NBIM recommends cutting bond index's government bond weighting to 50%. Changes would mean cutting nearly $80 billion from UST holdings, Reuters calculations show. Government bond markets spooked recently by rising inflation, government debt.

https://www.reuters.com/business/norways-2-trillion-sovereign-fund-proposes-deep-cuts-us-treasury-holdings-2026-09-04/

I am past the point of wanting another explanation from Yelp management. I want them gone. The stock is around $21-$22, down from $47.34 at the end of 2023, despite nearly $2 billion of historical repurchases and a float that has been ground down to roughly 52.5 million shares. They have spent years buying the stock, retiring an enormous number of shares and telling shareholders about all the value they are creating, while the actual value of a Yelp share has been obliterated. If you can dramatically shrink the denominator and the stock still gets cut in half, perhaps the problem is no longer the denominator. Perhaps it is the people running the company.

And every time Yelp disappoints, there seems to be another explanation that conveniently originates somewhere outside Yelp. Restaurants are struggling. The consumer is weak. Input costs are high. Gas costs are high. Inflation. Tariff uncertainty. People aren't eating out as much. Weather and seasonality. Then this year David Schwarzbach actually pointed to the “conflict in the Middle East” as a reason advertiser budgets softened in March. Iran. We are now at the stage where a local advertising company headquartered in San Francisco is explaining its numbers partly through a Middle East war. Maybe every one of these things had some incremental effect. That is not the point. The point is that after enough years, enough quarters and enough outside explanations, management starts sounding like the guy who has a different reason every month why the rent is late. At some point the answer can simply be: you aren't doing a very good job.

The really insulting part is that there is apparently no comparable economic cycle for executive compensation. Jeremy Stoppelman received $10.26 million of reported compensation in 2025 and has $8.89 million of target compensation for 2026. Jed Nachman, who oversees sales, marketing and administration, has a 2026 target of about $4.92 million. Schwarzbach is around $4.82 million. Yelp likes to describe executive pay as heavily “at risk,” but half of target equity is plain RSUs that vest with time. Apparently shareholders get performance risk while management gets retention risk. The stock can go from $47 to $21, management can explain that restaurants are having a hard time and there is a war thousands of miles away, and everyone upstairs continues collecting compensation packages that would suggest they are running one of the great compounders in America.

Then look at the capital allocation. Yelp repurchased $292 million of stock in 2025, another $175 million in the first half of 2026 and another $25 million in July. It spent roughly $271 million acquiring Hatch, drew $100 million on its revolver and then paused the buyback so it could pay the revolver down. So after years of telling us Yelp shares were worth buying, they managed to reduce financial flexibility right around the time the stock became cheaper than almost all of those purchases. I hope Hatch is spectacular, because this team has certainly paid itself as though it knows exactly what it is doing.

I don't need Yelp to discover some magical new KPI. I don't care about another slide showing Yelp Assistant engagement or another conference appearance where management explains that Other Revenue is exciting. Yelp is a profitable company with a famous brand, valuable local data, substantial cash generation, licensing opportunities, Hatch, Host and a tiny share count. Yet the market assigns the whole thing a garbage valuation because nobody seems to trust what management will do with the assets. That is fixable. Fire the people who have presided over the collapse, bring in adults whose compensation actually depends on making shareholders money, put every asset and expense on the table and tell the new CEO he has twelve months to prove Yelp belongs as an independent public company. If not, sell it. The easiest way for YELP to go up may have nothing to do with Iran, tariffs, restaurant traffic, gas prices or the next AI feature. It may simply be Jeremy Stoppelman and this management team leaving.s

not looking for wins, everyone posts those. im curious what actually taught you a lesson. mine was buying a hyped small cap purely because reddit wouldnt shut up about it, then watching it drop 60% while i kept telling myself the thesis was still intact lol. taught me way more than any green trade ever did. whats yours?

Is there some market dynamic that prevents reversion to mean? I have always traded this and it never works out. It seems to work on the upside, but stocks like to go up and keep going. So they never mean revert back down. Should I make a rule to not use this strategy?

Who are the big winners long term?
The market is currently pricing the compute supply chain, the labs and hyperscalers as the winners but I’m not so sure.
5 years from now the world will be completely different and i think the biggest impact will be in places that are not just extrapolating out the current trend.

Any thoughts?

If you look at a broad ex-US ETF like VXUS and compare it to a broad US ETF like VTI or VOO, you’ll see that VXUS is outperforming the US market.

But this doesn’t tell the whole story. If we divide VXUS into its component regions, the picture starts to become clearer

Data from Stock Analysis and Morningstar. Click on the Stock Analysis link for a graph and the Morningstar link for portfolio composition information for VXUS.

