, and multiple companies are filling in the shape of it. My post on “what comes next” from earlier this summer all but called this type of release, where the numbers aren’t shocking but the real world use cases are great, becoming more common.This is a different path for the industry and will take a different form of messaging than we’re used to. More releases are going to look like Anthropic’s Claude 4, where the benchmark gains are minor and the real world gains are a big step. There are plenty of more implications for policy, evaluation, and transparency that come with this. It is going to take much more nuance to understand if the pace of progress is continuing, especially as critics of AI are going to seize the opportunity of evaluations flatlining to say that AI is no longer working.To say it succinctly: Abilities will develop more slowly than products.The product overhang is being extended with each release. We’re still building untapped...
    続きを読む 一部表示
    11 分
  • gpt-oss: OpenAI validates the open ecosystem (finally)

    gpt-oss: OpenAI validates the open ecosystem (finally)
    2025/08/05
    OpenAI released two open-weight, text-only reasoning models today, both mixture of experts (MoE) sized to run efficiently on a range of hardware from consumer GPUs to the cloud. These models have the Apache 2.0 license, so they’re available for distillation into other reasoning models, deployment into commercial products, and are free of downstream restrictions. These two models, the smaller gpt-oss-20B with 3.6B active parameters and 21B total and the larger gpt-oss-120B with 5.1B active parameters, follow the trends we’ve seen with the other leading open models in architecture choices. Where this release shines is in the dramatic change in open model performance and strategy that comes with the leading name in AI releasing an open model that undercuts some of their own API products.We’ll get to the technical details on the model later, but the main point of this post is how much OpenAI has changed by releasing their first open language model since GPT-2. The larger 120B model “achieves near-parity with OpenAI o4 mini on core reasoning benchmarks‬” and is a major moment for the ecosystem:* OpenAI has released an open model at the frontier of current open model performance — highlighting how major concerns over open models that OpenAI leadership mentioned in 2023 were overblown. The marginal risks of open models have been shown to not be as extreme as many people thought (at least for text only — multimodal is far riskier). Once other organizations, particularly Meta and China showed OpenAI that there was no risk here, the path was opened to release a model.* OpenAI has revealed far more of their technical stack than any release to date. This blog post has light details on many things in the model, but community tinkering will begin to better understand what is going on here. This includes basic things like our first time seeing a raw chain of thought (CoT) for an OpenAI reasoning model, but also more interesting things like how this model is trained to use tools in the CoT like their o3 model. Other details include researchers being able to play with OpenAI’s instruction hierarchy in raw weights (where pieces of it are untouchable in the API), a new “harmony” prompt format, the same “reasoning efforts” of low, medium & high from the API, a huge proof of concept on how far basic, community standard architectures with MoEs can be pushed, and other small details for the AI community to unpack.* OpenAI has initiated a scorched earth policy on the API market, undercutting their own offerings and unleashing an extremely strong, trusted model brand with a permissive license. While adoption of any open model is much slower than an API due to testing, additional configuration, etc., this is set up to go about as fast as it can. Any API model that competes with current models like OpenAI o4 mini, Claude Haiku, Gemini Flash, DeepSeek R1 etc. are all going to have to compete with this model. OpenAI’s o4 mini model is currently served at $1.1 per million input tokens and $4.4 per million output. Serving this open model will likely cost at least 10x less. There are many potential strategic reasons for this, all of which paint OpenAI as having a clearer vision of what makes it valuable. What OpenAI hasn’t touched with this model is interesting too — “For those seeking multimodal support, built-in tools, and‬ seamless integration with our platform, models available through our API platform remain the‬ best option.” These are dropped for reasons above, and “headaches” discussed later in the post.Together, these paint a much clearer vision by OpenAI on how they’ll control the AI ecosystem. The top potential reasons on my mind are:* OpenAI could be trying to make all API models potentially obsolete on cost ahead of the GPT-5 release, which they hope to capture the top end of the market on. Or,* OpenAI could be realizing that models are no longer their differentiation, as ChatGPT users continue to steadily climb — and they’ll soon pass 1 billion weekly actives.There are plenty of other reasons, such as the politics alluded to at the end of the blog post, but OpenAI tends to only act when it serves them directly — they’ve always been a focused company on their goals.There’s also a long list of head scratchers or in-between the lines points that illuminate OpenAI’s strategy a bit more. OpenAI of course didn’t release training data, code, or a technical report, as expected. OpenAI is trying to make a big splash with the name that captures more of the enterprise market, but in doing so takes some collateral damage in the research and true “open source” AI communities. These future questions include:* The naming is bad — a mixture of cringe, confusion-inducing, and still useful for their marketing goals. For anyone following open-source AI for a long time it won’t be new that a major company is blurring the association of the term open-source with the ...
