搜索“AWS”

de Code和API的使用额度。 换句话说，这样做的目的就是 让后端拿到更多算力供给前端 。算力有了，模型强了，用户也留下来了。 于是模型公司的竞争开始出现两套同时运行的逻辑： 产品层面 ，大家还是争用户、争开发人才； 基础设施层面 ，谁能及时提供资源，谁都可能成为潜在合作对象。 它们的情况更具戏剧性。 这些公司难以自建数据中心，但过去，它们可以靠灵活租用AWS等云平台的GPU， 按需付费 。 然而随着头部AI实验室大量锁定算力资源，部分云服务商开始要求多年合约，预付款高达合同金额的 30% ，甚至要求创业公司寻找付款担保方。 以上种种，都让初创公司陷入了两难境地。 算力买太多，如果未来用户增长不及预期，持续消耗的现金就打了水漂；买太少，则可能因为没有计算资源处理需求而错失增长机会。 这样的情况反而催生出了一门新生意： 全球算力淘金。 比如代表性的San Francisco Compute公司就曾跑到美国乡村旧鸡舍里收服务器， 马斯克也和 台积电 讨论起了进一步的算力合作。 硅谷的顶级VC也开始亲自囤GPU ，YC在7月为旗下创业公司推出专供的GPU集群，Radical Ventur

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no one was monitoring for.¹⁸ The Accountability Gap The legal question raised by the incident is deceptively simple: who is responsible? Every major jurisdiction’s computer-crime laws require some form of criminal intent. The US Computer Fraud and Abuse Act requires “knowing” and “intentional” access.¹⁹ The UK’s Computer Misuse Act demands that the accused “know” the access is unauthorised.²⁰ These statutes were written for human hackers. When the Ninth Circuit held in Amazon v. Perplexity AI (A

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倍。甲骨文隐性承诺在2026年5月底超过2700亿美元，四年膨胀超30倍。Alphabet表外承诺估算约2500亿美元。 这套操作，很多人会联想到2001年安然。安然当年就是利用SPV把巨额债务移出资产负债表，同时用市值计价会计，在签约当年确认未来多年预期收入，最终轰然倒塌。 二者有本质区别：安然的收入几乎全部虚构。当下科技巨头的云算力收入真实存在，谷歌云、AWS确实在向客户售卖算力。问题在于，利润被股权投资浮盈严重美化，真实自由现金流持续恶化，产业链买卖双方高度绑定，形成内部循环。 三、一道算不平的算术题 我们把投入端和收入端拆开看，缺口一目了然。 投入端：五大巨头2026年合计资本开支预计达到7692亿美元，几乎是2025年的两倍，机构预估2027年资本开支最高或将冲击1万亿美元。 收入端：中金测算，AI应用赛道，每年需要形成约1万亿美元可持续稳定收入，才能覆盖全部资本开支与债务成本。当前美国头部模型厂商合计年度经常性收入ARR大约2000亿美元。中间存在5倍缺口。 伦敦证券交易所集团预测：2027年五大科技巨头经营现金流预计增加3400亿美元，但同期资本开支预计增加5340亿美元

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gle’s AI push in the classroom proves a nightmare. Nvidia CEO Jensen Huang’s son-in-law takes on a bigger role inside the company. USV among the VC firms raising new funds. A new lawsuit over Groq’s deal with Nvidia could throw a wrench in the reverse acquihire strategy. Tech moguls get the Hollywood treatment. The Main Item OpenAI Wanted Its Revenue Run-Rate to Match Anthropic’s. Cue the Chaos AI stocks got hammered Thursday after the Financial Times reported that OpenAI was spinning its annual

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Trump will have to convince the other branches of government to get on board with the term. He can’t bar Congress from using “artificial intelligence,” and it appears in existing laws like Trump’s signature Take It Down Act, which administration agencies will need to cite when arguing in court. As appetite for more AI regulation grows, congressional Republicans will have to decide whether to appease Trump by adopting a name many people find unintuitive, freighted with the baggage of Trump’s fair

