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Hello folks.
What do railroads in the 1880s, telecom fiber in 2000, and AI infrastructure in 2026 have in common? Each was a capex cycle where the shovel-makers got rich first and lost the most once the cycle finished. I believe this may happen in 2027-2028 and will be doing heavy shorts likely after the initial IPO pop, late 2026.
The AI bubble, the so-called "K-shaped economy", everything points towards one thing and one thing alone: the US economy right now is the Capex Economy. It is the only thing sustaining it.
*(Btw: No tldr here. Please read!)*
***Here's my thoughts:***
\- Capex as a % of GDP is now at an all-time high, sitting at 12.5%. Other historical highs included the Dotcom bubble in 2000 where it peaked at 11% (Bridgewater). But these Capex boom-and-bust cycles come and go, generally. Railroads in the late 1800s faced a similar capex boom and bust. The late 1970s had capex boom in oil and infrastructure, following the embargo. **Common theme: capex boom never lasts forever. And when they unwind, the shovel-makers lose.**
\- The source of liquidity is diminishing. First, market commentators touted the Mag7 as not needing debt and self-financing. They said it was healthy. Great. Well, now Amazon is projecting negative free cash flow for the first time in forever due to capex spend, and now many have turned to debt, vendor financing (circular financing), and of course, the IPO juggernauts coming to squeeze out the last sources of liquidity. **Bridgewater estimates AI financing in 2027 ($612bn) will exceed entire investment grade high yield net issuance (470bn).** This coupled with rising interest rates -- big problem. Spreads will widen -> AI issuers have to pay more interest -> ROI compresses -> capex demand degrades.
\- Equity financing is the **last source.** Everyone touts this time is different because there aren't 400 IPOs. But 400 IPOs worth a few billion vs. a few that are worth more than entire countries...well, do the math. The fact that companies have had to focus on circular financing and all sort of financial wizardry up until now is a sign of liquidity issues, whereby they hope later revenues will make up for it.
\- It is worth noting that free cash flow this time is real, but funding has still shifted towards debt markets -- and soon equity markets. Having strong cash flows does not secure highest Capex %GDP for all time going forward.
While the 'shovels' are making unprecedented money, people falsely equate the demand for the tools as the proof that the thing the tools build will be massively profitable. But OpenAI missed all its projections; Anthropic is likely soon profitable through its enterprise model, yes, but Anthropic isn't the entire AI market and cannot alone sustain the 12.5% GDP capex cycle. There is a real chance of LLM market consolidation whereby a few will make up total inference and training demand.
**Profitability demands inference efficiency, which reduces compute demand.**
\- Oviedo et al 2026: frontier-scale inference (>200B parameters running on H100 nodes) consume 0.31 Wh per query, 4 - 20 x below cited public estimates. This includes GPT-4, Claude, Gemini, Deepseek V3, Llama 405B, Qwen.
\- Reasoning queries (5,000 output tokens\~) use 13x energy of a standard query. Users perceive 'thinking' (reasoning) as better answer and default to this even when it isn't required. While unsourced, I remember reading 60-85% of reasoning queries don't need to use reasoning.
\- RouteLLM can cut costs by 85% while maintaining 95% of GPT-4 quality, per research (google LLM Routing for more info). This basically means they are kicking down queries to simpler models when the complexity isn't required. Claude's adaptive thinking does this to some extent, I believe. The bigger this becomes, the more massive needs for compute becomes obsoloete (because you avoid using reasoning where it isn't needed). The only danger here is rerouting hit rate: will the provider mistakenly reroute complex questions or will user perceive negative quality doing this?
I believe profitability pressures -- especially post-IPO -- will force firms to become leaner. There is an inherent tension between (a) margin protection by sending simple queries to cheap inference and (b) UX protection by avoiding subpar answers on misjudged routing. I believe force (A) will win in the name of EPS and net income, which means less compute need.
Furthermore: a CEO of a supplier in the 2000 said this about sudden demand degradation: "Institutional investors will not put more money into companies because they have not started towards revenue, which made them stop purchasing equipment,…and then things happened very fast."
**It is the Capex Demand that will break this cycle, if anything.**
While on the supply side, GPU depreciation is typically 3\~ years but savvy financial folks have pumped those numbers up to 4-6 years purely for GAAP net income boosts. However, anyone who knows anything about accounting **knows that this cycle reverses through deferred tax liabilites. The early benefit is a timing thing ONLY.** The firms will eventually have to recognize the cost...and this reversal will likely happen in the next 2\~ years. This will be interesting for all the firms who infamously jacked up depreciation lifespans of AI components like GPUs.
In addition, given GPU depreciation vs say fiber in 2000, is that an oversupply of fiber is valuable for a very, very long time (depreciation 20-25 years\~). Dark fiber which was a big woe in 2000 has suddenly become extremely popular nowadays, even. But GPUs made today will be useless come 2030, maybe even sooner.
**When margins are this high, competitors want in.**
\- ASIC takes inference share from NVDA.
\- China refused NVDA back into the market after Trump visit. They want their own shovels, so to say.
\- NVDA customer concentration: 3 folks = 54% of revenue. These big boys are public firms who cannot keep this cycle going forever; even MSFT or AMZN can only take on more debt or spend all their money until it catches up with their shareholders. They care about ROI.
\- Even Anthropic, the most valuable firm, trains Claude on TPU + Trainium, not NVDA GPUs.
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Other smaller points:
\- Markets are already punishing firms for too high capex spend; thijs will increase the sooner the end products, like OpenAI, Anthropic, and more, become public and the true ROI is revealed. Right now NVDA and memory are the litmus test for AI worthiness; once the LLM firms go public, they will be the new litmus test, because then we can finally gauge the end products.
\- Even if compute demand remains high, some folks, such as Liz Ann Sonders, Chief Investment Strategist at Schwab, believes compute *may* end up like a commodity traded on the market. This will reduce shovel-makers' pricing power and thus denigrate margins. That's when these firms start trading like oil; oil goes up, they go up, oil goes down, they go down.
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**Finally...I'll be putting my money where my mouth is.**
I intend to short late 2026 -- **unless** the timeline changes, which it may very well do. The question is WHEN the capex cycle dies...and timing that is a fickle thing, and you gotta be flexible. The names to short will be the ones with the most to lose: NVDA, MU, SNDK -- etc.
But right now, OpenAI and Anthropic are racing to IPO. After that initial pop and we start seeing a quarter or two from them, things could get interesting. If end products do not validate the spend, that's when institutional investors may pull the plug...and that's how capex demand dies.
**Some ethos to prove I'm not a lunatic:** Bridgewater believes in a capex reduction; perma-bull Brian Belski also has mentioned that a capex recession may hit 2027. And here I am, your somewhat unfriendly investment banker
(not financial advice im just showing off my thoughts)