Gavin Baker - Watts and Wafers - [Invest Like the Best, EP.473]

频道: Invest Like the Best with Patrick O'Shaughnessy
视频: https://colossus.com/episode/watts-and-wafers/
原文语言: en
统计: 共 77 轮 · Patrick O'Shaughnessy 5 · Gavin Baker 68

[0:00]

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[1:11]

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大家好，欢迎收听。

[1:30] Patrick O'Shaughnessy

I'm Patrick O'Shaughnessy, and this is Invest Like the Best.This show is an open-ended exploration of markets, ideas, stories, and strategies that will help youbetter invest both your time and your money.If you enjoy these conversations and want to go deeper, check out Colossus,our quarterly publication with in-depth profiles of the people shaping business and investing.You can find Colossus along with all of our podcasts at Colossus.com.Patrick O'Shaughnessy is the CEO of Positive Sum.All opinions expressed by Patrick and podcast guests are solely their own opinions and do notreflect the opinion of Positive Sum.This podcast is for informational purposes only and should not be relied upon as a basis forinvestment decisions.Clients of Positive Sum may maintain positions in the securities discussed in this podcast.To learn more, visit psum.vc.My guest today is Gavin Baker, the founding partner and CIO of Atreides Management, andthis is our sixth conversation.The central theme is watts and wafers, the two physical constraints that, in Gavin's view,will dictate the next phase of AI.On power, he thinks the near-term shortage starts to ease in 2027 and 28 as new sourcesof energy come online, and that orbital compute helps solve this problem in the long term.

我是 Patrick O'Shaughnessy，这里是《Invest Like the Best》。本节目是对市场、观点、故事和策略的开放式探索，目的是帮你更好地投资你的时间和金钱。如果你喜欢这些对话、想更深入，可以看看 Colossus——我们的季刊，深度刻画那些正在塑造商业与投资的人。Colossus 和我们所有的播客都在 Colossus.com。

Patrick O'Shaughnessy 是 Positive Sum 的 CEO。Patrick 和播客嘉宾表达的所有观点都只代表其个人，不代表 Positive Sum 的立场。本播客仅供参考，不应作为投资决策的依据。Positive Sum 的客户可能持有本播客中讨论的证券。了解更多请访问 psum.vc。

今天的嘉宾是 Gavin Baker——Atreides Management 的创始合伙人兼首席投资官（CIO），这是我们第六次对谈。中心主题是「瓦特与晶圆」（watts and wafers），也就是在 Gavin 看来将决定 AI 下一阶段走向的两个物理约束。在电力这一侧，他认为随着新能源上线，短期短缺会在 2027、2028 年开始缓解，而轨道算力（orbital compute）能从长期上解决这个问题。

[2:41] Patrick O'Shaughnessy

On wafers, he explains what is different this time from the dot-com bubble and why TSMC'scapacity decisions may be the single most important variable to watch.We also discuss Elon's Carafab, the disaggregation of GPUs, the role of new chip companies, andwhether economic value of AI will keep accruing to the frontier models.Please enjoy this awesome sixth conversation with Gavin Baker.All right, so this is our sixth time doing this, if you can believe it, which puts you back

在晶圆这一侧，他解释了这一次和互联网泡沫（dot-com bubble）究竟有什么不同，以及为什么台积电（TSMC）的产能决策可能是最值得盯住的唯一变量。我们还聊了马斯克的 Terafab、GPU 的解耦（disaggregation）、新兴芯片公司的角色，以及 AI 的经济价值是否会持续归属于前沿模型。请享受这场与 Gavin Baker 的精彩第六次对谈。

好，这是我们第六次做这件事了，你敢信——这让你重新回到第一名，或者至少和 Gurley（Bill Gurley）并列第一，

[3:07] Patrick O'Shaughnessy

into first place, or at least tied first place with Gurley, back into steam territory.Always my favorite conversation about markets and everything going on.Even since last time when we did this, which was so exciting and spectacular, I think we'rein an even more interesting time now.Maybe just start by riffing on how it felt for you living through March and April of thisyear, which felt to me just like a completely unique economic technology and market environment.And you're the biggest student of history and of these times.So what does it feel like?

重新进入常驻嘉宾的领地。这永远是我最喜欢的关于市场和当下一切的对话。就算跟上次相比——上次那次已经够刺激、够精彩了——我觉得我们现在处在一个更有意思的时点。也许就先从这里聊起：今年三月和四月，你亲历过来是什么感觉？在我看来，那是一个在经济、技术和市场层面都完全独一无二的环境。而你是历史和这个时代最认真的学生。所以，那是什么感觉？

[3:38] Gavin Baker

I would say broadly speaking, there are two kinds of drawdowns.There are drawdowns where you're wrong, company missed estimates, your hypothesis was invalidated,and you have to take your medicine and you crystallize that loss.And then there are drawdowns or periods of underperformance where you're underperformingbecause of companies you know really, really well, and where you profoundly disagree with theprice action.And you can lean in.And instead of crystallizing negative performance, you can kind of build pent-up alpha, pent-upfuture performance.And for me, that is what March felt like.The NASDAQ was selling off.At the same time, what was happening in AI was, I think, the most extraordinary moment inthe history of capitalism, the history of American business.What I just mean by that is that Anthropic, they added $11 billion of AR.And what is astonishing to me about this is that the SaaS and cloud revolution, it created,we'll call it between $5 and $10 trillion of value.I would say arguably the three highest profile SaaS companies in the last 10, 12 years arePalantir, Snowflake, and Databricks.And these three companies employ thousands of people, tens of thousands collectively.They've all spent 10 years building their businesses.

我会说，大致上有两种回撤（drawdown）。一种是你错了——公司业绩不及预期，你的假设被证伪，你只能吞下这剂药，把亏损兑现。另一种回撤、或者说跑输的时期，是因为你非常非常了解的那些公司，而你对价格的走法深深不认同。这时候你可以加仓。你不是在兑现负收益，而是在积攒“待释放的超额收益”（pent-up alpha）、待释放的未来业绩。对我来说，三月就是这种感觉。

纳斯达克在下跌。而与此同时，AI 领域正在发生的事，我认为是资本主义史上、美国商业史上最非凡的一刻。我的意思是：Anthropic 一个月增加了 110 亿美元的年度经常性收入（ARR）。让我震惊的是——SaaS 和云计算这场革命，创造了我们姑且说 5 万亿到 10 万亿美元的价值。我认为过去 10、12 年里最高知名度的三家 SaaS 公司大概是 Palantir、Snowflake 和 Databricks。这三家公司雇了几千人，加起来好几万人。它们都花了 10 年时间建立自己的生意。

[5:00] Gavin Baker

And Anthropic added their combined businesses in one month.Nothing like that has ever happened in the history of capitalism.Forget my career.Just the flat-out history of capitalism.The history of business.It's wild.And then Krishna comes on this show and shares some stats, 500% in DR.Yeah, you do the math on that for three years.So there's just no precedent for this.And we tech investors, we hear a lot of discussions about S-curves and investing in exponentials.I've just never seen an exponential like this.It felt even more extreme than DeepSeek, which was a very similar setup.They happened at about the same time.If we go back to 25, there's a huge sell-off on DeepSeek, which was very strange because the paper gets published seven days before DeepSeek Monday.It got published, I believe, on a Monday that was a holiday in America.I read it.I thought, hmm, this feels like it.Could be important.I read that positively for the AI trade.I took action.And then we had DeepSeek Monday, where AI really imploded.A week later.That was really strange because by DeepSeek Monday, it was super clear that this was going to be the most positive thing that had ever happened to compute demand.Prices of the AWS availability zones in Asia had already doubled.

而 Anthropic 在一个月里就把它们三家的生意加起来那么多的收入给加上了。资本主义史上从来没有发生过这样的事。别说我的职业生涯了，就是整个资本主义史、整个商业史都没有。这太疯狂了。

然后 Krishna（Krishna Rao，Anthropic CFO）来上了这个节目，分享了一些数据，净收入留存率（NDR）500%。是的，你按三年去算算这个数。所以这完全没有先例。我们这些科技投资人，天天听人讨论 S 曲线、投资指数增长。但我从没见过这样的指数曲线。

它给人的感觉比 DeepSeek 那次还极端，而那次的剧本非常相似，两件事发生的时间也差不多。回到 2025 年，DeepSeek 引发了一次巨大的抛售，这非常奇怪，因为那篇论文是在“DeepSeek 星期一”之前七天就发表的。我记得是在美国一个假期的星期一发的。我读了。我想，嗯，这好像有点意思。可能很重要。我把它读成了对 AI 交易的利好，并且采取了行动。然后就来了“DeepSeek 星期一”，AI 板块真的塌了。一周之后。

那真的很奇怪，因为到 DeepSeek 星期一那天，已经非常清楚：这将是对算力需求发生过的最正面的事情。亚洲 AWS 可用区的价格已经翻倍了。

[6:29] Gavin Baker

You were seeing GPU availability go down.And this was just the first time we saw how much more compute-hungry reasoning models are during inference than non-reasoned models.And so that was a similar setup.You had to do some work to see that.I mean, it's not that hard to say, oh, wow, stocks are selling off.The price of DRAM is going vertical.The price of GPUs in Asia are going vertical.GPU availability is going down.And then like two or three days later, GPU prices in America started going up, GPU rental prices.All you had to do in March was simply observe what was happening to Anthropic.And there's all these people who seem to regret not buying during 22, not buying during COVID, not buying during DeepSeek.You had the same valuation setup at the beginning of April and an even clearer AI inflection.So there have been all these chances to buy into AI.And then, of course, what complicated it was the straight and foremost.So I became a believer and am a believer that I think maybe one thing that the market was mispricing.I'm no macro expert.I do do a lot of pro-national security investing.So I do have access to people who are experts that are excited to share their thoughts and opinions with me.

你能看到 GPU 可用量在下降。那是我们第一次看到，推理模型（reasoning models）在推理（inference）阶段比非推理模型要吃掉多得多的算力。所以那是一个类似的剧本。你得做点功课才看得见。我是说，这其实不难：噢，股票在跌，但 DRAM（内存）价格在垂直上涨，亚洲的 GPU 价格在垂直上涨，GPU 可用量在下降。然后大概两三天后，美国的 GPU 价格、GPU 租赁价格也开始涨了。

而今年三月，你要做的只是单纯地观察 Anthropic 身上发生了什么。有那么一大批人，总在后悔 2022 年没买、疫情期间没买、DeepSeek 那次没买。四月初你面对的是同样的估值格局，而且 AI 的拐点更清晰。所以买入 AI 的机会一次又一次出现。

然后当然，把事情复杂化的是霍尔木兹海峡（Strait of Hormuz）。我变成了、并且现在依然是一个信奉者：我认为市场当时可能定错价的一件事是——我不是宏观专家，但我做了很多“亲国家安全”方向的投资，所以我能接触到一些真正的专家，他们也乐意跟我分享想法和判断。

[7:51] Gavin Baker

And that the straight and foremost being closed is actually relatively awesome for America.Why?Particularly for the goals of the current administration.So electricity is a very important industrial or manufacturing input.The key input into American electricity prices, which feeds into AI, is in G1, natural gas one on Bloomberg.That was down 20%.And natural gas in Asia, Europe, everywhere else doubled or tripled.Our relative manufacturing competitiveness improved overnight.And for better or worse, that is what the Trump administration seems to care about.They are very focused on America's relative position.And I think a lot of people had memories of the 1970s.What made the 70s so traumatic was it wasn't just that prices went up.It's that there were actual gas shortages.Then you go through, okay, well, the U.S. economy is dramatically less energy intensive than it was.The United States is now the world's largest producer of oil and gas.And we've become now the world's largest exporter of oil and gas.And on top of that, there's this relative manufacturing advantage.That made it easier to stay focused on AI fundamentals, stay focused on what were historically attractive valuations.I think on a relative basis, tech essentially got as cheap as it's been versus the rest of the market.

而霍尔木兹海峡被关闭，其实对美国来说是相当棒的一件事。为什么？尤其是对现任政府的目标而言。电力是一个非常重要的工业或制造业投入。美国电价的关键投入——它又会传导到 AI——是天然气，彭博终端上的代码 NG1（近月天然气合约）。那个价格跌了 20%。而亚洲、欧洲和其他地方的天然气价格翻倍甚至翻了三倍。我们的相对制造业竞争力一夜之间改善了。不管你喜不喜欢，这正是特朗普政府似乎最在意的东西。他们非常关注美国的相对位置。

我觉得很多人脑子里还是 1970 年代的记忆。70 年代之所以那么创伤，不只是因为价格上涨，而是真的出现了汽油短缺。然后你再往下看：美国经济的能耗强度比当年低太多了。美国现在是全球最大的石油和天然气生产国。而且我们现在已经成了全球最大的石油天然气出口国。再叠加上这个相对制造业优势。

这让人更容易保持专注在 AI 的基本面上，专注在那些从历史上看很有吸引力的估值上。我认为在相对基础上，科技股基本上跌到了相对于市场其余部分最便宜的水平——

[9:23] Gavin Baker

Has at any point over the last 10 years.And just think about that in the context of market efficiency.We have the most extraordinary moment in the history of capitalism that's wildly bullish for AI.And you get a chance to buy AI at really attractive valuation.What do you make of the multiples that specifically Anthropic and OpenAI,which in my mind are like the reference assets that are the most pure play takes on this trend,really being not that crazy?

过去 10 年里的任何时点都没这么便宜过。你就在市场有效性（market efficiency）的框架下想想这件事：我们迎来了资本主义史上最非凡的一刻，对 AI 是极度利好的，而你居然能以非常有吸引力的估值买到 AI。

（Patrick）你怎么看 Anthropic 和 OpenAI 的估值倍数？在我心里它们是这个趋势最纯粹的两个参照资产，可它们的估值居然没那么离谱。

[9:52] Gavin Baker

Like if you just look at the sales multiple and compare it to maybe what Databricks and Snowflakeand these companies traded at their peak.How do you process it?How do you make sense of it?I do think OpenAI and Anthropic are pretty different animals from a capital efficiency perspective.And Anthropic clearly has a dramatically lower cost per token than OpenAI.They just do.And you can just see that in the amount of money that they have burned to get to a roughly similar revenue scale.I think they burned maybe 80% less than OpenAI.So as businesses, they clearly have very different structural ROICs.I think Sarah Fryer is one of the most exceptional CFOs.I think they're doing a lot of things to try to improve this.And they've secured a lot of compute.They've secured a lot of compute.That's another big difference.It turns out being aggressive really paid.Anthropic at $900 billion for $50 billion in ARR.Growing at ridiculous rates.And I think maybe a true statement is that if Anthropic could just wave a magic wand and get all the compute they wanted, they'd probably be doing well north of $100 billion today.Maybe $150.They have clearly deprecated the intelligence of Claude.There's an analysis.

