The AI trade now runs on borrowed money, and the lenders are repricing it(greyswansignals.com) |
The AI trade now runs on borrowed money, and the lenders are repricing it(greyswansignals.com) |
After reading these articles go by so often, it feels like what I actually can’t keep up with is the bond market. To paraphrase Trotsky, you may not be interested in the bond market, but the bond market is interested in you. I want to be able to read the signals at the bottom of this article, and divine some kind of prediction that can guide me… I don’t know, to choose whether I should buy a house or change the investment strategy in my retirement fund or something. But I’m just seeing all these signals go by, waiting for the story to be written, which only happens when the dust settles.
I guess I’ll go back to not understanding AI, instead of not understanding the bond market.
By the time you have read this article, the professionals and their computers will have digested that material a thousand times over and have priced it in.
To be more precise: buy the lowest cost most diversified index fund you can buy and then hold it. If you want to spend some smarts to get a better return: look at how to minimise taxes and fees.
I think you overestimate traders. What we call smart money is very often really, really dumb from a macro perspective. Professional traders believe hype and follow trends. There is still at least 2 thesis playing out at the moment for the AI trade, and you don’t need to be a professional trader to take part: one is the AI impact on saas (the market has been very bearish on SaaS companies, and still hasn’t corrected meaningfully), and the ai infrastructure (hardware companies + hyperscalers)
Not sure how it's all going to play out, but this ginormous increase in passive investing over the past decade or so, mainly in S&P 500, seems like a vulnerability. Small cap might be a better (non-sexy) target long term.
That definitely was true.
I am unsure it is still true. Index funds have taken so much of the trade volume the are becoming momentum strategies
So long as you are happy following the market wherever it goes, and if the recent past is a guide then up is the direction, then yes.
But given the nepotism and corruption in the highest reaches of USAnian society (e.g. Trump's crypto currency scams and the blatant inside dealing and rule ignoring of the Space X float) the future looks much less certain than the past
The interesting thing is: you do not need to keep up. It’s actually way easier and cheaper to wait a bit for the chaos to stabilize, then learn to use the tools. You don’t need to have been someone who experienced the whole evolution, non stop at the edge. It’s ok to let the enthusiasts discover how things work and eventually learn from them. Just like any other technology. The whole „you will be left behind“ is nonsense. If AI is the future, then it will here to stay and you can let others map the domain first
Unfortunately the hype machine has far outstripped their capabilities so far, and the amount of money spent doesn’t look like being recouped, so somebody is going to lose money, as people lost money on the overpriced spacex ipo (overpriced because of AI).
I'm inclined to think the collapse has already started but nobody wants to see it yet.
In the last few weeks SP500 is down, kospi is down, nikkei is down, US inflation is still high and growth is softer than expected. Hyper inflated stocks (Tesla, Nvidia, SpaceX) are deflating. US bonds are at a 20 year high.
Interesting times ahead.
In what bubble does this pressure exist?
I have no illusions that I can time a bubble, but I'm hopeful I'm at least partially shielded, and most importantly I feel better about ignoring wall street again.
What do you mean? Selling everything before this bubble pops?
Revolutionary technology + massive adoption ≠ good investment
Investors have poured money into a bottomless pit, attracted by the growth and glamour of the industry. The airline industry since its birth has had a collective net loss, in aggregate, despite moving hundreds of millions of people.
Commodity Product, no switching costs. Infinite competition
- Gen AI: Too Much Spend, Too Little Benefit?: https://www.goldmansachs.com/insights/top-of-mind/gen-ai-too... (Goldman Sachs)
- AI’s $600 Billion Question: https://sequoiacap.com/article/ais-600b-question/ (Sequoia Capital)
- The Simple Macroeconomics of AI: https://www.nber.org/system/files/working_papers/w32487/w324... (MIT / Daron Acemoglu)
Directly conflicts with
> Alert and Critical signals represent readings that have historically been associated with meaningful financial stress.
These are all pretty standard things to track and are regularly (and publicly!)
Not saying we’re not in a bubble or near/far from it popping, but these metrics aren’t going to precisely tell you _when_, which is pretty much the only thing that matters.
Second, not all money is created via borrowing (but the vast majority is!)
And the YouTube video you linked to is very confused even about the money that is created via borrowing.
Government debt is not required to create money. The Bank of Japan bought stock ETFs to get 'freshly printed' money into circulation. ('Freshly printed' in scare quotes, because these days it's just entries in a database.) Another example: Singapore's central bank (MAS) does not use Singapore government debt to create Singapore dollars; I'm not even quite sure they would even be allowed to.
You can say that money itself is a debt of the central bank; and that's sort-of true, but it's not what David Graeber talks about.
A bit of a pedantic last point: silver coins or bitcoin also require no borrowing to create. Silver coins have been used as money, bitcoin could conceivably be used as money. (There are other problems with these options, but that's besides the narrow point.)
