估值神话与定价权的破裂
OpenAI 与 Anthropic 是否真的各自价值数万亿美元？华尔街很快就会给出答案。这两家公司目前正以超过 8000 亿美元的估值向投资者进行首次公开募股（IPO）的路演。市场曾对这场 AI 革命深信不疑，投资者迫切希望将资金注入其中。他们推销的核心叙事是：这些公司将成为下一个微软或谷歌，是能够在此后几十年内掌握持久定价权的科技巨头。如果将今年第一季度的表现折算为年化数据，我们甚至看到了每年 8 倍的惊人增长。
然而，在这个宏大的叙事背后，一个致命的问题正在浮出水面：这种定价权已经开始出现裂痕。中国的开源模型正在迅速吞噬低端市场，而美国本土的竞争对手则在向上猛攻高端市场。这些公司向二级市场投资者兜售的商业护城河，实际上正在以肉眼可见的速度萎缩。中美两国在 AI 领域的差距正在迅速闭合。随着企业客户从早期小规模的 AI 实验项目，转向在整个企业内部进行全员级别的规模化部署，他们开始冷静地审视一个核心问题：这种高昂的成本是否物有所值？他们的预算究竟花在了哪里？任务的具体性质是什么？如果在特定层级的任务上，切换到其他更具性价比的模型能节省多少成本？这种成本效益的重新评估，必将成为未来一年内的宏观主题。
Original English Source
Are OpenAI and Anthropic actually worth a trillion dollars each? Well, soon Wall Street is going to find out. Both companies are courting investors for IPOs at valuations north of 800 billion dollars. People really believed in this AI revolution, and they wanted to put their money to work behind it. The pitch: they're the next Microsoft, the next Google, tech giants with pricing power that will last for decades. In the first quarter of this year, we saw, if you were to annualize it, 8x growth per year. But here's the problem: that pricing power, it's already cracking. Chinese open source models—they're eating the low end. American competitors are coming for the high end, and the moat that these companies are selling to public investors, well, that's shrinking in real time. The gap between the two countries—it's closing rapidly. I'm Deirdre Bosa. The biggest IPOs of the year—they're price for market that is already splitting underneath them. So as enterprises go from a few experimental AI projects to now rolling out across entire workforces, they're beginning to ask: Is it worth the cost? Where their spend is going? What's the nature of the task? And if other models may be more performant, and how much could be saved by shifting volume based on the right level of task? I think this is going to be a mega theme for the next year.算力约束下的低成本突围
让我们来算一笔账。假设你的公司拥有 1000 万美元的 AI 预算。如果你将这笔钱全部投入到 Claude Opus（Anthropic 旗下的顶级模型）中，几周之内就会消耗殆尽。但如果将同样的预算投入到 DeepSeek（中国的开源模型）中，这笔资金可能足以支撑大半年的时间。
根据基准测试机构 Artificial Analysis 的数据分析，在执行同等任务时，Claude 的成本是相对最便宜的中国替代方案的九倍之多。不仅如此，在企业日常执行的绝大多数任务中，即便是那些已经发布了六个月、看似有些“过时”的模型，其性能表现也完全足够。它们不仅仅是便宜，更是极具竞争力。在过去的四个月里，诸如 Moonshot、小米、DeepSeek、Jibo 等中国实验室已经相继发布了多款开源模型，这些模型在核心基准测试中的表现，已经完全追平或极为接近美国的闭源前沿模型。
虽然从整体绝对能力来看，OpenAI、Anthropic 和 Gemini 等美国大模型仍然保持领先，但中国在开源模型赛道上却已经将美国远远甩在身后。在最大的 AI 流量聚合平台 OpenRouter 上，用户的采用率完全在跟随着价格波动。本月排名前五的模型中，有三个来自中国。它们的使用量份额从 2024 年的仅仅 1%，疯狂飙升至今年的超过 40%。一个不可忽视的现实是，2025 年，中国供应商的开源模型下载量首次超越了美国供应商——即便是在美国本土市场。
对于中国实验室而言，“约束本身反而成为了核心战略”。由于出口管制，他们被彻底切断了获取 NVIDIA 最顶尖芯片的渠道，这迫使他们无路可退，只能在创意和效率上寻找出路。这催生了更小巧的模型架构、更低廉的训练成本，以及效率极高的推理机制。因为计算资源受限，这群顶尖的 AI 研究人员反而逼迫自己设计出了极其聪明的底层算法。如果 AI 领域的绝大多数技术突破本质上都源于算法、计算机科学和编程范式的革新，那么这支庞大且高效的中国研究大军，无疑构成了他们最根本的战略优势——DeepSeek 的进步绝对不是微不足道的。
相比之下，美国的前沿实验室正陷入一场无止境的军备竞赛：他们将数千亿美元砸向 AI 基础设施，在 NVIDIA 最昂贵的芯片上，依托着不堪重负的电网，去训练参数规模越来越庞大的巨兽模型。而这些高昂的试错成本，最终都会被转嫁给终端消费者。因此，曾经支撑其产品享受高额溢价的护城河，正在被这种商业模式自身不断侵蚀。
Original English Source
Well, let's do the math. Say your company has a 10 million dollar AI budget. Run it on Claude Opus—that's Anthropic's top model—you could burn through it in weeks. Run the same budget on DeepSeek—that's China's open source model—that may stretch across most of a year. The benchmarking firm Artificial Analysis crunched the numbers. Claude costs nine times more than the cheapest Chinese alternative to do the same work. Even models that are out of date, six months old, are perfectly performant for most of the nature of tasks being done. And not just cheaper—they're competitive. Chinese labs, Moonshot, Xiaomi, DeepSeek, Jibo—they have shipped open source models in the last four months that match or nearly match American frontier models on the benchmarks that matter. Our models are better overall. OpenAI is better. Anthropic is better. Gemini is better. However, their open source models are well ahead of us. Adoption is following the price on OpenRouter, the largest AI traffic aggregator. Three of the top five models this month—they're Chinese—and they went from 1% of usage in 2024 to more than 40% this year. The reality is that so far, the ones dominating open source AI are Chinese companies. For the first time in 