TSM Reddit Mentions | ChartExchange

_(crossposted to [r/stocks](https://www.reddit.com/r/stocks/s/vDnl7cN04h))_
# (1) Exciting times
Until quite recently, I was living in blissful ignorance of AI and its impact on markets. As a set-it-and-forget-it investor with no employer to force AI on me, my personal projects and "work" at most required ignoring Google AI overviews when looking stuff up. But all that changed dramatically in 2026. I got my initial mind-blowing taste of AI coding after I finally decided to give it a spin on one of my projects (no, the output isn't that good, but it still works, and fast). Some major stories (or blog posts) broke out of the financial sphere and made it into general news - mainly sensationalism about half or all people losing their jobs.
Then, in March, a private fund that I had a small investment in went public as a CEF (directly listing on NYSE as \$VCX), immediately spiked, and briefly hit a premium to NAV of _nearly 3000%_ before collapsing, though trading continued to be volatile with a sizable irrational premium. (Pre-listing investors were subject to a lockup, so unfortunately I was unable to realize those gains.) Low float, overhyped advertising, and heavy retail buying played a role, but the strength of the AI-concentrated portfolio was undoubtedly the most important factor. Shortly before listing, VCX's holdings were about 20% Anthropic, 10% OpenAI, and 5% SpaceX, and included hefty positions in other well-known AI names like Databricks and Anduril.
After the Q1 2026 AI fire hose, I decided it was time to review my investments. I doubt I need to convince anyone that the AI trade is the main theme driving markets today.
Some of the fears about a possible AI bubble are just due to stocks going up. But AI isn't just a market narrative; it's a broad social and cultural phenomenon as well. The technology is powerful and has obvious utility, but my (oversimplified) view is that the hype is partly a kind of mass delusion. To interact with a chatbot, you use a _natural language interface_ (NLI), and it's not just you talking to the computer - the computer talks back to you, making the whole experience a _conversational NLI_ (CNLI). The CNLI part is critical, because it taps into a deep fascination, evident throughout millennia of history, that people have had with the creation of intelligent, humanlike beings. A few examples: God creates Adam and Eve from dust (the OG humans); Victor Frankenstein infuses life into a heap of inanimate matter; Professor Weizenbaum creates a basic pattern matching chatbot in the 1960s that convinces some users it has human feelings, in what has come to be known as the [ELIZA effect](https://en.wikipedia.org/wiki/ELIZA_effect). It really captures people's imaginations when human characteristics somehow emerge from beyond the usual egg-and-sperm sexual reproduction mechanism.
The conceit of our species is that being able to produce, understand, and "feel" complex language separates us from everything else on the planet. Publicly available prompt-based image generation had been around for months before ChatGPT was released, but OpenAI's DALL·E 2 (arguably the most accessible) accumulated only a few million users within two months of its launch, compared to ChatGPT's 100 million. While the image generators "used" natural language, they were not conversational and mainly perceived as fancy software tools. ChatGPT talked back to people. Some perceived it as being quasi-human, or even superhuman, and it has induced cases of [AI psychosis](https://en.wikipedia.org/wiki/AI-induced_psychosis) in a way that DALL·E never could. The obsession with AGI is an extension of the same phenomenon. (Voice modes and video avatars are certainly aggravating the issue.)
All this makes it much easier to AI-pill certain investors and convince them to commit huge sums of money, which I see as the primary support for most major stocks in the AI boom. If expectations for ROI and end-customer revenue (i.e., not investor-sourced revenue) run too far ahead of reality, then even a minor shock to investor confidence, or a few years' delay in the expected timeline, or a reprioritization within the industry of where money should be spent, can pummel share prices. And there are many, many reasons to be less than completely certain about today's ROI projections.
I am a common retail investor who holds index funds in my 401(k) and broad asset-class ETFs elsewhere - no individual stocks. I am handling my money responsibly because I have a family to support, and right now investments are our only source of income. Private deals, venture capital, and complex debt arrangements are important components of the system, of course, but my focus will be on the investments that are most relevant and accessible to me: public stocks and bond/fixed-income funds.
