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ow their future place in mathematics. Tsimerman said he wanted young people to keep “learning and improving themselves because you don’t know how the world will turn out.” He encouraged students to “engage with AI because it’s going to be a big part of our world going forward.” At the same time, he thought students were right to pay attention to how AI is disrupting the math profession. “I don’t think it’ll exist the way it exists right now,” he said. Not everyone agreed. Yu Deng, a University o

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In the Internet era, the more your software or application got used, the cheaper it got to run per user. The R&D cost was fixed, and each new user added a sliver of bandwidth, storage, and support. As your application grew, your fixed costs spread across more people, and cost per user fell. AI is different because inference is a real variable cost. It looks more like manufacturing business. Every query burns tokens, and you pay for those tokens whether you rent the model through an API or host

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onsumer taking on the maintenance, power bill, and ownership of the GPU, any use the training model gets is a net positive. Reply Edit Preview Upload images, audio, and videos by dragging in the text input, pasting, or clicking here . Tap or paste here to upload images Comment · Sign up or log in to comment Upvote 97 +85 Models mentioned in this article 1 Dharma-AI/Dharma-OCR-LITE Image-Text-to-Text • 4B • Updated Apr 17 • 486 • 22 Spaces mentioned in this article 1 Running on Zero Agents 6 Dhar

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Press Contact: Previous image Next image Before the advancement of scientific data storage and collaboration via the cloud, medical investigators seeking health research breakthroughs had to overcome significant obstacles to collaboration and key clinical data gathering. Data were siloed and difficult to distribute, so those looking to undertake research had no option but to gather them themselves. This not only made research more expensive, but it was challenging to compare findings across data

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orld to post a perfect score on a math olympiad benchmark. (SCMP); Baidu’s Ernie task-agent topped a major international agent leaderboard, beating Claude and GPT, per Chinese coverage. (量子位) Funding CXMT, China’s largest memory chipmaker, saw its blockbuster IPO draw scrutiny over cash-drain fears ahead of listing. (CNBC); The same pre-IPO pricing—reportedly making CXMT China’s most valuable company—is also facing scrutiny over ties to a crypto platform. (CNBC); Embodied-AI startup Psibot becam

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ame ou t, I created a brutalist city building game as a demo ( you can still play the original version ) with the prompt “make a procedural brutalist building creator where i can drag and edit buildings in cool ways, they should look like actual buildings,” and some suggestions for improvement. Less than a year later, I used GPT-5.6 Sol in Codex to do the same thing: you can play it here. If you don’t want to play it, the video shows the difference — it is quite stark! So how do you take advanta

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a historical event instead of reading about it, or following a red blood cell through the circulatory system, with the model tied to a textbook chapter so that, in her words, “no dragons will come in if we don’t want them to.” Robotics is still the hard part Hadsell has worked on robots for more than a decade and is blunt that the field is hard, an interplay of hardware, software, and understanding, with the goal of general-purpose machines that can drop into any task a human can do. DeepMind sp

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So, while all the Chinese labs have way less compute than their counterparts in the U.S., more of it can certainly go to training. When I joked around about how much compute an average researcher at OpenAI could have – say a few thousand H100 equivalent machines – the researchers at Kimi were shocked. The org chart and approach to building the Kimi models surely reflect this, but it is difficult to tease out what this looks like without substantial proprietary information. The state of affairs o

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); WAIC 2026 also opened in Shanghai this week with over 300 physical robots on the show floor, underscoring how much the “AI conference” has become an embodied-AI trade show. Coverage highlights included SenseTime’s new compute-power/electricity co-scheduling agent system claiming an 80% improvement in token output per unit of power cost, a humanoid-robot boxing exhibition, and Xiaomi detailing its robotics “data factory” approach to training humanoid systems. (量子位/雷峰网); SCMP reported a local C

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pletely changed the way we work at Replit. Engineering saw the impact first In late January we turned up infrastructure to experiment with internal agent use cases quickly. We leveraged our agent harness, microVMs, and remote filesystem infrastructure so any engineer could orchestrate swarms of agents in parallel. Then we locked the whole thing behind access policies, token proxies, audit logging, and our ZeroTrust network. At that point we felt safe giving the agent access to all the things we

