SemiAnalysis 的 Jeremie Eliahou Ontiveros 谈 AI 与数据中心的供需动态
Andrew WalkerJeremie Eliahou Ontiveros
Ontiveros 不接受把“无限”作为投资前提，但其审慎预测仍指向 AI 数据中心短缺加剧。 全球数据中心 IT 容量在2019年至2023年间每年增加约4 GW；2024年，仅 NVIDIA 硬件就新增约5 GW 需求，AI 加速器合计新增7-8 GW。SemiAnalysis 预计，未来数年数据中心建设速度仍将落后于需求，基础设施链条各环节因此都有上行空间。
DeepSeek 并未推翻算力支出逻辑，其效率提升仍处于一条已经极其陡峭的趋势之内。 Ontiveros 预计，2年后，达到 GPT-3 水平的模型，其推理成本应降至当前约1/1,200。他认为 Gemini 2.0 Flash 的服务成本更低、表现也优于 DeepSeek。DeepSeek 自身仍受推理容量约束，说明更便宜的智能依然可能带来对更多算力和数据中心容量的需求。
易得文本数据耗尽，增加的是算力需求，而不是终结训练规模扩张。 合成数据生成、强化学习、更严格的验证，以及用超大模型改进小模型，都会消耗大量算力；Ontiveros 认为 GPT-4o 和 Claude 3.5 Sonnet 都是这条更广泛路径的产物。当前最大集群约有100,000块 Hopper GPU、功耗130 MW；头部实验室正规划到2027年落地 GW 级、部分达到2 GW 的园区。
NVIDIA 被低估的推理优势，可能在于 scale-up 网络，而非市场熟悉的训练软件护城河。 GB200 NVL72 通过 NVLink 将72块 GPU 以全互联方式连接，带宽无出其右；机架级系统使用3,000-4,000根铜缆。更长上下文的推理模型需要更多内存和网络，SemiAnalysis 因此认为 NVIDIA 的护城河“可能实际上在推理侧强于训练侧”（might actually be stronger in inference than in training）。
AI 上行周期的拐点，不是模型变得更高效本身，而是采用率和变现无法支撑实体投资。 Ontiveros 预计，超大规模云厂商的资本开支仍将超预期20-30%，但 GW 级园区最终需要的不只是用户增长。他会关注产品流量、能够获得的 OpenAI 收入指标以及可能的 Claude 收入指标，以及消费者是否继续每月支付20美元或200美元。他说，投资规模最终会大到市场无法承受。
供给短缺使“time to power”（接入电力所需时间）成为最有价值的基础设施产品。 Schneider Electric、Eaton、Vertiv、台湾液冷企业，以及规模较小的工程或制冷公司都能参与其中；大量设备的商品化程度高于 NVIDIA GPU。Talen 式表后核电方案看起来比预想中棘手得多。
Bitcoin 矿工拥有稀缺的已接入电力场址，但将其改造成 AI 园区是团队与资本问题，不是简单更换设备。 矿场成本约为50万美元/MW，AI 数据中心则超过1,000万美元/MW，而客户还需要可信赖的运营团队。Walker 的比喻概括了其中的期权价值：矿工是“掉进金矿的一队小丑车”（clown cars that fell into a gold mine）；但 Core Scientific 和 Applied Digital 更突出，因为它们较早建立了数据中心能力和行业关系。
矿工手里的电力并不天然适配 AI。 Walker 指出，西德州限电协议以及间歇性的风电、光伏电力，可能不适合 AI 全天候运行的负载。Ontiveros 认为，发电机、现场电池，以及数据中心本已使用的 UPS 系统可以覆盖停电时段，电池储备也可能延长至通常5-10分钟以外。
尽管海外能源更便宜，美国仍是默认选址，胜在速度、劳动力、供应链和经验。 超大规模云厂商自建的大型数据中心中，约75-80%原本就在美国，使其积累了100 MW 规模建设经验。Ontiveros 表示，“目前没有其他国家比美国更懂得如何建设大型数据中心。”
1. AI 已打破旧有数据中心增长曲线
Ontiveros 一开始就拒绝“无限”这个词，随后量化了市场热情的来源。2019年至2023年，全球数据中心 IT 容量每年增加约4 GW；2024年，仅 NVIDIA 硬件就带来约5 GW 的新增需求，而 AI 加速器——包括定制 ASIC 和 AMD 的小部分贡献——合计带来约7-8 GW。
SemiAnalysis 的测算方式，是把每一块采购的 GPU、CPU 或其他 IT 设备，折算为其运行所需的电力和物理空间。Ontiveros 表示，自2024年以来，市场“几乎全部”由 AI 加速器驱动，生成式 AI 由此明显偏离了此前的行业增长轨迹。
其对2024年、2025年及随后数年的供给侧预测显示，运营商的建设速度不够快。按 Ontiveros 的框架，投资含义很直接：不断扩大的供给缺口，为数据中心容量、电气系统、制冷设备以及更快获得电力的供应商留下了空间。
2. DeepSeek 位于既有效率曲线之下，而非超越曲线
Walker 的核心反驳是光纤过度建设的类比：数据使用量可以持续数十年复合增长，但在景气高点建设基础设施的投资者仍可能亏钱。如果 AI 开发者在能力提升放缓后开始优化功耗，今天曼哈顿级别的数据中心可能变成闲置的过剩容量。
Ontiveros 的回答是，优化已经在以惊人的速度进行。他表示，2年后，达到 GPT-3 水平的模型，其推理成本应降至当前约1/1,200；因此，DeepSeek 并没有改变这条趋势。
他给出的更有力对比对象是 Gemini 2.0 Flash：服务成本更低，表现也优于 DeepSeek。DeepSeek 的竞争成果确实存在，但这并不意味着前沿模型开发者突然只需要比此前预期更少的基础设施。
DeepSeek 自身的约束反而强化了这一点。Ontiveros 称，DeepSeek“完全没有推理服务容量”，拒绝新用户请求，运行在最大 batch size 下，交互性也非常低。他还将其 CEO 与中国排名第二的政治人物会面，以及次日宣布的1,400亿美元国家补贴联系起来，认为这反映出的是对更多算力的需求，而不是更少算力。
3. 真实数据稀缺使训练更加耗算力
目前最大的 GPU 集群约包含100,000块 Hopper GPU，IT 功耗约为130 MW。未来2至2.5年内，Ontiveros 称主要实验室正在规划单体 GW 级园区，部分项目预计到2027年达到2 GW。
Walker 问道，既然“互联网上的大部分内容”都已经进入训练语料，集群为什么还要继续扩大？Ontiveros 的简短回答是：正因为真实数据稀缺，实验室必须消耗算力生成合成数据、通过强化学习构建推理能力，并进行成本越来越高的质量验证。
Walker 保留了关键的失败模式：高度合成的数据集可能递归放大错误，直到模型自信地编造出一场发生在1942年的 Apple 产品发布会。Ontiveros 并不否认验证难题，他的回答是“再次遵循 scaling law”——投入更多算力，建立更强的校验机制并生成质量更高的合成数据。
更大的模型本身也不一定要成为面向消费者的经济型产品。实验室可以用它们微调和改进更小的模型，Ontiveros 认为这是 GPT-4o 和 Claude 3.5 Sonnet 诞生路径的一部分。前沿模型的价值因此可以体现在教会更便宜的模型，而不是直接服务每一次查询。
4. 尚未开发的视频提供了另一个数量级的数据池
在企业付费让楼宇业主记录现实世界之前，Ontiveros 认为，眼下有一个更大的数据集正处于闲置状态。模型会吸收 YouTube 字幕和其他文本衍生内容，但通常不会用底层视频进行训练。
纳入电影和 YouTube 视频，将提供比 Common Crawl 等网页文本来源“多出几个数量级的数据”。Walker 指出，这个语料库存在偏斜——1,000个 MrBeast 视频并不能代表普通人的生活——但 Ontiveros 强调的是规模：在实验室需要设计方案付费让人们生成新的现实世界数据之前，现有视频已经足够使用数年。
用户平台也在为其提供商生成更多数据。每一次与 ChatGPT、Claude 或类似平台的互动，都会存储在提供商服务器上，因此广泛分发既是变现入口，也是持续获取使用数据的来源。
5. NVIDIA 的推理护城河建立在网络之上
Walker 完整陈述了看空逻辑：NVIDIA 估值昂贵，半导体具有周期性，Microsoft、Amazon、Google、Apple 等大客户都可以打造竞争产品。它们并不需要所有场景都达到最先进的性能；性能达到90%，或者落后18个月的加速器，也可能夺取有意义的市场份额。
Ontiveros 同意，NVIDIA 的软件优势在推理侧可能不如训练侧重要，但他认为市场低估了公司的整体工程能力。硬件和软件是人们熟悉的两层；高带宽网络是第三层，而且正变得越来越关键。