# (2) Companies are making real money
The Owenomics blog analyzes markets from a quantitative and behavioral-economics angle. There's a good ["Bubble Watch" series](https://www.acadian-asset.com/investment-insights/owenomics?keyword=bubble%20watch&sortBy=AlphabeticalAsc) you can read for a relevant introduction to the blog's approach and style. Mr. Lamont has a [data-driven approach ("the four horsemen")](https://www.acadian-asset.com/investment-insights/owenomics/no-we-are-not-in-a-bubble-yet#main-subsection-4) to calling a bubble, and has (so far) not been wrong in the sense of predicting impending doom right before the markets keep going up, but his [updates](https://www.acadian-asset.com/investment-insights/owenomics/getting-bubbly) have shown increasing concern about the [arrival of more bubble indicators](https://www.acadian-asset.com/investment-insights/equities/we-are-not-in-an-ai-bubble). There is [only one left ("the coming IPO wave")](https://www.acadian-asset.com/investment-insights/owenomics/waiting-for-the-ipo-wave#main-section-3) until he would officially call a bubble, though even then the market top could still be years away.
While I deeply appreciate Mr. Lamont's willingness to share all this information, including many indicators worth watching which I used to build my own bubble watch dashboard, the particulars of the AI boom could be an important blind spot. The "Four Horsemen" indicators haven't really changed since the dot-com bubble. But, as has been widely reported, AI is totally different from the dot-com mania, right? By 1999, many _public_ internet firms, consumer-facing ones especially, had empty bank accounts, no serious income, and a heavy reliance on alternative metrics like "eyeballs" to support their stock prices (a source of financing through follow-on offerings). As investments, the quality of these internet IPOs was not much better than the quality of the shitcoin ICOs during one of the recent crypto bubbles. In contrast, nearly all the _public_ AI winners have and/or make piles and piles of cash. So maybe we shouldn't be lulled into complacency by the gap between low-quality dot-com IPOs and low-quality AI IPOs.
[\>>> 📊 Figure 1: The AI financing flowchart <<<](https://postimg.cc/w308f2HW)
This flowchart traces how money moves through the AI-[industrial complex](https://en.wikipedia.org/wiki/Industrial_complex). It can roughly be summarized by its four columns:
* The **actors** (drawn as stick figures) are individuals and businesses who ultimately make the financial decisions, including end-customers (who _use_ AI) and investors (who fund expansion in order to _sell_ AI, and hopefully make a return on investment).
* The companies in the **software stack** design/engineer the stuff AI needs to do on chips/computers to work. Cash flows between these companies for various reasons, and they are also prominently consumer-facing, taking in most of the industry's end-customer revenue.
* Everything funnels into **data centers**, the only box in the third column. This is where the physical chips/computers live.
* From there, everything fans out into **beneficiaries of data center construction**.
There are some immediately obvious takeaways. First, centralized data centers are the linchpin of the whole shebang. Yes, AI can also be local, edge, decentralized, etc., but [one can reasonably conclude that 90%+ of AI-related public stock growth so far is associated with massive data centers (ChatGPT analysis)](https://chatgpt.com/share/6a8b0801-0048-83e8-8f2d-552d52b15b54).
Second, investor cash that flows through the system quickly morphs into "Wall Street results" well before any evidence supporting the primary AI thesis has to appear. Whoever receives the initial investment doesn't report it as revenue - OpenAI didn't raise $122 billion at the end of March and then immediately turn around and say "we earned $122 billion this quarter." But once that investment is used to prepare land, procure NVIDIA chips, build the actual building, and install gas turbines, it _does_ become revenue (usually with a fat profit margin because of how intense the build-out is) for \$EQIX, \$NVDA, \$FIX, \$GEV, and a whole host of other companies.
Be aware of the following simplifications:
* Aside from the green investor arrows, I only drew in customer relationship flows, where money is exchanged for something of non-monetary value (not a financial asset like equity). This keeps things clean and avoids showing customer-investor funding loops, which are visualized in the [Bloomberg article](https://www.bloomberg.com/graphics/2026-ai-circular-deals/) that has been making the rounds all year.
* Circular deals are still basically captured in the "Investors" stick figure, which includes decision-makers working for companies from other boxes, like NVIDIA (box 8), Google (box 6), and OpenAI (box 5), in addition to external actors like SoftBank and VC funds.
* I didn't include stock/equity investor flows, because then there would be bidirectional green arrows between investors and every other box on the diagram. Equity is still very important to track - it's Mr. Lamont's final indicator, and we are seeing important developments like \$GOOG issuing $80+ billion of equity, the \$SPCX IPO, and the upcoming Anthropic and OpenAI listings.
