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  • Burning out

    Burning out
    2025/10/25
    One of the obvious topics of the Valley today is how hard everyone works. We’re inundated with comments on “The Great Lock In”, 996, 997, and now even a snarky 002 (midnight to midnight with a 2 hour break). Plenty of this is performative flexing on social media, but enough of it is real and reflecting how trends are unfolding in the LLM space. I’m affected. My friends are affected.All of this hard work is downstream of ever increasing pressure to be relevant in the most exciting technology of our generation. This is all reflective of the LLM game changing. The time window to be a player at the most cutting edge is actually a closing window, not just what feels like one. There are many different sizes and types of models that matter, but as the market is now more fleshed out with resources, all of them are facing a constantly rising bar in quality of technical output. People are racing to stay above the rising tide — often damning any hope of life balance.Interconnects is a reader-supported publication. Consider becoming a subscriber.AI is going down the path that other industries have before, but on steroids. There’s a famous section of the book Apple in China, where the author Patrick McGee describes the programs Apple put in place to save the marriages of engineers traveling so much to China and working incredible hours. In an interview on ChinaTalk, McGee added “Never mind the divorces, you need to look at the deaths.” This is a grim reality that is surely playing out in AI.The Wall Street Journal recently published a piece on how AI Workers Are Putting In 100-Hour Workweeks to Win the New Tech Arms Race. The opening of the article is excellent to capture how the last year or two has felt if you’re participating in the dance:Josh Batson no longer has time for social media. The AI researcher’s only comparable dopamine hit these days is on Anthropic’s Slack workplace-messaging channels, where he explores chatter about colleagues’ theories and experiments on large language models and architecture.Work addicts abound in AI. I often count myself, but take a lot of effort to make it such that work expands to fill available time and not that I fill everything in around work. This WSJ article had a bunch of crazy comments that show the mental limits of individuals and the culture they act in, such as:Several top researchers compared the circumstances to war.Comparing current AI research to war is out of touch (especially with the grounding of actual wars happening simultaneously to the AI race!). What they really are learning is that pursuing an activity in a collective environment at an elite level over multiple years is incredibly hard. It is! War is that and more.In the last few months I’ve been making an increasing number of analogies to how working at the sharp end of LLMs today is similar to training with a team to be elite athletes. The goals are far out and often singular, there are incredibly fine margins between success and failure, much of the grinding feels over tiny tasks that add up over time but you don’t want to do in the moment, and you can never quite know how well your process is working until you compare your outputs with your top competition, which only happens a few times a year in both sports and language modeling.In college I was a D1 lightweight rower at Cornell University. I walked onto a team and we ended up winning 3 championships in 4 years. Much of this was happenstance, as much greatness is, but it’s a crucial example in understanding how similar mentalities can apply in different domains across a life. My mindset around the LLM work I do today feels incredibly similar — complete focus and buy in — but I don’t think I’ve yet found a work environment where the culture is as cohesive as athletics. Where OpenAI’s culture is often described as culty, there are often many signs that the core team members there absolutely love it, even if they’re working 996, 997, or 002. When you love it, it doesn’t feel like work. This is the same as why training 20 hours a week while a full time student can feel easy.Many AI researchers can learn from athletics and appreciate the value of rest. Your mental acuity can drop off faster than your physical peak performance does when not rested. Working too hard forces you to take narrower and less creative approaches. The deeper into the hole of burnout I get in trying to make you the next Olmo model, the worse my writing gets. My ability to spot technical dead ends goes with it. If the intellectual payoffs to rest are hard to see, your schedule doesn’t have the space for creativity and insight.Crafting the team culture in both of these environments is incredibly difficult. It’s the quality of the team culture that determines the outcome more than the individual components. Yes, with LLMs you can take brief shortcuts by hiring talent with years of experience from another frontier lab, but that doesn’t...
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    10 分
  • The State of Open Models

    The State of Open Models
    2025/10/16

    This talk covers everything that’s happened this year in the open model landscape — DeepSeek kickstarting the Chinese open model norms, Llama’s fade, Qwen’s dominance, GPT-OSS — and what comes next. It is my attempt to share what people need to know about where open models are heading, building on all of my research here at Interconnects and in my day job of training these models, in order to help us take the actions we need to steer it in a better direction.