（Patrick）如果你只看市销率（sales multiple），拿它跟 Databricks、Snowflake 这些公司在最高峰时的估值比一比。你怎么消化这件事？你怎么理解它？

（Gavin）我确实认为，从资本效率的角度看，OpenAI 和 Anthropic 是两种很不一样的动物。Anthropic 每 token 成本明显低得多。就是这样。你从它们烧到大致相似的收入规模所花的钱就能看出来。我估计 Anthropic 烧的钱比 OpenAI 少 80% 左右。所以作为生意，它们的结构性投入资本回报率（ROIC）非常不同。

我认为 Sarah Friar（OpenAI CFO）是最出色的 CFO 之一。我觉得他们在做很多事来改善这一点。而且他们锁定了大量算力。他们锁定了大量算力，这是另一个很大的区别。事实证明，激进是真的有回报的。

Anthropic 的估值 9000 亿美元，对应 500 亿美元 ARR。而且增速离谱。我觉得一个可能成立的说法是：如果 Anthropic 能挥一挥魔杖、拿到它想要的全部算力，它今天的收入很可能远超 1000 亿美元。也许 1500 亿。他们显然把 Claude 的智能水平做了降级。有一个分析显示——

[11:05] Gavin Baker

Claude is even on Opus is generating 70% less tokens for the exact same question.As we talked about last time, token quantity equals quality of answer and quality of thinking at some level.And there is an intelligence density per token that also matters.I felt that as a user.So I think they would be doing materially more.$100, $150, maybe $200 billion.So you might be buying it at more like five times unconstrained.I'm going to make up a new number.URR.Unconstrained revenue.Why do you think they don't raise $100 billion at a $3 trillion valuation or something like this?

Claude，哪怕是 Opus，对完全相同的问题现在生成的 token 少了 70%。就像我们上次聊过的，token 的数量在某种程度上等于答案的质量、思考的质量。而且每个 token 里的“智能密度”也很重要。我作为用户是能感觉到的。

所以我认为他们本来能做到多得多的收入——1000 亿、1500 亿，甚至 2000 亿美元。所以你买的其实更像是 5 倍的……我发明个新词吧，URR，「无约束收入」（unconstrained revenue）。

（Patrick）你觉得他们为什么不按 3 万亿美元的估值去融 1000 亿美元这种量级的钱？

[11:47] Gavin Baker

If you were the Anthropic CFO, Christian is awesome.We just had him on.Or if you're Sarah, it seems to me like if the inbound I received following the Krishna episode is any indication,everyone I've ever met is trying to invest in both these companies.I think it's wise.The future is uncertain.You are clearly in a very capital intensive game.Even if you are Anthropic, I'm sure is at very positive gross margins on inference today.I think Anthropic probably starts generating cash this year if they are not already generating cash,which I think is probably the case.But still, you probably want to be able to raise more capital, access more compute.The world is uncertain.Ukraine is starting to really, really win.How is Russia going to respond?

（Patrick）如果你是 Anthropic 的 CFO——Krishna 很棒，我们刚请他上过节目——或者你是 Sarah，在我看来，从 Krishna 那期播出之后我收到的问询量来看，我认识的每一个人都在想办法投这两家公司。

（Gavin）我觉得这是明智的。未来是不确定的。你显然处在一个非常资本密集的游戏里。哪怕你是 Anthropic——我相信它今天在推理业务上的毛利率已经是很正的了，我认为 Anthropic 今年很可能开始产生现金流，如果它还没有在产生现金的话，而我认为它大概已经在产生了——但即便如此，你还是想保有融更多钱、拿更多算力的能力。世界是不确定的。乌克兰开始真的、真的在赢。俄罗斯会怎么回应？

[12:31] Gavin Baker

I think there's still a lot of uncertainty in Iran.All this uncertainty, I think, probably amplifies geopolitical uncertainty over time.So it's an uncertain world.If I think about Elon, Elon has always made investors money.He treats it like a sacred covenant.And as a result, because he's made people money for now 20 years, he has a superpower.That is, he can essentially raise as much capital as he wants, whenever he wants.I do think being focused on making investors money is wise and creates benefits that don'tjust last for like a year or two.They can last for the next 20 to 30 years.And the way Elon did this was systematically underpricing SpaceX or whatever else.Like what is the actual method?

我觉得伊朗那边也还有很多不确定性。所有这些不确定性，我认为随着时间推移可能会放大地缘政治的不确定性。所以这是个不确定的世界。

如果我想想马斯克：马斯克一直让投资人赚到钱。他把这当成一份神圣契约来对待。结果是，因为他已经让人赚了 20 年的钱，他获得了一种超能力——他基本上可以在任何他想要的时候，融到任何他想要的金额。我确实认为，专注于让投资人赚钱是明智的，它创造的好处不只持续一两年，而是能持续未来 20 到 30 年。

（Patrick）马斯克做到这一点的方式，就是系统性地把 SpaceX 或别的什么给定价定低。具体的方法到底是什么？

[13:21] Gavin Baker

Just never being greedy on valuation.Never pushing valuation.Just that simple.My friend Antonio pointed out SpaceX compounded it low 30% per year for a decade.And that was just because Elon was, I think, focused on preserving the superpowerand having, trying to strike a fair balance between investors and employees.I think it's wise.But could Anthropic raise money at probably at least a 100% premium to this rumored latest mark?

（Gavin）就是在估值上从不贪心。从不硬把估值往上顶。就这么简单。

我朋友 Antonio 指出，SpaceX 十年间以 30% 出头的年化速度复利增长。这就是因为马斯克，我认为，专注于守住那个超能力，努力在投资人和员工之间取得一个公平的平衡。我觉得这很明智。

（Patrick）但 Anthropic 有没有可能以比这次传闻中的最新估值至少高 100% 的价格融资？

[13:51] Gavin Baker

Of course.Let's get to the Watson wafers part of the discussion.Always my favorite thing to talk about with you.The importance of this infrastructure build out.Every time I feel like it's getting overheated.And then the next time I talk to you, it seems like we should have done way more than we did.You studied S-curves and the steepness of those S-curves a lot.And you know a lot about history.Talk us through how you're thinking about Watson wafers today as the key to inputs into this whole thing.I think capitalism is going to solve the Watts shortage absent big regulatory political blowback,which I think is a real possibility.The head of data center infra investing at one of the big PE firms, Blackstone Apollo,KKR said it used to be energy and chips were our biggest gating factors.Now it's zoning and approval, much more important.I think a lot of companies are waiting till after the midterms to take action in terms of maybe workforce reductions.Nobody wants to be a pinata during the midterms.You've seen a lot of companies that make turbines, announce a plan to significantly increase capacity.There's like two of these machines that can cast these big blades.We haven't made one in 80 years in the West.

（Gavin）当然可以。

（Patrick）我们进入「瓦特与晶圆」这部分吧。这永远是我最想跟你聊的话题：这轮基础设施建设的重要性。每次我都觉得是不是过热了，然后下一次跟你聊完，又觉得我们建得远远不够。你研究 S 曲线、研究这些曲线的陡峭程度研究了很多，你也懂历史。跟我们讲讲你今天怎么看瓦特与晶圆这两个关键投入。

（Gavin）我认为，只要没有大的监管和政治层面的反弹——我觉得这是一个真实的可能性——资本主义会解决电力（Watts）短缺。黑石、阿波罗、KKR 这几家大 PE 里某一家的数据中心基础设施投资负责人说：过去能源和芯片是我们最大的卡点，现在是分区规划和审批，重要得多。

我认为很多公司在等中期选举结束之后再采取行动，比如裁员。没人想在中期选举期间当那个被打的皮纳塔。你也看到很多做涡轮机的公司宣布要大幅扩产。全世界只有两台机器能铸造这种大叶片。西方已经 80 年没造过一台这样的机器了。

[15:06] Gavin Baker

We don't know how to make them anymore.All of that is true.By no means am I minimizing the industrial engineering magic and artistry that goes into those.But capitalism is very good at solving problems like these over time.There's other sources of energy besides these turbines with a longer time frame.So I think the Watts shortage will probably begin to alleviate 27, 28.And then I think orbital compute will really solve that.I do want to reframe orbital compute because I think when people hear data centers in space,which we discussed in our last episode, they picture a pentagast-sized building in space.They're like, well, we can't do that.That's not what it is.A Blackwell rack weighs 3,000 pounds.It's eight feet high.It's four feet deep, three feet wide.It's racks in space.And SpaceX has showed you an illustration.And it's a rack.That's the satellite.But it's probably about the size of a Blackwell rack.It has these solar wings that are probably 500 feet long on each side.You keep it in a sun-secretous orbit.So those solar panels are always in the sun.Because it's in an exactly sun-secretous orbit,the radiator, which extends behind it for hundreds of feet, is in the shape.This is a common criticism.

我们已经不知道怎么造了。这些都是事实。我绝不是在轻视这里面的工业工程魔法和手艺。但资本主义在解决这类问题上，随着时间推移是非常擅长的。除了这些涡轮机之外还有别的能源来源，只是时间尺度更长。所以我认为电力短缺大概会在 2027、2028 年开始缓解。

然后我认为轨道算力（orbital compute）会真正解决这件事。我想重新定义一下“轨道算力”，因为我觉得人们一听到“太空里的数据中心”，就会想象一栋五角大楼那么大的建筑飘在太空里。然后说，我们做不到那个。但那不是它的样子。

一个 Blackwell 机柜（rack）重 3000 磅，高八英尺、深四英尺、宽三英尺。它是“太空里的机柜”。SpaceX 已经放出过一张示意图。那就是一个机柜——那个机柜就是卫星本身。大小差不多就是一个 Blackwell 机柜。它两侧各伸出大概 500 英尺长的太阳能翼。你把它放在太阳同步轨道（sun-synchronous orbit）上，这样太阳能板永远朝着太阳。而正因为它在精确的太阳同步轨道上，那个向后延伸几百英尺的散热器（radiator）就正好处在阴影里。这是一个常见的批评点。

[16:30] Gavin Baker

Yeah.How are you going to go over there?I've spent a lot of time at Starbase.Over the years.And I've talked to a lot of SpaceX engineers.And I do think it is the most talented group of engineers on planet Earth.And they're very confident they have solved this.And they're not always confident.There's some engineering that needs to happen to turn the Starship into a Mars colonial transporter.Will they do that?

（Patrick）是啊，散热你打算怎么解决？

（Gavin）这些年我在星舰基地（Starbase）待了很多时间，也跟很多 SpaceX 的工程师聊过。我确实认为那是地球上最有才华的一群工程师。而他们非常有信心已经解决了这个问题。他们并不是对什么都有信心——要把星舰（Starship）变成火星殖民运输船，还有一些工程问题要解决。他们会做到吗？

[16:55] Gavin Baker

Absolutely.What are they more focused on?I'd say probably the repair and maintenance.Those are the two big responses.Yeah.The radiator and how do you repair whatever issue goes wrong on the rack?And the answer is, until you have probably floating optimuses, you don't.Starship is going to change the space economy in ways we cannot imagine.And particularly if regulation becomes a constraint to data centers, none of it's going to matter.You're going to sell as much orbital compute as you can make.And then obviously you link these racks using lasers traveling through vacuum, which are already on every Starlink.And it's just mind-blowing to me that SpaceX operates the world's largest satellite fleet, which is 98% or 99% of all satellites in orbit.Every Starlink, they're cooling it today.I think Starlink V3 is going to operate at 20 kilowatts.A Blackwell rack is only 100 kilowatts.And people talk a lot about density.Well, if you're connecting the racks with lasers through vacuum, you can make the rack bigger.Physically, you're focused on weight, not size.In a data center on Earth where you're trying to connect racks, ideally using copper, minimize lengths, cabling is a big cost.You do want that rack to be small.

绝对会。他们更关注的是什么？我会说大概是维修和维护。这是（对散热质疑）最主要的两个回应：散热器，以及机柜上出了问题你怎么修。答案是：在你有了能在太空里飘着干活的 Optimus 机器人之前，你不修。

星舰将以我们无法想象的方式改变太空经济。特别是如果监管变成地面数据中心的约束，那其他一切都不重要了——你造多少轨道算力就能卖出去多少。

然后显然，你用激光把这些机柜连起来，激光在真空中传播，而这个东西每一颗 Starlink 卫星上都已经有了。让我觉得非常震撼的一点是：SpaceX 运营着全世界最大的卫星舰队，占在轨卫星总数的 98% 或 99%。每一颗 Starlink 今天都在做散热。我认为 Starlink V3 会工作在 20 千瓦。而一个 Blackwell 机柜也才 100 千瓦。

大家很爱谈“密度”。但如果你是用真空中的激光把机柜连起来，那你可以把机柜做得更大。物理上你关心的是重量，不是尺寸。而在地球上的数据中心里，你想用铜缆连机柜、想把长度压到最短，布线是一大笔成本，所以你确实希望机柜小一点。

[18:15] Gavin Baker

Copper when you can, optics when you must.But in space, you know, there's all sorts of things that SpaceX can do that I think maybe some of these naysayers are not contemplating.They operate more satellites than anyone.They have a 20 kilowatt satellite today.So maybe you just scale that up to 60 kilowatts to start.They seem very confident they're going to go right to 100 to 120.The same company now also operates the largest data center on Earth.They have the world's best hardware engineers.And all sorts of people, almost all of whom are not smart enough or practical enough to work at SpaceX, are these armchair skeptics.You know, I don't want to quote Larry Ellison, but somebody was being skeptical.And Larry was just like, listen, he's out there landing rockets.I don't see anybody else landing rockets.And the reality is, 10 years later, no other company is consistently capable of landing and fully reusing an orbital rocket.None of this makes sense without reusability.That means you have to land it.I would like to redefine orbital compute as racks in space.Not giant floating pentagon sized data centers in space.That's silly.What makes a data center is you're connecting these racks with lasers.

能用铜就用铜，不得已才上光模块。但在太空里，SpaceX 能做的事情有很多，我觉得是一些唱衰的人没有考虑到的。他们运营的卫星比谁都多。他们今天就有一颗 20 千瓦的卫星。所以也许你只要先把它放大到 60 千瓦起步。而他们看起来非常有信心可以直接上到 100 到 120 千瓦。

同一家公司现在还运营着地球上最大的数据中心。他们有全世界最好的硬件工程师。而各种各样的人——几乎所有人都不够聪明、或者不够务实到能进 SpaceX 工作——却在当键盘上的怀疑论者。我不太想引用 Larry Ellison 的话，但当时有人在唱衰，Larry 就说：听着，人家在那儿把火箭降落回来。我没看见别人在降落火箭。

而现实是，十年过去了，没有任何别的公司能稳定地降落并完全重复使用一枚入轨火箭。没有可重复使用，这一切都不成立。而可重复使用就意味着你必须能把它降落回来。

我想把轨道算力重新定义为“太空里的机柜”，而不是巨大的、五角大楼那么大的太空数据中心——那太蠢了。构成一个数据中心的，是你用激光把这些机柜连起来。

[19:30]

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（Gavin）所以它会是太空里的一堆机柜，用激光连成一个虚拟数据中心。

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[20:47] Patrick O'Shaughnessy

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（Patrick）那如果你想象那个世界的状态——假设这一切都发生了，我们真的很擅长把这些东西经济地送上去，让矩阵乘法在太空里到处跑。那对地面数据中心意味着什么？

[21:17] Gavin Baker

Someone once said America was going to suck as hard as it can on every energy source it can get.And I just think the same is true of compute.It's why I'm probably less worried about like an edge AI bear case than I was.We're going to consume as much compute as we can.Inference, I think, is very sensible for Orvo compute.Training will be done on Earth for a long time.So I don't think that this is super bearish for terrestrial data centers.I think those are going to be valuable for my lifetime.But I do think if you're in this ecosystem of power production and cooling and you are massively ramping capacity, a lot of these capacity ramps are going to be hitting.Just as I think all of the silly skeptics start to understand that orbital compute is very real.I think it's worth thinking long and hard about that if you're one of those companies.And then all sorts of cool stuff is happening in the interim.We're getting really good at repurposing jet engines.There's that boomerang space that is doing this.Capitalism is hard at work on watts.On wafers, though, it's just this group of plenty older humans in Taiwan who are the most important humans in Taiwan, whatever they are.The overwhelming fraction of the country's GDP, water usage, electricity usage.