2025, the volume of download of open models from Chinese providers was higher than from American providers, right? Even in the U.S. For Chinese labs, constraint became the strategy. Cut off from NVIDIA's best chips amid export restrictions, Chinese labs—they had no choice but to get creative. So smaller models, cheaper training, more efficient inference. The best AI researchers in the world, because they are limited in compute, they also come up with extremely smart algorithms. If most advances came from algorithms and computer science and programming, tell me that their army of AI researchers is not their fundamental advantage, and we see it. DeepSeek is not inconsequential advance. Meanwhile, American frontier labs—they are spending hundreds of billions of dollars in AI infrastructure, training ever larger models on the most expensive chips that NVIDIA sells on a power grid that cannot keep up. That gets passed to the consumer. So the edge that justified premium pricing—that's eroding.信任护城河与企业级生态防线
中国开源力量正在快速缩小能力差距，而美国本土竞争对手则将矛头对准了高敏感度、高信任需求的核心企业级市场——在这里，客户愿意为了安全和合规支付高额溢价。对于美国前沿实验室而言，他们手中目前还握有一张王牌：信任机制。
银行、电网运营商、国防部门、医疗机构等受到严格监管的行业，无论中国模型变得多么便宜或强大，都绝不会轻易触碰它们。对于政府机关和关键基础设施行业来说，那些技术完全不在可选项之列。因此，那些亟需为其 AI 决策机制、代码生成质量提供绝对信任背书的组织，会心甘情愿地为接入与西方民主价值观对齐的技术体系支付高昂溢价。这正是高定价模式目前唯一能够稳固立足的堡垒。
然而，这也正是 OpenAI 和 Anthropic 即将面临美国同侪联合围剿的腹地。以 Cohere 为例，这家由引发现代 AI 浪潮的关键论文联合作者 Aidan Gomez 创立的公司，正精准地瞄准这一市场缝隙。他们专门为受监管行业打造体积更小、运行效率更高的高安全级别模型。凭借这一战略，Cohere 的营收在去年实现了 6 倍的暴涨，并仍在持续高速扩张。
与此同时，早已在企业级市场确立信任霸权的 NVIDIA，也亲自下场发布了自家的开源模型 NemoTron，将其定位为既能替代中国产品、又能制衡封闭前沿实验室的完美选项。NVIDIA 清醒地意识到，若要繁荣生态，必须让所有企业都能自由构建 AI，而不是将权力垄断在少数几个寡头手中。正是这种战略格局，使得 NVIDIA 成为了企业级开源领域的真正霸主，像 Palantir、Salesforce、ServiceNow、CrowdStrike 等企业软件巨头，都已经全线接入 NVIDIA 的开源生态。
在这个围剿阵营中，还包括刚刚以数十亿美元估值完成融资的明星初创公司 Reflection AI。他们的目标同样明确：构建足以媲美前沿能力的本土开源模型，以此作为 DeepSeek 的美国平替。这三家代表性公司正在合力瓜分同一个战略真空：在企业早已信任的美国本土基础设施上，以极低的价格提供具备前沿竞争力的模型。这表明，如果更多本土初创公司向开源社区贡献力量，美国完全有能力在开源战线上追平中国的步伐。
不仅如此，由于孤立运营 AI 实验室的资金消耗过于庞大，连 Elon Musk 都已经放弃了单打独斗的念头。今年二月，他将旗下的 AI 公司 xAI 整体并入了 SpaceX，这笔交易极可能成为历史上规模最大的并购案。在此之前，xAI 每月的烧钱速度高达 10 亿美元；并购完成后，这笔巨额开销将由 SpaceX 每年数百亿美元的稳定营收来兜底。这位曾建造过史上最大 AI 超级计算机、在 AI 领域投入了极高个人资产的商业大亨，最终还是选择利用多元化业务来为高风险的 AI 研发“输血”。
相形之下，OpenAI 和 Anthropic 并没有这种退路。华尔街只能基于纯粹的“AI 经济学”来冷酷地审视它们的资产价值。残酷的真相是：这两家公司正在以“垄断企业级 AI 市场”的预期被资本市场高位定价，但种种迹象表明，他们仅仅掌握了其中一个非常有限的细分切片。当曾经承诺的“长达数十年的定价权”被接连不断的反面证据所削弱时，二级市场的投资者即将用真金白银投票，来决定谁才是这场估值游戏的赢家。
Original English Source
Chinese open source is closing the gap on capability, while American competitors—they're coming for the sensitive, high trust workloads where customers will pay up. American frontier labs—they do have one stronghold left: trust. Banks, grid operators, defense, healthcare, regulated industries that won't touch Chinese models, no matter how cheap or good they get. For governments, for regulated industries, critical industry, they're not going to be using and leveraging that technology. Chinese models just simply aren't an option, and so organizations that need to trust the models, the decisions that they're making, the code that they're writing, are going to be willing to pay a premium to access Western democratically aligned technology. That is where premium pricing holds, but it's also where OpenAI and Anthropic are about to get squeezed by American competitors building exactly for this gap. Take Cohere, founded by Aidan Gomez, one of the authors of the paper that kicked off the modern AI era. Cohere builds for that niche: smaller, more efficient models, specifically for regulated industries. I see things moving heavily in Cohere's direction. We've seen that over the past year with our revenue 6xing last year and continuing to grow very rapidly this year. And then there's NVIDIA, a company that U.S. enterprise already trusts. It's now shipping its own open source models called NemoTron, positioning them as the alternative to both Chinese options and the closed frontier labs. I think NVIDIA recognizes that they need everyone to be able to build AI, not just a few players, and so that's why they're supporting open source AI so much. That's why they—they became, in my opinion, the American king. Palantir, Salesforce, ServiceNow, CrowdStrike—they're all already