    I strongly recommend watching (or listening, as it’s in the podcast feed) if any of the discussions around open models or Chinese AI impacts your decision making. This felt like one of the better talks I’ve given in a bit and I’m excited to keep expanding my coverage here.

    You can click through the slides here.

    Thanks to the organizers of The Curve for inviting me (and encouraging me to give this talk), and for permission to post this video.

    EDIT: I noticed sometimes the audio jumps weirdly, not sure what caused it (from slideslive export, raw is here: https://slideslive.com/39046297/open-models-in-2025-stakes-state-and-strategy)

    Chapters

    00:00 2025 so far05:53 China takes the lead15:54 What comes next21:20 What we should do25:00 Q & A

    (Podcast feed / Audio only version trims 7 seconds of silence to start)

    References & Recommended Reading

    * The ATOM Project

    * On China’s open-source community & trajectory

    * Ranking China’s open AI labs

    * On GPT-OSS

    * Recent open models

    * More on The Curve conference

    Of course, you can watch on YouTube:

    Listen on Apple Podcasts, Spotify, YouTube, and where ever you get your podcasts.



    This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.interconnects.ai/subscribe
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    47 分
  • Thoughts on The Curve

    Thoughts on The Curve
    2025/10/07
    I spent the weekend debating AI timelines, among other things, at The Curve conference. This translates as spending the weekend thinking about the trajectory of AI progress with a mix of DC and SF types. This is a worthwhile event that served as a great, high-bandwidth way to check in on timelines and expectations of the AI industry.Updating timelinesMy most striking takeaway is that the AI 2027 sequence of events, from AI models automating research engineers to later automating AI research, and potentially a singularity if your reasoning is so inclined, is becoming a standard by which many debates on AI progress operate under and tinker with. It’s good that many people are taking the long term seriously, but there’s a risk in so many people assuming a certain sequence of events is a sure thing and only debating the timeframe by which they arrive.I’ve documented my views on the near term of AI progress and not much has changed, but through repetition I’m developing a more refined version of the arguments. I add this depth to my takes in this post.I think automating the “AI Research Engineer (RE)” is doable in the 3-7 year range — meaning the person that takes a research idea, implements it, and compares it against existing baselines is entirely an AI that the “scientists” will interface with.In some areas the RE is arguably already automated. Within 2 years a lot of academic AI research engineering will be automated with the top end of tools — I’m not sure academics will have access to these top end of tools but that is a separate question. An example I would give is coming up with a new optimizer and testing it on a series of ML baselines from 100M to 10B parameters. At this time I don’t expect the models to be able to implement the newest problems the frontier labs are facing alone. I also expect academics to be fully priced out from these tools.Within 1-3 years we’ll have tools that make existing REs unbelievably productive (80-90% automated), but there are still meaningful technical bottlenecks that are solvable but expensive. The compute increase per available user has a ceiling too. Labs will be spending $200k+ per year per employee on AI tools easily (ie the inference cost), but most consumers will be at tiers of $20k or less due to compute scarcity.Within 3-4 years the augmented research engineers will be able to test any idea that the scientists come up with at the frontier labs, but many complex system problems will need some (maybe minimal) amount of human oversight. Examples would include modifying RL implementations for extremely long horizon tasks or wacky new ideas on continual learning. This is so far out that the type of research idea almost isn’t worth speculating on.These long timelines are strongly based on the fact that the category of research engineering is too broad. Some parts of the RE job will be fully automated next year, and more the next. To check the box of automation the entire role needs to be replaced. What is more likely over the next few years, each engineer is doing way more work and the job description evolves substantially. I make this callout on full automation because it is required for the distribution of outcomes that look like a singularity due to the need to remove the human bottleneck for an ever accelerating pace of progress. This is a point to reinforce that I am currently confident in a singularity not happening.Up-skilling employees as their roles become irrelevant creates a very different dynamic. The sustained progress on code performance over the next few years will create a constant feeling of change across the technology industry. The range of performance in software is very high and it is possible to perceive relatively small incremental improvements.These are very complex positions to hold, so they’re not that useful as rhetorical devices. Code is on track to being solved, but the compute limits and ever increasing complexity of codebases and projects (ie. LLMs) is going to make the dynamic very different than the succinct assumptions of AI 2027.To reiterate, the most important part of automation in the discussion is often neglected. To automate someone you need to outcompete the pairing of a human with the tool too.Onto the even trickier argument in the AI 2027 standard — automating AI research altogether. At the same time as the first examples of AI systems writing accepted papers at notable AI venues, I’m going to be here arguing that full automation of AI research isn’t coming anytime soon. It’s daunting to try and hold (and explain) this position, and it relies on all the messy firsthand knowledge of science that I have and how it is different in academia versus frontier AI labs.For one, the level and type of execution at frontier labs relative to academic research is extremely different. Academia also has a dramatically higher variance in quality of work that is accepted within the community. For this ...
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    12 分
  • ChatGPT: The Agentic App