有人说过，美国会把它能拿到的每一种能源都吸干。我觉得算力也是一样的。这也是为什么我现在对“边缘 AI”那套看空逻辑没那么担心了。我们能消耗多少算力就会消耗多少。

推理（inference）我认为非常适合放在轨道算力上。训练在地球上还会做很长时间。所以我不认为这对地面数据中心是特别看空的。我觉得它们在我这辈子里都会是有价值的。

但我确实认为，如果你身处电力生产和冷却这个生态里，而你正在大规模扩产能，那么很多这些产能爬坡到位的时点，恰好会撞上所有那些愚蠢的怀疑论者开始明白“轨道算力是真的”的时点。如果你是那类公司，我觉得值得好好想想这件事。

中间阶段也有各种很酷的事在发生。我们越来越擅长把喷气发动机改造成发电机——有一家叫 Boomerang Space（音，ASR 存疑）的公司在做这个。资本主义正在拼命解决“瓦特”。

但在“晶圆”这一侧，就只是台湾一群上了年纪的人，他们是台湾最重要的人，不管你怎么算——那家公司占了这个地方 GDP、用水量、用电量中压倒性的比例。

[22:47] Gavin Baker

They talk about the silicon shield.They all view themselves as inheritors of Morse Chang's sacred legacy.I vividly remember visiting Science Park more than 20 years ago and talking to them.Do you think you could catch Intel?

他们谈“硅盾”（silicon shield）。他们都把自己看作张忠谋（Morris Chang）神圣遗产的继承者。我清楚地记得 20 多年前去新竹科学园区拜访他们，问他们：你们觉得自己能追上英特尔吗？

[23:06] Gavin Baker

And they said, this is such a beautiful dream, but it's a dream for our grandchildren.And they did it, partly because of Intel's self-inflicted wounds.They think very differently.One reason Jensen flies over there so much is he wants them to expand capacity.I do think it's wild that Jensen has never had a contract with Taiwan Semi.They do business on what seems fair in handshakes.Just fascinating.No contract.It's going to be fair over time.We're partners.We're going to be fair to each other.The truth is, based on every prior market precedent for a foundational new technology like AI, you've always had a bubble.Carlotta Perez wrote this great book about this.Markets are efficient.They correctly understand that this is a foundational new technology.There's what Mobison calls a breakdown in diversity.Everyone becomes bullish on this new technology.And I am beginning to worry a little bit about a diversity breakdown.And then you get a bubble.That bubble funds the build out of this new technology.But supply gets ahead of demand.And you get a crash.And it's a particularly severe crash if it's a debt-fueled build out like the year 2000.And one thing that's really good about the current build out is it's still overwhelmingly funded out of operating cash flows, which is a really important fundamental difference versus the year 2000.

他们说：这是一个很美的梦，但这是留给我们孙辈的梦。结果他们做到了，部分原因是英特尔的自我伤害。他们的思考方式非常不一样。

黄仁勋（Jensen）之所以频繁飞过去，一个原因就是他想让他们扩产。我确实觉得很疯狂的是：黄仁勋跟台积电从来没签过合同。他们靠“什么看起来公平”和握手来做生意。太迷人了。没有合同。长期看会是公平的。我们是伙伴。我们会彼此公平。

真相是：按照以往每一个基础性新技术的市场先例，你总会先有一个泡沫。卡萝塔·佩雷斯（Carlota Perez）写过一本很棒的书讲这个。市场是有效的。市场正确地理解到这是一项基础性新技术。于是出现了 Mauboussin（迈克尔·莫布森）说的“多样性崩溃”——所有人都变得看多这项新技术。而我确实开始有点担心多样性崩溃了。然后你就得到一个泡沫。那个泡沫资助了这项新技术的基础设施建设。但供给最终跑到需求前面，于是你迎来一场崩盘。

而如果这是一场债务驱动的建设——比如 2000 年那样——崩盘会格外惨烈。当前这轮建设有一点真的很好：它仍然压倒性地是用经营性现金流在出资，这是相对 2000 年一个非常重要的基本面差别。

[24:26] Gavin Baker

Has is valuation.Has is the fact that every GPU is running at 100% utilization when 99% of fiber was unutilized.So there's all these fundamental differences.History doesn't repeat, but it rhymes.And as an investor, we have to be very cognizant of it and recognize that based on the last 200 or 300 years, forget the internet bubble.We had a railroad bubble, a canal bubble, every kind of bubble.South Sea bubble.We should expect a bubble.That's terrifying.Nobody wants a bubble.The reason it's terrible is if you're valuation sensitive, you like massively underperform.You get fired by probably all your clients.George van der Heiden, who is no longer with us, Great Fidelity Portfolio Manager, he fought the bubble in 99.And he retired in early 2000 because I think he just couldn't take it.He knew it was wrong.His clients were deeply skeptical.George, you're out of step.He had white hair.He's a truly great man.I only overlapped with him briefly, but he was a very important mentor and friend to my good friend and mentor, Jennifer Urig.So I have a lot of van der Heiden DNA through her.He was the same person who said being early is the same thing as being wrong.George retires because he can't take the underperformance and he can't take clients saying, what's wrong with you?

另一个是估值。还有一个是：现在每一块 GPU 都在 100% 利用率上跑，而当年 99% 的光纤是闲置的。所以有一大堆基本面上的差异。

历史不会重复，但会押韵。作为投资人，我们必须对此高度警觉，并且认识到：按过去 200 年、300 年的经验——别说互联网泡沫了——我们有过铁路泡沫、运河泡沫，各种各样的泡沫，南海泡沫。我们理应预期会有一个泡沫。

这很吓人。没人想要泡沫。它可怕的原因是：如果你对估值敏感，你会被大幅甩在后面。你大概会被所有客户炒掉。

George Vanderheiden——已经不在了，富达（Fidelity）一位伟大的基金经理——他在 1999 年跟泡沫对抗。他在 2000 年初退休了，因为我觉得他实在扛不住了。他知道那是错的。他的客户对他深度怀疑：George，你跟不上时代了。他一头白发。他是个真正伟大的人。我只跟他短暂共事过，但他是我的好友和导师 Jennifer Uhrig 非常重要的导师和朋友。所以我身上有很多通过她传下来的 Vanderheiden 的基因。就是他说过：过早等于错误。

George 退休，是因为他受不了跑输，受不了客户问他：你到底怎么了？

[25:42] Gavin Baker

You don't get it.And he has like 40% of his funded tobacco, 40% did home builders.And literally, he probably outperformed the NASDAQ by like 20 or 30x over the next three years.And I have been optimistic that this fundamental shortage of wafers, which really today is controlled by Taiwan Semi, will prevent one.If Taiwan Semi did what Jensen wanted, I think NVIDIA could sell $2 trillion of GPUs in 26 or 27.Maybe $2.5 trillion.Maybe $3 trillion.But there is a limit where consumers would consume so much, they probably would be in an overbuild.So Taiwan Semi, if we don't get a bubble, we need to throw a party for them because they will have single-handedly prevented a bubble.You are starting to see companies go to Intel and Samsung.Plus, just assume TSM stays super supply constrained versus blatant demand.What happened?

你不懂。而他当时大概 40% 仓位在烟草、40% 在房屋建筑商。然后就在接下来三年里，他跑赢纳斯达克大概 20 到 30 倍。

而我一直乐观地认为：这种根本性的晶圆短缺——今天实际上由台积电控制——会阻止泡沫出现。如果台积电真按黄仁勋想要的去做，我认为英伟达在 2026 或 2027 年能卖出 2 万亿美元的 GPU。也许 2.5 万亿。也许 3 万亿。但存在一个上限：消费者能消耗的量是有限的，超过了大概就会变成过度建设。

所以台积电——如果我们没等来泡沫，我们得给他们办个庆功宴，因为是他们凭一己之力阻止了泡沫。

你现在开始看到有公司去找英特尔和三星了。而且，就假设台积电相对于明摆着的需求依然处在严重供给受限的状态。会发生什么？

[26:43] Gavin Baker

The history of markets is, I don't know who, but one of Intel and Samsung, they're not going to stay disciplined.They will break.And then at some level, that will force everyone else to break.I think a lot of this may come down to the degree to which Taiwan Semi can maintain a lead over Intel and Samsung.You got to remember, it's whatever it is.It's 9, 12, 15 months.The leading node edge, you mean?

市场的历史告诉我们——我不知道会是哪一个——英特尔和三星里总有一个不会守纪律。它会破功。然后在某个程度上，那会逼得所有人都跟着破功。

我认为这里面很大程度上取决于台积电能在多大程度上维持对英特尔和三星的领先。你得记住，这个领先幅度不管具体是多少——9 个月、12 个月、15 个月。

（Patrick）你是说最先进制程节点（leading node）上的差距？

[27:08] Gavin Baker

Exactly.The pace at which they expand capacity.If I were to watch one thing to understand where there's a bubble, it's Taiwan Semi's capacity decisions.And I think there's a Goldilocks zone where they expand enough.They make it hard for Intel or Samsung to really, truly emerge as like a at-scale second source with something well north of 30% market share.And yet they also keep this fundamental constraint on wafers that helps us avoid a bubble.And then obviously, I think the TerraFab is going to play into this too.Say more about that.It's a SpaceX, I believe Tesla's involved as well, joint venture to build the world's largest fab here in America.I think they're going to be successful.One, they have a partnership with Intel, which is very important because they're getting access to 50 years of institutional knowledge.That's just nine months, a few quarters, 12 months, three to five quarters behind the front.That's an advantage.It's also an advantage that I believe the TerraFab is going to get attention from the A-teams, all the semi-cap equipment companies.One big reason Taiwan semi-caught up is ASML and KLA-10 core and LAM research and applied materials.They wanted them to catch up.

（Gavin）正是。以及它扩产的节奏。

如果只让我盯一件事来判断有没有泡沫，那就是台积电的产能决策。我认为存在一个“金发姑娘区间”（Goldilocks zone）：他们扩得足够多，让英特尔或三星很难真正崛起成一个有规模的第二供应源、拿到远超 30% 的市场份额；但同时他们又保持住晶圆这个根本性约束，从而帮我们避免泡沫。

然后显然，我觉得 Terafab 也会进入这个变量里。

（Patrick）再多说说这个。

（Gavin）那是 SpaceX——我相信特斯拉也参与了——的一个合资项目，要在美国本土建全世界最大的晶圆厂。我认为他们会成功。

第一，他们和英特尔有合作，这非常重要，因为这让他们拿到了 50 年的机构性知识积累。那只落后最前沿大概九个月、几个季度、12 个月、三到五个季度。这是一个优势。

另一个优势是，我相信 Terafab 会得到所有半导体设备公司 A 队人马的关注。台积电当年能追上来，一个很大的原因就是 ASML、KLA-Tencor、Lam Research 和 Applied Materials——它们希望台积电追上来。

[28:29] Gavin Baker

They don't like having a monopsony.The A-teams were in Taiwan working, Intel made some mistakes, and presto.So the A-teams will be here because of Elon's reputation in hardware engineering.And then to a degree that I think is maybe hard for people to imagine in America, where politics has replaced religion.I think because Elon had his foray into politics, that makes it hard for some people in America to see him clearly, which is sad because I do think he's probably doing more for America than any other American.He's single-handedly bringing manufacturing back to America.He's revived defense tech.I think SpaceX is in some ways the most important defense contractor in America.He's doing the Starlink.It's amazing for the world.He's creating all these blue-collar manufacturing jobs, which is like a goal, I think, of a lot of liberals and good for America.He's done more than any living human to decarbonize the world.And if you are upset about data centers on Earth for environmental reasons, well, here you go.Boom, boom, boom, boom.It's sad, but he is a living deity in China, Taiwan, South Korea, and Japan.Having watched him for a long time, what he's going to do is they're going to recruit the best people because the best engineers want to work for Elon, especially in hardware engineering.

它们不喜欢面对一个买方垄断（monopsony）。A 队人马当年都在台湾干活，英特尔犯了些错，然后，噔——就成了。

所以因为马斯克在硬件工程上的声誉，A 队人马这次会来这边。

然后还有一点，可能是美国人自己很难想象的——在美国，政治已经取代了宗教。我认为因为马斯克涉足过政治，这让一部分美国人很难客观地看他，这挺可惜的，因为我确实觉得他为美国做的事可能比任何其他美国人都多。

他凭一己之力把制造业带回美国。他复兴了国防科技——我认为 SpaceX 在某些方面是美国最重要的国防承包商。他在做 Starlink，这对全世界都很了不起。他创造了这么多蓝领制造业岗位，这我认为是很多自由派的目标，也对美国有好处。他比任何在世的人都为世界脱碳做得更多。而如果你因为环保理由对地球上的数据中心不满，那好，给你——砰，砰，砰，砰。

很讽刺的是，他在中国、台湾、韩国和日本是活着的神。看了他这么多年，我知道他会怎么做：他们会招到最好的人，因为最好的工程师想为马斯克工作，尤其是在硬件工程领域。

[29:55] Gavin Baker

He's going to recruit incredible engineers.Next to the TierFab, there'll be a Taiwan town.Oh, these are your favorite restaurants?I'm going to move them and their whole staff from Taiwan to Texas.We're going to make everything the way they like it.And then we'll have Japan town.Same thing.We're going to have Korea town.We're going to have all these things exactly dialed to recruit the best engineers.And that's just not the way that the people who run Intel at Samsung think.So he's going to have the best talent.He's going to have the A-teams at the Wafer Fab equipment companies.He has Intel, which is important.It's so good for all of any administration's political goals.And I think it's different enough that it will not alienate Taiwan's semi.And these have long lead times, right?

他会招到一批不可思议的工程师。Terafab 旁边会有一个「台湾城」。哦，这些是你最爱的餐厅？我把它们连同全体员工从台湾搬到德州。我们会把一切都按他们喜欢的样子做出来。然后我们再来一个「日本城」，一样。再来一个「韩国城」。我们会把所有这些东西调到刚好能招到最好的工程师。而这根本不是英特尔和三星的管理层的思考方式。

所以他会有最好的人才。他会有晶圆厂设备公司的 A 队人马。他有英特尔，这很重要。这对任何一届政府的政治目标都太有利了。而且我认为它跟台积电的差异足够大，不会疏远台积电。

（Patrick）而这些东西的交付周期都很长，对吧？

[30:44] Gavin Baker

TerraFab is going to be pumping out whatever GPs, whatever chips, quite a long time from now.We'll see.Elon tends to do things differently.Everybody else has taken three years to build a data center.He built one in 122 days.Samsung had to give him an office in their fab in Texas because he was so unhappy about the pace at which they're expanding and building.Are you surprised by, you mentioned DeepSeek earlier, the simple reaction to that was, okay, these models are just going to get 95% as effective for some tiny fraction of the cost to still Chinese open source models.We'll be able to use these for most of what we want to do.Fast forwarded a little bit of time, two years from now, there's no reason I have to spend a million dollars a year in my small little firm on tokens or something.But then the actual reality seems quite different than this.And I'm curious why there's that dissonance in your mind.I do think it's fascinating, the returns to the frontier, all the economic returns to AI at the model layer.Not all of them, but an overwhelming amount of them have been at the frontier, which is surprising to me.And I think it's been surprising to a lot of people.This is one of the most important questions to be answered, and you need to have a hypothesis on it as an investor.