他给出的最佳样本是 GB200 NVL72：72块 GPU 通过 NVLink 实现全互联，带宽在当时没有任何超大规模云厂商方案能够匹敌。这一机架级设计使用“3,000或4,000根铜缆”，展现出一组表面上可互换的芯片背后所需的系统工程能力。
推理模型和更长的上下文窗口需要更多内存；扩展内存又需要强大的 scale-up 网络。这条因果链引出了 SemiAnalysis 的反常识结论：即使 NVIDIA 的软件差异化收窄，其护城河“可能实际上在推理侧强于训练侧”。
6. 采用率与收入决定上行周期何时结束
Ontiveros 对周期的判断非常直接：“如果这是上行周期，事情就会向上；如果是下行周期，事情就会向下。”只要盈利预期仍在不断上调，高估值的重要性就相对较低，这也是 SemiAnalysis 继续看好 NVIDIA、电力基础设施及其他 AI 相关公司的原因。
他预计，超大规模云厂商的资本开支仍将超出预测，不是5%，而可能是20-30%。DeepSeek 不会改变这一判断，因为 Google 已经拥有一个 Ontiveros 认为更好、更便宜的模型；它更直接的影响，可能是压低最先进 API 的高利润率。
真正可能打破周期的是采用率恶化。Ontiveros 会跟踪 ChatGPT、Gemini 和 Claude 的流量，同时关注能够获得的 OpenAI 收入指标以及可能的 Claude 收入指标。最初的抢跑，反映的是下一个用户数超过10亿的平台可能出现；但未来2年规划的实体扩张最终需要收入支撑。
这类似于 Google 收购尚未产生收入的 YouTube：超大规模云厂商历来先锁定用户，再建设变现能力。AI 可能将能力越来越强的免费模型——如 GPT-4o Mini 或 Gemini 2.0 Flash——与高级工具、广告、API，以及每月20美元或200美元的订阅结合起来；但 GW 级投资最终要求这些尝试奏效。
7. 接入电力的时间让价值扩散至发电商之外
Ontiveros 更偏好把数据中心基础设施视为 GPU 部署的资本开支驱动型代理。Schneider Electric、Eaton 和 Vertiv 在部分电气及制冷设备领域占据主导；台湾液冷企业，以及规模较小的本地工程或冷却塔企业，也能从中受益，因为大量设备的商品化程度高于 NVIDIA GPU。
供给缺口使“time to power”尤其有价值。Talen 式表后核电安排起初看起来像是解决方案，但 Ontiveros 表示，这类方案后来证明比预期棘手得多，迫使开发商考虑其他办法。
他也同意，发电商和天然气是值得研究的领域，但个人关注重点仍是支撑 GPU 需求所需的基础设施。供需错配会让一些方案变得有价值，而在正常环境下它们可能并不具备经济性。
8. Bitcoin 矿工拥有电力，但转型 AI 的成本高出20倍
矿工过去主要围绕西德州、Wyoming 或 North Dakota 的充裕闲置电力进行优化。这段“电力优先”的历史留下了超大规模云厂商如今看重的资产：一个500 MW 的 AI 项目可能需要约50亿美元，而 Walker 举的例子是，收购现有场址可以在6个月内上线，不必等到2029年自行建成。
矿场的物理外壳并不是 AI 数据中心。Ontiveros 估算，矿场基础设施成本约为50万美元/MW，而 AI 数据中心超过1,000万美元/MW；尤其在矿工需要外包大量工作时，尚未购买 GPU，成本就已经跃升约20倍。因此，客户需要相信矿工的团队、技术执行能力和交付能力。
Walker 提出了最有力的反对意见：Core Scientific 数月前就签署了与 CoreWeave 的标志性安排，但尽管需求看起来极其旺盛，几乎没有其他矿工拿下类似的100 MW 以上承诺。如果这些资产真是显而易见的“金矿”，熟悉行业的内部人士就应该更快完成转型、保护股权，而不是激进发行股票融资。
Ontiveros 给出两种解释，并未回避这一担忧。一些 Bitcoin 矿业从业者近乎宗教般地坚持 Bitcoin，并预计其价格将达到100万美元；另一些人则缺乏推进数十亿美元项目所需的数据中心高管、技术团队和客户关系。“你总得做出选择。”
9. Core Scientific 和 Applied Digital 的执行路径最清晰
Core Scientific 的优势不只是电力。公司更早就与 CoreWeave 建立关系，并拥有 Ethereum 挖矿经验；在旗舰安排之前，已与 CoreWeave 签署 Austin 的16 MW 合同，聘用了有经验的托管业务人员，并提前建立客户关系。这段历史有助于解释为什么 CoreWeave 承担了大部分转型资本开支。Walker 称，市场传闻中的早期报价对应约10亿美元市值，而最终合同的净现值约为18亿美元。
Ontiveros 将 Applied Digital 称为可信的第二名。公司在更大的 North Dakota 600 MW 园区内，已经建成100 MW 数据中心容量，建设成本约10亿美元；公司还获得了 Macquarie 的新支持，并签署了一份不具约束力的意向书，Ontiveros 认为该意向很可能发展为约400 MW 的交易。
IREN 是他更具投机性的候选，因为公司已在西德州锁定1.4 GW。Walker 指出，当地的限电协议以及间歇性的风电、光伏电力，可能不适合 AI 全天候运行的负载。Ontiveros 认为，发电机、现场储能以及数据中心已经使用的 UPS 电池可以应对停电；通常5-10分钟的电池储备，可能延长至20或30分钟，或者延长到实际需要的时长。
其他例子包括 TeraWulf 约70 MW 的合同，以及 Hut 8 在 Louisiana 的项目。Ontiveros 预计，后者在2025年达到100 MW，并在存在一定不确定性的情况下，于2026年扩展至约200 MW。他认为 Riot 和 Marathon 仍将继续专注挖矿。因此，单纯的兆瓦数无法替代赢得 AI 合同、获得托管业务15-20倍 EBITDA 估值——有时甚至更高——所需的团队和关系。
10. 美国继续在执行速度上胜出
Walker 不解的是，超大规模云厂商为什么不去 Qatar 追逐几乎免费的天然气，或去 Brazil 等资源丰富的地方。Ontiveros 的答案是上市速度：超大规模云厂商自建的大型数据中心中，约75-80%原本就在美国，因此美国的供应链、劳动力、并网电力和经验都支持100 MW 规模建设。
便宜能源无法替代专业的电气、机械和管道劳动力。Walker 还提出，政府可能没收一项50亿美元的数据中心与 GPU 投资。Ontiveros 认为这是重大风险，但他表示，即使不考虑这一点，劳动力问题本身也足以让海外建设变得困难。
头部 AI 实验室、超大规模云厂商及其周边生态都在美国，进一步强化了产业在国内的集中。Ontiveros 的结论是：“目前没有其他国家比美国更懂得如何建设大型数据中心。”
完整逐字稿
Before we get started, a quick disclaimer: Nothing on this podcast is investing advice. Please consult a financial adviser and do your own research. That's always true, but we were talking ahead of time about a long list of companies we can talk about today, so we might end up hitting 50 different companies. Please remember that we're not recommending any of them.
I'm super excited to have you on today. You come highly recommended, obviously, and you're over at SemiAnalysis. We started planning this podcast before the DeepSeek stuff even came out, and AI, semiconductors, data centers, and everything around them have been the hottest areas of the market to talk about and debate for the past year. They've gotten even hotter and more interesting over the past 2 weeks after DeepSeek, so we have a lot to talk about.