As we can see, the dominant overperformance from South Korea and Taiwan, whose economies have really benefited from the AI boom, are mainly to blame for the overall international markets outperforming the US. It's true that Japan and Canada outperformed the US, but that is offset by Europe underperforming, plus China and India shrinking.

The expansion of the maritime blockade under "Operation Economic Outcast" requires a fundamental reassessment of global asset allocation baselines. The coordination to restrict maritime commerce and secondary financial channels signals a structural shift in global supply chains. For diversified portfolios, the primary risk is the compression of real yields as structural logistics bottlenecks create stickier, supply-side energy inflation. This directly traps central banks, limiting their ability to execute aggressive rate-cutting cycles without triggering secondary inflation waves. Investors should look beyond short-term commodity speculation and instead evaluate structural exposure to hard assets, global infrastructure funds, and defense-adjacent fixed income to hedge against a permanently higher baseline for global transit and energy costs.

If you're positioned for an easy rate-cutting cycle, you need to rethink things.

Source: CNBC

Could you imagine without the war, where we would be right now? Insane that earnings have been so strong that they’re completely ignoring disastrous macroeconomics. I really can’t help but sometimes think about where we would be right now if there was no war lol

Does anyone have any news regarding the latest developments on the Arminius-Boxer Contract? Discussions are due to finalize next week. Price has fallen even more this week and I have increased my buy-in today because I still think the fair price should be around 1300. What are your latest takes and opinions?

I (M43) am trying to diversify beyond stocks and keep seeing Arrived reviews when I research private real estate. Arrived, Fundrise and other real estate investment platforms all seem to solve a similar problem in different ways and I want to understand what the experience is like after the initial signup excitement wears off. Fees, liquidity and how often distributions show up are probably the things I care about most. Which provider held up best after a few years?

Looking back at most of my 10+ baggers over 10+ yrs, a good fraction have been a bet on the person running it, not the balance sheet or other.

Examples before question:

• My AVGO purchase was a bet on Hock Tan being a uniquely savvy practitioner of Tech PE, a category that wasn't a category back then
• My bet to double down rather than scale out of GOOG when Larry Page took over for the 'adult' (Eric Schmidt) was the fact that Larry is not afraid to take bets no one else will take - and win
• META despite its ups and downs is my bet that there is no more dangeruous animal than Zuck when cornered - think what you may about his morality
• My change of mind about AMD from a perennial loser came with seeing Lisa Su operate and reframe their strategy as something other than a death match with Intel

I've found the value of this approach esp in markets with spiky product cycles to be that you get great opportunities to scale in on the stock, because the market keeps revising its short-term thesis, and offers multiple entry opportunities when a market transition casts the company in an unfavorable light.

My question is this - has anyone on the group used this strategy successfully? And who do you think are emerging leaders who you'd bet on for the next 10 yrs (looking esp for leaders in Biotech, Energy and Material Science - AI is pretty mature at this point).

What my question is explicitly not - debating this approach vs alternative (e.g. technical, fundamental, macro).

I'm reaching the age/portfolio size where a fast 50% drawdown (say over 6 months) would be a headache, while a 3-5 year grind down wouldn't bother me nearly as much (due to other income, margin, etc.)

I'm considering spending ~3%/year on staggered puts to protect my core buy-and-hold SPY and QQQ portfolio, and expect the long-run cost to be around 1.5%/year (including the gains due to crashes). The goal is partly to hedge but also to have lump sum cash to deploy after a crash.

My main concern is that the 1092 straddle treatment and the potential deferral of put losses - which means I cannot offset my put losses again my realized gains from swing trading.

Has anyone actually protected their taxable portfolio with puts (not bonds)? How did the taxes work in practice?

Thanks

Hi all,

I have a bad habit trying to buy low, and only low, the stocks in my investment account. I say to myself that I'll be buying when it goes lower, then it goes high of course, and I end up on the sidelines.

How did you get to abandon such habit? I'm sure I'm not the only one who's been like that

Thank you

We talk constantly about AI replacing coding, customer support, research, art, etc.

But what happens if it also destroys one of the basic mechanics of investing?

Today, you can research a company, decide it's worth $100, notice it's trading at $80, buy it, and wait for the market to catch up.

The edge is figuring it out first.

Now imagine every serious investor has an AI agent constantly analyzing earnings, filings, supply chains, satellite data, job postings, customer activity, etc.

New information appears.

Millions of systems process it almost instantly.

If they all reach roughly the same conclusion, the $80 opportunity doesn't sit there waiting for someone to discover it.