（Patrick）Terafab 真正开始量产 GPU 或者别的什么芯片，是相当久以后的事了。

（Gavin）走着瞧。马斯克做事往往跟别人不一样。别人建一个数据中心要三年，他 122 天就建好了一个。三星不得不在他们德州的晶圆厂里给他一间办公室，因为他对他们扩产和建设的速度太不满意了。

（Patrick）你刚才提到 DeepSeek——当时最简单的反应是：好，这些模型会以极小的成本达到 95% 的效果，而且是中国的开源模型，我们大部分想干的事都能用它们干。再往前推一点，两年后，我没理由在我这家小公司上每年花 100 万美元买 token。但实际发生的现实似乎跟这个很不一样。我好奇在你脑子里，这个反差从何而来。

（Gavin）我确实觉得这件事很迷人：回报都归了前沿（frontier）。AI 在模型层创造的经济回报，虽然不是全部，但压倒性的大部分都归到了前沿模型身上，这让我很意外。我想这让很多人都很意外。这是最重要的待解问题之一，作为投资人你必须对它有一个假设。

[31:56] Gavin Baker

Are frontier tokens going to continue capturing the overwhelming majority of economic value created at the model layer?And it is surprising.I remember when Gemini 3.1 Pro came out.It was mind-blowing to me.It was so good.Today, it's intolerable.There's probably a little bit of a dynamic where companies prototype with frontiers.Then when they put something into production, you're hearing a lot of people do use Vertex or open source.But still, it is a fact today that the overwhelming majority of these economic returns come from frontier tokens.And that's surprising.And whether or not it continues, I think, is a very interesting question.And I'm much more open-minded to that, having had the experience I've had with Gemini 3.1 and then Opus.And then I do use Grok 4.3 a lot.It is on the Pareto frontier.The companies that are on the Pareto frontier are, and this is, by the way, a big change in a consequence of what we talked about last time,Google losing their per-cost token leadership as a result of making very conservative design decisions with TPUB8 to try and take it away partially from Broadcom.And NVIDIA continuing to make aggressive choices.But Google dominated the Pareto frontier, the Pareto frontier being intelligence versus cost.

前沿 token 会继续攫取模型层创造的绝大部分经济价值吗？这确实很意外。

我记得 Gemini 3.1 Pro 刚出来的时候，我惊为天人，它太好了。而今天，它已经难以忍受了。

这里面可能有一点动态是：公司用前沿模型做原型验证，等到真正上生产的时候，你会听到很多人确实在用 Vertex（谷歌的模型托管平台）或者开源模型。但即便如此，今天的事实依然是：这些经济回报的绝大部分来自前沿 token。这很意外。它会不会持续下去，我认为是一个非常有意思的问题。而在经历了 Gemini 3.1 和 Opus 给我的体验之后，我对这个问题的心态更开放了。

我也大量使用 Grok 4.3。它处在帕累托前沿（Pareto frontier）上。而处在帕累托前沿上的公司——顺便说一句，这跟我们上次聊的东西相比是个巨大变化，也是那件事的后果：谷歌丢掉了每 token 成本的领先地位，原因是他们在 TPU v8 上做了非常保守的设计决策，想从博通（Broadcom）手里把一部分价值拿回来。而英伟达继续做激进的选择。但当时谷歌是统治帕累托前沿的——所谓帕累托前沿，就是「智能水平 vs. 成本」这条边界。

[33:14] Gavin Baker

And I think this is the most important thing to look at to analyze AI labs.Google dominated that nine months ago.Every point on the Pareto frontier, OpenAI, XAI, and Anthropic were inside of them.Now, the Pareto frontier is dominated by Anthropic, OpenAI.And then Grok 4.3 is on the Pareto frontier.It's clearly the best, lowest cost, 500 billion parameter model.And then Gemini 3.1 is hanging on to the Pareto frontier.And if I were to bet, I would bet that they were subsidizing that out of pride.I would just say a violation of Richard Sutton's bitter lesson is for sure the biggest risk to this trade, to all of AI.The closer someone is to AI, the more skeptical they are that this will occur.One thing I think contributed to weakness in March was, you know, a much more stupid version of DeepSeek, which was this thing called TurboQuant.And TurboQuant is some Google memory optimization that was written up in a paper a year ago.And then during the middle of an agreement, while Google was negotiating with Micron, Samsung, and Hynix to sign some LTA that would lock in really high prices for a long time, they released this.What people do is always more important than they say, and they just kind of publicize it on X.

我认为这是分析 AI 实验室最该看的东西。九个月前谷歌统治着这条前沿。前沿上的每一个点，OpenAI、xAI 和 Anthropic 都在谷歌的内侧（即更差）。

而现在，帕累托前沿被 Anthropic 和 OpenAI 主导。然后 Grok 4.3 在前沿上——它显然是最好、成本最低的 5000 亿参数模型。而 Gemini 3.1 是勉强挂在前沿上。如果让我打赌，我赌他们是出于面子在补贴那个价格。

我要说，违反理查德·萨顿（Richard Sutton）的「苦涩的教训」（bitter lesson），肯定是这笔交易、也是整个 AI 面临的最大风险。而一个人离 AI 越近，就越怀疑这种事会发生。

我觉得三月的疲弱有一个诱因，是一个比 DeepSeek 蠢得多的版本，叫 TurboQuant。TurboQuant 是谷歌的某个内存优化方法，一年前就写在一篇论文里了。然后就在谷歌正在跟美光、三星和海力士谈判、要签一份把高价长期锁死的长期协议（LTA）的中途，他们把这个东西放了出来。人做了什么永远比说了什么重要——他们就那么在 X 上宣传了一下。

[34:26] Gavin Baker

And it goes viral.Like, oh my God, DRAM is cooked.Here's this DRAM optimization.I was unable to find a single AI engineer on planet Earth who believed that TurboQuant would have any impact on DRAM demand.But nonetheless, a violation of Richard Sutton's bitter lesson, you know, more compute will always outperform human algorithmic ingenuity, more compute and data, chinchilla optimal, beyond chinchilla optimal, I guess what people increasingly do today.That's a real risk, man.And the people who are building these models are skeptical of that risk.The reason I am a little less skeptical is I think we're very close to ASI.And who knows if the bitter lesson holds for 400 IQ models.Maybe we get a temporary period where these, you know, if you get to ASI, the first thing it wants is probably to be smarter and have more resources.How does it do that?

然后它就病毒式传开了：天哪，DRAM 完蛋了，这里有个 DRAM 优化技术。

而我在地球上找不到任何一个 AI 工程师相信 TurboQuant 会对 DRAM 需求产生任何影响。

但尽管如此，「违反萨顿的苦涩教训」——你知道，苦涩教训是说更多算力永远会跑赢人类的算法巧思，更多算力加更多数据，Chinchilla 最优、乃至今天人们越来越多地做的超越 Chinchilla 最优——那确实是一个真实的风险。而正在造这些模型的人，反倒对这个风险持怀疑态度。

我之所以没那么怀疑，是因为我觉得我们已经很接近 ASI（超级人工智能）了。谁知道苦涩的教训对 400 智商的模型还成不成立？也许我们会迎来一个短暂的时期——你想，如果你造出了 ASI，它想要的第一件事大概就是变得更聪明、拥有更多资源。它怎么做到？

[35:20] Gavin Baker

It makes itself more efficient.I think that is an actual risk.The bitter lesson literally, I believe, includes humans in it.So we're about to find out whether the bitter lesson, we'll find out if it applies to 300 IQ, AI, then 400, then 500 and 600.And at some point, we may have like a temporary violation of the bitter lesson based upon AI and ASI.So I'm curious how you think about some other parts of the innovation around the model, continual learning and memory being two that people seem to be most focused on as things that might create yet another new paradigm that we would enter.What do you think about the role of those two things?

它把自己变得更高效。我认为这是一个真实的风险。苦涩的教训这句话本身，我相信是把人类也算进去的。所以我们即将验证：苦涩的教训对 300 智商的 AI 适不适用，然后是 400、500、600。而在某个时点，我们可能会因为 AI 和 ASI 本身，迎来对苦涩教训的一次暂时性违反。

（Patrick）我好奇你怎么看模型层面另外几项创新——持续学习（continual learning）和记忆（memory），这两件事似乎是大家最关注的、可能会带来又一个新范式的东西。你怎么看它们的作用？

[36:01] Gavin Baker

Yeah, well, I think we've done a lot with memory through these harnesses.And it turns out that harness engineering is not as important as the model, but it really matters.And these harnesses in these models are increasingly being co-developed.One of the big things a harness does, what you just think of as like a runtime that the model operates in.And it knows where the tools are.It creates context, memory, state, has very specific prompts or instructions.It makes a huge difference.Even simple versions.It makes an incredible difference.I think the last time I was on here or one of the other times, I just said like, hey, as an investor, it's very important that you pay for the $250 a month version to get your own intuitive sense.That's no longer possible.To understand what Frontier AI is capable of today, even for a non-coding use case, you need to have Claude Code or Codex.And you need to be on an enterprise plan.And the reason for this is, and this is another dynamic that's enabled by Google losing their cost leadership, is these AI models just shifted to usage-based pricing.And if you're on that $250 or $300 or $280 a month plan or whatever it is, you are getting severely rate limited.

是这样，我觉得我们通过这些「壳」（harness）在记忆上已经做了很多事。事实证明，壳的工程做得好不好，重要性不如模型本身，但它真的很重要。而且这些壳和模型现在越来越是协同开发的。

壳做的一件大事是——你可以把它理解成模型在其中运行的一个运行时（runtime）：它知道工具在哪里，它构造上下文、记忆、状态，有非常具体的提示词或指令。它带来的差别是巨大的。哪怕是很简单的版本，差别也大到不可思议。

我记得上次或者前几次来这里，我说过：作为投资人，你花那 250 美元一个月订最高档非常重要，这样你才能对 AI 有自己的直觉。这条建议现在已经不成立了。

今天要理解前沿 AI 到底能干什么，哪怕是非编程的用途，你也必须用上 Claude Code 或 Codex。而且你必须在企业版套餐上。

原因是——这又是谷歌丢掉成本领先地位所催生的另一个动态——这些 AI 模型刚刚转向了按用量计费。如果你还在那个 250、300 或 280 美元一个月的套餐上，你会被严重限流。

[37:21] Gavin Baker

You are getting a lobotomized version of the AI.Because like we talked about, Claude now produces 70% less tokens.You want the tokens that Claude and its harness really think it needs to produce to get you a good answer?

你拿到的是一个被做了额叶切除手术的 AI。因为像我们刚才说的，Claude 现在少产出 70% 的 token。你想要 Claude 和它的壳真正认为需要产出、才能给你一个好答案的那些 token 吗？

[37:34] Gavin Baker

You need to be on a usage-based plan.And by the way, this is so bullish for AI.If we go back to 05 to 07, cellular had been a great growth industry really for the last 10 years.And the reason was you had a combination of fixed pricing.You had 900 minutes for whatever it was, and then usage-based pricing over that.When did cellular stop being a great growth industry?

那你就得上按量计费的套餐。

顺便说，这对 AI 是极大的利好。回到 2005 到 2007 年，此前十年蜂窝通信一直是一个很棒的成长行业。原因是它有一个组合：固定资费加超额按量计费——你有 900 分钟包月，超出部分按用量付费。蜂窝通信是什么时候不再是好成长行业的？

[37:57] Gavin Baker

When everybody just went to all-you-can-eat.And by the way, long distance is the same thing.AI is just shifting from all-you-can-eat to pay-by-the-drink.And it turns out people really like to talk to their friends long distance.They really like to talk to their friends on the phone.And people really like to use AI.And particularly now that one person can have 100 agents working.So I think the shift to usage-based pricing is probably why you will see OpenAI and Anthropic exceed well over $200 billion in ARR this year.Not only is MoreCompute going to become online, but they're going to be able to push frontier token pricing with these usage enterprise models.It's sad.It's sad for the world because it just means if you can't afford that, you're not at the frontier.And I think it's going to throw off a lot of investors' intuitive sense of the capabilities of AI.But yeah, continual learning, man.I mean, if we solve that.How do you conceptualize that?

是所有人都转成「无限量吃到饱」的时候。顺便说，长途电话也一样。

AI 现在正在从「吃到饱」转向「按杯买」。事实证明，人们真的很喜欢跟朋友打长途，真的很喜欢在电话上跟朋友聊天。而人们真的很喜欢用 AI。尤其是现在一个人可以有 100 个智能体在干活。

所以我认为，转向按量计费大概就是为什么你今年会看到 OpenAI 和 Anthropic 的 ARR 都远远超过 2000 亿美元。不仅是更多算力会上线，而且他们还能靠这些企业级按量模式把前沿 token 的定价往上推。

这有点悲哀。对世界来说是悲哀的，因为它意味着如果你付不起那个钱，你就不在前沿上。而且我觉得这会把很多投资人对 AI 能力的直觉判断带偏。

但话说回来，持续学习，天哪。我是说，如果我们把它解决了。

（Patrick）你怎么理解这件事？

[38:52] Gavin Baker

So AI is constantly updating its weights.I mean, it may end up being something different.There's so many mysteries about the human mind.We're such sample-efficient learners relative to AI.Many orders of magnitude.Now we have a crude variant of continual learning today when something is verifiable.And that's just reinforcement learning during mid-training.Continual learning is a model that dynamically adjusts its weights, or adjusts in some way in real time.Like as a human, the first time I put my hand in a fire, I've learned I never put it in there before.That model today needs to put its hand in the fire a million times, and then have the designers effectively put a fire in the next training run or an RL gym for it to learn.I think it has to be dynamically updating the weights.But I think people are working on really smart techniques beyond this.But if we get that, then we have a really fast takeoff.And people seem confident that continual learning is kind of just around the corner.And I do think this is like the third big question.Bitter lesson violation as a result of ASI are less likely human ingenuity.Will Frontier tokens still command the premium they do?

（Gavin）就是 AI 在不断更新自己的权重。当然最后的形态可能是别的东西。人脑还有那么多未解之谜——我们是比 AI 高出好几个数量级的「样本高效」学习者。

今天在可验证的场景里，我们已经有了一个粗糙版本的持续学习，那就是在中期训练（mid-training）阶段做强化学习。而真正的持续学习，是一个能实时动态调整自己权重、或者以某种方式实时自我调整的模型。

就像作为人类，我第一次把手放进火里，我就学会了以后再也不放进去。而今天的模型需要把手放进火里一百万次，然后还要让设计者把「火」这件事放进下一轮训练、或者做一个强化学习环境（RL gym），它才学得会。

我认为它必须是动态更新权重的。不过我也相信人们在研究比这更聪明的技术路线。但如果我们拿到了这个能力，那我们就会有一次非常快的「起飞」。而大家看起来相当有信心，持续学习差不多就在拐角处了。

我确实觉得这是第三个大问题：一是 ASI 带来的苦涩教训违反（人类巧思带来的违反可能性更小）；二是前沿 token 还能不能维持它今天的溢价；

[40:12] Gavin Baker

And will we get continual learning?And if so, when?What is the role of new chip companies in all of this?We talked a lot about NVIDIA and their relationship with DSMC and Intel and all these sorts of things.There's a thousand flowers blooming.I think literally probably a thousand flowers blooming.Trying to create a new chip to address some part of this bottleneck.I'm curious how you process this space, this opportunity, what role it will play.So I think this is good and healthy for the world.It's good for Jensen too, because a different administration might take a different view.Competition, I think, is good for everyone.And seeing different architectures explored is good.And the reason is, in tank design, they talk about the iron triangle.The iron triangle of tank design is that all designers of a tank, they have to make trade-offs between attack, defense, and mobility for obvious reasons.The more defense you have, which is just armor, the heavier the tank is, the less mobile it is.So you have to live in this triangle and make trade-offs.The Merkava in Israel is optimized for defense.Russian tanks and like the Leopard are generally more optimized for mobility.Chip design is the same.