I'm going to start with my main interest in AI and everything around it. Aside from how I can use it as an investor to improve my work, my main interest is data centers and electrification. I know that's an area you specialize in, so we can get into different companies and industries, but broadly, if I said that most investors I talk to are still working from the thesis that AI equals unlimited demand for data centers—that you should buy nuclear energy and buy anything you can because there's going to be unlimited demand—what would you think about that trend? How are you thinking about it evolving as we sit here? We're recording on February 5, 2025.
Unlimited is a pretty strong word. At SemiAnalysis, we do have a comprehensive tracker of supply-and-demand dynamics. Demand is basically this: Every time someone buys a GPU, or even a CPU—whatever IT equipment they want to put into a data center—there is corresponding demand for power and data center space. That's something we track very closely.
Since 2024, and going forward, everything has been driven by generative AI. NVIDIA hardware and all the custom ASICs are driving the whole market. AMD contributes a little bit, but it's pretty much all AI accelerators.
To give some perspective, the global data center industry from 2019 to 2023 grew by about 4 gigawatts per year. That's the amount of IT capacity added annually. In 2024, NVIDIA alone added 5 gigawatts of demand. AI is roughly 7 to 8 gigawatts, including all the custom ASICs and everything else. It's a complete change in trend, and it's all driven by GenAI.
That's the demand side. On the supply side, which we spend a lot of time tracking, the question is whether people are building data centers quickly enough to power AI and provide the space and power where they can place their GPUs. The answer, based on our forecasts for 2024, 2025, and a few years beyond, is no. They need to build data centers faster, which means upside for the companies exposed to that supply chain.
Let me start with a basic question. DeepSeek highlighted this concern for me. I worry as an investor about the power companies people are buying—Talen, Vistra, Constellation Energy, and all these other companies that have become investor darlings because of what seems to be unlimited demand. Talen, in particular, has a great nuclear asset.
Right now, there's an AI race, and these companies don't have to optimize for anything. They're just trying to go, go, go and get the best model. But I worry that if we run this out 12 or 24 months, at some point you have to start optimizing. The gains get smaller, and you start optimizing. It seems like a natural first place to optimize would be to say, "We're literally consuming Manhattan-level power in one data center. How hard would it be to design one that uses a little less power?"
You could get into a really overbuilt scenario. We've seen it before with fiber. Data demand went straight up for 20 years—I think it was growing at about 30% from 2000 to 2020—but if you built fiber in 2020, there was a huge oversupply. I worry that we're building all these data centers, not to mention bringing all this new energy online, and then 12 months from now we start optimizing and realize we way overbuilt.
That's a totally fair point. I would start by highlighting that the progress on the efficiency of inference for AI models is tremendous. Everyone is freaking out about DeepSeek, but in reality, DeepSeek isn't changing the trend. If anything, DeepSeek is below the trend.
The trend is that, in 2 years, a model with the quality of GPT-3 will cost about 1,200 times less to run inference on. The amount of improvement in the AI space is gigantic. Something we also like to highlight is that everyone is freaking out about DeepSeek, but if you look at other competing models, Gemini 2.0 Flash is much cheaper to run inference on than DeepSeek and is actually a better model.