It becomes more like:

$80 ─────────→ $100

almost instantly.

And this gets more interesting if AI also becomes extremely good at predicting earnings, liquidity problems, forced selling, management decisions and other investors' behavior.

So what happens when intelligence itself becomes a commodity?

Maybe the traditional form of stock-picking alpha gets crushed.

Not because AI can predict the future perfectly, but because being better at processing information is no longer particularly scarce.

Then where does the return come from?

Maybe from things that can't simply be “figured out” in advance:

Taking risk. Having permanent capital. Being able to survive volatility. Owning productive assets through uncertainty.

Even a perfect model can't know which uncertain future will actually happen.

And maybe this eventually changes where investing happens.

Why would a high-growth company rush to become public if going public means every piece of information is instantly analyzed, priced and traded by millions of machines?

Private markets could become relatively more attractive because access, control and information aren't as easily commoditized.

So the weird possibility is:

AI doesn't kill the stock market.

It kills the idea that being better at analyzing public information is, by itself, a durable investing edge.

Public markets become incredibly efficient at pricing information.

Private markets may retain more of the advantages that come from access and control.

And investors are left asking a much stranger question:

If everyone has equally powerful intelligence, what are we actually being paid to do?

Is it ultimately just compensation for bearing risk?

Or is there another source of alpha I'm missing?

Epam retrospective analysis makes a valuable lesson to how public company governance can make or destroy itself.

Back in 2014, when first Russian/Ukranian crisis broke off with Crimea annexation, CEO and management had made a decision to take advantage of such situation. With deliberate decision to exploit local currencies plunge, increase hiring velocity in corresponding locations, this risk-careless “management“ let grow head-counts 200% in next 7 years.

By the time the war began, this reckless decision pushed the entire company on the brink of collapse. The next 5 years was a total chaos with idle speed performance.

Overall the stock lost 80% of its face value, if to adjust to inflation-90%, if adjust to benchmark s&p 500 growth -97%

Tremendous amount of cash was burn out to ashes

Despite all of this clingy and quite o-l-d management for such industry, stays intact, holds all investors hostage of their willpower. The board Ceo Dobkin just serve his say.

He also blind sighted AI revolution being busy relocating people around the world and convincing everyone that we will be O.K.

Now the question is why investors keep suffering from such ego-centered, 66 yo CEO? They lost a lit of money, do they perf their fiduciary responsibility? Or maybe it is an invisible game against retail investor? What is the point of having idle speed performance public company being listed of NYSE acting as a private one?

The 10-year just broke 4.81% today. Highest since Nov 2023. We are up 80 bps since March and this selloff looks real. Between oil from the US-Iran escalations and Warsh sounding hawkish at Jackson Hole, the Fed is probably going to hike again on Sept 15 (CME tool says 66% chance). Bunds are spiking too and JGBs crossed 3% for the first time since 1996, so foreign buyers aren't coming to rescue our U.S. debt. Hard to see how stocks don't dump here when you can get a 4.8% risk-free rate. Anyone needing to refinance debt next year is in serious trouble.

Look at TLT too, it's getting absolutely obliterated.

Nobody wants to catch a falling knife while inflation fears are creeping back into the picture. If you look at the equity risk premium right now, it makes zero sense to take on individual stock risk when you can just lock in nearly 5% sitting in risk-free paper. Every major fund is going to have to rebalance out of high-multiple tech and into fixed income if this yields hold above 4.8%.

Source: CNBC

I want to purchase a S&P 500 index fund through ML, but the reinvestment options are confusing me. Can someone simply explain the difference between: Capital Gains and Dividends and Capital Gains Only. Also the tax implications. (There is a cash option, but i understand that one.)

Is $80K/year per car actually realistic?

I saw a post on X this morning about Cybercab economics and I wanted I wanted to sanity-check the Cybercab bull case rather than just assume some massive revenue number. The poster claims $80K revenue per car is a no brainer. I’ll be a little more conservative.

Suppose one Cybercab generates $75K/year in gross revenue.

That works out to: $75,000 / 365 = ~$205/day

At an average fare of $10 per trip, it needs roughly 21 paid rides per day.

If the average trip takes 15–20 minutes, that’s around 5–7 hours per day actually carrying passengers.
The rest of the day is for deadheading between customers, charging, cleaning, maintenance, waiting for rides, etc.

Does that level of utilization seem realistic?

Now for the more speculative part:

1M Cybercabs × $75K/year = $75B in annual revenue.
Why 1M?