三是我们会不会拿到持续学习？如果会，什么时候？

（Patrick）在这一切里，新兴芯片公司扮演什么角色？我们聊了很多英伟达、它跟台积电和英特尔的关系等等。现在是千花齐放——我觉得真的可能有一千家在开花，都想造一颗新芯片去解决这个瓶颈的某一部分。我好奇你怎么消化这个领域、这个机会、它会扮演什么角色。

（Gavin）我觉得这对世界是好的、健康的。这对黄仁勋也是好事，因为换一届政府可能会有不同的看法。竞争我认为对所有人都好。看到不同的架构被探索也是好事。

原因在于——坦克设计里有个说法叫「铁三角」。坦克设计的铁三角是：所有坦克设计师都必须在攻击、防护和机动性之间做取舍，原因很明显。你防护越强——防护就是装甲——坦克就越重，机动性就越差。所以你必须活在这个三角形里做权衡。以色列的梅卡瓦（Merkava）为防护优化。俄罗斯坦克和豹式（Leopard）一般更偏机动性。

芯片设计也一样。

[41:21] Gavin Baker

There are these fundamental constraints imposed by the laws of physics,as embedded in the Taiwan semi-design rules that you need to live within.You have TPU, Tranium, and AMD, which are all essentially trying to be a better GPU.And today, I think probably Tranium is doing the best.Nobody's a better GPU.But Tranium is tugging on Superman's cape.That hadn't started yet.The Tranium 3 needs to ramp into production because it has a switch scale-up network,which you really need to economically inference MOE models.A lot of companies have a Taurus architecture.That's where Google was.Google's developing a switch scale network.And then AMD is like always kind of flying from a bit behind.Yeah.AMD, we'll see.The MI450, we don't know yet.We'll see.We probably know more about Tranium 3 than the MI450.But that's a hard game to play.So you have to do something different.And you have to do something different that is also hard to do.So I think the best path for these startups, my rule of thumb, is 1% market share is goingto be worth $100 billion.$100 billion is a pretty good venture outcome.I think what Jensen would say is, okay, if somebody does something different and it getsto 1% or 2% or 3% share, we'll make that chip.

有一些由物理定律施加的根本约束——它们体现为台积电的设计规则，你必须在里面活。

你有 TPU、Trainium 和 AMD，它们本质上都是在试图做一个更好的 GPU。而今天我觉得做得最好的大概是 Trainium。没有人是「更好的 GPU」。但 Trainium 是在拽超人的披风——那还没开始呢。Trainium 3 需要爬产上量，因为它有一个交换式（switch）的纵向扩展（scale-up）网络，而要经济地推理 MoE（混合专家）模型，你真的需要这个。

很多公司用的是环形（Torus）架构，谷歌当年就在那个位置。谷歌现在也在开发交换式的纵向扩展网络。

然后 AMD 就是那种一直略微落后一点在飞的状态。AMD，我们看看吧。MI450 我们还不知道，走着瞧。我们对 Trainium 3 的了解大概比对 MI450 还多。

但这是一场很难打的仗。所以你必须做点不一样的事。而且你做的那件不一样的事，还必须是难做的事。

所以我认为这些初创公司最好的路径——我的经验法则是：1% 的市场份额将值 1000 亿美元。1000 亿美元是个相当不错的风投回报。我觉得黄仁勋会说的是：好，如果有人做了点不一样的东西，并且做到了 1%、2% 或 3% 的份额，那我们就把那颗芯片也做出来。

[42:41] Gavin Baker

And that's coming for everyone.But if you're trying to make a better GPU, good luck.If you are doing something different, it also needs to be hard to do.And you can make different trade-offs.The disaggregation of pre-fill and inference really have opened the aperture for makingthese different trade-offs because you can make very aggressive trade-offs for decode,aggressive trade-offs for pre-fill.Pre-fill being taking in the context, decode being, you know, write the output.Yeah.I have a great colleague named Andrew Fox who said, pre-fill, picture British naval shipfrom the 18th century.Pre-fill is loading the cannon, decode is firing.And what pre-fill literally is, is just the model understanding the question, the prompt,and then keeping track of its own answer.And that is fundamentally a memory capacity bound problem.Decode is the process of generating new tokens, and that is memory bandwidth constraint.So if you're a chip designer, this gives you a richer canvas to paint on.But even so, it needs to be hard.Because if you make different trade-offs in that iron triangle to optimize for memory capacity,and they're not hard trade-offs to make, NVIDIA is going to make those same trade-offs,

这一点对所有人都成立。但如果你是在试图做一个更好的 GPU，那祝你好运。

如果你在做不一样的东西，它还得是难做的。而且你可以做不同的取舍。

预填充（pre-fill）和解码（decode）的解耦，真正打开了做这些不同取舍的口子——因为你可以为解码做非常激进的取舍，也可以为预填充做激进的取舍。预填充就是把上下文吃进去，解码就是把输出写出来。

我有个很棒的同事叫 Andrew Fox，他说：预填充，你想象一艘 18 世纪的英国军舰。预填充是装填炮弹，解码是开炮。

预填充实际上就是模型理解问题、理解提示词，并且持续记住它自己给出的答案。这本质上是一个受内存容量限制的问题。解码是生成新 token 的过程，那是受内存带宽限制的。

所以如果你是芯片设计者，这给了你一块更丰富的画布去画。但即便如此，它还得难。因为如果你在那个铁三角里做了不同的取舍来优化内存容量，而这些取舍并不难做，那英伟达也会做同样的取舍，

[43:49] Gavin Baker

they get better prices from Taiwan Semi than you're ever going to get.Good luck.And they have the advantage of working with every model company and optimizing their designs.By the way, another very funny thing is there's this process.If you're a VC and you're investing in a semiconductor company,that is telling you they are going to have an advantage because of a Taiwan Semi processthat they have special access to.I promise you that Jensen saw that process when it was a twinkle in Taiwan Semi's eyes.They know more about it than this little company with 200 people can imagine.Taiwan Semi, everybody in the supply chain is showing Jensen everything.The same way they're showing Amazon everything, AMD everything, TPU everything.And that's another reason.Don't go try to make a better GPU.So you can do something different.You can paint in the pre-fill canvas.You can paint in the decode canvas.But you also have to do something hard because if it gets to scale,you're going to have those four companies has very fast followers.My firm was a venture investor in Cerebrus.What Cerebrus has done is something hard and fundamentally different.Way for scale computing.It comes with a set of trade-offs.

而且他们从台积电拿到的价格是你永远拿不到的。祝你好运。而且他们还有一个优势：跟每一家模型公司合作、据此优化自己的设计。

顺便说另一件很好笑的事：如果你是一个风投，你在投一家半导体公司，而这家公司告诉你，它有优势是因为它拿到了台积电某个工艺的特殊使用权。我向你保证，黄仁勋在那个工艺还只是台积电眼里的一丝念头时就已经看到它了。他们对这个工艺的了解，远超一家 200 人的小公司所能想象。

供应链上的每一个人都在把一切摊给黄仁勋看。同样地，他们也把一切摊给亚马逊看、AMD 看、TPU 团队看。这也是另一个理由：别去试图做一个更好的 GPU。

所以你可以做不一样的东西。你可以在预填充这块画布上画，可以在解码这块画布上画。但你还必须做难的事——因为一旦它做到了规模，你就会面对那四家「非常快的快速跟随者」。

我的公司作为风险投资投过 Cerebras。Cerebras 做的是一件又难又根本不同的事：晶圆级计算（wafer-scale computing）。它伴随着一整套取舍。

[45:03] Gavin Baker

But that architectural decision they made was hard and lets them do something that no one else can do.And we'll find out how big that is.They're working on really cool things.One of the problems Cerebrus has is once you start needing to glue a lot of chips togetherand scale up networks or scale out networks, you need a lot of IO.An IO is bound by what's called the shoreline, the sides of the chip.Cerebrus has an overwhelming ratio of on-chip computed memory relative to shoreline IO.Well, they're really smart people.They did something really hard.They're trying to see if they can put an optical wafer right on top of that.And then that solves that problem.I'm sure they're looking at hybrid bonding of DRAM to get around the much discussedon X, these alleged limitations that are not true.A Cerebrus machine can theoretically run any size model.There are sizes of models where they're much better than other sizes.So Cerebrus, what I think is interesting, is they did something different that's hard to do.Really hard to do, wafer-scale computing.I do think there's a role for these.I would just encourage them all, make a different trade-off.Try and do something hard.Everybody's going to get funded after this Cerebrus IPO.

但他们做的那个架构决策是难的，而且让他们能做到别人做不到的事。它到底能长多大，我们会看到。他们在做一些很酷的事。

Cerebras 面临的问题之一是：一旦你开始需要把很多芯片粘在一起、组成纵向扩展或横向扩展（scale-out）网络，你就需要大量 IO。而 IO 是受所谓「海岸线」（shoreline，即芯片的边缘长度）限制的。Cerebras 的片上算力和内存相对于海岸线 IO 的比例高得离谱。

但他们是非常聪明的人，他们做了一件非常难的事。他们正在尝试能不能把一片光学晶圆直接叠在上面，那样这个问题就解决了。我相信他们也在看 DRAM 的混合键合（hybrid bonding），来绕开 X 上被大谈特谈的那些所谓限制——那些说法其实并不成立。一台 Cerebras 机器理论上可以跑任意规模的模型。只是在某些模型尺寸上，它比在别的尺寸上强得多。

所以 Cerebras 我觉得有意思的地方在于：他们做了一件不一样、而且很难做的事。真的非常难做——晶圆级计算。

我确实认为这些公司有它们的位置。我只想劝他们所有人：去做一个不同的取舍，去试着做难的事。这次 Cerebras IPO 之后，所有人都会拿到融资。

[46:16] Gavin Baker

It's not going to be a problem.But it took Cerebrus three generations of chips to get it right.Andrew Feldman, the CEO, you can just see how hard it was, what he did, and that whole team didto get where they are today.And they need to have the grit to do that, the resilience.This first chip is a failure.It happens.Can you come back and make a second chip?

钱不会是问题。但 Cerebras 花了三代芯片才做对。Andrew Feldman，他们的 CEO——你能直接看出来，他和整个团队为了走到今天，做的事有多难。

他们需要有那个韧劲、那个抗打击能力。第一颗芯片失败了，这很正常，会发生。你能不能爬起来再做第二颗？

[46:42] Gavin Baker

But the one last thing on this topic is this is going to be amazing for the useful lives of GPUsand may single-handedly save private credit.Say more about that.What do you mean by the private credit?Well, just private credit, they're in pain from these SaaS loans.And however much they're marked down, they probably need to be marked down more.Because if the public companies are struggling to adapt, how's like a debt-laden company goingto adapt and invest in what is a very different margin structure business?

这个话题上最后一件事：这件事对 GPU 的可用寿命将是巨大的利好，而且可能凭一己之力拯救私募信贷（private credit）。

（Patrick）再多说说这个。你说的私募信贷是什么意思？

（Gavin）就是私募信贷现在因为那些放给 SaaS 公司的贷款而很痛苦。不管它们已经减记了多少，大概都还需要再多减记一些。因为如果连上市公司都在艰难地适应，一家背着债的公司又要怎么适应、怎么投入到一个利润结构完全不同的生意里去？

[47:07] Gavin Baker

There's a lot of private credit in GPUs too.And they were underwriting that to, I think, three or four years.The disaggregation of inference means that I think these GPUs are going to have 10 or 15-year lives.The AI skeptics are like, oh, these companies are all cooking their books.The useful life of GPU is only a year or two.The useful life of CPU is only four years because of the rapid technological change.No.What rapid technological change has done with the disaggregation of pre-fill and inferenceis mean that you can put a Cerebra system or Grok LPUs that NVIDIA acquired effectively in front of a hopper or even an Ampere.Use that hopper and Ampere for pre-fill and extend the useful life of that GPU until it melts.They do melt, so they have a time.But maybe you don't have to run them as fast.This is going to be really good for the whole private credit industry.It's going to help finance the AI buildout.Because if you can start to finance GPUs at more like 5% or 6% instead of, I think, Corwin's lowest financing was like low 7s.That actually mathematically changes the cost of finance this buildout.We had this technological innovation that's going to lower the cost of financing, extend the useful life of compute on Earth.

GPU 上也有大量私募信贷。而他们当初是按三到四年去做信贷模型的。

推理的解耦意味着，我认为这些 GPU 会有 10 到 15 年的寿命。

AI 怀疑论者会说：噢，这些公司都在做账。GPU 的可用寿命只有一两年，CPU 的可用寿命只有四年，因为技术变化太快了。不。技术的快速变化——通过预填充和解码的解耦——真正带来的结果是：你可以把一台 Cerebras 系统、或者英伟达收购来的 Groq LPU，摆在一台 Hopper 甚至 Ampere 前面，用那台 Hopper 和 Ampere 去做预填充，把这块 GPU 的可用寿命一直延长到它烧掉为止。它们确实会烧掉，所以是有时限的。但也许你不必让它们跑那么快。

这对整个私募信贷行业会是非常大的好事。它会帮助为 AI 建设融资。因为如果你能以 5% 或 6% 而不是——我记得 CoreWeave 最低的一笔融资成本大概是 7% 出头——那样的利率给 GPU 融资，那从数学上就真的改变了这轮建设的融资成本。

我们得到了一项技术创新，它会降低融资成本、延长地球上算力的可用寿命。

[48:17] Gavin Baker

And then I do think the one last thing that's interesting about that is my friend Jamin from Kotu just did a podcast and Kotu had a deck.And they talked about, hey, sellers of shortage are doing so much better than the buyers of shortage, buyers of shortage being the hyperscalers.But if you own a giant installed base of what is currently in shortage, that's also a very good place to be.And we're hearing CPUs are way more important than they were in an agentic world.They do all these things around orchestration, tool calls.The biggest CPU fleets in the world sit at the hyperscalers.Some of these hyperscalers may catch up a little bit to the sellers of shortage.I want to talk about this idea of different and hard applied outside of the infrastructure piece of this.Now you're starting to interact with new founders, existing CEOs and founders that have to adjust to this new world.What are you seeing the most AI native founders that aren't building chips or infrastructure or models, but just people using this technology to build other stuff?