People are freaking out about DeepSeek, but it's nothing out of the ordinary. It's very good, to be clear, but it's not fundamentally better than the best models from Google, OpenAI, and others. The trend of massive improvement was always the case.
People talk about the Jevons paradox, but the trend that matters here is the overall scaling law. You want to have the best model, and we've seen tremendous improvement in the capabilities of those models. Maybe you've tried Deep Research recently; the things it can do are getting pretty insane. You've surely seen the benchmarks for o3, including the ARC-AGI-1 benchmark. Those are pretty incredible as well.
The point is that we're going to keep building better and better models. That means more demand for training, but even more demand for inference. There's also a funny thing to note: Despite all its efficiency, DeepSeek has zero capacity to serve inference. It's extremely constrained.
DeepSeek has 2 problems. On the training side, its CEO met with the No. 2 person in the Chinese Communist Party, and the next day China announced $140 billion of state subsidies. It's pretty clear that DeepSeek said, "I need subsidies to build more compute." On the inference side, it can't serve new users. It has to turn down new user requests, and it operates with a maximum batch size and very low interactivity. Its user interface is not very good because it has to maximize the capacity of its GPUs.
The trend is that even if you're building the most efficient model—like people think DeepSeek is doing—you still want much more compute, many more data centers, and much more capacity overall.
I guess I'm just a journalist. Every time I email you or someone like you and ask what you think about something, the DeepSeek news, for example, everybody freaks out. It was about 2 weeks ago that every journalist freaked out, and I'm sure that every specialist knew about it 2 months before it filtered through to the general public.
When did you first hear about DeepSeek?
DeepSeek actually started popping up about a month ago.
If I were to talk to you and ask whether you're at all worried about scaling laws at this point, or what worries you about the entire AI trade, what would be on your mind?
The way we think about it is that we split it into 2 phases: training and inference.
If you think about training alone, over the next 2 years all the big AI labs still have massive plans. A nice way to visualize that is that today the largest GPU clusters are about 100,000 Hopper GPUs. In terms of power, that's roughly 130 megawatts of IT power. All these companies want to scale that massively, and they have plans for gigawatt-scale data centers, including 2-gigawatt-scale facilities for a few specific sites, toward 2027.
In 2 to 2½ years, they want to scale their biggest single cluster from 100,000 GPUs and 130 megawatts to gigawatt scale. That's massive scaling, and that's for training alone. We're talking about tens of billions of dollars in capital expenditures, which they partially have the funds to finance at this point.
At some point, though, they're going to have to face adoption of the underlying technology and generate inference revenue.
Can I pause you there and ask a really stupid question? I've always wondered about this. You mentioned that we're scaling from roughly 100,000-GPU clusters to gigawatt-scale clusters. I keep hearing that we're near the end of the amount of data available to train these models on.
If I told you that, why do we need to go from 10,000 to 100,000, or whatever the numbers are, when there's only so much more data we can put into the models? Yes, the world generates more data, and we can go find books from the 1500s, but most of the data has already been input into these training models. Most of the internet is in them. Why do we need to go from 10,000 to 100,000 when it seems like the amount of new data we can add is getting smaller? Does that question make sense?
The answer is exactly that there's not enough real data, so we have to use a lot of compute to generate synthetic data. When you go from 10,000 to 100,000, a lot of that additional compute is used to generate synthetic data and train on it.
It's not only synthetic data. Techniques for adding more reasoning capabilities also use reinforcement learning, which uses a lot of synthetic data. Another interesting area is pre-training scaling laws—building models with more parameters and more data. One issue with pre-training scaling laws is that at some point the models become too expensive to run inference on.
You can use those huge models to fine-tune smaller models and make the smaller models much better. That's a technique all the AI labs have used. That's how they came up with models like GPT-4o and Claude 3.5 Sonnet.
That's why I have experts on, so I can ask super-generous questions. You read about this stuff all day, while I read about a lot of other things.
If you start having models that run more and more on synthetic data instead of real-world data, don't you risk the synthetic data becoming corrupted? I understand that they have checks, but you can't check everything in a model. If 90% of the data is synthetic and 10% is real, don't you risk ending up with models that believe the sky is red or the ocean is green?
Those would be obvious things to correct, but we're all familiar with the phenomenon where you ask Google, "What was Apple's big product launch in 1942?" and it tells you Apple was excited to launch the Catapult that year. You wonder where that came from, and no one can point to it. Doesn't synthetic data create an increasing risk of hidden, phantom answers?
The simple answer is the scaling law again: You just need to spend more compute to have better checks and make sure the synthetic data is of better quality.
Let me ask another question about data. Most of the data right now is stuff that's online. Do you foresee a rush to generate more data tokens, where companies say, "Facebook has cameras and interactions everywhere. Google has cars driving around. Forget that—I'm going to put a camera on every corner and in every building so I can record everything happening in the world"?
Maybe ChatGPT could say, "We really need a lot of real-time physical data, so we're going to go to every building owner and say, 'We'll pay you $1,000 per month if you let us record everything and collect the audio and video.'" Is that something you could see happening?
You could argue that using platforms like ChatGPT, Claude, or whatever is one way to contribute more data to those companies. Every time you have an interaction, it is stored on their servers. Having a platform with many users is very valuable because it generates much more data.
I get that. One argument for Facebook is that it has all this private data—the interactions, direct messages, and everything you like on Instagram. Facebook has more data on you than anyone else, which is great for advertising.
I was talking more about bringing it into the real world. I could imagine a world where ChatGPT says, "We really need a lot of real-time physical data, so we're going to go out and pay people to record everything happening in the world." That would generate enormous amounts of additional data in some way, shape, or form.
There's a step before that, which is starting to use all the video data that's already out there.
You mean YouTube data and things like that?
That's right—all the movies and all the YouTube videos. We don't really use that data currently. We use transcripts and things like that, but we don't actually use the video data itself.
Using all of YouTube would be orders of magnitude more data than what people use from internet text data, like Common Crawl. That would be the first step. We need to use the existing video data, which is massively larger than what exists in text format.
YouTube is interesting because you and I have completely different YouTube feeds, and you'd never even realize it. There are millions and millions of hours of video uploaded to YouTube every day, but it isn't really being used for training yet.
I would also argue that if you started analyzing 1,000 MrBeast videos for data, that would be interesting, but it's very different from me sitting in my closet interviewing you, or petting my dog on the street. It would be a very skewed view of the world.
