AI Infrastructure & Investment - OpenAI, Oracle, SoftBank 等公司计划开设五个新的数据中心，最终电力容量将达到 7 吉瓦 [1] - 7 吉瓦的电力大约可供 600 万家庭使用 [1] - 行业普遍认为，AI 领域的持续投入巨大，需要创新的融资方式来支持 [2][6] - 行业对 AI 的需求仍然巨大，尤其是在药物研发、癌症研究等尚未解决的问题领域 [3] - 解决这些问题需要大量的训练、GPU 和电力，因此需要大量的资本投入 [4] - ARM 与 NVIDIA 加强合作，下一代数据中心将使用下一代 ARM CPU [6] - 行业正在寻找更具创意的方式来完成融资，因为所需的资本非常庞大 [6] Power & Sustainability - 数据中心对电力的需求巨大，可能需要相当于数百万家庭的用电量 [8] - 本届政府对使用化石燃料和天然气持更加开放的态度，这在短期内有所帮助 [8] - 随着需求的增长，包括小型模块化反应堆（SMRs）和核能在内的所有能源解决方案都在被考虑 [9] - 行业正在考虑包括小型模块化反应堆（SMRs）和核能在内的所有方案，以满足电力需求 [9]

I always like to go back to basics. Jonathan Like Grok stacks up against some video and Google's TPU and we're largely focused on inference. Why did you raise this round in that context.What is it going to allow you to do to be more competitive against those two giants. Well, the bottom line is that the demand for inference is insatiable. The total amount of capacity that people are trying to deploy is mind boggling. The numbers that people are putting up.And that's only growing. And in our case, we don't f ...

We felt bad about the slowdowns as we were adding GPUs, so we reset everyone's limits to make up for it:Tibo (@thsottiaux):We have reset everyone's limits for gpt-5-codex to allow you all to benefit more from it today and as a way to make up for the slowdowns earlier while we were provisioning additional GPUs. We'll continue to add capacity throughout the week to maintain the speed.Enjoy! ...

Market Trends - Lambda, a GPU rental company, is reportedly considering going public, similar to its competitor CoreWeave [1]

It's either a gold rush or a sugar rush. >> And how long that how long you think that buildout really lasts because it may take several hundred people to build a data center, but it only takes five or 10 to operate it. >> I think we are so in the early inning.I think a lot of prognosticators want to call it like we're on the sugar rush. We're not. It's more like the railroads or the buildout of electricity than anything I've seen.The internet, it turns out in hindsight, was actually a relatively capex-like ...

Artificial General Intelligence (AGI) Race - The core equation for achieving intelligence is "Compute + Data = Intelligence" [1] - The report questions what is preventing China from achieving AGI first [1] Key Resources for AGI - China possesses significant electricity generation capacity to power GPUs, which in turn drives compute [1] - The report raises the question of what data sources China is utilizing to train its models [1]

Partnerships - The company acknowledges key partnerships with Microsoft, NVIDIA, Oracle, Google, and Coreweave [1] - These partnerships are crucial for enabling the company's operations [1] Infrastructure - The company relies heavily on a large number of GPUs [1] - These GPUs are working overtime, indicating significant computational demands [1]

Scalability Challenges in AI Inference - Current AI inference systems rely on brute-force scaling, adding more GPUs per user, leading to unsustainable compute demands and spiraling costs [1] - Real-time use cases are bottlenecked by latency and costs per user [1] Proposed Solution - Rethinking hardware is the only way to unlock real-time AI at scale [1] Key Argument - The current approach to inference is not scalable [1]

GPU Capacity - OpenAI plans to bring over 1 million GPUs online by the end of this year [1]

GPU Deployment - The company anticipates exceeding 1 million GPUs online by the end of the year [1] Future Goals - The company aims for a 100x increase in GPU deployment [1]