还有一件我觉得有意思的事：我朋友 Jamin（Coatue）刚做了一期播客，Coatue 有一份材料，里面讲到：短缺的卖方（sellers of shortage）表现远好过短缺的买方（buyers of shortage），而买方就是超大规模云厂商（hyperscalers）。

但如果你手上握着一个巨大的、正处于短缺状态的存量装机基础，那也是一个非常好的位置。而我们听说，在智能体化（agentic）的世界里 CPU 比以前重要得多——它们负责编排（orchestration）、工具调用这些事。而全世界最大的 CPU 机群就在这些超大规模厂商手上。所以有些超大规模厂商可能会稍微追上一点「短缺卖方」的表现。

（Patrick）我想把「不一样且难做」这个想法，拿到基础设施之外去聊。你现在开始接触新一批创始人，还有那些必须适应这个新世界的现任 CEO 和创始人。那些最 AI 原生、但并不造芯片、不做基础设施、不做模型，只是用这项技术去造别的东西的人，你看到了什么？

[49:14] Gavin Baker

How do they feel the most different to you if you've observed differences?I do think this is just for chip design.To me, it's always been a fundamental question for venture.So there are different ideas that are obvious to everyone on planet Earth as soon as they hear it.And if that's where you are in venture, if it's not hard to do, if it becomes obvious to the world before you have built scale, scale is the ultimate advantage.You're in trouble.And the great thing Amazon had was, I think it was obvious to a lot of people, but it wasn't obvious to the retail CEO.Amazon, they were very smart.Any e-commerce company that VCs invested in, they would destroy.They'd be like, oh, that's so cute.We're going to take our margins of that to negative 10,000%.And that's why the guys at Wayfair, they did something hard.Amazon tried to kill them and they failed.Those were like tough, operationally, really competent CEOs.For me in venture, I always look, is this going to be obvious to the world before this company could build scale?

（Patrick）如果你观察到了差异的话，他们身上最不一样的感觉是什么？

（Gavin）我觉得这跟芯片设计是一回事。对我来说，这一直是风险投资的根本问题。

有些点子，全地球的人一听就懂、觉得显而易见。如果你在风投里处在这个位置——如果它不难做，如果它在你还没建立起规模之前就已经对全世界显而易见了，而规模才是终极优势——那你就有麻烦了。

亚马逊的了不起之处在于，我觉得它对很多人来说是显而易见的，但对零售业的 CEO 们来说不是。亚马逊，他们非常聪明。任何一家风投投的电商公司，他们都能碾死。他们会说：噢，好可爱，我们把这块的利润率打到负 10000%。

而这就是为什么 Wayfair 那帮人了不起——他们做了难的事。亚马逊试图杀死他们，失败了。那是一群强悍的、运营上极其能干的 CEO。

在风投里，我永远在看的是：在这家公司能建立起规模之前，这件事会不会对全世界变得显而易见？

[50:13] Gavin Baker

Or is this both not obvious, different, and really hard to do?I think a lot of founders are really struggling with this in AI.I think people are becoming worried today in Jensen's five-layer cake of AI.The profits, they're accruing to energy, they're accruing to data centers, they're accruing to chips, they're accruing to models.Not really accruing to the applications.I think cursor and cognition got to a scale.They focused on coding.18 months ago, the people were focusing on coding.Open AI was doing everything under the sun.The people focused on coding were cursor, cognition, anthropic.And it was really right to focus on code.Amjad Massad, the founder of Replit, tweeted something that I thought was so smart.Just something like, better lesson adjacent is the fact that coding might be the shortest path to ASI and useful AI.Because if you're really good at coding, you can write yourself code to do anything.So I think it was really smart of those companies to focus intensely on coding.They all probably got to a scale where they have a place.I think cognition is doing something really, really different.But I think a lot of founders are really struggling, man.I think they're trying to get confidence that in nichier areas...

还是说，它既不显而易见、又不一样、而且真的很难做？

我觉得很多创始人在 AI 里正被这个问题折磨得很痛苦。

我觉得今天人们开始担心的是黄仁勋那个「AI 五层蛋糕」：利润在往能源积累、往数据中心积累、往芯片积累、往模型积累。就是没怎么往应用层积累。

我认为 Cursor 和 Cognition 做到了规模。他们聚焦在编程上。18 个月前，聚焦在编程上的人是 Cursor、Cognition 和 Anthropic，而 OpenAI 在无所不做。事实证明，聚焦在代码上是非常正确的。

Replit 的创始人 Amjad Masad 发过一条推我觉得特别聪明，大意是：跟「苦涩的教训」相邻的一个事实是，编程可能是通往 ASI 和有用 AI 的最短路径。因为如果你真的很擅长写代码，你就能给自己写代码去干任何事。

所以我觉得那几家公司高强度聚焦在编程上是非常聪明的。它们大概都做到了一个能站住脚的规模。我觉得 Cognition 在做的事非常、非常不一样。

但我觉得很多创始人真的很挣扎，兄弟。我觉得他们在努力给自己找信心：在更细的niche（细分领域）里……

[51:27] Gavin Baker

They won't get steamrolled....that they can get to them and get like a data moat before the model companies get to that niche.Or that it's a small enough niche that the model companies won't do it themselves, but it can still produce their venture outcome.Is this related to what you would call like the token path?

他们不会被碾平……在模型公司还没打到那个细分领域之前，他们能先到那儿、并且建立起一条数据护城河；或者那个细分领域小到模型公司不屑于自己做，但又仍然大到足以支撑一个风投级别的回报。

（Patrick）这跟你说的「token 路径」（token path）有关系吗？

[51:43] Gavin Baker

I know you've used that phrase with me before.Yeah, I think it comes from a guy, an altimeter, Jamin Ball.But he just said, if you're a software company or an AI company of any kind, you have to be in the token path.So Databricks, that's in the token path.Comparable companies are in the token path.If you're not in the token path and you're not in some really niche thing, life may be hard.And even for these vertical niches, I think if you talk to the people at the model companies, they're even skeptical of some of these.Because all of the data that's being generated in these niches come from humans.But then you're betting that you're able to use that proprietary data in this narrow vertical to train a model that's lower cost than the frontier labs can ever get to.And maybe that's a good bet.But I just think you have to be very, very careful.Now, on the other hand, if the returns to these frontier tokens relative to other tokens come down, there's going to be an explosion in value creation at the application layer.And I think another really important point is I have a belief that whenever he wants, Jensen can probably get pretty close to the frontier.With his own model.With his own model.

（Patrick）我知道你以前跟我用过这个说法。

（Gavin）对，我觉得这个说法来自 Altimeter 的 Jamin Ball。他说的是：如果你是一家软件公司、或任何一种 AI 公司，你必须站在 token 路径上。Databricks 就在 token 路径上。同类公司也都在 token 路径上。如果你不在 token 路径上，又不在某个真正很窄的细分里，日子可能会很难过。

而且哪怕是这些垂直细分，我觉得你去跟模型公司的人聊，他们对其中一些也是怀疑的。因为这些细分领域里产生的所有数据，都来自人类。然后你是在赌：你能用这个窄垂直里的专有数据训出一个模型，成本比前沿实验室能做到的还低。也许这是个好赌注。但我只是觉得你必须非常、非常小心。

反过来说，如果这些前沿 token 相对于其他 token 的回报下降了，那应用层的价值创造就会迎来一次爆发。

我还有一个我认为非常重要的判断：我相信只要黄仁勋想，他大概能把自己做到相当接近前沿的位置。

（Patrick）用他自己的模型。

（Gavin）用他自己的模型。

[52:59] Gavin Baker

I don't think he wants to do that.But that is what OpenAI and Anthropic are kind of trying to do to him unsuccessfully.He's a very logical thinker.This is the logical counter move.You will see that open source frontier, which today consists of Chinese models with stolen American tokens.Somebody told me that like DeepSeq, maybe the original one was only 150,000 reasoning traces.There's many ways to launder this if you're a Chinese company.You can hit all these different APIs.You can make it hard.Now, the American labs are working really hard on anti-distillation technology.But I just think Chinese open source, they're doing really impressive things in a very resource constrained way.But there's a lot of distillation.And this is why I think in addition to there not being enough compute to serve Mythos, they did not want it to be distilled.They wanted to use Mythos, distill it themselves, use it to RL their next model, whatever it is.And then I think what they and eventually I think OpenAI, anyone on the frontier will do is just say there's going to be some very interesting game theory.Because it's a new kind of prisoner's dilemma.We talked about the old prisoner's dilemma being just around like, hey, you're in a prisoner's dilemma where you have to spend.

我不认为他想那么做。但这正是 OpenAI 和 Anthropic 在试图对他做、而没成功的事。他是个非常讲逻辑的思考者，这是逻辑上的反制动作。

你会看到，今天的「开源前沿」基本上由中国模型构成，而它们用的是偷来的美国 token。有人告诉我，DeepSeek——大概是最初那一版——只用了 15 万条推理轨迹。如果你是中国公司，有很多办法把这件事洗干净：你可以去打各种不同的 API，你可以把它弄得很难查。

现在美国的实验室在非常努力地做反蒸馏（anti-distillation）技术。但我只是觉得，中国的开源确实在资源极度受限的情况下做出了非常令人印象深刻的东西。不过里面有大量蒸馏。

这也是为什么我认为，除了算力不够去服务 Mythos（ASR 存疑：一个前沿模型的代号）之外，他们也不希望它被蒸馏。他们想自己用 Mythos、自己蒸馏它，用它去对下一代模型做强化学习，不管那个模型是什么。

然后我觉得他们、以及最终我认为 OpenAI、任何在前沿上的人都会走到那一步：这里会出现非常有意思的博弈论。因为这是一种新的囚徒困境。我们以前聊过的旧囚徒困境是：嘿，你陷在一个「你必须花钱」的囚徒困境里。

[54:14] Gavin Baker

The new prisoner's dilemma is going to be if you are at the frontier, do you release that model via API or not?If everyone at the frontier agrees not to do that, then Chinese open source, if one person defects, they're going to have the best model.They're going to have a lot of revenue and cash flow.And then, of course, resources equal intelligence.So they'll start to pull ahead and then that will lead to everybody else releasing it.So it's a new game theory.It's kind of the same game theory that you have with Taiwan Semi, Samsung and Intel.The reality is if a company like NVIDIA or AMD were to ever really, really use one of these other foundries, that foundry would get better really quickly.So I do think Jensen is going to keep open source a certain time frame behind the frontier.I think that's going to be a very interesting thing to watch.And then, by the way, open source gets monetized.There's this misnomer that open source is free.Open source tokens, they cost energy to produce.You need to make up on GPUs.And the open source model companies almost always get a revenue share.How are you preparing Atreides for the world of Mythos 3, Mythos 4?

而新的囚徒困境将是：如果你在前沿上，你到底要不要通过 API 把这个模型放出去？

如果前沿上的所有人都同意不放，那么中国的开源就……而只要有一个人叛变，他就会拥有最好的模型，会拿到大量收入和现金流。然后当然，资源等于智能，所以他会开始拉开差距，接着这就会导致所有人都放出来。所以这是一个新的博弈论。

这跟台积电、三星、英特尔那个博弈其实是同一种。现实是，如果像英伟达或 AMD 这样的公司真的、认真地去用另一家代工厂，那家代工厂会很快变强。

所以我确实认为黄仁勋会让开源始终落后前沿一段固定的时间。我觉得这会是非常值得盯着看的事。

顺便说，开源是会被货币化的。有一个误称是「开源是免费的」。开源的 token 也要耗电才能产出，你还得把 GPU 的钱赚回来。而开源模型公司几乎总是能拿到一份收入分成。

（Patrick）你在怎么为 Mythos 3、Mythos 4 的世界做准备，为 Atreides 做准备？

[55:24] Gavin Baker

We're just trying to overinvest in cybersecurity.And I really believe everybody needs to have a safe word.Everybody needs to go leave your digital devices behind, literally go to the ocean and have a family safe word or a company safe word.And it can't be one that can be like socially engineered.And this is just to avoid cybercrime where what looks like your son or your daughter or your grandparents or your parents or whatever FaceTimes you.It's an utterly accurate simulation of them.They know everything and can extrapolate based on what they're likely to say and says, you know, wire me a million bucks.So doing everything we can with cybersecurity.That's defensive.What about analytical or processing?

（Gavin）我们就是在拼命在网络安全上超额投入。

而且我真心认为每个人都需要有一个「暗号」（safe word）。每个人都应该把数字设备放下，真的走到海边去，和家人约一个暗号、或者公司约一个暗号。而且不能是那种能被社会工程学套出来的。

这纯粹是为了防范这种网络犯罪：一个看起来像你儿子、女儿、祖父母或父母的人给你打 FaceTime。那是对他们完全准确的模拟，知道一切，还能根据他们大概率会说的话去外推，然后说：给我汇 100 万美元。

所以我们在网络安全上能做的都做了。

（Patrick）那是防守。那分析和处理这一侧呢？

[56:06] Gavin Baker

What will you still be able to do that it won't be able to do, I guess, on the analytical side?So it's a good question.I just watched The Last Samurai and I asked people at my firm to watch it.And The Last Samurai, if you haven't seen it, I highly recommend watching it.It's actually a movie that's aged really well.It's a Tom Cruise movie from 20 years ago.You know, the conceit is Tom Cruise.It's this bitter, washed up Civil War veteran who's actually a very good soldier.I mean, he's bitter and washed up because he feels like he participated in negative actions against the Native Americans.It's during the Miji Restoration.And he's hired by the modern elements of the Japanese government to train like an army of peasants how to fight the samurai.There's a first battle.Of course, the samurai win, even though they don't have guns.He fights valiantly.So the samurai decide not to kill him, take him to their village.He becomes a samurai.It feels like the Civil War to him.So he fights on the side of the samurai.At the end of it, he's massacred by a peasant with a machine gun.The machine gun is here.If we do not all become masters of the machine gun, we're going to get mastered.So I am trying to become a master of the machine gun.

（Patrick）在分析这一侧，你觉得你还能做到而它做不到的是什么？

（Gavin）好问题。我刚看了《最后的武士》（The Last Samurai），还让我公司的人都去看。如果你没看过，我强烈推荐。这部电影其实老得很好，是汤姆·克鲁斯 20 年前的片子。

设定是：汤姆·克鲁斯演一个愤世嫉俗、落魄的南北战争老兵，但其实是个非常优秀的军人。他愤世嫉俗和落魄，是因为他觉得自己参与过针对印第安人的恶行。故事发生在明治维新期间。他被日本政府的现代派雇来，训练一支农民组成的军队去打武士。

第一场仗打下来，武士当然赢了，尽管他们没有枪。他英勇作战，所以武士决定不杀他，把他带回村子。他变成了一名武士——这对他来说就像南北战争的感觉。所以他站到了武士这一边。

而到最后，他被一个拿机关枪的农民屠杀了。

机关枪就在这儿。如果我们不全都成为机关枪的大师，我们就会被别人拿机关枪支配。所以我正在努力成为机关枪的大师。

[57:09] Gavin Baker

And then I'm optimistic.There's a long period of time where just like if you were a 50-year-old samurai veteran of many wars,I fought many wars, Master Dwarf, you will have advantages using the machine gun.I'm optimistic as a lifelong student of investing.I'm going to be able to master the machine gun, this new technology, integrate it into my own process,integrate it into our firm's process in ways that let me contribute value as a human being for a long time.Like everyone, I have agents running all the time now.What's your most useful agent?