Imagine all those great podcasts that YouTube could train on. It would learn a lot, especially about investing. There's just a tremendous amount of data out there in video. Again, it's orders of magnitude more than what exists in text format, so before we get to your scenario of paying people to generate data, there are multiple years of video data that we're yet to uncover.
I want to ask about some more data center questions, but let me completely switch gears for a moment.
Matt Levine called it the most market-moving short report ever—the short report on NVIDIA that came out the weekend of DeepSeek. People think it was partly the reason NVIDIA opened down 10% or 15%, losing $600 billion of market capitalization.
The basics of the report were that NVIDIA has had great success, but success attracts competitors. For various reasons, NVIDIA's future is not as bright as the stock market is giving it credit for. One reason was that all the big technology companies and hyperscalers—Amazon, Apple, and Google—are trying to build NVIDIA competitors.
As we move from training to inference, people aren't sure NVIDIA has such a big advantage over its competitors. There are all kinds of reasons you can list. I'd love to hear your thoughts on NVIDIA.
I'm in a privileged position because I'm part of the SemiAnalysis team. We're 23 people with a lot of technical backgrounds and very different areas of expertise. We have an AI engineer who knows everything about model architectures, networking experts, and so on.
We answer these questions from a technologically informed point of view. What we see when analyzing the engineering of all these systems and comparing it with common market narratives is that NVIDIA's engineering talent tends to be underappreciated.
People know that NVIDIA is a great hardware designer. That's not a secret. People increasingly understand that it has a great software moat, although there is a discussion about whether that software moat applies to inference.
The third area, which I think is less often discussed, is networking. Our position at SemiAnalysis is that NVIDIA's moat might actually be stronger in inference than in training. Even if we agree with the market position that the software aspect is less important for inference than for training, everything related to networking—especially the scale-up network—is more important.
Think about the engineering process behind building the NVIDIA GB200 NVL72, where you have 72 GPUs interconnected in an all-to-all configuration with NVLink at incredible bandwidth. That bandwidth is unmatched by any hyperscaler. The amount of engineering involved is pretty incredible.
If you look at the rack-scale solution, you have 3,000 or 4,000 copper cables. It's a state-of-the-art system, and no one else has a solution as good as that. That's a huge moat for inference.
As we move into more reasoning models, especially when reasoning models have a longer context window, they need more memory. To scale memory, you need that scale-up network, and that scale-up network is one of NVIDIA's fortes.
Let me pause there. I have no view on the NVIDIA stock, so I'll be honest about that. It seems like a great company that's growing rapidly, but it is priced pretty richly, and semiconductors overall are very cyclical. Every semiconductor company has a cycle at some point.
One point in that short report was that nobody is questioning whether NVIDIA is a great company. It's just priced very richly for what is ultimately a cyclical industry. The second point was that Microsoft, Amazon, Apple, and Google are not stupid. NVIDIA is earning huge margins, so why couldn't they recreate 90% of NVIDIA's performance?
Why do they need 100% of state-of-the-art performance all the time for every AI model? Why can't it be 90%? If I gave Microsoft's engineers the NVIDIA technology from a year ago, couldn't they recreate it within 6 months? Why isn't Microsoft creating its own stuff 18 months behind NVIDIA, with half the cost of the capital expenditures, and then using NVIDIA's state-of-the-art technology for the other half?
I know I'm talking about hypothetical worlds, but the underlying question is this: Nobody disputes NVIDIA's moat, but the stock is quite richly priced. Why can't there be a world where NVIDIA is still a great company but the stock is overvalued?
That's why investing is hard. I think my friend Byrne Hobart from The Diff said about 2 years ago that the way you'll know AI is here is when NVIDIA's stock starts going up and all your smartest friends who are into AI start buying calls.
I think about you and the SemiAnalysis team all the time because Doug said that demand for power is unlimited and that you should just keep buying. But at some point, where does it end? Where's the limit?
I used to be a buy-side guy. You can invest however you want, but generally you want to be with the cycle. If it's an up cycle, things are going to go up, and if it's a down cycle, things are going to go down.
Even if a company is richly valued during a strong up cycle, nobody cares. It keeps going up because earnings estimates keep getting revised upward. That's why we generally have a positive stance on NVIDIA and many other AI-related stocks, including power companies. The earnings are going to rise so much more than consensus expects.
I would briefly talk about hyperscaler capital expenditures. You've seen Google's capex, Meta's capex, and Microsoft's capex. Microsoft hasn't given full-year guidance, but at the end of calendar 2025, when we look back at what people project today versus what they actually did, I think the answer will be that capex went up massively.
It keeps beating estimates—not by 5%, but by 20% or 30%. That's how we see the trend playing out in this up cycle: very strong upward revisions in what people are spending.
Those are words of wisdom. If we're in an up cycle, numbers are going up and you can buy everything. But nobody has a crystal ball. When does the down cycle start?
I do think there were people who thought that Project Stargate would be announced, with $500 billion from Masayoshi Son, Oracle, and Trump, and that everything was going straight up. People saw it as a huge check and a silly number, and said, "This is your license to buy anything."
Then DeepSeek came out, and a lot of people got worried. My first question is about the timing. Stargate was announced after Sam Altman had committed to an $80 billion check, and Mark Zuckerberg had talked about spending $50 billion or more in capex. I would imagine they knew about DeepSeek before making those commitments, because they continued to see upside.
Would you agree or disagree?
I fully agree. As I said, Gemini 2.0 Flash is actually better and cheaper than DeepSeek, so DeepSeek doesn't change the trend. It might have an impact on margins because the companies want to charge high API prices for state-of-the-art models, but overall it doesn't change the trend.
You just said we're in an up cycle, the numbers are going up, and you can buy everything while the numbers are going up. What breaks that?
A couple of things could break it. First, it's very important to carefully track adoption of AI. You can do serious work, like looking at website visits for ChatGPT, Gemini, Claude, and whatever else. You can also use whatever tools are available to track OpenAI's revenue and perhaps Claude's revenue. The overall adoption obviously matters.
I think the reason this has taken on such proportions is that we've somehow seen the AI work. Hyperscalers built their businesses around users first, not revenue first. You had acquisitions like YouTube in 2007 or 2008, where people said it was way overpriced because YouTube had no revenue. In Google's mind, it was a great platform with great users. They wanted the users first and would figure out the revenue model later.
WhatsApp was a similar story. You could argue it was less successful, but Instagram has obviously been extremely successful. Hyperscalers are user-first and revenue-second, and it turned out to work pretty well for them.