# (3) Fragility on the way up, exacerbation on the way down
The current AI financing system is overdependent on investors. I got an admittedly rough, but reasonably supported, [guesstimate from ChatGPT that less than 25% of the cash flows in the industry can be traced back to end-customers](https://chatgpt.com/share/6a8c6892-1ec4-83e8-8e64-2a879bab6bf0). (Here I'm categorizing hyperscaler capex as "investing," which it effectively is.) This isn't a bad thing per se. It's normal for investors to foot most of the bill when building a new business and expanding physical assets. Usually, 100% of the money put up to, say, construct a new apartment building is "investor" money, and no "end-customer" rent is paid until a renter moves in (after construction is finished).
It's important to distinguish between primary markets (which directly inject cash) and secondary markets, where investors trade financial interests with each other and without company involvement. It's well understood that primary market investments often lead to effectively total losses without any wider systemic impact (think of the $90 billion Meta has spent so far on Reality Labs, essentially an "internal startup," or the $14 billion that SoftBank lost on WeWork). What I'm actually concerned about is the impact on secondary markets, where everyone's public stocks are held.
As soon as money moves around, it shows up in the earnings (and future projections, and stock prices) of downstream public companies. And the stock prices of upstream public companies injecting/investing/incinerating their cash may not be commensurately penalized, because they are expected to make a healthy return on their investments, just like the rest of us, right? So long as the cash keeps flowing, the stock market keeps rising on both ends of the stream. With 75%+ of the ecosystem's money ultimately coming from investors, _investor optimism_ is the key ingredient that will make or break the market.
_None of this necessarily indicates a bubble is inflating or about to pop._ If the financial growth projections around AI are eventually proven correct (on time), then cash flows become mostly customer-based (on time), and the investors all get their money back (on time). Sure, then today's optimistic stock prices are the "correct" prices. A lot is riding on those projections, though, which seem to be growing in tandem with all the hyperscaler capex and checks being written to AI labs. So, are they plausible? Is the entire financing machine resilient, or is it fragile?
Anything that could dent investor optimism, and consequently slow or stop investor cash flows, is a risk to stock prices. Here's my (incomplete) list of those risks:
* **Hyperscaler competition:** Before AI, many big tech firms did not seriously compete, padding margins by operating monopolies or oligopolies. Today, there are enough data center operators (hyperscalers and neoclouds) to make running a cartel significantly more unstable. Data center investing has turned into a [prisoner's dilemma](https://sequoiacap.com/article/ai-optimism-vs-ai-arms-race), with at least Google and Meta on the record saying that the risks of underbuilding outweigh those of overbuilding. (You could think of this as "one of the most expensive games of chicken in history," roughly on the same order as the US/USSR nuclear arms race.) Overbuilding is likely, which makes a compute supply glut likely, which can pressure pricing power, profits, and hyperscaler and neocloud stocks. On the other hand, stopping the competition will hammer the stocks of data center construction beneficiaries.
* **Model commoditization:** LLMs and related technology are accessible enough to support several serious competitors. The technical recipe is straightforward and the IP is not that protected. Many model-makers have an incentive, business or geopolitical, to underprice high-capability models or give them away free, and end-customers have already responded to price signals by [using Chinese models more than US models on the OpenRouter marketplace](https://openrouter.ai/blog/images/deepseek-v4-china-share.png) ([original article](https://openrouter.ai/blog/insights/deepseek-v4-adoption/)).
* **Potential small-model or local AI competition:** High prices are enough to push end-customers to smaller, self-hosted, and unmetered AI - even before concerns about privacy, security, reliability, and data sovereignty. It's underappreciated [how well the hardware and software already perform at this level](https://archive.is/y6Cf9), and how widespread compatible platforms (like NVIDIA or AMD consumer GPUs and the Apple M-series SoC) already are. This directly competes with compute providers and impairs the companies that serve them.
* **Chip design evolution:** The commonly heard claim that GPU circuitry is "perfect" for powering AI compute is not entirely true. As the name suggests, GPUs are optimized for computer graphics, and their stickiness in AI today is largely due to a mature GPGPU computing ecosystem built around NVIDIA's two-decade-old CUDA platform. Moving an AI workload from a CPU to a GPU is a transition from "horribly inefficient" to "rather inefficient." All major hyperscalers, as well as startups like Groq, Cerebras, and Etched, have introduced competing designs that are tailored to AI's particular usage patterns and demonstrate clear performance improvements at the cost of some flexibility, an excellent tradeoff to make when targeting standardized, widely deployed inference workflows. As always, true competition sacrifices margins (and stock prices) to offer lower prices to customers. (However, NVIDIA did recently neutralize some competition, without antitrust scrutiny, by doing a stealth acquihire on Groq. Those sellouts!)