adopting NVIDIA open source. And then there's Reflection AI, a startup that just raised at a multi-billion dollar valuation, building open source frontier models as an American alternative to DeepSeek. All three are going after the same gap: capable models at a fraction of frontier prices on infrastructure U.S. enterprises already trust. If we can see more and more American startups contributing to open source, we can definitely catch up to Chinese open source. OpenAI and Anthropic—they don't just have a China problem; they have an America problem too. Even Elon Musk isn't betting on a standalone AI lab anymore. In February, he merged his AI company, XAI, into SpaceX. Elon Musk's rocket company, SpaceX, acquiring Musk's artificial intelligence company, XAI, in what would be the largest M&A deal in history, and ahead of a possible blockbuster IPO for SpaceX. The stated reason was more capital for XAI. Before the deal, XAI was reportedly burning roughly a billion dollars a month, but after the deal, that burn would be backstopped by SpaceX's billions in annual revenue. So the man who built the largest AI supercomputer in history, who has bet more of his own money on AI than maybe anyone else, decided his AI lab needed to diversify, other businesses to support it. OpenAI and Anthropic—they do not have that option. So Wall Street will have to judge them on AI economics alone. So here's where we end up: OpenAI and Anthropic—they're being priced as if they own enterprise AI, but the evidence says they own one slice of it. And even Elon Musk hedged his bet. The pitch was pricing power for decades, but the evidence is stacking up against it. Public investors—they're about to decide who's right.极致安全：私有化部署与算力微缩化
当谈及企业级 AI 采用的真实阻碍时，成本（Cost） 与 安全（Security） 始终是卡在企业喉咙里的两大核心瓶颈。特别是对于电网、金融服务、电信和政府机关等高度敏感的实体而言，模型所接触的业务数据已直接上升至国家安全的层面。在这些极其严苛的环境中，Cohere 找到了一条完全差异化的护城河：极致的私有化安全部署。
无论是传统的本地化部署（On-prem），还是完全断开外网的物理气隙隔离（Airgapped），Cohere 都能够实现零网络风险的系统级交付。这种交付能力甚至极端到可以在深潜于海底一公里、毫无互联网连接的核潜艇内部署大模型。然而，这种近乎偏执的安全要求也带来了极端的物理计算限制。在超大规模云计算厂商动辄需要数万张 GPU 集群来支撑算力的今天，这些高安全级别的客户根本无法在封闭环境内获取如此庞大的算力阵列。
因此，技术上必须实现一种激进的妥协与重构：模型必须被极度压缩，使得它们能够在仅仅 2 到 4 张 GPU 的微缩算力足迹上流畅运行。这就彻底宣判了那些动辄拥有数十万亿参数规模的超级巨无霸模型，在这些核心基础设施场景中的出局。那些为了展示极致黑客攻防能力而被锁在保险箱里的“神秘巨型模型”（如传闻中的 Mythos），尽管拥有骇人的网络攻防能力，却因为无法塞进这两三张显卡里，而根本不会在真实的政企安全市场中与 Cohere 产生任何正面交锋。
更为严峻的系统级反馈是，随着大型语言模型开始全面接管并自动编写软件代码，代码安全性的根基正在发生动摇。如果你打算用 AI 模型来替代整个软件工程团队的代码输出，你绝对无法容忍这些代码源自一个可能暗中植入难以察觉的系统级后门的外部不可信架构。如果因为图便宜而将金融交易、医疗系统或电网调度的底层代码控制权暴露给潜在的第三方威胁，这种被利用和攻击的风险比历史上任何时期都要致命。
Original English Source
You heard from Aidan Gomez in the piece. He co-authored the paper that started the modern AI era, and he now runs Cohere, building for regulated industries. We think the full interview is worth your time. He gets into why he thinks the market is moving Cohere's direction, how he sees that China threat, and where premium pricing actually holds. Is premium AI frontier AI? Is it still worth it? I think you can see in the demand that people are willing to pay, and you can see in the level of capex spend, the reduction in free cash flow, that companies can see the future demand ramping. So yes, people will continue to spend to get access to extremely high quality models. In terms of the enterprise and how their adoption of AI is being shaped, two of the core bottlenecks to that are cost and security, and so trust is really deciding who they're going to choose. And to your point about DeepSeek V4, I think that's an example where, for governments, for regulated industries, critical industry, they're not going to be using and leveraging that technology. Chinese models just simply aren't an option, and so organizations that need to trust the models, the decisions that they're making, the code that they're writing, are going to be willing to pay a premium to access Western democratically aligned technology. Talk a little bit about Cohere's business model then, because my understanding: you guys serve the enterprise, and you're a model builder. How are you thinking about it, and the cost payoff and benefits? Yeah, absolutely. So we we build our models from scratch. Our business is exclusively on the enterprise side, so we focus in particular