    ChatGPT: The Agentic App
    2025/09/30
    Ever since ChatGPT exploded in popularity, there has been a looming “how” to its monetization plans. Much has been said about shopping and advertising as the likely paths, especially with Fidji Simo joining as CEO of Applications under Sam Altman. Advertising as a business model for AI is logical but difficult to personalize and specialize. We know tons of people spend a lot of time using AI models, but how do you best get the sponsored content into the outputs? This is an open technical problem, with early efforts from the likes of Perplexity falling short.Shopping is another, but the questions have long been whether AI models actually have the precision to find the items you want, to learn exactly what you love, and to navigate the web to handle all the corner cases of checkouts. These reflect a need for increased capabilities on known AI benchmarks, rather than inventing a new way of serving ads. OpenAI’s o3 model was a major step up in search functionality, showing it was viable; the integration was either a business problem — where OpenAI had to make deals — or an AI one — where ChatGPT wasn’t good enough at managing websites for you.Yesterday, ChatGPT launched its first integrated shopping push with Buy It in ChatGPT, a simple checkout experience, and an integrated commerce backend built on the Agentic Commerce Protocol (ACP). The announcement comes with the perfect partners to complement the strengths of OpenAI’s current models. GPT-5-Thinking is the best at finding niche content on the web, and ChatGPT’s launch partner for shopping is Shopify (*soon, Etsy is available today), the home to the long tail of e-commerce merchants of niche specialties. If this works, it will let users actively uncover exactly what they are looking for — from places that were often hard to impossible to find on Google. This synergy is a theme we’ll see reoccur in other agents of the future. The perfect model doesn’t make a useful application unless it has the information or sandbox it needs to think, search, and act. The crucial piece that is changing is that where models act is just as important as the weights themselves — in the case of shopping, it is the network of stores with their own rankings and API.The ACP was built in collaboration with Stripe, and both companies stand to benefit from this. Stripe wants more companies to build on the ACP so that its tools become the “open standard for agentic payments” and OpenAI wants the long-tail of stores to adopt it so they can add them to their ever-growing internal recommendation (or search) engine. The business model is simple, as OpenAI says “Merchants pay a small fee on completed purchases.” OpenAI likely takes a larger share than Stripe, and it is a share that can grow as their leverage increases over shoppers.I’m cautiously optimistic about this. Finding great stuff to buy on the web is as hard as it has ever been. Users are faced with the gamification of Google search for shopping and the enshittification of the physical goods crowding out Amazon. Many of the best items to buy are found through services like Meta’s targeted ads, but the cost of getting what you want should not be borne through forced distraction.OpenAI will not be immune to the forces that drove these companies to imperfect offerings, but they’ll come at them with a fresh perspective on recurring issues in technology. If this works for OpenAI, they have no competitor. They have a distribution network of nearly 1B weekly users and no peer company ready to serve agentic models at this scale. Yes, Google can change its search feed, but the thoroughness of models like GPT-5 Thinking is on a totally different level than Google search. This agentic model is set up to make ChatGPT the one Agentic App across all domains.The idea of an agentic model, and really the GPT-5 router itself, shows us how the grand idea of one giant model that’s the best for every conceivable use-case is crumbling. OpenAI only chooses the more expensive thinking model when it deems a free user to need it and they have an entirely different model for their coding products. On the other hand, Claude released their latest model, Claude 4.5 Sonnet, yesterday as well, optimizing their coding peak performance and speed yet again — they have no extended model family. The reality that different models serve very different use-cases and how AI companies need to decide and commit to a certain subset of them for their development points to a future with a variety of model providers. Where coding is where you can feel the frontier of AI’s raw intelligence or capabilities, and Anthropic has turned their entire development towards it, the type of model that is needed for monetization of a general consumer market could be very different. This is the web-agent that OpenAI has had the industry-leading version of for about 6 months. Specialization is making the AI market far more interesting, as ...
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    9 分
  • Thinking, Searching, and Acting