If you apply that logic to AI, it doesn't work perfectly because AI is more capital-intensive. But in terms of users, ChatGPT demonstrated 200 million users in about 2 months. It's pretty clear that GenAI has the capability to become the next billion-user-plus platform.
That's why everybody is in such a rush. A billion-user-plus platform means tens of billions of dollars in potential revenue, perhaps even hundreds of billions. The consumer adoption of these tools is extremely strong, and having hundreds of millions or billions of users was enough to support the spending level we're seeing today.
However, what we forecast will happen over the next 2 years—and there is meaningful physical evidence of it, especially in data centers—is that they're building huge gigawatt-scale data centers, many gigawatt-scale data centers dedicated to AI. At some point, to justify those investments, you're going to need more than users. You're going to need revenue.
Tracking whatever indicators you can to understand adoption is crucial. If people stop using ChatGPT, or if these companies struggle to sell $20-per-month or $200-per-month subscriptions, at some point that will simply be too much for the market.
I just paid for my first ChatGPT subscription. I started using it more, and I realized I needed to commit to it. If you're a professional in almost any intellectual field—including investing, although sometimes I say yes and sometimes I say no—you probably need to be using it constantly or you're falling behind pretty rapidly.
But is a subscription ultimately how you pay for these things? Bloomberg is a subscription product, although it's also a networking tool. Most of these tools, including Google Search, ultimately monetize through advertising. I'd be surprised if subscriptions are how they all monetize, especially if data is the way they get better.
One way to generate proprietary data is to have more searches and more usage go through your platform. How do you get the most searches? You make the product free and monetize it through advertising.
I would say that the business models people are exploring right now are generally based on having a free model with decent capabilities. That would be GPT-4o Mini or Gemini 2.0 Flash.
Those free models will see their capabilities increase. You can think of the free version as an advertising tool and a way to build a massive user base—hundreds of millions, perhaps billions of users. On top of that, once users see the capabilities, you can sell all kinds of additional products. Advertising could definitely be part of it.
As someone with your finger on the pulse of this more than 99.9% of the population, what AI tool do you use the most?
I like Gemini.
Have you tried Deep Research?
It's pretty incredible. If you want to do a quick stock initiation report, it's fairly decent. There are some good prompts you can use with it.
One of the reasons I paid for ChatGPT is that over the past couple of weeks I was researching things, started playing around with the prompts, and used the reasoning function. Some of the outputs I got would have taken me a month to put together. It was really incredible. I decided that, as an investor, I had to use it.
For me personally, the 2 areas that are most interesting are power and data centers. Power can mean the actual power producers—Talen, Vistra, Constellation Energy—or it can mean whether we're going to have to fund all this additional demand with nuclear, natural gas, or even coal. All of those are interesting.
The other area is the rush for data centers, which brings us to the Bitcoin miners in particular. Let's start with power. We talked earlier about the demand for power and all the construction that's happening. When you think about the power side, what are the most interesting plays? Is it what everybody likes to do and just buy the power producers? Or is there a way to play the underlying commodities?
We have such a supply constraint that we need all this additional power. We can't retire coal plants, or we might need more natural gas. How do you think about playing that?
I personally look at data center infrastructure, which is a pretty good proxy for GPU demand. It's a capex-driven type of business, so everything related to electrical equipment and cabling equipment is interesting. There are big companies like Schneider Electric, Vertiv, and Eaton.
If you want to be creative, you can look at the Taiwanese stock market. There are some liquid-cooling stocks with fairly high exposure to liquid cooling, so there are interesting plays there. There are also smaller companies with exposure to MEP engineering for data centers or cooling towers for data centers.
There's a fairly large supply chain. The thing is, those pieces of equipment are generally commodities. They don't have the level of product differentiation that NVIDIA has with GPUs. Even if Schneider dominates the electrical and cooling market, and Vertiv and Eaton are major players, there's room for smaller companies to play in local ecosystems. Everyone can benefit in that area.
That's one area because I personally look at equipment, but you're right to also mention power producers and natural gas.
Vertiv is what I was going to say.
Exactly. I think an area you want to look into is time-to-power solutions. In our forecast, we have an extremely granular view of the supply-and-demand dynamics of the global AI and data center market. We forecast that the deficit will increase in the coming years despite the data center boom.
That means if you want to solve the problem, you need time-to-power solutions. For a while, people thought behind-the-meter nuclear, like the Talen-type deals, would be the solution. It's actually much trickier than expected, so you have to look at other solutions.
This supply-and-demand mismatch is creating value that would not be economically viable without the mismatch. That obviously affects the miners because they're not traditional data center companies. They might have a few people from Digital Realty or Equinix, but they don't have the full experience of building these solutions.
In a normal environment, people would never go to those miners, especially for a 500-megawatt project, because it's a huge amount of capital. We're talking about a $5 billion investment. But in this environment of supply-demand mismatch, the miners have historically been power-first.
They found stranded power available in large quantities at extremely low prices, which is why they went to West Texas, Wyoming, North Dakota, and similar locations. The miners have the power and can provide time-to-power solutions. Suddenly, they're a very valuable asset to hyperscalers and other companies deploying GPUs.
Let me ask about the Bitcoin miners. For people who don't know, mining Bitcoin is similar to AI in that you take some GPUs or Bitcoin miners, put them in a facility, run a lot of power through them, and get Bitcoin. It's a very commoditized and difficult business.
A lot of these companies woke up and said, "There are all these AI companies desperate for places with cooling, lots of power, and huge facilities where they can put GPUs. We're Bitcoin miners. We can take the Bitcoin miners out, put AI GPUs in, and suddenly we have an AI data center."
Those facilities are getting valued at huge multiples. Core Scientific did this last summer. It struck a deal with CoreWeave, and the stock went up about 4 times in a year because it went from being a mediocre Bitcoin miner to being AI data infrastructure.
That was Core Scientific, but there are tons of Bitcoin miners out there. I know some of them well and others less well. I'd love to talk about the Bitcoin miners as potential AI plays.
Before getting into specific miners, I want to add something to the previous question. It's all about time-to-power. We forecast a deficit that will increase in the coming years despite the data center boom. That means you need time-to-power solutions.
For a while, people thought behind-the-meter nuclear, like the Talen deals, would be the answer. It's much trickier than expected, so you have to consider other solutions.
The value created by this supply-demand mismatch affects the miners. They're mediocre miners, and they don't have the data center expertise. They might have a few people from companies like Digital Realty and Equinix, but they don't have the full experience of building those solutions.