on the high security settings. So think grid operators, financial services, telco, government, and in those settings, the data and the systems that these models are accessing are really national security concerns, and so there's just no way that there's going to be a reliance on a non-democratically aligned technology stack. And what Cohere does uniquely well among all the the labs is very secure deployments. So whether it's on-prem or completely airgapped, we can deploy in a way that there is no cyber risk. We can deploy inside a customer's data center. We can deploy into a submarine, a kilometer under the surface of the ocean, with no ability to talk to the internet, and so that level of security just gives us a completely unique value proposition. It also presents constraints. We have to deploy on extremely limited compute footprints, and so we talk about how these large models are driving massive build out and spend for the hyperscalers, but for these high security settings, they can't get their hands on enough chips either, and so we need to be able to deploy on two to four GPUs, and so that presents a completely new technological constraint that basically rules out massive, massive models, and so we focus on something that is right sized for the market that we we serve. Right. It you know, two of the biggest frontier labs, OpenAI and Anthropic, certainly turning their attention to security with Mythos, and you know, Sam Altman, OpenAI were out with you know big plan last night. How do you compete with those ones? And those bigger models, you know, Mythos—the whole narrative surrounding it is it's so powerful they can't release it. When they do, how does Cohere fit into that picture? Yeah. So the massive models, you know, rumor is that it's something like 10 trillion parameters in scale. That does not fit on two to four GPUs, and so it's not going to be served inside of these extremely compute constrained environments. So we don't really run into those models when we're serving the market. What I will say is that these massive models that are capable of very sophisticated cyber offense and defense use cases are a very interesting new capability that exists in the world. We can now at scale find exploits inside very sensitive software, and so it reinforces the significance of private deployments, ensuring that our grid operators aren't putting the code that operates the power that flows into all of our homes, all of our our businesses in a place that it could be accessed by a third party, where that third party could use AI models to find exploits that they could use to shut off the grid, or the same thing in financial services or in healthcare, etc. If we expose the infrastructure, the software that powers our economy, whether it's in finance, whether it's in healthcare, whether it's in energy, the risk now that someone will be able to exploit vulnerabilities and use them against us is higher than it's ever been. So I think the notion of private deployment is essential. We've known that these cyber capabilities were going to emerge in models for a while, and as we've started to see software increasingly be written by these large language models instead of by humans, you're going to care a lot about which models you're using, whether they're like who they're coming from, essentially. So if you're going to be replacing your entire software engineering team with models that are writing your code, you probably don't want that coming from China because it might be subtly introducing vulnerabilities that you're not going to catch, and so the the trust barrier that I was describing, the trust barrier to enterprise adoption, continuously gets higher and higher.降本增效拐点与模型的隐性收敛
尽管中国模型在底层安全信任上存在争议，但诸如 AWS 和微软等科技巨头依然在公有云上大规模托管并推广这些低成本的开源模型。这背后的商业逻辑异常清晰：对于敏感度极低的通用场景、亦或是尚未达到规模效应且对成本极度敏感的初创公司而言，廉价的算力永远具有致命的吸引力。
然而，企业市场的整体心态正在发生根本性的转向。在过去的一年里，几乎所有企业都在不计代价地狂飙突进，试图通过快速上马 AI 项目来抢占风口。但如今，随着大量概念验证（POC）项目开始向真正的生产环境落地，当企业级规模化部署的账单摆在 CFO（首席财务官）的办公桌上时，昂贵的显卡账单瞬间改变了整个采购方程式。下一阶段的市场主轴，将从不计成本的“能力试探”，彻底转向极为苛刻的“成本结构优化”。
这种基于算力瓶颈倒逼出来的效率觉醒，正在驱动整个行业将资源向更小、更精悍的模型倾斜。尽管中国通过对巨型模型进行“知识蒸馏（Distillation）”（一种涉嫌违反服务条款的技术捷径）在短期内实现了能力的快速追赶，但总体而言，各大主要 AI 供应商之间的底层技术能力正在快速收敛并趋同。