    続きを読む 一部表示
    14 分
  • Towards American Truly Open Models: The ATOM Project

    Towards American Truly Open Models: The ATOM Project
    2025/08/04
    I’m very excited to share a substantial project on invigorating investment in open language models and AI research in the U.S. The ATOM (American Truly Open Models) Project is the mature evolution of my original “American DeepSeek Project” and I hope it can help be a turning point in the current trajectory of losing open model relevance vis-a-vis China, and even the rest of the world.I’ve included the full text below, but I encourage you to visit the website for the full version with added visuals, data, and a place to sign your support. This is a community movement, rather than me fundraising, starting an organization, or anything like thatIf you can help get the word out and or sign your support, I’d greatly appreciate it. (Or watch a 5 minute overview on YouTube)The ATOM Project: Towards fully open models for US research & industryReinvigorating AI research in the U.S. by building leading, open models at homeAmerica's AI leadership was built by being the global hub and leading producer of open AI research, research which led directly to innovations like the Transformer architecture, ChatGPT, and the latest innovations in reasoning models and agents. America is poised to lose this leadership to China, in a period of geopolitical uncertainty and rising tensions between these two nations. America's best AI models have become more closed and restricted, while Chinese models have become more open, capturing substantial market share from businesses and researchers in the U.S. and abroad.Open language models are becoming the foundation of AI research and the most important tool in securing this leadership. America has lost its lead in open models – both in performance and adoption – and is on pace to fall further behind. The United States must lead AI research globally, and we must invest in making the tools our researchers need to do their job here in America: a suite of leading, open foundation models that can re-establish the strength of the research ecosystem.Recommendation: To regain global leadership in open source AI, America needs to maintain at least one lab focused on training open models with 10,000+ leading-edge GPUs. The PRC currently has at least five labs producing and releasing open models at or beyond the capabilities of the best U.S. open model. Regaining open source leadership is necessary to drive research into fundamental AI advances, to maximize U.S. AI market share, and to secure the U.S. AI stack.OverviewOpen language model weights and data are the core currency of recent AI research – these are the artifacts that people use to come up with new architectures, training paradigms, or tools that will lead to the next paradigms in AI to rival The Transformer or Inference-time Scaling. These research advances provide continued progress on existing products or form the basis for new technology companies. At the same time, open language models create potential for a broader suite of AI offerings by allowing anyone to build and modify AI how they see fit, without their data being sent through the cloud to a few, closed model providers.Open language models are crucial for long-term competition within American industry. Today, substantial innovation is happening inside of large, closed AI laboratories, but these groups can only cover so many of the potential ideas. These companies spend the vast majority of their resources focusing on the next model they need to train, where the broader, open research community focuses on innovations that’ll be transformative in 2, 5, 10, or more years. The most progress in building useful, intelligent AI systems will come when the most people can participate in improving today's state-of-the-art, rather than the select few at certain companies.The open AI ecosystem (regarding the models, not to be confused with the company OpenAI) has historically been defined by many parties participating. The United States emerged as a hub of the deep learning revolution via close collaboration between leading technology companies and academic institutions. Following ChatGPT, there have been countless contributions from around the globe. This distribution of impact on research has been collapsing towards clear Chinese leadership due to their commitment to open innovation, while a large proportion of leading scientists working in the United States have joined closed research organizations.The playbook that led Google to invent and share the Transformer – the defining language model architecture of which all leading models such as ChatGPT, Gemini, or Claude are derived from – is now the standard mode of operation for Chinese companies, but it is increasingly neglected by American companies.The impact of China’s models and research are growing because the institutions focused on open models have access to substantial compute resources for training – e.g. some have formed a close relationship between leading AI training laboratories and academic ...