* **Deadlines for turning profits:** We're still in the AI honeymoon period, happy to overlook high spending and low income on a promise that things will change soon. That can't last forever. The hyperscalers are forecast to start generating [rapidly increasing FCF starting in 2028](https://hermes.media.static.aol.com/media/2026/07/09/a442e43b-7a81-341e-b503-86744cf7659f/52b93cb0-262d-4c45-955c-18ec1bf93d94.png) ([original article](https://www.apollo.com/wealth/insights-news/insights/daily-spark/a-slower-ai-payoff-would-be-everyones-problem)). Yes, some of that is from an expected drop in capex. But, with the exception of Microsoft, the trend in the forecast, compared to the pre-AI era, plainly reflects expected FCF growth far beyond the pre-AI baseline. As for the AI labs, they are about to run into a payment wall from the take-or-pay contracts they've signed with data center operators. The cash impact is like a [turbocharged version of an ARM mortgage payment reset after the intro period](https://substack-post-media.s3.amazonaws.com/public/images/19f0fdfd-6e2f-4f86-8bf2-6a8c32b58781_2038x1218.png) ([original article](https://www.groundbrkr.com/p/the-teaser-period-why-the-ai-boom)). Investors won't be happy if profits still aren't showing up at these critical dates.
* **Political opposition and inadequate infrastructure:** Regulation and lack of electricity are making it harder and slower to build data centers. Without continual construction and upgrades, construction beneficiaries make less money, though compute scarcity may benefit upstream companies.
* **Strain in the macroeconomic backdrop:** Inflation is still around and the Fed might raise rates soon. Many have already pointed out foreboding parallels to the 2000 and 2008 crashes, when rising Fed rates peaked at 6.5% and 5.25% (in 2006). AI could be making this worse by pushing up the prices of RAM and electricity.
A separate kind of bubble risk is overinflation, which won't increase fragility on the way up, but will exacerbate the pain on the way down. A sharp drop really hurts those who bought in near the top or are overleveraged. Stock prices that are too high have farther to fall and might cause "AI collapse contagion" that spreads outside the industry:
* **Hyperscaler competition (again):** The longer that the hyperscalers pay to build data centers, the more money that data center builders and suppliers will make. Investors may overpay by underestimating the risk that this level of growth turns out to be unsustainable.
* **Contamination of compute demand:** I asked ChatGPT whether the hyperscalers disclose how much compute usage is for training (largely not end-consumer financed) versus inference (what end-customers actually pay for). [The response is essentially "it's not that easy to figure out."](https://chatgpt.com/s/t_6a908f88a0ec8191b4c9471dbfa459e7) End-customer demand is what determines optimal capacity and long-term stock prices. Without enough clarity, investors might assume things are better than they really are.
* **Weak earnings quality:** One of the most important metrics that investors use to price a stock is earnings - that's why the P/E ratio is the most prominent valuation measure. If earnings numbers are inflated, stocks are also likely to be inflated. Well, AI companies may be inflating their earnings in multiple (completely legal) ways. [Many](https://www.acadian-asset.com/investment-insights/owenomics/waiter-theres-a-p-in-my-e) [different](https://www.cnbc.com/2026/08/03/big-techs-anthropic-and-openai-stakes-distort-corporate-earnings.html) [articles](https://www.groundbrkr.com/p/peak-cheap-the-ai-boom-isnt-2000#:~:text=The%20second%20accrual%20is%20equity%2Dmethod%20and%20fair%2Dvalue%20mark%2Dups%20on%20AI%2Dlab%20stakes) have pointed out that _unrealized_ investment gains get dumped straight into quarterly earnings, which is great for Microsoft, Google, and Amazon, as they hold stakes in OpenAI, Anthropic, and SpaceX. Those earnings are stacked on top of "real cash" earnings and have little to do with end-customer demand specific to the holding company. The Groundbreaker piece also details [other ways to pad earnings](https://substack-post-media.s3.amazonaws.com/public/images/79d0e2b7-c868-4951-9709-0872d305eabf_2039x1110.png): self-selecting longer depreciation timelines, and the much-maligned circular/vendor financing strategy used to double-count cash as profits after passing it around a bit.