在这种技术趋同的背景下，超大规模云厂商们宣称的 7000 亿美元基建狂潮，注定无法垄断整个未来的 AI 版图。因为随着对公有云安全隐患的认知加深，越来越多的企业意识到将核心命脉全部迁移上云是一个战略性错误。最终的市场形态很可能是割裂的：超大云厂商最多吃下 50% 的市场份额，而另一半则将无可避免地流向由新一代私有云构建者所主导的本地安全部署方案。而在这个过程中，整个 AI 产业不仅需要强大的工程突破，更需要领导者在面对公众对于能源消耗危机与职业焦虑的质疑时，展现出更多的同理心与沟通的智慧，而非单纯的技术碾压。
Original English Source
Right. And it sounds like you know Cohere's models have a very specific use case when you can have a model on-prem and you need the utmost security. When you say though that sort of the the whole, so I think also what you're talking about is sort of the back door, right? That's been a concern with these Chinese models. You can self-host them, but you don't know if there's a back door that can get into your data. Why then does AWS, the hyperscalers, Microsoft, Dell, why are they hosting and promoting these open source Chinese models? Well, for certain applications, I think they're great, right? Like in low sensitivity settings, it's pretty reasonable to to use a Chinese model. I think for startups that are looking for the lowest cost option and aren't operating at scale yet, it seems like a reasonable and very helpful tool. Of course, I think everyone would prefer to be using a democratically supported and aligned piece of technology. So I hope there is, and Cohere is contributing to much more democratic open source. We want to continue to put that out there, but you know there is a market for these, and they are cheap, as you as you say, and for the less secure settings, maybe there's a place for them. Right. And you know, you see sort of usage go up, and we hear all the time about how work compute constrained. So is that calculation for enterprises changing, especially if you're not in an industry that requires so much security? Is that calculation changing, especially because some of these open source models are getting very close to the frontier, and that sort of lag is closing? Yeah. Cost control, and then the other thing is like the compute bottleneck, right? So there's simply not enough compute to support using these massive models. So we need efficient small models that are good enough for the use cases that people are pursuing. Otherwise, we're just not going to have sufficient compute to meet demand. So there is a shift in the market. I think everyone over the past year has been racing to, you know, at all costs adopt AI. There's going to be a another phase that we enter into where CFOs are looking at the expenses being spent on some of these models, and they're going to try to optimize. They certainly aren't going to pull back, and it's going to continue to grow, but we're going to need to find ways to use smaller models, more efficient models. I hope that doesn't mean shifting over to a Chinese tech stack to satisfy that. I think there's companies like Cohere and others who are building, you know, more aligned technology that satisfies that efficiency need. Right. But it does sort of beg the question: Are the American bottle, are the American models better than Chinese ones? Like, what is the gap? Is DeepSeek V4 a real threat, especially when we see sort of it matching or surpassing some of the frontier ones on the benchmarks? I think it's it's very close. The gap between the two countries, in addition to Canada, in addition to France, Germany, it's closing rapidly. Right. And you're saying that there's options. I mean, Cohere's working on it. NVIDIA's working on it. The startup Reflection is working on open source. Is there enough companies working on it in America? And what happens to OpenAI and Anthropic if they aren't working on this sort of open source model race? I think there's a lot of folks contributing to open source. Cohere, we've been investing in it for years now. I'm not so concerned about the number of players. I think there will continue to be good open source options coming from democratic nations like the U.S., like Canada, like Germany, but I do think that the thing that China is doing that's setting it ahead is effectively distilling the large models, which is against the terms of service of a lot of these large models, and so that gives them a shortcut. They can very quickly catch