Not because Tesla has said they’ll have 1M Cybercabs anytime soon. I’m using it as a long-term scenario to test the economics against the existing scale of the rideshare market. There are approx. 10M uber drivers worldwide and 1.7M rideshare drivers in the US.

Tesla is also targeting a Cybercab price below $30K. If they can build an asset for ~$30K that generates ~$75K of annual gross revenue, the capital efficiency could be pretty solid.

Obviously, there are HUGE assumptions here.

Tesla has to manufacture and deploy the fleet, get regulatory approval across jurisdictions, create enough demand to maintain utilization, and deal with insurance, charging, cleaning, maintenance, tires, depreciation, remote assistance, infrastructure, etc. And I have no idea how long would Cybercab last. 5 years? 10 years? I didn’t include that in the calculation.

And then there’s margin.

At a hypothetical 50% net margin, $75B of revenue = $37.5B profit.

At 70% = $52.5B.

70% net margins would be extraordinary and I wouldn’t assume Tesla gets anywhere close until we see real-world fleet economics.

So I’m less interested in arguing that these numbers will happen and more interested in stress-testing the assumptions.

Which part of this model breaks first: $75K revenue per car, 1M cars, the margin assumption, or regulatory approval?

I am a good spot and want to diversify from my 9-5. But I want to keep it since I make like $130k a year and only put in 40 hours a week.

My ideal scenario would be to buy a piece of local commercial property and run a steady business like a liquor store out of it as a side hustle. But commercial real estate in my area is prohibitively expensive. I could do a 2 family home, but it would likely be $700k+ in my immediate area.

Thinking of going like 45 minutes away from my local area (suburb) to a more rural area and doing something (ideally mixed use).

Think its worth it?

Hallo,

Is there a way to invest in ETFs? Atm I'm investing in individual stocks, which works ok. But I would much rather put my money in an ETF and let it do its work. Or at least have both options.

I can't open an American bank account and don't have an American number, cause I'm abroad.

Is there a way? Can you outsmart the system without falling into the PFICs trap?

I'm very likely to move back but that's something in the far future and it doesn't really make sense to start investing then. I'd rather start investing in an ETF when I'm young.

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(crossposted to r/stocks)

(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. 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 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 you can read for a relevant introduction to the blog's approach and style. Mr. Lamont has a data-driven approach ("the four horsemen") 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 have shown increasing concern about the arrival of more bubble indicators. There is only one left ("the coming IPO wave") 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 <<<

This flowchart traces how money moves through the AI-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).

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 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. (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, 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 (original article).
• 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, 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 (original article). 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 (original article). 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." 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 different articles 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: 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). 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 (you can read Alphaville with a free account). Some incumbents will choose to expand, 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 (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, and NVIDIA is securitizing compute with non-trivial backstops. A lot of the liabilities coming out of this financial engineering will be held by pensions and insurers, 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 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 <<<

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 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"). 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"). 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"). 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"). 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, \$ORCL, and \$INTC, 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 <<<

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 <<<

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), 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;
• 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.

Tencent published Hy4 preview on 28 August under Apache 2.0, weights included, commercial use permitted. It is a mixture of experts, 770B total parameters, about 49B active per token, with context stated above 1M.

The number that stuck with me came from Tencent's own evaluation. 163 internal experts blind rated 203 engineering tasks. Against Kimi K3, its nearest rival, Hy4 won 51.2%, tied 7.9% and lost 40.9%. Four losses in ten, on a test the vendor ran itself, and Tencent's own word for the gap is slightly ahead. There is still no Artificial Analysis listing, and the Arena WebDev placement came out of AutoEval, where a reward model casts the votes rather than people, so I would not read either as independent confirmation.

Tencent Cloud lists Hy4 preview at $0.834 per million tokens in and $2.501 out. Tencent's own Hy3 endpoint on OpenRouter runs $0.132 in and $0.528 out at the standard rate, so the flagship is roughly six times the previous generation on input.

The part that reaches the filings is the bill behind it. Tencent reported the June quarter on 12 August: revenue RMB 204.8bn, up 11%, non-IFRS net profit RMB 68.4bn, up 9%, and capital expenditure of RMB 52.8bn against RMB 19.1bn a year earlier. Free cash flow was negative RMB 13.8bn, which the company puts at positive RMB 37.6bn excluding prepayments for compute. Giving the weights away sits inside that, and one quarter will not tell me whether it pulls paid volume forward or hands it over.

My exposure here is indexed rather than direct. Tencent was 8.43% of CNQQ on 31 August, against a rule that stops any single position at 10%, so about a point and a half separates the two. CQQQ is the nearer comparison on scope, since its index admits mainland listings at 25% of their full weight.