In a normal environment, nobody would go to those miners for a 500-megawatt project, because it requires a huge amount of capital—around a $5 billion investment. But in this environment, the miners have historically been power-first. They found stranded power available in large quantities at very low prices, which is why they went to West Texas, Wyoming, and North Dakota.
The miners know how to secure power and can provide time-to-power solutions. Suddenly, they're valuable assets to hyperscalers and other companies deploying GPUs.
I've called them clown cars that fell into a gold mine. They built these huge facilities because they thought Bitcoin was going to a million dollars, often without much regard for cost, economics, or competitive analysis. Then the economics came down, and suddenly people realized, "You have 300 megawatts of power. If we build that ourselves, we might not be online until 2029. We could buy your facility, throw the Bitcoin miners away, and be online in 6 months."
If you go back roughly a year, remember that CoreWeave wanted to buy Core Scientific. They were effectively saying, "I'll buy this company and get roughly a gigawatt of available power, then develop it myself." It was a cheap way to secure that power.
The rumor is that CoreWeave offered to buy Core Scientific, but the contract they ultimately struck was worth twice what CoreWeave had offered. CoreWeave might have offered a purchase price implying a $1 billion market capitalization, while the contract was worth roughly $1.8 billion in net present value. Core Scientific kept the upside from Bitcoin mining on its other assets as well.
It was strange. If CoreWeave had offered $1.8 billion, maybe it could have had a deal. Instead, they signed a contract worth more than the purchase price.
That's essentially what happened. There are a few other companies we've looked into, including fuel cells, which we can discuss later if you want.
Tell me about one Bitcoin miner you're most bullish on as an AI player. As a Bitcoin mining business, I understand that it isn't very good, but as an AI play, which one do you think will successfully make the leap?
It's all about how much power you have available and how much uncovered upside remains. The easy answer would be Core Scientific, but they've already made the leap.
The most speculative one, but one that I think makes sense, is IREN. They have 1.4 gigawatts secured in West Texas. It remains to be seen exactly how they manage that, but just having that amount of power is valuable.
The downside is that, based on IREN's earnings calls, the management team seems hesitant about how much they want to move into AI versus Bitcoin mining. They still seem very committed to developing the Bitcoin mining business. I think that's also true of other companies like Riot and Marathon. In my personal opinion, they should go all-in on AI because the valuation of a colocation business is much higher.
You're talking about 15 to 20 times EBITDA for companies like Digital Realty, and sometimes more than 20 times EBITDA. You're talking about 15 years of revenue visibility and the same amount of revenue, but with a valuation of 20 times EBITDA.
Let me prove that I've done the work over the past few months. You mentioned IREN. There was a Culper Research short report in July 2024 that I thought was very good on the economics of Bitcoin mining.
I think one of the issues with IREN is that, although it has 1.4 gigawatts of power, a huge amount, all of the West Texas miners had to sign curtailment agreements with the government to be allowed to build. If the retail load is too high, they have to shut down and support the grid.
A lot of that power is also intermittent, coming from wind and solar. When you put all of that together, and when you think about the Bitcoin miners that say they're going to become AI plays, I think intermittent power is a major issue. You can't use intermittent power for AI, whether it's curtailment, solar, wind, or whatever else, because AI needs to be on 100% of the time.
You're going to spend $100 million a year on power and put $5 billion worth of GPUs in the facility. If you're offline for 5 minutes, the cost of the GPUs completely overwhelms the power cost. Given those issues, do you think IREN can actually become an AI data center, or does it have certain assets that can overcome them?
Generally, the issue isn't that difficult to overcome. Most data centers in the world have backup generators, which are meant to cover periods when you don't have grid power. If there's a grid power failure, the backup generators maintain power.
You can build more sophisticated systems. Some people have started deploying on-site battery systems to manage those peaks. Another thing to keep in mind is that when you build a data center, it's not just about the diesel generators. There are also batteries inside all data centers as part of the UPS system.
Typically, people have 5 to 10 minutes of battery capacity, which is just enough time for the generators to turn on. You could imagine going further if it makes sense—20 minutes, 30 minutes, or whatever is appropriate. Overall, the issue can be overcome fairly easily.
Let me push back in a different way. The Core Scientific-CoreWeave deal was announced in June 2024, and the Talen-Amazon deal was announced in March 2024. If you're a Bitcoin miner, you've had 6 to 9 months to try to make the AI transition.
These AI companies are desperate for power and facilities. Six to 9 months isn't a long time in the grand scheme of switching a company over, but it is a long time when there's a gold rush. We still haven't seen anyone other than Core Scientific sign a definitive, 100-megawatt-plus AI contract.
IREN has experimented with buying GPUs and doing AI, and Applied Digital has talked about doing the same. But if you choose any of the other miners, you're betting that they can make the transition. Why haven't we seen another miner land a major contract if any of these companies are going to make that leap?
TeraWulf did sign a contract. I think it was 70 megawatts, so it isn't insignificant.
Applied Digital is probably the closest. They have a real data center that's already built in North Dakota. It's a 600-megawatt site, and 100 megawatts of data center capacity is already built.
It's not a mining data center; it's a real data center. They paid roughly $10 million per megawatt for it, so they paid about $1 billion for the data center. It's essentially ready. They'll need more capital to build the other data centers, which are supposedly covered by their nonbinding letter of intent. They recently got capital backing from Macquarie, so Applied Digital is a credible number two behind Core Scientific.
I think Applied Digital is likely to get its 400-megawatt deal finalized. Why have those companies succeeded while others have struggled? I would argue that it's about the technical capabilities within the team.
Both Applied Digital and Core Scientific already had experience in the colocation business. Before the flagship deal, Core Scientific signed a 16-megawatt deal with CoreWeave in Austin, Texas. I don't remember whether that was in 2023 or early 2024, but it was early 2024.
The point is that those companies saw the trend earlier than others, hired people accordingly, and already had decent industry relationships. By "hired," I mean they hired senior people from the colocation companies. They built good teams, which is ultimately what matters. It's a human business.
If you look at a company like IREN, it needs to hire more technical expertise. It probably needs to be more focused and streamlined about exactly what it wants to do. In its last earnings call, the company said it wasn't sure whether it wanted to focus on Bitcoin mining or AI data centers. At some point, you have to choose.
This is the other thing that makes me want to smash my head against the wall. I'm not blaming you; I'm ranting, and I'd love to hear your response because I think there's an interesting question here.