up just from distillation of someone else's work, whereas for the rest of us that are trying to build a completely independent stack from scratch, it requires more effort and more work to build competitive great models. But I do see things converging. I do see capability capabilities converging between all the major providers. So what does that tell us about sort of the huge infrastructure spent that we see from the hyperscalers and Meta? Just talked about this coming off a massive earnings day. They're all planning to spend more than 700 billion dollars in infrastructure this year. From where you sit, especially as Cohere is building these very specialized models on just a few GPUs, does that money come back, or do you think that the trend is going towards these smaller, more specific models—the kind that Cohere is doing? I definitely think it's pointed towards more efficient models. There's going to be a big wave in the market seeking to reduce costs and make things more efficient. It's easy to do POCs on some of these large models, but then as soon as you push that into production and you're dealing with production level scale, suddenly the price tag just changes the buying equation, and cost becomes a huge constraint. So I see things moving heavily in Cohere's direction. We've seen that over the past year with our revenue 6xing last year and continuing to grow very rapidly this year. So I I do think there's going to need to be a lot more infrastructure because the demand is virtually insatiable, but as that infrastructure comes online, we're also going to make a big push towards efficiency. So there will be much more AI in the world, but those AIs will be using less GPUs to do the same work as they were doing six months ago. Are the hyperscalers plus OpenAI and Anthropic are they building for that kind of world? The hyperscalers are definitely building the infrastructure necessary to serve a significant amount of this demand. Although it's important to say that beyond cloud, on-prem deployments, private deployments, they're growing massively as well. And this is becoming an increasingly well-understood security threat that moving too much to the cloud is a mistake and exposes you to vulnerabilities, especially in this new frontier of potential cyber attacks thanks to these models. And so on-prem deployments, folks building their own data centers, neo-clouds, you're going to see a whole new array of compute providers to satisfy all this demand. So certainly the hyperscalers are building out the infrastructure that is needed, but they won't capture the entirety of the market. They'll probably capture half of it, and the rest will be these on-prem secure deployments. So where does that leave an OpenAI or Anthropic? That's not, you know, the cloud business is not the main business. It's frontier models. I think the demand for frontier models. I think the demand for all models is going to be extraordinary. I think there's going to be so it's basically a rising tide lifts all boats. Everything is going to benefit from this. I'm not too worried about the demand for their products or our own. There is a huge market out there. The applications for AI in the enterprise side, it feels like we're just scratching the surface. We're still doing very simple things. We know the models are capable. We know the models from 18 months ago are capable of way more than we're doing actually out in the industry. So even if you don't update the models for a year and a half, the industry is still catching up. So right the demand piece, I feel extremely confident. There's a lot to go do in the global economy. Good question from Carson Allen, who's a regular viewer of our live streams, asking: Are is your pricing increasing as you see these compute constraints? No, so we don't actually charge our customers for the compute. We deploy inside of their private deployment, so it's their own compute. What we see with our customers purchasing their own compute, yes, the the GPUs are becoming more expensive. There is a scarcity. You know, it's it's something that we try to help our customers with, but our model of sales is not to provide the