    続きを読む 一部表示
    22 分
  • Interviewing Ross Taylor on the state of AI: Chinese open models, scaling reasoning, useful tools, and what comes next

    Interviewing Ross Taylor on the state of AI: Chinese open models, scaling reasoning, useful tools, and what comes next
    2025/07/29
    I’m excited to welcome Ross Taylor back on the podcast (and sorry for the lack of episodes in general – I have a lot going on!). The first time Ross came on we focused on reasoning – before inference-time scaling and that sort of RL was popular, agents, Galactica, and more from his Llama days. Since then, and especially after DeepSeek R1, Ross and I have talked asynchronously about the happenings of AI, so it’s exciting to do it face to face.In this episode we cover some of everything:* Recent AI news (Chinese models and OpenAI’s coming releases)* “Do and don’t” of LLM training organizations* Reasoning research and academic blind spots* Research people aren’t paying enough attention to* Non language modeling news & other topicsListen on Apple Podcasts, Spotify, YouTube, and where ever you get your podcasts. For other Interconnects interviews, go here.Show outline as a mix of questions and edited assertions that Ross sent me as potential topics.00:00 Recent AI newsRelated reading is on Kimi’s K2 model, thoughts on OpenAI’s forthcoming open release.* What did you think of Z.ai’s GLM 4.5 model (including MIT licensed base model) with very strong scores? And Kimi?* What will OpenAI’s open model actually be?* What do you make of the state of the ecosystem?12:10 “Do and don’t” of LLM training organizationsRelated reading is on managing training organizations or the Llama 4 release.This is one of my favorite topics – I think a lot of great stuff will be written on it in the future. For now, Ross asserts…* Most major LLM efforts are not talent-bound, but politics-bound. Recent failures like Llama 4 are org failures not talent failures.* Most labs are chaotic, changing direction every week. Very different picture from the narrative presented online.* Most labs represent investment banks or accountancy firms in that they hire smart young people as “soldiers” and deliberately burn them out with extremely long hours.36:40 Reasoning research and academic blind spotsRelated reading is two papers point questions at the Qwen base models for RL (or a summary blog post I wrote).I start with: What do you think of o3, and search as something to train with RL?And Ross asserts…* Most open reasoning research since R1 has been unhelpful - because not enough compute to see what matters (underlying model and iterations).* Best stuff has been simple tweaks to GRPO like overlong filtering and removing KL divergence.* Far too much focus on MATH and code - AIME has tens of samples too so is very noisy.* People are generally building the wrong kind of environments - like puzzles, games etc - instead of thinking about what kind of new capabilities they’d like to incentivise emerging.50:20 Research people aren’t paying enough attention toThe research area I hear the most about right now is “rubrics” – a per-prompt specialized LLM-as-a-judge to replace reward models. SemiAnalysis reported OpenAI scaling this approach and lots of great research is coming out around it.I start with: What do you think of the state of RL scaling and generalization? What of models losingRoss asserts…* Rubrics are underhyped on social media - they were driving force behind projects like DeepResearch - and GenRMs are interesting but perhaps slightly overhyped.* There is an evals crisis - there are not enough high quality evals, particularly for frontier tasks like automating research and real life work. Impediment to anyone building agents or ASI.01:02:46 Extra stuff!I ask Ross: What AI are you using today? Why?To conclude, Ross wanted to discuss how AlphaEvolve has been underhyped on social media, and means the future isn’t just RL. Shows there are other effective ways to use inference compute.Interconnects is a reader-supported publication. Consider becoming a subscriber.TranscriptCreated with AI, pardon the minor typos, not quite enough time this week but I’m hiring someone to help with this soon!Nathan Lambert: Hey, Ross. How's it going? Welcome back to Interconnects. I took a many month break off podcasting. I've been too busy to do all this stuff myself.Ross Taylor: Yeah, I was trying to think of all the things that happened since the last time we did a podcast a year ago. In AI time, that's like two hundred years.Nathan Lambert: Yeah, so I was looking at it. We talked about reasoning and o1 hadn’t happened yet.For a brief intro, Ross was a co-founder of Papers with Code, and that brought him to Meta. And then at Meta, he was a lead on Galactica, which was a kind of language model ahead of its time relative to ChatGPT. So if people don't know about Galactica, there's a great paper worth reading. And then he was doing a bunch of stuff on reasoning with Llama related to a lot of the techniques that we'll talk about in this episode.And now he's doing a startup. I don't know if he wants to talk about this, but generally, we talk a lot about various things. This got started through o1 and trying to ...