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cal Science and Law (CUPL), Tsinghua’s School of Public Policy, and the ministries’ expert pools. They write the expert readings that accompany every CAC rule and draft the model laws the Ministry of Justice consults. The two camps are asking different questions. Scientists ask what a frontier model can do. Their answer is loss of control and misuse. Thus, their prescriptions reach the model before it ships. They have been advocating for registration of training runs above compute thresholds, sa

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at’s interesting to me is that one man, Peter Steinberger, was able to create an actually useful AI agent for the consumer with OpenClaw — an always-on tool, despite its privacy flaws. It had a lot of privacy and security issues. But it was an agent that was useful enough that people still wanted to use it anyway and try to find ways around these privacy issues that it was having. That inspired these companies to say, “If one man can do this over the course of one weekend, we’ve really been slac

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I driver’s steering and braking change what it sees next. Other road users follow their recorded paths, but the driver can explore the consequences of its own actions. Odyssey-3 draws on visual pre-training to learn controls for robots, cars, and games. In the company’s experiments, robot arms learned from tens of hours of demonstrations and recovered from missed grasps without being shown those recoveries. These early results suggest that physical AI may need fewer demonstrations of every situa

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pecific pooled investment vehicles — which should be read in their entirety, and only to those who, among other requirements, meet certain qualifications under federal securities laws. Such investors, defined as accredited investors and qualified purchasers, are generally deemed capable of evaluating the merits and risks of prospective investments and financial matters. There can be no assurances that a16z’s investment objectives will be achieved or investment strategies will be successful. Any

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持22个月之久，却最终屈服于庞大的民间反对浪潮。 只要有钱就能大干快上的时代过去了，欢迎来到比拼资源的新时代。 芯片买完就能用，但数据中心不是 Meta也在调整芯片组合，增加CPU的采购力度。它认为随着智能体的发展，单纯GPU训练已经无法满足需求，越来越多工作需要CPU来处理持续性的任务。 今年3月Meta宣布与Arm共同开发新一代数据中心CPU，4月宣布与AWS合作，将数千万个Graviton CPU核心纳入Meta的计算资源。 7月路透社援引知情人士报道称，2026年Meta可能投入1450亿美元在AI基础设施上，部署约14GW用电规模的基础设施。 这个体量已经不是传统意义上的买几台服务器，它背后是一套复杂的大型工业基础设施体系。毕竟芯片和服务器买完就能立刻用，但数据中心不是下单之后就能交付的。 黑石被迫放弃的明星项目Digital Gateway就是一个典型案例。 该数据中心项目最早在2023年12月获得Prince William County政府批准，规划在弗吉尼亚州Gainesville附近进行建设。项目最终规划面积超过2200万平方英尺，涉及约2000英亩土地。 当地居民

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pecific pooled investment vehicles — which should be read in their entirety, and only to those who, among other requirements, meet certain qualifications under federal securities laws. Such investors, defined as accredited investors and qualified purchasers, are generally deemed capable of evaluating the merits and risks of prospective investments and financial matters. There can be no assurances that a16z’s investment objectives will be achieved or investment strategies will be successful. Any

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g it from both sides from the government. Zoë Schiffer : Yeah. And we have Lina Khan writing an op-ed in The New York Times today saying they actually can be tried under existing laws. We don't necessarily need to make new laws, with the big caveat that Congress would have to act. Zoë Schiffer : I personally think Republicans are going to do great in the midterms, but you can cut that because I am not reporting on politics and that's just my vibe check. Leah Feiger : I'm changing my mind constan

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n (sometimes 1,000 digits): if few enough options remain, those numbers pin down the remaining coefficients exactly. The semi-numerical bootstrap seemed ideal for agentic AI. It draws on mathematics, physics, and computer science that no one person has mastered; it needs a great deal of coding and algorithm development; and it is checkable, since the same numerics let anyone, expert or not, verify the final answer against the integral to as many digits as they like by running two scripts. The co