* **Scarcity rents in pricing power:** If you're going to be totally sold out anyway, it's rational to increase prices as much as your customers will tolerate. This is great when they're all "rich" and want something very badly. (Anyone who has shopped for houses or luxury goods before may find this familiar.) Incumbents in non-competitive industries are also incentivized to just overcharge and expand more slowly than customers would like (looking at you, [TSMC](https://sequoiacap.com/article/ai-in-2026-the-tale-of-two-ais#:~:text=while%20TSMC%20had%20ramped%20revenues%20by%2050%25%20since%202022%2C%20they%20had%20only%20ramped%20CapEx%20by%2010%25)). However, it's unrealistic to assume that scarcity rents will remain durable in a booming market, though there are signs that [some stocks are priced assuming exactly that](https://www.ft.com/content/d5b45659-ecd5-4bb5-93d0-56b99c798b9d#:~:text=The%20big%20five,level%20are%20heroic) (you can read Alphaville with a free account). Some incumbents [will choose to expand](https://www.cnbc.com/2026/08/13/inside-sk-hynixs-720-billion-bet-to-build-enough-memory-for-ai.html), and highly motivated challengers will eventually appear on the scene, like Intel and Rapidus trying to break into TSMC's market. More supply and competition come online, prices for customers go down, stock prices adjust.
* **Debt, debt-like instruments, and overleveraging:** When companies need to raise money, there are broadly two ways to do it - issue debt, or issue equity. Common stock equity holders (like me and most shareholders) are scared of debt because it is strictly more senior in the capital stack. Common stock is always worth only what is left over _after all the debt is paid off_. Naturally, companies _visibly_ taking on lots of debt (as some hyperscalers are) pressure their own stock prices. So it's not reassuring to see companies taking on [very large amounts of _invisible_ debt](https://www.wsj.com/tech/ai/why-big-techs-ai-spending-is-3-trillion-higher-than-it-seems-e1067bb2) (interactive graphic is above the paywall), in the sense that the details are hidden in private contracts and nonstandard disclosures rather than the quarterly reports that investors are used to analyzing. Hyperscalers have engaged in [future lease commitments and purchase commitments](https://www.ft.com/content/a0a07cce-6d19-4b1e-a73b-9855a06ba7b3), and NVIDIA is [securitizing compute with non-trivial backstops](https://www.sascha-steffen.de/updates/nvidia-500bn-ai-financing-credit-risk). A lot of the liabilities coming out of this financial engineering will be held by [pensions and insurers](https://illuminem.com/illuminemvoices/the-great-train-robbery#:~:text=assets%20with%20short%20economic%20lives%20and%20limited%20revenue%20visibility%20are%20being%20financed%20as%20though%20they%20were%20long%2Dlived%20infrastructure%2C%20with%20pension%20funds%20and%20insurers%20invited%20to%20provide%20the%20patient%20capital), who have little tolerance for the synchronized defaults that could occur if the whole AI trade begins to unwind. And while a lot of this stuff may not be classic debt, it is certainly [legally enforceable](https://www.quinnemanuel.com/the-firm/publications/client-alert-emerging-litigation-risks-in-financing-ai-data-centers-boom) in ways that will compel repayment and prioritize it over common equity. Just like with classic leverage, equity returns are pretty great as long as everything keeps going up, but they can get very nasty if things start going down.
* **Strain in the macroeconomic backdrop (again):** The US national debt is high. Yes, people have been complaining about this for decades, but it has recently reached the point where it starts to actually matter. 2024 was the first full year in which interest payments exceeded the military budget, politicians across the spectrum don't want to fix entitlements or taxes, deficit spending looks more wartime than peacetime, and growth projections are not rosy enough to overtake the debt unless AI really delivers. Just like in 2022, bonds may once again not be an effective portfolio counterweight in a bear market, even though today's yields have more room to fall. This time around, the government may need to issue so much debt to cover the tax shortfalls that long-term bond yields stay elevated. AI could also be making this worse via "investment-grade" hyperscaler debt issuance crowding out demand for the government's own debt, raising yields and interest payments even further.