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s More from this author 20 August 17, 2026 GPU Management: Why Idle GPUs Are the New Grounded Aircraft 97 July 30, 2026 Community Edit Preview Upload images, audio, and videos by dragging in the text input, pasting, or clicking here . Tap or paste here to upload images Comment · Sign up or log in to comment Upvote 58 +46 Models mentioned in this article 1 Dharma-AI/Dharma-OCR-LITE Image-Text-to-Text • 4B • Updated Apr 17 • 486 • 22 Spaces mentioned in this article 1 Running on Zero Agents 6 Dhar

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rt of the harness all along. 1 reply yarizk Article author Jul 28 Agreed, routing should disappear into the harness and just be part of the system's execution logic. Our goal is exactly that—making model choice an intrinsic capability rather than a separate concern. deleted 26 days ago • This comment has been hidden Edit Preview Upload images, audio, and videos by dragging in the text input, pasting, or clicking here . Tap or paste here to upload images Comment · Sign up or log in to comment +81

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t now we have a lot of frantic analysis of ‘time to power’, but the question at the end of that remains whether the foundation models have sustainable pricing power, strategic leverage and value capture, or whether they become low-margin commodity infrastructure providers. At the moment, I think every dynamic we can see points to the latter. Clearly, the situation today is transitory. On the supply side, a trillion dollars or more of data centre capex is coming down the pipe (and plenty more sem

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t he would say that when his team started using Mythos this year, it felt like it had better research taste than anything before it. With one caveat: still “not as good as your average researcher.” Average researcher where, I asked. “Maybe at Anthropic.” A high bar to be measured against, and one the models are now climbing. Safety is a capability, not a tax on it Ask what safety research has ever done for product quality and Ted reaches for cars. Seat belts, crumple zones, airbags, an oven that

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of them, across problems like language modeling and image classification, with an agent free to target a loss function, an optimizer, or an architecture. The early signal is encouraging, with more and more diverse training tasks improving performance on held-out problems the agent has never seen. The complementary curve Stack the three together and you have the recipe: reinforcement learning to discover better solutions where progress can be measured, divergent search to find new problems rathe

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line bookseller and e-tailing giant Amazon ever did was let you pay for its enormous IT expenses as the company was growing at a crazy pace. You did this by renting compute and storage capacity on the nascent Amazon Web Services cloud. It is not a “public cloud,” it is a private compute utility that people rent capacity on and a massively profitable one at that. I do not know precisely where the annual Amazon IT budget and the operating profits from Amazon Web Services crossed each other. Amazon

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ng compute portfolios: NVIDIA for the broadest software ecosystem and fastest path to scale, TPUs and Trainium for strategic supply and cost control, custom silicon for future leverage, and neoclouds or sovereign projects when hyperscaler capacity is not enough. “CUDA is still king” remains true in the papers. But in frontier-lab procurement, the question is becoming broader: who can turn contracted gigawatts into reliable, liquid-cooled, networked, usable intelligence infrastructure? Looking ah

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ced. Code is the thing that produces some end product, and you want a functional end product. Whereas in writing, the end product is directly the thing the AI is producing. Each paragraph, sentence, and word matters because that is the substance. It’s not some separate thing produced out of the writing. It can’t be slop in the way that code can be slop and still produce the outcome you want. Grant Sanderson But you were just pointing out how we’ve actually gotten much better at agents writing no

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rope can match the American frontier dollar for dollar. On today’s capital it cannot, and pretending otherwise only wastes the little it has. But the goal is not parity, it is leverage. A country does not need the best model in the world to be sovereign; it needs a credible one of its own, on its own soil, good enough that being cut off is survivable rather than catastrophic. That is the difference between negotiating your access from dependence and negotiating it with an alternative in hand. Th

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ye, holding all the constellations together, is an unimaginably massive black hole of data. Intermission: Comparing human and AI sample efficiency If a person hears and sees on average 2,000 words an hour, then from birth to adulthood, they’ll see 200 millions tokens. By contrast, frontier models are trained on somewhere between 10s to 100s of trillions of tokens. That is close to a million fold difference; A person can learn to teleoperate any random humanoid or robot arm within hours. The reas

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