Any of these miners say, "We're not sure whether to focus on Bitcoin mining or AI." If you're a Bitcoin miner, you're going to be valued at $100 or $200 per kilowatt, or whatever the number is. If you're an AI data center, look at the deals people are signing. Even $1,000 per kilowatt would be low.
There's a gold rush. You've got insiders who should know the opportunity best, but they're not enthusiastic about making the transition, and they're not protecting their equity. A lot of these companies have issued shares at an incredible pace. Some of them probably increased their diluted share count by 4 times in 2024, and I'm not exaggerating.
If making the switch to AI could make the stock go from being worth 200 to 2,000 overnight—not financial advice, just talking about the magnitude—why are the people who should see the gold mine not seeing it? Are we wrong, or are they doing something we don't understand?
That's a fair point. I would start with something that might be controversial. Many people involved in Bitcoin mining, and in Bitcoin generally, have a religious way of thinking about it. I don't know whether that's the right word, but think of the laser eyes.
Some of them said as recently as 6 months ago, "We're never going to do AI. We're Bitcoin miners to the core." The AI numbers became so large that they started saying they might consider allocating some of their assets to AI. To their credit, they are coming around.
I agree with what you're saying. I personally like Bitcoin, so I think they're right about several things in their overall pitch. If you're in their mindset, you see Bitcoin going to $1 million, so Bitcoin mining is the business to be in.
At these current prices, if you assume Bitcoin goes from $100,000 to $1 million overnight, these companies would be value-neutral between Bitcoin mining and switching to AI. My view is that they should make the switch to AI and then buy Bitcoin in their personal accounts, but that isn't how they see it.
Another issue is that people are generally bullish on AI, but there is still some skepticism. There's also a lot of fear. The capital required to build an AI data center is much higher than what these companies are used to building.
When you build a Bitcoin data center, I'm talking only about the physical infrastructure, not the hardware, it typically costs about $500,000 per megawatt. When you build an AI data center, especially if you don't have much internal capability and have to outsource a lot of the work, you're talking about more than $10 million per megawatt.
That's a 20-times factor. These companies are used to building a Bitcoin mining facility for $500,000 per megawatt, and now they need to build an AI data center for more than $10 million per megawatt.
But you don't have to do it on spec. CoreWeave paid for most of the capital expenditures for Core Scientific. If these assets are so good, why can't the miners find someone to fund the construction?
NVIDIA could write a check to Applied Digital or Core Scientific. You could find somebody to write a check and cover the capex. Why haven't we seen another proof point yet?
I would go back to the industry relationships. I think Core Scientific and CoreWeave had a partnership for several years. Core Scientific was involved in Ethereum mining a few years ago, so they knew each other well.
I'm sure they had internal discussions before doing the deal, including negotiations over how much capital CoreWeave would contribute. Companies like IREN, Marathon, and others probably aren't as involved in the data center market. They may be hiring now, but they don't yet have the industry relationships.
You need to become familiar with the industry. I personally go to a lot of data center conferences. I was at PTC a few weeks ago, which is a pretty large conference. I met people from Applied Digital and Core Scientific, and Hut 8 was there as well. They had a credible project.
A lot of the newer companies weren't at those events. They're new to the industry, so they need to become familiar with it. That's work Core Scientific started much earlier. You need credibility and relationships.
This has been a super interesting conversation. Just to wrap up the miners: I've done a decent amount of work on Applied Digital. I really like the North Dakota asset, although I'm not a super expert on it.
It sounds like you think it's a pretty solid asset.
I think it's a pretty solid asset. They've done a lot of dilution, but when you start talking about a 600-megawatt asset, you realize how much it could be worth if all 600 megawatts were applied to AI. It's a big number.
It sounds like you like the IREN asset as well. Any other miners you want to touch on? We're coming up on an hour, so I don't want to drag this on forever.
Very briefly, IREN is the more speculative one. It has a lot of power, but I'm not sure about the human capital and whether the company has the right people to execute.
Hut 8 has an interesting project in Louisiana. It has an interesting data center that is 100 megawatts in 2025 and expandable to, I think, 200 megawatts by 2026. It already has a solid site plan and secured power, so I think that's a pretty solid one as well.
Let me ask you a completely left-field question. Most of the data center activity is happening domestically in the United States. Forget Europe, which has power issues, but I'm always surprised that we haven't seen a company say, "Qatar has basically free natural gas. We're going to build a kajillion-dollar, jillion-megawatt plant there."
You could say the same thing about Brazil, which has huge energy resources. I'm surprised by how domestically focused the AI data center buildout has been. I'm sure I'm missing a few things, but why do you think it's been so focused on the United States?
You're 100% right. I would say it's mostly about time to market.
An interesting way to visualize it is to look at where the large-scale data centers are today. Let's go back to 2022 or 2023. Where were the large-scale data centers? Most of them were self-built by U.S. hyperscalers, and 75% to 80% of those large-scale data centers were in the United States.
Large-scale data centers in the United States are nothing new. The country has the experience of building 100-megawatt-scale data centers. It has the supply chain, a decent amount of labor, and a decent amount of grid power. The United States is simply a good place to build data centers.
One risk I've heard is that you build a data center, spend $1 billion on it, put $4 billion worth of GPUs in it, and then the government shows up with machine guns and says, "Thanks for the $5 billion investment. We'll be taking that now."
That's a big risk, but even without considering that, labor is an issue. You might want to build in the Middle East, but getting specialized labor isn't easy. Low-level labor is available, but specialized electrical, mechanical, and plumbing labor is more difficult to find.
You hear the horror stories from Qatar when it hosted the World Cup and what it had to do with labor to build all those stadiums. That shows how difficult it can be to build overseas.
The data center infrastructure has specific requirements, and no other country in the world currently knows better how to build large-scale data centers than the United States. The big AI labs, hyperscalers, and the surrounding ecosystem are all American, so it's all happening in the United States.
How can people find you if they want to get in touch or learn more?
I have a very underfollowed Twitter account with almost no posts. LinkedIn is another option.
The best way to become familiar with our work is to check out SemiAnalysis. The website has incredibly deep content, and the other people on the team are amazing as well. The best way to reach out would probably be through SemiAnalysis, Twitter, or LinkedIn—whatever works for you.
This was a ton of fun. The industry is developing so quickly that we could probably do one of these every week and find a way to talk about something new. We probably won't do one every week, but we'll have to have you back for a follow-up conversation.
Thanks so much for coming on.
Thanks for having me.
A final disclaimer: Nothing on this podcast should be considered investment advice. Guests or hosts may have positions in any of the stocks mentioned during the podcast. Please do your own work and consult a financial adviser.