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Swyx [00:38:30]：对。我一直会回到的那个类比，我们就以此收尾吧，就是，你在构建一朵 AI 云，对吧？ Nikunj Handa [00:38:35]：嗯哼。 Swyx [00:38:35]：就像，这是 Sam 一年前说过的东西 Nikunj Handa [00:38:38]：嗯哼 Swyx [00:38:38]：在那里，而你，这几乎就像你在做 AWS 的发明，你必须做，好，这是 EC2 Nikunj Handa [00:38:45]：对 Swyx [00:38:45]：而这是 S3，而这是像……但你做的是每一个的 AI 原生版本。 Vibhu [00:38:48]：有很多类比，所以你在为你明知会用到的东西预热缓存 Nikunj Handa [00:38:53]：对。 Vibhu [00:38:53]：而且很好的一点是，这一切都暴露给构建者 Nikunj Handa [00:38:56]：嗯哼 Vibhu [00:38:56]：因为它就打开了你可以构建新东西的各种方式。 Nikunj Handa [00:38:59]：对，绝对是这样。 Swyx [00:39:00]：好。

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AI debate because I saw way too many people treating repeat evidence of lawbreaking as wholly unreachable by current law. Is there any reason to think that OpenAI has broken any laws that (at least in another time) might be enforced? Which and why? In the world, in general, law enforcement starts when there’s evidence of a crime, and then investigates. A DA doesn’t wait for all the evidence to investigate a crime, and an AG doesn’t wait for all the evidence in the newspaper to investigate illeg

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r, there’s currently no legal framework or industry standardization to enforce, or even recommend, methods for controlling agents that collaborate. That’s where AI governance—new laws, regulations, and industry practices—can come into play. Noam Kolt , an assistant professor at the Hebrew University of Jerusalem who leads its Governance of AI Lab, says companies should place more emphasis on legal compliance when training and deploying models. In a recent paper , Kolt and a coauthor questioned A

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t you might be surprised to learn that OpenAI likely wasn’t legally required to disclose these incidents. (OpenAI did not respond to a request for comment.) State AI transparency laws like California’s SB 53 , New York’s RAISE Act , and Illinois’s SB 315 require that AI developers report “critical safety incidents.” These are defined as incidents that cause more than 50 deaths or physical injuries or $1 billion in damage. They also include incidents where the model deceives developers outside an

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ith Greg Brockman’s massive donations to MAGA. I will let you connect your own dots. Except to leave you with some perspective: § The other thing to note, and this may come up in lawsuits, is that it was all foreseeable. In fact, I foresaw it, in tweets like this: and in a series of essays on agents, like this one from over a year ago: LLMs + Coding Agents = Security Nightmare Last October, I wrote an essay called “When it comes to security, LLMs are like Swiss cheese — and that’s going to cause

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of the early days stuff. Anjney Midha [00:14:54]: Well, I was gonna say that, like, the biggest objection we got is big model win, which is - all of the Swyx [00:15:03]: Scaling laws. Anjney Midha [00:15:04]: Huh? Swyx [00:15:04]: Scaling laws. Anjney Midha [00:15:05]: Yeah, scaling laws, and natural network effects are just gonna accrue to one company, which will be - It’ll be a Google-style monopoly, just like how Google won the search market, by a large margin, and you’ll just be fighting fo

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nd $125 billion for short-lived equipment/chips.” Yet arguably the most revealing thing I could find was a quote from CEO Andy Jassy on Amazon’s Q1 2026 earnings call from April: …AWS has to lay out cash for land, power, buildings, chips, servers and networking gear in advance of when we can monetize it, typically 6 to 24 months before we start billing customers depending on the component. So, if we assume the number is roughly $374 billion, plus (at minimum) $50 billion from Microsoft, plus ano

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