然后我是乐观的。有很长一段时间——就像如果你是一个身经百战的 50 岁武士老兵，我打过很多仗，那么你在使用机关枪时会拥有优势。

作为一个终身学习投资的人，我很乐观。我能够掌握这挺机关枪、这项新技术，把它整合进我自己的流程、整合进我们公司的流程，让我在很长一段时间里仍然能作为一个人类贡献价值。

跟所有人一样，我现在有智能体一直在跑。

（Patrick）你最有用的智能体是什么？

[57:44] Gavin Baker

My single most useful agent is a really good summary of the points that would be interesting to me from podcasts.There's just six hours a day of stuff that I feel like it's in my job description to watch.Every time somebody from OpenAI, XAI, Google, Cursor, Fireworks, Base 10, I'm going to say nothing of Jensen, Elon, Dario.I feel compelled to watch and I just don't have that much time.And there's some real needles in heat stacks.That is what I would say for me is the most useful.I do think there's a set of things that I always like to see.Like I'm very sensitive to management compensation.What are they insented to do?

（Gavin）我单个最有用的智能体，是从播客里帮我提炼出「我会感兴趣的那些点」的一份高质量摘要。

每天有整整六个小时的内容，是我觉得按我的岗位职责就该看的。每次 OpenAI、xAI、谷歌、Cursor、Fireworks、Baseten 的人出来讲话——更别说黄仁勋、马斯克、Dario 了——我都觉得非我看不可，而我根本没那么多时间。而且那里面确实有大海捞针捞出来的针。这就是我认为对我最有用的。

我确实有一组我总是想看的东西。比如我对管理层薪酬非常敏感：他们被激励去做什么？

[58:30] Gavin Baker

Do they just have stupid RSUs or do they have PSUs?And if they have PSUs, what are those PSUs that incent them to do?And we now have systems that do a very good first pass at that.That saves people a lot of time.It frees them up for more creative work than like going through the proxy,pulling the PSU thing, looking at how it's changed versus all the proxies because there's signal in that.And that's very labor intensive and that's so good for an AI.And there's obviously all sorts of same things within investing.Pressuring the organization in those ways I think has been helpful.This is the most exciting, thrilling time to be an investor.I'm getting a little bit worried.The diversity breakdown thing?

他们拿的只是傻乎乎的 RSU（限制性股票单元，时间到了就归属），还是 PSU（业绩股票单元，达成目标才归属）？如果是 PSU，那些 PSU 在激励他们去做什么？

我们现在有系统能把这件事的第一遍做得非常好。这为人省下大量时间，把人解放出来去做更有创造性的工作——相比之下，翻代理投票说明书（proxy）、把 PSU 条款一条条抠出来、再跟历年所有的 proxy 对比看它怎么变的（这里面是有信号的），那是极度耗人力的活，而且特别适合交给 AI。投资里显然还有一大堆同类的事。用这些方式给组织施加压力，我觉得是很有帮助的。

这是当投资人最激动人心、最刺激的时代。不过我开始有一点点担心。

（Patrick）是那个「多样性崩溃」的事吗？

[59:12] Gavin Baker

Say just like a little bit more about the kinds of people that are capitulating.I don't know anyone like me who's not really bullish on DRAM.There's all these interesting things happening with AI right now.One is cross-sectionally, the valuations do not make sense.They just flat out do not make sense.They cannot all be true.In other words, you have semi-cap equipment companies trading at 40 times next quarter's annualized earningsand DRAM companies trading at mid-single digit.At the peak of the last cycle, that was 5 versus 12.At one point, it was like 3 versus 45.Those can't both be true.And yes, semiconductor CapEx business models have improved more than the memory business models.We don't know how much HBM is going to improve memory business models yet.Yes, they have some element of recurring revenue with parts and maintenance,but it's not worth a thousand percent multiple cap.I think it's hard to square the valuation of something like NVIDIA,which is still in early April, was essentially as cheap as it gets relative to the market,like in the last 10 or 12 years or whatever it is, and very cheap absolute.It's very hard to square that valuation with something like GE Vernova's valuation,

（Patrick）再多说一点：那些正在缴械投降（capitulating）的都是什么样的人？

（Gavin）我不认识任何一个像我这样、但对 DRAM 不看多的人。

现在 AI 领域有一堆有意思的事在发生。第一，横截面上看，这些估值根本讲不通。它们就是彻底讲不通。它们不可能同时为真。

换句话说：半导体设备公司在按下个季度年化利润的 40 倍交易，而 DRAM 公司在按个位数中段的市盈率交易。上一轮周期的顶点，这个对比是 5 倍对 12 倍。某个时点甚至是 3 倍对 45 倍。这两个不可能同时成立。

是的，半导体资本开支类公司的商业模式改善的幅度确实大于存储公司的商业模式。HBM（高带宽内存）能把存储的商业模式改善多少，我们还不知道。是的，他们有一部分零配件和维保带来的经常性收入，但那不值一千个百分点的估值溢价。

我觉得很难把英伟达的估值——它到今年四月初还基本是相对市场最便宜的时候，过去 10 年、12 年不管怎么算都是，而且绝对估值也很便宜——很难把那个估值跟 GE Vernova 这类公司的估值放在一起自洽，

[1:00:19] Gavin Baker

because it builds in an unfathomable amount of share loss for NVIDIA.So valuations cross-sectionally are really different.Because we are in shortages, the lowest quality companies are doing the best.So if you're an oil and gas investor, a mining investor, natural resources investor,and you're well-versed in thinking of costs, this is very intuitive to you.And a real bull market for a commodity.The commodity suppliers with the highest costs go up the most,because it's the most beneficial to them.They go from on the verge of bankruptcy to gushing cash.And this is, I think, one reason commodity investing is really, really hard,because quality outperforms during the cycles, but you get all of the outperformance during thedownturns when the high-cost guys that mooned during the shortages and the commodity bull marketsgo bankrupt or whatever.You're seeing that happen in every industry.The lowest quality players, companies that are hated and detested by the hyperscalers and the buyers,because they have high costs, they're unreliable, the parts fail at a high rate,they're sold out and raising prices.And then that activity gets the interest of these retail accounts on X,and these stocks get bid to the moon, whereas some of the higher quality expressions

因为后者的估值里隐含着英伟达要丢掉一个深不可测的市场份额。所以横截面上估值差异真的很大。

第二，因为我们身处短缺之中，质量最差的公司表现最好。如果你是石油天然气投资人、矿业投资人、自然资源投资人，习惯用成本曲线去思考，这对你来说非常直观：在一轮真正的商品牛市里，成本最高的那些供应商涨得最多，因为对他们的边际改善最大——他们从濒临破产变成现金哗哗地流。

我觉得这也是商品投资特别难做的一个原因：高质量公司在整个周期中是跑赢的，但你所有的超额收益都是在下行期拿到的——那时候在短缺和商品牛市里被炒上天的高成本玩家纷纷破产。

你在每一个行业里都能看到这件事在发生。质量最低的玩家、那些被超大规模厂商和买家又恨又嫌弃的公司——因为它们成本高、不可靠、零件故障率高——现在订单排满了还在涨价。然后这种动静又引起了 X 上那些散户账号的兴趣，这些股票被炒到月球上；而一些质量更高的表达方式反而

[1:01:35] Gavin Baker

have actually really underperformed.As an investor, it's hard because you know, within a shadow of a doubt,that that thing that's mooned 10x in three months or six months is going to go right back down,subject to what they do with all the cash.And so it worries me a little bit that people who are very skeptical a year agoare no longer skeptical.But then I just contrast that with the valuations of these high-quality companies,which are just not extended, and it makes me feel better.I always thought it was funny in 24 and 25 that anyone asked about an AI bubble or talked about it.You have this nuclear bubble and this quantum bubble right here, right in front of you.What are we talking about? This is so real.Some of that nuclear quantum silliness has maybe spread into more speculative,lower quality, smaller cap names, where if you have a big presence on X or Reddit,it's easy to move them.And that frightens me a little bit.But I just wish there were more AI bears.I wish there were more memory bears.Astera is a stock I've been close to a long time.And there's a lot of bears on that.I love that.Great.I first invested in the Series C.Good luck thinking that's a copper loser.And then there's also you can feel the baskets in the market and the leverage baskets.

严重跑输了。

作为投资人这很难受，因为你毫无疑问地知道，那个三个月或六个月涨了 10 倍的东西一定会跌回去——具体取决于他们拿这些现金去干了什么。

所以让我有点担心的是：一年前还非常怀疑的人，现在不怀疑了。但我又拿这个去对照那些高质量公司的估值——它们根本没被拉伸——这又让我感觉好一些。

我一直觉得很好笑的是，2024 和 2025 年居然有人问「AI 泡沫」、谈论 AI 泡沫。你眼前明明摆着一个核电泡沫和一个量子计算泡沫。我们在说什么呢？AI 这件事太真实了。

那些核电和量子的荒唐劲儿，可能已经蔓延到更投机、质量更低的小市值标的里去了——在那里，如果你在 X 或 Reddit 上有很大的影响力，是很容易把它们拉动的。这让我有点害怕。

但我真希望能有更多 AI 空头。我希望有更多做空存储的人。Astera（Astera Labs）是我关注很久的一只股票，上面有很多空头。我爱死这个了，太好了——我从 C 轮就开始投它。祝你把它当成「铜的输家」还能赚到钱。

然后你还能感觉到市场里那些「篮子」（baskets）和杠杆篮子的存在。

[1:02:56] Gavin Baker

And what baskets you're in is really important.You know, copper, optical, DRAM, NAND.And a very interesting thing that's happened this year is in 24 and 25, the AI trade traded together.You could be long GPU compute, scale-up networking and optical scale across and short power or whatever it was.That trade worked from like a risk management sense because, you know, I'm very factor aware.That all blew out in January of this year.Scale-up networking would go crazy while scale-out was going down.Our DRAMs massively underperforming NAND and HDDs, which had not happened.So these cross-sectional correlations within AI really fell apart and you had to get very fine-grained.You couldn't hedge your memory anymore with some semi-cap equipment or NAND.Everything cross-sectionally really changed in a very interesting way in January.And I think maybe one reason for that was AI got to a quality where it was all of a sudden really easy for a bunch of people to get really smart on these different subsectors, start trading them, and then they get put into baskets.And those baskets influence-AI creating price efficiency.Yeah, exactly.I think some of the biggest opportunities outside of these higher quality names that I think can compound for a long time, and they're safe, unlike these low-quality names, which are terrifying, is in names that are miscategorized.

你被放进了哪个篮子里非常重要：铜、光模块、DRAM、NAND。

今年发生了一件很有意思的事：在 2024 和 2025 年，AI 这笔交易是同涨同跌的。你可以做多 GPU 算力、纵向扩展网络和光模块横向扩展，同时做空电力或别的什么。那个组合从风险管理的意义上是奏效的，因为我对因子（factor）非常敏感。

而这一切在今年一月全部崩了。纵向扩展网络在疯涨，同时横向扩展在跌。而 DRAM 大幅跑输 NAND 和硬盘（HDD），这是以前从没发生过的。

所以 AI 内部这些横截面相关性真的散架了，你必须做到非常细颗粒度。你没法再用半导体设备或 NAND 去对冲你的存储敞口了。今年一月，横截面上的一切都以一种非常有意思的方式变了。

我觉得一个可能的原因是：AI 达到了某种质量水平，突然之间一大批人可以很容易地在这些不同子板块上变得很懂，开始交易它们，然后它们又被塞进篮子里，而这些篮子又反过来影响价格——

（Patrick）AI 在创造价格效率。

（Gavin）对，正是。我认为除了那些能长期复利、而且安全（不像那些低质量标的那么吓人）的高质量名字之外，最大的机会之一，在那些被错误归类的标的上。

[1:04:25] Gavin Baker

Astera was in a lot of copper loser baskets.Astera, their biggest product is going to be a switch.You use both copper and optics to connect switches to accelerators.Definitionally, if you're a switch company or an accelerator company, you cannot be a copper loser because you're going to be on the other side of that connection.I wonder if you could riff just for like a sentence or two on each of the major companies.Google, Microsoft, Amazon, the major players that are public.All the conversation is centered around these exciting new companies.Maybe run through them in riff.Google was incredible last year because they had that TPU advantage, which is now gone.The reason I think they're still in a great position is just they have the most compute of everyone.We talked about the value of installed bases being higher as a result of shortages.They have the biggest installed base of compute.Google I.O. is this week.If they don't release something that even slightly leapfrogs, open AI and or clawed, that's interesting.And it's not a disaster for Google.It's just interesting.And it just means this NVIDIA effect we discussed is even more powerful than maybe I'd imagined.But I'm very curious to see what the Pareto frontier looks like literally in five days after Google's announced its new stuff.

Astera 被塞进了很多「铜的输家」篮子里。但 Astera 最大的产品将会是一颗交换芯片（switch）。你连接交换机和加速器时，铜和光都要用。从定义上讲，如果你是一家交换机公司或加速器公司，你就不可能是「铜的输家」，因为你永远站在那根连接的另一端。

（Patrick）我想请你对每一家主要公司都即兴说一两句：谷歌、微软、亚马逊，这些主要的上市玩家。现在所有讨论都围着那些激动人心的新公司转。要不你挨个过一遍？

（Gavin）谷歌去年非常了不起，因为他们有 TPU 那个优势——现在这个优势没了。我认为他们仍然处在很好的位置，原因很简单：他们的算力比谁都多。我们刚说过，因为短缺，存量装机基础的价值更高了。而他们拥有最大的算力存量。

本周就是 Google I/O。如果他们没发布出哪怕稍微超越 OpenAI 和/或 Claude 的东西，那就很有意思了。这对谷歌不算灾难，只是很有意思。而且那就意味着我们讨论的这个「英伟达效应」比我想象的还要强。所以我非常好奇，五天后谷歌发完新东西，帕累托前沿会长成什么样。

[1:05:42] Gavin Baker

This is a big card for them.But Google, between the amount of data they have and the YouTube data is actually really genuinely valuable.It is valuable in a world of robotics.The amount of compute they have, the search business they have.Google's never not going to be in a good position.And then you see that with GCP going crazy.You got to give Zuckerberg a mince credit, what he's done in terms of making Meta an AI-first company internally.And I do think he is the only one of those true internet giants to have done that.I give him a lot of credit for that.I give him a lot of credit for paying up when he did for building our contracts, the talent.And Muse, I think, was a really big upside surprise.It was the first model from MSL.And it's not on the Pareto frontier with XAI, Google's one entrant, and then OpenAI and Cloud, but it's pretty close.That was very impressive to me.So I think Meta is in a better position.Still not as strong of an absolute position as Google, but their better position and rates of change matter more than level, as you know, in markets, particularly over short three-year timeframes.Over long timeframes, the level of competitive advantages tends to dominate.But even within that, changes really matter.