# (4) Bubble watch
I put together a folder of bookmarked webpages to answer this question: **Is it likely that we're in the late stages of an AI stock market bubble?** I want to have a confident "yes" answer before making any significant changes to my portfolio, regardless of the numbers. No signal indicates a bubble all by itself, but when a large number of them are flashing red, it's time to prepare your trades.
It would be great if I could have just two high-quality indicators - one for "strength of AI investor optimism" and one for "proportion of reported AI-related revenues that ultimately comes from durable end-customer demand." As long as at least one of those is high, I'd be very confident in saying that, while we may or may not be in a bubble, it's certainly not in a _late_ stage yet, and there's still time to ride the market up. Unfortunately, I don't think I'll be able to find comprehensive-enough data to ever give a precise score to either of them, certainly not free on the internet. The best I can do here is to keep following the news, letting the professionals pick apart the quarterly results of all the major public companies and report on what they find. Still, I consider these two indicators to be more important than all of the bookmarks put together, and will keep updating my gut feeling on them as the news rolls on.
My bookmarks mainly focus on different ways of looking at stocks to try to discern if equity investors are nearly tapped out. For various reasons, companies tend to avoid issuing equity as long as they can (it's bad optics, and executives are heavily paid with stock, so they'll dilute themselves by issuing too much). Once they become reliant on selling stock, that signals other options have been exhausted, making it harder to find new cash to inject into the system. If the equity then starts running out, some flows may slow or freeze up. I think that will wreck confidence and prevent the market from retaking its highs once a downturn takes hold.
This is more complicated than tracking standard watchlist data like prices, P/E ratios, and 52-week range, so I only plan to look through everything once a month. The sites are all free, by the way. I don't think anything besides Yahoo Finance requires a sign-up.
[\>>> 🖇️ Attachments: AI Bubble Watch Bookmarks <<<](https://gist.github.com/citizinf/c769e2c9e24d5a95da1196f7d6a6ca5d)
Use "Download ZIP" or right-click on the "Raw" button and select "Save link as" to get the HTML file. There should be an "Import Bookmarks" menu option in your browser's bookmarks manager (in Chrome: Settings > Bookmarks > Open Bookmarks Manager) which you can use on that file. The other two JS files are [Tampermonkey](https://chromewebstore.google.com/detail/tampermonkey/dhdgffkkebhmkfjojejmpbldmpobfkfo) userscripts which you can directly highlight-copy-paste into the Tampermonkey UI; the discountingcashflows.com one filters out irrelevant rows and the stockanalysis.com one highlights large IPO deal sizes to make it easier to track oversized or mega-IPOs.
Section A includes different market-wide metrics, starting with CAPE, the real 10-year yield, and forward P/E. These are inputs to [one way Mr. Lamont suggests tracking overvaluation ("first horseman")](https://www.acadian-asset.com/investment-insights/equities/we-are-not-in-an-ai-bubble#main-subsection-2). For what it's worth, inverse CAPE minus the 10-year is slightly negative right now, which is a bubble indicator, though it seems I'm using a different data series because I can't get the November 2025 result to match Mr. Lamont's post.
The next two pages are rough measures of total and net equity issuance, [Mr. Lamont's favorite bubble indicator ("third horseman")](https://www.acadian-asset.com/investment-insights/equities/we-are-not-in-an-ai-bubble#main-subsection-4). I don't really understand what the FRED graph is showing, but it roughly matches Mr. Lamont's line graph, so I'll run with it.
Then there are two IPO pages. The stockanalysis.com one requires you to open the "Indicators" dropdown and select "Deal Size" each time a page loads to see IPO size.
Finally, there are graphs tracking EFFR, SOFR, and corporate bond spreads. Just following the news is probably going to be a lot more valuable than looking at these, but I think it can be useful to compare rates to recent history.
Section B attempts to track investor sentiment. The first two links are actually the same. They both point to the Yale School of Management's investor survey data on whether prices are overvalued and whether they will be higher in a year. [Mr. Lamont thinks that it is a clear bubble indicator when investors simultaneously believe both ("prices are too high")](https://www.acadian-asset.com/investment-insights/owenomics/bubble-beliefs-prices-are-too-high-but-going-higher#main-section-0). You need to select the correct datasets yourself from the dropdown.
The last page is Robinhood's IR site. The "Monthly Metrics Dashboard" provides useful glanceable data on customer count, stock trading volume, and options volume. It displays a one-year trend and updates monthly.
