内容来源：https://techcrunch.com/2026/06/07/is-this-the-dawn-of-the-tokenpocalypse/

内容总结：

微软GitHub Copilot大幅涨价引发行业连锁反应，“代币末日”担忧蔓延

近日，微软宣布对旗下编程辅助工具GitHub Copilot进行重大价格调整，从原先的固定费率模式转向按令牌（token）数量计费，且费用显著提高。这一变动在Reddit上引发热议，有用户直言其公司已将此举称为“代币末日”（Tokenpocalypse）。

在最新一期TechCrunch《股权》播客节目中，三位主持人就此次调价对整个人工智能生态的潜在影响展开讨论。随着Anthropic等头部AI公司筹备上市，盈利能力成为难以回避的尖锐问题。业界普遍预期，为控制成本，其他AI产品也将跟进涨价，并收紧使用限制。

从“烧钱狂欢”到成本转嫁：AI商业模式之困

记者Sean O’Kane指出，当前整个AI生态系统严重依赖投资者资金补贴，许多看似免费的服务实际上成本高昂。如今，这些成本正加速转嫁给终端消费者。他质疑：“这些AI实验室能否在压缩成本的同时，让技术进步到足以匹配客户的支付意愿？”

Kirsten Korosec则强调，这一现象凸显了行业变化之快。短短数月内，企业从狂热追求“tokenmaxxxing”（最大化使用令牌）转向因成本过高而限制使用。她提出一个现实难题：“当市场每天都在变化，AI公司在IPO文件中如何准确描述这些风险？”

Uber式盈利之路能否复制？

讨论以Uber为参照案例。Uber曾在长期亏损后通过业务转型、压缩成本实现盈利，但其过程伴随对司机和用户的利益调整。Anthony Ha认为，当前AI公司若要生存，也必须经历类似阵痛——通过商业模式重塑和成本控制找到盈利平衡点。

然而，Sean O’Kane对此表示怀疑：“Uber可以通过压榨司机来缩减开支，但AI公司的成本结构更为刚性透明，它们能找到类似‘可挤压’的环节吗？”

政府监管追赶，IPO文件风险因素成焦点

与此同时，美国政府也在加速跟进。本周，总统特朗普签署了一项行政命令，旨在让政府有权审查强大AI模型。Kirsten感叹，监管、商业模式、定价机制几乎同时在飞速演变，这在她职业生涯中前所未见。因此，她特别关注即将提交的Anthropic等公司的IPO注册文件，其中如何列举风险因素将成为观察行业走向的关键窗口。

中文翻译：

微软近期宣布了GitHub Copilot的重大定价调整——调整幅度之大，以至于有Reddit用户称其公司已开始将此事称为“Token末日”。在最新一期《TechCrunch》的《Equity》播客中，我与Kirsten Korosec、Sean O'Kane共同探讨了这些变化对更广泛AI生态可能产生的影响。毕竟，随着Anthropic等大型AI公司计划上市，盈利问题将变得棘手，我们很可能会看到其他AI产品也出现类似的价格上涨，以及更多使用限制，因为企业正努力控制成本。

Sean提出了一个疑问：“这些AI实验室能否压低成本，同时推动技术足够进步，最终在客户愿意支出的水平上达成平衡？”

与此同时，Kirsten认为这也反映了“事情变化之快”。短短几个月内，企业先是痴迷于“最大化Token使用量”，随后又因高昂成本而转向反对。因此，在AI公司撰写IPO文件时，她问道：“你们该如何写入这些风险？因为风险本身正在我们眼前日新月异地演变。”

以下是我们对话的节选，已为篇幅和清晰度进行编辑。

Anthony Ha：Sean，我们在策划这期节目时，你提到了“Token末日”。我想听听你对此更深入的想法，但作为例子，微软决定对GitHub Copilot按Token收费（而非统一费率）。整个生态系统严重依赖投资者资金补贴，所以那些看似零成本的东西，实际上极其昂贵。如今，我们将面临更多成本转嫁给终端用户和客户的情况。这会如何改变用户行为？我不确定，但痛苦肯定会很多。

Sean O'Kane：我的意思是，Anthropic的S-1文件中会出现多少与Token相关的风险因素？这是个重大问题。我在节目中多次提及此事，而我们似乎总绕不开这一点。优步（Uber）在一个半月内完成了整个轮回：先是说“天哪，我们在这方面的预算消耗速度远超预期”，接着又说“哦，这或许太贵了，我们需要设限，控制公司内部的使用量”。

这有点令人担忧。想象一下，像优步这样大量使用AI的公司，变化如此之快，这引出了一个问题：这些AI实验室能否在成本上做减法，同时在技术上做加法，最终与客户的支出意愿找到平衡？

回想起来有趣的是，ChatGPT最初推出时，每月20美元的定价我怀疑是否真有策略可言，更像是“随便定个数字”。此后我们一直在为此付出代价。显然，用户愿意为更先进的模型支付更多，但这仍不足以弥补实际成本。因此，这无疑是最核心的问题。

Kirsten：在我看来，这一切都说明了事情变化之快。仔细想想，“最大化Token使用量”这个风潮从兴起、达到顶峰，到如今被视为不利策略，仅仅用了六个月。整个定价机制，正如你所说，是在AI实验室商业模式尚未真正成型并稳固之前就已确立的。

与此同时，政府也在努力追赶。就在本周，特朗普总统签署了一项行政命令——虽为缩略版，但旨在让政府有机会审查强大的AI模型。所有这些变化发生的速度，我认为是前所未有的。