    Thinking, Searching, and Acting
    2025/09/22
    The weaknesses of today’s best models are far from those of the original ChatGPT — we see they lack speed, we fear superhuman persuasion, and we aspire for our models to be more autonomous. These models are all reasoning models that have long surpassed the original weaknesses of ChatGPT-era language models, hallucinations, total lack of recent information, complete capitulations, and other hiccups that looked like minor forms of delusion laid on top of an obviously spectacular new technology.Reasoning models today are far more complex than the original chatbots that consisted of standalone model weights (and other lightweight scaffolding such as safety filters). They're built on three primitives that'll be around for years to come:* Thinking: The reasoning traces that enabled inference-time scaling. The "thoughts" of a reasoning model take a very different form than those of humans that inspired the terminology used like Chain of Thought (CoT) or Thinking models.* Searching: The ability to request more, specific information from non-parametric knowledge stores designed specifically for the model. This fills the void set by how model weights are static but living in a dynamic world.* Acting: The ability for models to manipulate the physical or digital world. Everything from code-execution now to real robotics in the future allow language models to contact reality and overcome their nondeterministic core. Most of these executable environments are going to build on top of infrastructure for coding agents.These reasoning language models, as a form of technology are going to last far longer than the static model weights that predated and birthed ChatGPT. Sitting just over a year out from the release of OpenAI's o1-preview on September 12, 2024, the magnitude of this is important to write in ink. Early reasoning models with astounding evaluation scores were greeted with resounding criticism of “they won’t generalize,” but that has turned out to be resoundingly false.In fact, with OpenAI's o3, it only took 3-6 months for these primitives to converge! Still, it took the AI industry more broadly a longer time to converge on this. The most similar follow-up on the search front was xAI's Grok 4 and some frontier models such as Claude 4 express their reasoning model nature in a far more nuanced manner. OpenAI's o3 (and GPT-5 Thinking, a.k.a. Research Goblin) and xAI's Grok 4 models seem like a dog determined to chase their goal indefinitely and burn substantial compute along the way. Claude 4 has a much softer touch, resulting in a model that is a bit less adept at search, but almost always returns a faster answer. The long-reasoning traces and tool use can be crafted to fit different profiles, giving us a spectrum of reasoning models.The taxonomy that I laid out this summer for next-generation reasoning models — skills for reasoning intelligence, calibration to not overthink, strategy to choose the right solutions, and abstraction to break them down — are the traits that'll make a model most functional given this new perspective and agentic world.The manner of these changes are easy to miss. For one, consider hallucinations, which are an obvious weakness downstream of the stochastic inference innate to the models and their fixed date cutoff. With search, hallucinations are now missing context rather than blatantly incorrect content. Language models are nearly-perfect at copying content and similarly solid at referencing it, but they're still very flawed at long-context understanding. Hallucinations still matter, but it’s a very different chapter of the story and will be studied differently depending on if it is for reasoning or non-reasoning language models.Non-reasoning models still have a crucial part to play in the AI economy due to their efficiency and simplicity. They are part of a reasoning model in a way because you can always use the weights without tools and they'll be used extensively to undergird the digital economy. At the same time, the frontier AI models (and systems) of the coming years will all be reasoning models as presented above — thinking, searching, and acting. Language models will get access to more tools of some form, but all of them will be subsets of code or search. In fact, search can be argued to be a form of execution itself, but given the imperative of the underlying information it is best left as its own category.Another popular discussion with the extremely-long generations of reasoning models has been the idea that maybe more efficient architectures such as diffusion language models could come to dominate by generating all the tokens in parallel. The (or rather, one) problem here is that they cannot easily integrate tools, such as search or execution, in the same way. These’ll also likely be valuable options in the AI quiver, but barring a true architectural or algorithmic revolution that multiplies the performance of today’s AI models, the efficiency and ...
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    9 分
  • Coding as the epicenter of AI progress and the path to general agents