C is just my Yahoo Finance watchlist (it is surprisingly better than Google). I can't copy the contents over into the bookmarks, so you'll have to set this up yourself. I have the following columns to get a general sense of valuations: Symbol, Last Price, 52-Wk Range, P/E Ratio (TTM), Forward P/E, PEG Ratio. The actual tickers in the list are:
* Tier 1: NVDA, GOOG, MSFT, AMZN, ORCL
* Tier 2A: MU, AMD, CRM
* Tier 2B: AMAT, APLD, ASML, AVGO, CAT, CBRS, CIFR, CRWV, CSCO, DELL, EQIX, FIX, GEV, GFS, HUT, INTC, IREN, KLAC, LITE, LRCX, LTBR, NBIS, OKLO, SMCI, SMR, TSM, VRT
* ETFs: SPY, QQQ, SMH, AIQ, DTCR, CHAT, RACK, FPX, IPO
I plan to keep adding more tickers as relevant companies go public.
This selection is broad and somewhat arbitrary. I probably don't need three speculative nuclear power companies, but why not? Also, the 2A/2B split is not substantial. I was trying to divide the list into subgroups to make it easier to manage, but things overlapped too much and it wasn't worth the hassle. When I gave up and alphabetized everything, I just forgot to include those three companies.
D is the "advanced chart" view for all these tickers. What I specifically care about is the trading volume (Tools > Indicators > Volume chart). Increasingly high volumes could indicate saturation of retail investor participation, short holding periods, and general speculative excess.
Section E tracks short interest across all tickers. Mr. Lamont has noted that [lengthy bubbles eventually discourage short sellers because they keep losing money ("how do bubbles end")](https://www.acadian-asset.com/investment-insights/owenomics/a-trillion-reasons-were-not-in-an-ai-bubble#main-section-2). Fewer short sellers means fewer participants expressing the "price is too high" opinion, which inflates a late-stage bubble even more.
Section F tracks put-call ratios of options. The rationale is similar to that for following short interest: higher relative call volumes (lower put-call ratios) indicate optimistic excess.
Section G tracks shares outstanding for the non-ETF tickers. This was inspired by Mr. Lamont's views on issuance. Most of the charts still look calm, in line with the trend of regular buybacks, but the beginnings of a turnaround are visible with [\$GOOG](https://investorsengine.com/GOOG/shares-outstanding), [\$ORCL](https://investorsengine.com/ORCL/shares-outstanding), and [\$INTC](https://investorsengine.com/INTC/shares-outstanding), and probably others soon.
In Section H, I included a subset of tickers to track debt levels and earnings quality. It's still worth a shot, even with all of the industry's hidden debt. Here are the rows I'm interested in (my userscript hides all the others):
* Debt Ratio: Gives a sense of how healthy a company's "net worth" is. As it approaches 1, net worth approaches 0.
* Total Debt to Capitalization: Shows how reliant a company is on debt for its financing needs.
* Cash Flow to Debt Ratio: Roughly shows how quickly a company can make enough money to pay off its debt.
* Cash Conversion Ratio: This is just OCF over earnings. It compares the actual dollars coming in to the income that a company reports after all their accounting adjustments. If this gets too low, it can indicate cash flow issues or that a lot of a company's performance is not coming from core money-making activities. For example, unrealized gains from a startup investment boost earnings without touching OCF, lowering this ratio.
Section I contains just 1Y price charts for the ETFs and \$NVDA. In this case, I think Google has a cleaner, more familiar look than Yahoo Finance. I'm not doing anything fancy here - just trying to see if there are any obvious peaks that could indicate a top. The ETFs include broad indexes, specific industry plays, and IPO-focused funds.
# (5) My investing strategy
I'm an asset class allocator, and I'll refer to different asset classes using popular representative ETFs. So, unless noted otherwise, "SPY" is a stand-in for "large-cap US stocks" or "basically the whole US stock market," not specifically SPY or any individual stocks it holds. In my view, any fund that is strongly correlated with SPY is basically the same as SPY. The important thing is to avoid picking one with high fees.
It's not my goal to simply "avoid the bubble." If that were all I cared about, I could just sell everything now and hide in cash forever, guaranteeing that I skip this bubble and all future ones too. I want to stay invested and capture as much of the wealth-generating benefits of modern capitalism as my risk tolerance will allow. That includes the rationally priced long-term background upside as well as any irrational bubble premium.