compute with the model. Instead, we just provide the software. So are you in the server building business as well? Is that sort of what this looks like in the future? Yeah, no, no infrastructure from us. We we just focus purely on the models, the agentic platform that the models power, so that integrate with all the different tools and data that enterprises use to automate things, right? Like to automate workflows for big banks, you know, inside capital markets divisions, wealth management, investment banks. We automate work and augment employees inside those organizations, and we don't build the actual data centers themselves. Or help source it. They they do that, and you come in and implement the tools, the AI tools. Got it. Now, Aiden, part of the reason I want to talk to you so much as well is of course you co-author that famous paper that started essentially the modern AI age. This is a really general question. Watching where it's gone in you know a small number of years, you've got infighting at the top, Musk versus Altman's a big focus in San Francisco this week. You see growing backlash in the public. What do you think? Did you expect to be here? Do you think that the industry has mishandled the public image side of AI? I certainly, I mean, it's a completely general technology. It's like computing, right? Like it can be deployed into any sector. It can be deployed against any use case. It's the most general piece of technology humanity has created, and so it's going to have very sweeping impacts and and touch everyone's lives, both professionally and as consumers. So of course it was very important that that technology be developed in a way and communicated to the public in a way that was accessible, that they could try for themselves, that they could test and find the faults and and give feedback. In terms of how the sentiment has evolved throughout that process over the past three years, I I am concerned. I do think it's you know there's certain folks who feel skeptical of the technology, whether it's AI slop and in the creative industry, there's a lot of resistance to this technology. I think we need to better compensate artists and ensure that they can contribute to the development of the next generation of models, or whether it's the energy crisis, right? Like to power all of this adoption, we need a ton of energy, and if that is making things more expensive for people, I think that's a hugely regrettable outcome. So investing in energy infrastructure, ensuring that prices don't go up for consumers as a result, is a is a massive priority. I think people are rightly skeptical. I think criticism is a good thing, being aware of the weaknesses, being aware of the shortcomings about how the technology is being rolled out, the potential consequences is net positive in the long run. It's a it's a tricky thing that both sides are figuring out, both the public and the companies building the technology. We both want it to go well. That's a really thoughtful answer. Do you think we have the right spokespeople? Thinking of Dario at Anthropic and Sam Altman at OpenAI, Elon Musk. What's what's needed here? It it is kind of like a narrative, an image problem where you did mention really real things, but also you know there is does feel like this narrative problem at the top. Yeah, you know, since you're Canadian, I can say it. I try to bring a Canadian touch to to things. I think we need to be empathetic, kind, you know, thoughtful in this, and not just steamroll people. That's that's fair. Yeah, it's really great that the conversation is happening. I think it's essential. There should be criticism. We should respond to that criticism with action to try to mitigate the potential downsides for people. Well, we hope to hear a lot more from you, especially that sort of nuanced, thoughtful answer acknowledging both sides of this and the real criticisms. Aiden, thank you so much for taking the time. We really appreciate it. And from fellow Canadian to another, thanks again. Thank you for having me.📌 文中提及的人物和组织
人物: Aidan Gomez, Elon Musk
公司/组织: OpenAI, Anthropic, DeepSeek, Cohere, NVIDIA, SpaceX
产品/模型: Claude, Claude Opus