    Coding as the epicenter of AI progress and the path to general agents
    2025/09/18
    Coding, due to its breadth of use-cases, is arguably the last tractable, general domain of continued progress for frontier models that most people can interface with. This is a bold claim, so let’s consider some of the other crucial capabilities covered in the discourse of frontier models:* Chat and the quality of prose written by models has leveled off, other than finetuning to user measures such as sycophancy. * Mathematics has incredible results, but very few people directly gain from better theoretical mathematics. * The AIs’ abilities to do novel science are too unproven to be arguable as a target of hillclimbing. Still, coding is a domain where the models are already incredibly useful, and they continue to consistently stack on meaningful improvements. Working daily with AI over the last few years across side projects and as an AI researcher, it has been easy to take these coding abilities for granted because some forms of them have been around for so long. We punt a bug into ChatGPT and it can solve it or autocomplete can tab our way through entire boilerplate. These use-cases sound benign, and haven’t changed much in that description as they have climbed dramatically in capabilities. Punting a niche problem in 1000+ lines of code to GPT-5-Pro or Gemini Deep Think feels like a very fair strategy. They really can sometimes solve problems that a teammate or I were stuck on for hours to days. We’re progressing through this summarized list of capabilities:* Function completion: ~2021, original Github CoPilot (Codex)* Scripting: ~2022, ChatGPT* Building small projects: ~2025, CLI agents* Building complex production codebases, ~2027 (estimate, which will vary by the codebase)Coding is maybe the only domain of AI use where I’ve felt this slow, gradual improvement. Chat quality has been “good enough” since GPT-4, search showed up and has been remarkable since OpenAI’s o3. Through all of these more exciting moments, AIs’ coding abilities have just continued to gradually improve. Now, many of us are starting to learn a new way of working with AI through these new command-line code agents. This is the largest increase in AI coding abilities in the last few years. The problem is the increase isn’t in the same domain where most people are used to working with AI, so the adoption of the progress is far slower. New applications are rapidly building users and existing distribution networks barely apply. The best way to work with them — and I’ll share more examples of what I’ve already built later in this post — is to construct mini projects, whether it’s a new bespoke website or a script. These are fantastic tools for entrepreneurs and researchers who need a way to quickly flesh out an idea. Things that would’ve taken me days to weeks can now be attempted in hours. Within this, the amount of real “looking at the code” that needs to be done is definitely going down. Coding, as an activity done through agents, is having the barriers to entry fully fall down through the same form factor that is giving the act of coding re-found joy.Why I think a lot of people miss these agents is that the way to use the agents is so different from the marketing of incredible evaluation breakthroughs that the models are reaching. The gap between “superhuman coding” announcements and using an agent for mini projects is obviously big. The best way to use the agents is still mundane and requires careful scoping of context. For example, yesterday, on September 17, 2025, OpenAI announced that GPT-5 as part of a model system got a higher score than any human (and Google’s Gemini Deep Think) at the ICPC World Finals, “the premier collegiate programming competition where top university teams from around the world solve complex algorithmic problems.” Here’s what an OpenAI researcher said they did:We competed with an ensemble of general-purpose reasoning models; we did not train any model specifically for the ICPC. We had both GPT-5 and an experimental reasoning model generating solutions, and the experimental reasoning model selecting which solutions to submit. GPT-5 answered 11 correctly, and the last (and most difficult problem) was solved by the experimental reasoning model.These competitions often get highlighted because they’re “finite time,” so the system must respond in the same fixed time as a human does, but the amount of compute used by GPT-5 or another model here is likely far higher than any user has access to. This is mostly an indication that further ability, which some people call raw intelligence, can be extracted from the models, but most of that is limited by scaffolding and product when used by the general population.The real story is that these models are delivering increasing value to a growing pool of people.For followers of AI, coding with AI models is the easiest way to feel progress. Now that models are so good at chat, it takes very specialized tasks to test the...
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    16 分
  • 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. ...
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    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 ...
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