这对他们是一张大牌。但谷歌手上有那么多数据，YouTube 的数据是真正、实实在在有价值的——在一个机器人的世界里它是有价值的。加上他们拥有的算力、拥有的搜索业务，谷歌永远不会处在一个不好的位置。你从 GCP（谷歌云）的疯涨也能看到这一点。

你得给扎克伯格记一笔功：他在把 Meta 内部变成一家 AI 优先的公司这件事上做到的程度。我确实认为，在那批真正的互联网巨头里，他是唯一做到这件事的人。这点我很服他。我也很服他在合同和人才上肯出价、而且是在对的时点出价。

还有 Muse（ASR 存疑：Meta 超级智能实验室的首个模型代号），我觉得是一个很大的正面惊喜。那是 MSL（Meta Superintelligence Labs，Meta 超级智能实验室）的第一个模型。它没能进到 xAI、谷歌那一家入围者、以及 OpenAI 和 Claude 所在的帕累托前沿上，但它相当接近了。这让我印象非常深刻。

所以我认为 Meta 的位置变好了。绝对位置仍然不如谷歌强，但他们的位置在改善，而在市场里——尤其在三年这种短周期里——变化率比水平值更重要。长周期里，竞争优势的绝对水平往往会占主导。但即便如此，变化也真的很重要。

[1:06:57] Gavin Baker

Amazon, I think, is in a really strong position because of Tranium.I do think you're going to see real P&L efficiencies from robotics over the next 18 months in their retail business.I actually think Nova, their internal models are not where Muse is, but they're better than they get credit for.Then Microsoft, I like Satya.I admire him.I think he's an exceptional CEO, and I give him a lot of credit for the decisions he's made.But he did go from, we're going to make Google dance to being the product manager of Copilot in like three years.I would love to know during the coup attempt against OpenAI, does Satya regret his decisions?

亚马逊我认为处在非常强的位置，因为 Trainium。我确实认为，未来 18 个月你会在他们的零售业务里看到机器人带来的真实损益改善。我其实觉得 Nova（亚马逊自研模型）没到 Muse 的水平，但它比外界给的评价要好。

然后是微软。我喜欢 Satya（纳德拉），我很敬佩他，我认为他是一位卓越的 CEO，我也很服他做的那些决定。但他确实从「我们要让谷歌跳舞」，在大约三年时间里变成了「Copilot 的产品经理」。

我特别想知道，在那次针对 OpenAI 的逼宫未遂事件中，Satya 后不后悔他当时的决定？

[1:07:36] Gavin Baker

Does Satya wish that he had supported Ilya instead of Sam?And that Ilya and Mira were really running OpenAI today?In his heart of hearts, I would love to know.Because I think the Microsoft OpenAI partnership might look very different in that world.I think that's a very interesting question that we'll never know the answer to.But I give him a lot of credit.What he is doing now, he's taking risk.This goes to the decisions you have to make in that cone of uncertainty, or not only how much you spend, but what you're going to spend it on.I think Microsoft flinched for like a moment in early 25.They have this algorithm.We spend this much CapEx dollars.We get this return.That algorithm was kind of off.And if you flinch, you lose position.You lose all these allocations.It's difficult to get it back.So they flinched.And now the decision Satya is making, which the market has punished him for, but I think is the right decision.I mean, who knows how fast Azure could be growing if they're willing to just sell GPUs to OpenAI.We're going to use our compute internally to make our own products better.One reason Copilot is so bad, or has been so bad, is just one enough compute available.They're fixing that.

Satya 会不会希望自己当初支持的是 Ilya 而不是 Sam？希望今天真正在管 OpenAI 的是 Ilya 和 Mira？他内心深处怎么想，我特别想知道。因为我觉得在那个世界里，微软和 OpenAI 的合作关系可能会非常不一样。我觉得这是一个我们永远不会知道答案的、非常有意思的问题。

但我还是很服他。他现在在做的事，是在承担风险。这就涉及到你在那个「不确定性锥」里必须做的决定——不光是你花多少钱，还有你要把钱花在什么上。

我认为微软在 2025 年初退缩了一瞬间。他们有一套算法：我们花这么多资本开支，我们拿到这么多回报。那个算法当时有点不灵了。而一旦你退缩，你就丢掉位置，你丢掉所有那些产能配额，再想拿回来非常难。所以他们退缩了。

而 Satya 现在做的这个决定——市场为此惩罚了他，但我认为是对的决定——我是说，谁知道如果他们愿意就把 GPU 卖给 OpenAI，Azure 能长多快？他们选择的是：我们要把算力留在内部，用来让我们自己的产品更好。Copilot 之所以那么糟、或者说一直那么糟，一个原因就是可用的算力不够。他们正在修这个问题。

[1:08:52] Gavin Baker

He's the product manager at Copilot.I do think he's a great CEO.They're trying to use their compute to train their own models.I am a little skeptical that they have the right team to succeed there.But just like Betta, they can afford to hire maybe a different team.But I think he's making good decisions that are risky decisions to position Microsoft for this world where frontier models are no longer API accessible.And I think it's a really courageous decision that I give him a lot of credit for.And he is foregoing.I mean, Microsoft would probably be an $800 stock today if they were using their GPUs to serve solely OpenAI and Anthropics capacity instead of using them for their own products.So I give him a lot of credit for making a great decision.I think what's really interesting is the degree to which these companies are outward facing in their decisions.The two companies who are the most deeply engaged with startups are Amazon and NVIDIA by a mile.Then there's a really intense engagement with Google.They're next most intense.Broadcom is engaged in a different way.They're just everybody's favorite ASIC supplier.If you're a startup, it's considered like a level up if you get to work with Broadcom for your second gen chip.

他就是 Copilot 的产品经理。我确实认为他是一位伟大的 CEO。他们在试图用自己的算力去训自己的模型。我对他们是否有对的团队能在这件事上成功，稍微有点怀疑。不过就像 Meta 一样，他们付得起钱去请一个不一样的团队。

但我认为他在做好的决定、有风险的决定，为微软在「前沿模型不再通过 API 开放」的那个世界里卡好位置。我觉得这是一个非常有勇气的决定，我很服他。而且他是在放弃收益——我是说，如果微软把 GPU 单纯用来给 OpenAI 和 Anthropic 供产能，今天股价大概会是 800 美元。所以我很服他做出了一个伟大的决定。

我觉得真正有意思的是：这些公司在决策上「向外看」的程度差别有多大。跟初创公司接触最深的两家公司，是亚马逊和英伟达，遥遥领先。然后谷歌的接触也相当密集，排第二。博通是另一种方式的参与——它是所有人最爱的 ASIC（专用集成电路）供应商。如果你是一家初创公司，能在第二代芯片上跟博通合作，会被视为「升级了」。

[1:10:07] Gavin Baker

And it's considered mana from heaven if Broadcom works with you for their first gen chip.And then you see essentially zero engagement with startups from AMD, Microsoft, and Meta.When I say zero, it's a little.And I just wonder about that decision.Because some of the best teams are no longer at big public companies.They're at these smaller startups.And I think it's going to end up being a pretty big advantage for NVIDIA, AMD, Google right behind them to have this engagement that you just don't see from these other hyperscalers.As we wrap up, I'm curious for you to riff on any other out there knock-on effects that you've started to think about for this giant trend.We've talked about the specific companies in a lot of detail that this most impacts.We talked a little bit about the application layer and what would have to happen for there to be more value occurring to that layer of the stack.I'm curious, any other just fun knock-on things that you've been thinking about as this world changes so quickly?

而如果博通愿意跟你合作做你的第一代芯片，那简直是天降甘露。

然后你会看到，AMD、微软和 Meta 跟初创公司的接触基本为零。我说「零」的意思是很少。我很好奇他们为什么这么决定。因为一些最好的团队已经不在大型上市公司里了，他们在这些更小的初创公司里。我认为这最终会成为英伟达、AMD、以及紧随其后的谷歌一个相当大的优势——这种接触是你在其他几家超大规模厂商身上看不到的。

（Patrick）快结束了，我好奇你能不能就这个巨大趋势，聊聊你开始想到的其他「外溢效应」。我们已经很详细地聊了受影响最大的具体公司，也聊了一点应用层，以及要让价值更多归到那一层需要发生什么。我好奇，在这个变化如此之快的世界里，你还想到了哪些好玩的连锁反应？

[1:11:08] Gavin Baker

And it is wild.I mean, at the application layer, forget value accruing.Just value has been destroyed.AI has net destroyed.Even if you count cursor cognition, the most successful AI natives, trillions of dollars of value has been destroyed by AI at the application layer.And just in this context, the companies that are doing the best today, that are seeing their values increase the most, that are creating economic value,are the companies with the highest effective ratio of utilized GPUs per human.Maybe this just means that every human is going to get a lot of GPUs, but I think that's an interesting fact that we kind of need to be cognizant of.I will just say, and maybe this is a little dark, I am more and more worried about personal safety.And I worry about this a lot more for people who have a much bigger public presence and are much more associated with AI.I hope nothing tragic happens.There is this upsurge in political violence here in America.And as AI increasingly becomes political, I worry that's going to get directed at more and more AI political leaders.Whatever I may think or may not think of open AI, I think it is terrible that someone threw Molotov cocktails at Sam Altman's house.

确实很疯狂。我是说，在应用层，别说价值归属了——价值是被摧毁的。AI 净摧毁了价值。哪怕你把 Cursor、Cognition 这些最成功的 AI 原生公司算进去，AI 在应用层已经摧毁了数万亿美元的价值。

而在这个背景下，今天表现最好、价值增长最多、真正在创造经济价值的公司，是那些「有效利用的 GPU 数 / 人数」比例最高的公司。也许这只是意味着每个人都会分到很多 GPU，但我觉得这是一个我们需要意识到的有意思的事实。

我还想说——这可能有点阴暗——我越来越担心人身安全。而且我为那些公众曝光度大得多、跟 AI 关联紧密得多的人担心得更厉害。我希望不要发生什么悲剧。

美国现在政治暴力在抬头。而随着 AI 越来越政治化，我担心这会指向越来越多的 AI 政治领袖。不管我对 OpenAI 怎么看或不怎么看，我觉得有人往 Sam Altman 家里扔燃烧瓶这件事是很糟糕的。

[1:12:18] Gavin Baker

I am worried that we are headed into a higher variance, higher beta, higher risk world because of AI.And that's for me as an individual and then for people who are big players on the chessboard.Think about what it means geopolitically.We're watching the Ukrainians are really starting to win.And the reason they're winning, I think is not really because they have better drones.I think they do have better drones.That's part of it.I think the reason Ukraine is really winning is they have the best battlefield AI outside of probably America and Israel.And as China, as our adversaries begin to process that, how do they respond?

我担心因为 AI，我们正在走向一个方差更高、贝塔更高、风险更高的世界。这对我个人是如此，对那些棋盘上的大玩家更是如此。

想想这在地缘政治上意味着什么。我们看到乌克兰真的开始在赢。而我认为他们赢的原因，其实主要不是因为他们的无人机更好——我确实认为他们的无人机更好，那是一部分原因。我认为乌克兰之所以真的在赢，是因为他们拥有除美国和以色列之外最好的战场 AI。

而当中国、当我们的对手开始消化这件事，他们会怎么回应？

[1:12:58] Gavin Baker

If the United States, because of its edge in AI, it's great if you're America, but it is destabilizing for the rest of the world.Something I think a lot about is creating a charity to just educate the world on how awesome the West has been.And slavery was endemic to essentially almost every civilization and slavery was really ended by the British Empire.Tell that story.But America, after 1945, we had the nuclear bomb.No one else had it.We could have controlled the world forever.Instead, we rebuilt Germany and Japan, who are America's most reliable allies.Israel, South Korea, that's a testament to like the American spirit in our country.We didn't take over the world.There were these fears that were documented at the time that the American generals, MacArthur was a little bit of an American emperor in Japan, were just going to take over the world.And they could have.And they didn't.They came home.We demilitarized.And then you had this period of great global stability between, you know, it was scary.There were terrible moments.Pax Americana.Yeah, you had the Pax Americana.So maybe it's not destabilizing.Maybe it leads to another Pax Americana informed by our AI dominance.And I'm so optimistic that AI is going to be amazing for the world.

如果美国因为它在 AI 上的优势——那对美国当然很好，但对世界其他地方是不稳定因素。

我经常想的一件事，是搞一个慈善机构，专门去向世界讲清楚西方到底有多了不起。奴隶制在几乎每一种文明里都是普遍存在的，而真正终结奴隶制的是大英帝国。把那个故事讲出来。

而美国，1945 年之后我们有了原子弹，别人都没有。我们本可以永远统治世界。可我们做的是重建德国和日本，而他们成了美国最可靠的盟友。还有以色列、韩国。这是对美国精神、对我们这个国家的一种证明。我们没有接管世界。

当时是有过记录在案的担忧的：美国的将军们——麦克阿瑟在日本某种程度上就像个美国皇帝——会不会直接接管世界。他们本来可以。但他们没有。他们回家了。我们裁军了。然后就有了那段全球高度稳定的时期。当然，也很吓人，有过一些可怕的时刻。

（Patrick）美利坚治世（Pax Americana）。

（Gavin）对，是的，你有了美利坚治世。所以也许这不是不稳定因素。也许它会带来另一个由我们的 AI 主导地位塑造的美利坚治世。

而我非常乐观地认为，AI 对这个世界会是极好的事。

[1:14:11] Gavin Baker

There's someone like me whose daughter was diagnosed with a very rare disease.There's no cure.He was able to assemble a lot of resources.He was able to get a lot of compute from the labs.We were made aware of what was happening.Spun up an immense amount of agents.Spun up an immense amount of agents.Using AI with a drug on the market that can actually impact his daughter's disease.And then has spun up a company to cure it.Her life is already immeasurably different because of AI.So I'm like an AI optimist maximalist.But I also just acknowledge it's like an event horizon.It for sure, I think, is going to be a discontinuity.We need to navigate as a society.I think the Luddites are going to be wrong.But we need to be like really thoughtful in how we address their concerns.We need to make sure that it's good for everyone.Like it is a little dystopian that now the best AI is only available to people with a lot of money.We need to solve that.We need to approach this with humility, recognize there's a lot of uncertainty and be thoughtful.When I do this with you, I tell people afterwards, I'm like,may you find something that you love as much as Gavin loves markets and companies and capitalism

有一个跟我情况类似的人，他女儿被诊断出一种非常罕见的疾病，无药可治。他有能力调动很多资源，从各个实验室拿到了大量算力。我们被告知了正在发生的事。他启动了海量的智能体。启动了海量的智能体，用 AI 找到了一种市面上已有的药，那种药真的能作用于他女儿的这个病。然后他又成立了一家公司去攻克它。她的人生已经因为 AI 而变得截然不同了。

所以我算是个 AI 乐观主义的极端派。但我也承认，这就像一个事件视界（event horizon）。我认为它肯定会是一次断裂式的变化，我们作为一个社会必须去导航它。

我认为「卢德分子」（Luddites，反技术者）会被证明是错的。但我们必须非常认真地去回应他们的关切。我们必须确保这件事对每个人都是好的。

比如现在最好的 AI 只有有钱人才用得上，这确实有点反乌托邦。我们必须解决这个问题。我们必须带着谦逊来面对它，承认这里面有大量不确定性，并且深思熟虑。

（Patrick）每次跟你录完，我事后都会跟别人说：愿你也能找到一样东西，让你像 Gavin 爱市场、爱公司、爱资本主义

[1:15:18] Patrick O'Shaughnessy

and history on display today as always.Gavin, thanks for your time.Thank you.Thanks, Patrick.If you enjoyed this episode, visit Colossus.com.You'll find every episode of this podcast complete with hand edited transcripts.You can also subscribe to Colossus, our quarterly print, digital, and private audio publicationfeaturing in-depth profiles of the founders, investors, and companies that we admire most.Learn more at Colossus.com slash subscribe.You know how small advantages compound over time.

和历史那样地去爱它——今天一如既往地展现得淋漓尽致。Gavin，谢谢你抽时间。

（Gavin）谢谢。谢谢你，Patrick。

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[1:16:05]

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