正因如此，我非常期待一些S-1 IPO注册文件，尤其是其中的风险因素部分。这些风险正日新月异地演变，你该如何把它们写进文件？

Anthony：Sean，你提到优步是个有趣的例子。他们的AI支出是一方面，但优步也在AI讨论中被提及，因为那些认为存在泡沫的人常会指出这些工具和公司盈利有多糟糕，而反驳者则举出优步的例子。人们曾谈论优步如何不盈利，但最终它通过规模化缩小了差距。

我认为这有道理。但优步要实现这一点，必须在很多方面对公司进行彻底转型。从创立之初到如今，它扩展了多个业务领域，对客户和司机施加了不同压力，这些都是它最终成为盈利公司所必须经历的。我认为，如果许多AI公司要生存下去，也必须经历类似的转型。

Sean：这些AI实验室有没有可能像优步多年来压榨司机那样，一分一厘地节省成本？它们是否有足够的弹性空间来做到这一点？我不知道。在很多方面，这似乎意味着更硬性、更直接的成本，所以未来会很有趣。

英文来源：

Microsoft recently announced major pricing changes for GitHub Copilot — changes that were drastic enough that a Reddit user said their company has started calling it the Tokenpocalypse.
On the latest episode of TechCrunch’s Equity podcast, Kirsten Korosec, Sean O’Kane, and I discussed what those changes might mean for the larger AI ecosystem. After all, as Anthropic and other big AI companies plan to go public, leading to awkward questions about profitability, we’re likely to see similar price increases for other AI products, and more usage restrictions as businesses try to keep costs under control.
“Can these AI labs collapse that cost [and] progress the tech enough in a way that it eventually meets in the middle with customers’ appetite for spending?” Sean wondered.
Kirsten, meanwhile, suggested that this also reflects “how quickly things are moving.” In just a few months, companies became obsessed with “tokenmaxxxing,” then turned against it due to the high costs. So as AI companies write their IPO filings, she asked, “How do you even write these risks in, because they are evolving before our eyes?”
Keep reading for a preview of our conversation, edited for length and clarity.
Anthony Ha: When we were planning for this, Sean, you called this the Tokenpocalypse. And I want to hear more about what you think about it, but there was an example of Microsoft deciding with GitHub Copilot that they’re going to start charging more per token [instead of a flat rate].
This whole ecosystem is heavily, heavily subsidized by investor money. And so stuff that seems like it has no cost is, in fact, incredibly expensive. And now we’re going to get to a point where more of that cost is going to get passed on to the end consumer, to the customer. How is that going to change behavior? I don’t think we know, but there’s going to be a lot of pain.
Sean O’Kane: I mean, how many token-related risk factors do we think are going to be in the Anthropic’s S-1? This is a big question. It’s something that I’ve mentioned a lot on this show and we seem to just keep running into it, where Uber has done like the full arc in the span of a month and a half of saying, “Boy, we kind of blew through our budget on this stuff way quicker than we thought this year.” And then, “Ooh, maybe this is going to be a little too expensive, we need to put caps on this, and we need to limit people’s usage inside the company.”
That’s just a little worrying. Imagine if you see that happen so quickly at a company like Uber, that is using this stuff a lot, and it’s just a question of: Can these AI labs collapse that cost [and] progress the tech enough in a way that it eventually meets in the middle with customers’ appetite for spending?
A funny thing to think back on is, I don’t think there was really any strategy involved in charging $20 a month [for ChatGPT Plus] when ChatGPT originally came out. It was just sort of like, “Let’s spit out a number.” And we’ve all been reckoning with that ever since. Clearly, people pay more for the more advanced models, but even that still isn’t enough to close that gap to the true cost. So that’s clearly the biggest question here.
Kirsten: All of this, to me, illustrates how quickly things are moving. I mean, when you really think about it, the whole tokenmaxxxing thing has become a thing, peaked, and now is seen disfavorably, within six months. The scale of this, the whole pricing mechanism, to your point, was put in place before business models were really shaped and solidified around AI labs.
And then, at the same time, you have the government trying to catch up. Also this week, President Trump signed an executive order — it is a narrow version, but this is designed to give the government a chance to review powerful AI models. So you have all this happening at a pace that I don’t think I’ve ever experienced.
That’s why I’m really looking forward to some of these S-1 IPO registration statements, because of the risk [factors]. How do you even write these risks in, because they are evolving before our eyes, and day by day?
Anthony: Uber is an interesting example, Sean, because you mentioned their AI spend, but they’ve also come up in the AI discourse because sometimes, people who think there’s this bubble, they’ll bring up just how wildly unprofitable these tools are, these companies are, and then people will bring up Uber as a response. People talked about how unprofitable Uber was, but eventually you get to scale and then you close that gap.
And I think that’s true. But also, for Uber to do that, it had to really transform itself as a company in a lot of ways. What Uber was at the beginning and what it is now, all the different areas of business that it’s had to expand into, the different ways that customers and drivers have gotten squeezed, those are things that had to happen to get to the point where it could be a profitable company.
And I think you’re going to have to see similar transformations for a lot of these AI companies if they’re going to survive.
Sean: Is there any way that these labs can squeeze pennies like Uber has squeezed the drivers over the years? Is there something squishy enough there for them to do that? I don’t know. This seems like harder, more straightforward costs in a lot of ways, so it’ll be interesting.