# AI capex — X 热门讨论 (2026-10-11 12:54 UTC)

## @ParadisLabs (Paradis) · 10-11 10:55 · ♥61 ↻0 💬16 With the Singapore Grand Prix in a few hours, I can't help but draw comparisons to the financial markets and the AI trade.

One slow pitstop or a miscalculated overtake can undo weeks of preparation and cost millions of dollars.

So with AI and semiconductor earnings fast approaching, it feels like we need a quick pitstop to get the race back on track.

Rates remain high. Oil prices are elevated. Fresh frontier lab ARR concerns. AI capex. Circular financing.....all create some dark clouds which have resulted in de-risked positioning and low sentiment.

Similarly, consumer confidence is low. New research from Morgan Stanley finds that net sentiment from Americans towards AI is at +14% compared with -20% for data center construction, with water requirements and environmental concerns among the biggest objections.

It therefore seems like AI policy will become an even bigger stumbling block in the years to come...

But the positive is that business fundamentals have remained strong highlighted by recent sold-out comments from $MU and $LITE CEOs. Plus $TSM reporting 55% YoY revenue growth as the de-facto proxy for the wider AI trade.

My beloved Chelsea won 5-1 yesterday. But conceding even just 1 goal won't be good enough for the AI trade.

With sentiment at fresh lows, I feel like all the key infrastructure companies need to report without any hiccups. https://x.com/ParadisLabs/status/2109236535090434110

## @KrisPatel99 (Kris Patel 🇺🇸) · 10-11 11:04 · ♥46 ↻3 💬10 The Difference between the dot com bubble and the AI bubble is actually every simple yet no one seems to be talking about it surprisingly...

The dotcom bubble was pumped up by mal investment in unprofitable equities that got pumped to moon by expectations of future profitability from the revolutionary technology called "The Internet". Investors paid for companies that either made no money or bought companies at valuations that made no sense based on their future growth rate.

The AI Bubble is a concentration bubble. Alot of capital is being pumped into VERY profitable companies but those companies are cyclical in nature. The expectation for most investors is that the AI CAPEX cycle will never and we're never going back to a time when semiconductors end up at negative growth and thus multiples should reflect that durability.

You also have an entire generation that is hyper focused on equities and have never done any work on understand how debt impacts enterprise value and the multiples the market will pay on that debt overhang risk as well as cashflow that has to be diverted to debt servicing instead of buybacks.

The worst case scenario is that we get a simultaneous bust of capital allocators waking up and realizing... holy crap the moats we thought we had dont actually exist.

The lynchpin to EVERYTHING is OpenAI and Anthropic.

If at any point, if the market believes that OpenAI and Anthropic cannot meet its obligations, the entire AI economy collapses.

All that capital will rush out of the AI trade and into safe haven assets like Treasuries. > 引用 @dividendology: If we are in a bubble-

Then the bubble is not in the price… it’s in the earnings. https://t.co/pbyDwBVLeA https://x.com/KrisPatel99/status/2109238589556985896

## @rickawsb (rick awsb ($people, $people)) · 10-11 00:10 · ♥33 ↻1 💬8 市场上又开始质疑ai泡沫，capex见顶

于此同时，ai4s三千公里/小时的海啸已经近在眼前

而科学探索，根本没有终点

图：美国1945年发表的著名科学政策报告《科学：无尽的前沿》

1945年，美国科学界认识到科学前沿没有尽头；

过去两个月，前沿实验室，顶尖科学家们开始探索前沿能否自我加速扩张。。。 https://t.co/ztAU5AHKZe > 引用 @rickawsb: 当数学证明不再稀缺，科学革命才真正开始

---陶哲轩今天发表在加州理工的最新演讲ppt，Math 2.0，要点及解读（2分钟版）

要点： 数学问题空间是无限的，问题难度没有上限，因此数学研究的前沿会持续扩张。

开放的数学问题像是“灯塔”。科学家研究一个数学问题，不只是为了最终证明它，而是在探索过程中发现周围的数学结构、研究方法、规律和新的问题。从而发现更广的待探索科学空间。

Math 2.0 最有革命性的方向：从研究几个问题，到同时研究几百万个问题。它让科学家能够研究过去根本无法系统观察的现象，比如：哪些数学问题容易被 AI 解决？哪些问题需要全新的方法？不同领域之间是否存在隐藏规律？

陶哲轩给出了具体的新研究方向（如图）

“实现 Math 2.0 的首要瓶颈，不是技术，甚至不是制度文化，而是想象力。”

解读： 数学 2.0 可能是整个 Science 2.0 的先行版本。 数学有一个特殊优势：理论证明可以通过形式化工具快速验证。但随着精密测量、机器人、实验自动化、测序技术的进步，现实世界也开始能够回答过去无法回答的问题。

陶强调，未来数学家需要更加重视数学理解、知识整合、新原理的发现，以及将数学正确应用到现实世界的能力。这一点，对所有科学家，企业家，创业者来说，都有非常大的借鉴意义。 https://x.com/rickawsb/status/2109074146869354865