I am already somewhat defensively positioned, in part because of worries about AI overvaluation and in part because I have no active income. Rather than the usual SPY/VEU/AGG/cash split, the majority of my US stock allocation is tilted towards quality, value, and lower volatility (VIG, SPYV, JEPI). This is a broad allocation decision, not a precise expression of an anti-AI thesis - \$AVGO is in many dividend funds, plenty of hyperscalers can be found in value funds (like \$AMZN in SPYV), and JEPI holds \$NVDA. I am also holding more AGG and cash than is commonly recommended. However, I do think there's still plenty of time to ride the market up. The Anthropic and OpenAI IPOs, which will inject hundreds of billions, are coming up. Some hyperscalers still have FCF available to burn and are expected to keep squeezing cash flow through 2027. And exotic data center financing instruments designed to raise funds from institutional debt investors have ramped up mostly within the last year, with plenty of room for expansion. People are still expecting continued investment and low end-customer growth, so it will be hard to disappoint for quite a while.
[\>>> 📊 Figure 2: The NASDAQ dot-com peak and subsequent bear rally peaks <<<](https://postimg.cc/MfWV5MKQ)
I don't think it's worth trying to call the tippy top right before it happens. There's too much uncertainty. Instead, the chart pattern I'm looking for is an obvious peak followed by a "big" pullback and rallies that fizzle without retaking the high. Using the dot-com bubble as an example, multiple instances of this pattern are visible throughout 2000. My plan is basically to guess when a bear market rally is occurring, use my gut and bubble watch bookmarks to come up with some probability for "a bubble has already popped and a steep decline is coming," then do a major reallocation weighted by that probability. (That way I can try again later with the remaining original position if I'm wrong.) Yes, the title of this post was a bit misleading; I am actually planning to get off _after_ the market peaks.
[\>>> 📊 Figure 3: NASDAQ dot-com bear traps and false bear rally peaks <<<](https://postimg.cc/LYkjjBRv)
Of course I am leaving room for flexibility and judgment. The above plan is not a rigid set of rules that outputs a binary yes/no sell decision. Maybe the bubble didn't inflate enough to cause real damage upon bursting. Or maybe the news is so bad that it makes no sense to sit around for weeks waiting for a bear rally. It's especially important to filter out sharp market movements that are not related to AI optimism, financing, or the bookmarks. [Large pullbacks are expected (ChatGPT analysis)](https://chatgpt.com/share/6a92fafb-ffd4-83e8-af30-0b0c3ac09b81), and I don't want to be misled by "false bear rallies" and get out years too early. A brief list of things that don't mark the end of an AI bubble, even if stocks sell off:
* anything from the "overinflation" list above;
* deals announced by investors or cash-rich companies to inject more cash, [even if they're circular](https://www.morningstar.com/stocks/nvidia-reported-openai-deal-raises-circular-deal-fears-we-think-stock-is-undervalued);
* random, idiosyncratic, short-term variations in quarterly earnings, especially if not correlated across the industry;
* exogenous shocks that the US will probably contain with policy (tariffs, oil);
* pure valuation concerns without any direct connection to sustainability of cash flows;
* fears of Fed tightening without actual tightening.
The major reallocation would look like this (if I do 100% of it at once): Exit SPY (if hyperscalers are in the bubble). Slightly reduce VEU (it has TSMC, Samsung, SK Hynix, ASML). Slightly reduce VIG, SPYV, and JEPI. For counterweights, boost bonds substantially, and mix BSV and AGG to tilt towards a shorter duration, because national debt problems might lead to AGG underperformance in an equity crash. Also, commit a few percentage points to long-dated, slightly OTM puts (rolling as needed) on something liquid like QQQ or SMH, depending on whether the largest constituents are part of the bubble or not (hyperscalers, labs, neoclouds, chips, etc. may not all be simultaneously overvalued). This is responsible insurance, not an attempt to gamble for a huge payout, and it more cleanly makes money if AI-related stocks drop, without interference from the Fed or inflation or the national debt. And keep a big chunk in cash.
As for calling the bottom, or backing out of the trade, that will also be tough in different ways. I haven't thought about it as much. Right now I'm assuming I'll go with the standard contrarian stance and wait for AI ROI pessimism to abound, then shift back into a "normal" allocation, slightly defensive due to risk tolerance but not as much as right now. I hope I gave others some useful ideas about how to ride this market out. Good luck!
_This work is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)._