    続きを読む 一部表示
    1 時間 15 分
  • The White House's plan for open models & AI research in the U.S.

    The White House's plan for open models & AI research in the U.S.
    2025/07/23
    Today, the White House released its AI Action Plan, the document we’ve been waiting for to understand how the new administration plans to achieve “global dominance in artificial intelligence (AI).” There’s a lot to unpack in this document, which you’ll be hearing a lot about from the entire AI ecosystem. This post covers one narrow piece of the puzzle — its limited comments on open models and AI research investment.For some context, I was a co-author on the Ai2 official comment to the Office of Science and Technology Policy (OSTP) for the AI Action Plan and have had some private discussions with White House staff on the state of the AI ecosystem.A focus of mine through this document is how the government can enable better fully open models to exist, rather than just more AI research in general, as we’re in a shrinking time window where if we don’t create better fully open models then the academic community could be left with a bunch of compute to do research on models that are not reflective of the frontier of performance and behavior. This is why I give myself ~18 months to finish The American DeepSeek Project.Important context for this document is to consider what the federal government can actually do to make changes here. The executive branch has limited levers it can pull to disperse funding and make rules, but it sends important signaling to the rest of the government and private sector.Overall, the White House AI Action Plan comes across very clearly that we should increase investment in open models, and for the right reasons.This reflects a shift from previous federal policy, where the Biden executive order had little to say about open models other than them getting grouped into models needing pre-release testing if they were trained with more than 10^26 FLOPS (which led to substantial discussion on the general uselessness of compute thresholds as a policy intervention). Later, the National Telecommunications and Information Administration (NTIA) released a report from under the umbrella of the Biden Administration that was far more positive on open models, but much more limited in the scope of its ability for agenda setting.This is formatted as comments in line with the full text on open models and related topics in the action plan. Let’s dive in, any emphasis in italics is mine.Encourage Open-Source and Open-Weight AIOpen-source and open-weight AI models are made freely available by developers for anyone in the world to download and modify. Models distributed this way have unique value for innovation because startups can use them flexibly without being dependent on a closed model provider. They also benefit commercial and government adoption of AI because many businesses and governments have sensitive data that they cannot send to closed model vendors. And they are essential for academic research, which often relies on access to the weights and training data of a model to perform scientifically rigorous experiments.This covers three things we’re seeing play out with open models and is quite sensible as an introduction:* Startups use open models to a large extent because pretraining themselves is expensive and modifying the model layer of the stack can provide a lot of flexibility with low serving costs. Today, most of this happens on Qwen at startups, where larger companies are more hesitant to adopt Chinese models.* Open model deployments are slowly building up around sensitive data domains such as health care. * Researchers need strong and transparent models to perform valuable research. This is the one I’m most interested in, as it is the one with the highest long-term impact by determining the fundamental pace of progress in the research community.We need to ensure America has leading open models founded on American values. Open-source and open-weight models could become global standards in some areas of business and in academic research worldwide. For that reason, they also have geostrategic value. While the decision of whether and how to release an open or closed model is fundamentally up to the developer, the Federal government should create a supportive environment for open models.The emphasized section is entirely the motivation behind ongoing efforts for The American DeepSeek Project. The interplay between the three groups above is inherently geopolitical, where Chinese model providers are actively trying to develop mindshare with Western developers and release model suites that offer great tools for research (e.g. Qwen). The document is highlighting why fewer open models exist right now from leading Western AI companies, simply “the decision of whether and how to release an open or closed model is fundamentally up to the developer” — this means that the government itself can mostly just stay out of the way of leading labs releasing models if we think the artifacts will come from the likes of Anthropic, OpenAI, Google, etc. The other side of this is that ...
    続きを読む 一部表示
    13 分