If "money" is the some function of all outstanding credit, then yes, it is created (mostly) by bank lending
If you define money as a web of trust then it is mostly created by those that create the rules. The state
Until they didn't.
Amazon also added/pivoted to AWS, which is where a huge part of its value comes from today.
Not sure what your point is.
so everything is going according to plan, and nobody knows the future, and predicting collpses has never been a profitable business.
I didn't have to read past the first few confusing contorted and convoluted paragraps of this article to decide to come over here and explain it, this is all straightforward corporate finance 102 and the article is fluff
The industrialisation essentially socializes the cost across a lot more people though, so even though it doesn't make a profit it does mean people can have air travel without it costing millions per flight for the few people who can afford it. Essentially the economies of scale from having lots of flights isn't enough to make it profitable but they are enough to make it affordable.
There's no spare money to extract from the airline industry but it's still very useful. The same could be true for AI in the long term.
Sometimes the goal of an industry is to exist rather than to make a profit, because the benefit to society is more important than profit. People don't like that though so they do a bit of creative accounting or head-in-the-sand denial around it.
Of course it could, let’s start with making the models open weight and entirely open source. Fully publicly owned and not shaped to maximise profits for the shareholders.
Oh wait, Scam Altman entered the chat and turned a non-profit lab into the next biggest IPO vehicle the world has ever seen.
OpenAI launched as a nonprofit research institution. Its announcement explicitly said it wanted to pursue AI “unconstrained by a need to generate financial return,” produce value for everyone rather than shareholders, publish research and share patents broadly.
True. But it has added enormous benefits to many other parts of the economy.
Airlines do not capture that value.
That is where the AI companies are. Adding value they cannot capture
The number of competitors for SOTA drops by a few every year. The winners make more money, get more revenue, buy more compute, train better model with compute, buy best talent, and the cycle goes.
I think it's easier to fall behind and never catch back up than people think. One disastrous training run can leave a lab months to a year behind. For example, Meta's disastrous LLAMA 4 models. Meta is lucky to have their ads business as a funding source. However, Anthropic's revenue is growing so fast, that ability to use ads as a funding source to stay in the race may not last much longer for Meta.
To me, SOTA LLM training is very much like new chip fab nodes. One disastrous node can put you behind for many years or forever. The cost to build the next chip node doubles every every 4 years (Rock's law). The cost to train the next SOTA model likely has some similar power law which means over time, it's too costly for losers to keep up. The only reason TSMC isn't a defacto monopoly for advanced chip nodes is strictly due to geopolitics.
Obviously there is risk, but can't we really extrapolate the AI gains forward and just see how big it's ahead to become?
Where is this disruption? The longer we go, the more people report that the supposed net-gain of easily 100s of percents is not visible.
I do strongly believe "It's just a tool" - A powerful one, but not one like the invention of the steam machine.
The benefit is neither here nor there - it's whether the borrowed money will ever be repaid on the lenders' terms.
We’ll see how they develop but so far they are not capable of operating independently.
That’s the point: making growth sustainable over time.
The internet and railroads were highly beneficial, still crashed the economy.
Writing a lot of code doesn’t mean much when the moat was never “writing a lot of code”
I was called quite disruptive in class when I was young. I'm sure my teachers never meant it as a compliment.
Isn't that the point here? That everyone thinks massive disruption is happening and everyone is 100xing their productivity, but it's not actually showing up in the numbers anywhere?
Because what many of us are seeing is meaningless “productivity” improvements.
If at the end of the day you don’t have more users paying for your product or the same users and paying more, then what’s the point of being more productive?
It’s just coding. It’s not every technical profession at all.
Lenders are doubting their return. People's benefits have nothing to do with it. The benefits would go in a minute, if doing so yielded a better return.
That's the ideal scenario, but you can also have immense debt because someone once thought you had assets.
Question is: is that worth enough to cover the debt after the market crashed?
You increased the amount of code written by 10x but unless there’s a 10x increase in demand, its worth nothing
Meta and xAI announcing they are leasing out capacity is a version of this already happening.
I was considering writing a tool that simply follows any index you choose with a .toml of simple config options, like which stocks to exclude, potential fixed locks for specific stocks (or maybe upper and lower percentage of portfolio settings), a hard per stock cap (say AAPL at 3%), and drift threshold. Something you just run once a day and it spits out your buy / sell orders. Seems like this is something brokerages are already offering in some variation though, and I'm not sure what, if any, API access looks like, or export / import options.
Your idea for the tool sounds interesting. I suspect even just copy-and-pasting the paragraph you wrote here into your favourite AI programming agent would get you pretty close to a prototype you can play around with. At least in terms of 'spit out buy / sell orders' and leaving out the API integration.
https://stockanalysis.com/quote/lon/VWRA/holdings/
~ 5% Nvidia as biggest holding and 20% in US listed tech companies (most of which are heavily invested in AI), over 60% in the US market, so this ticker is very similar to investing in the US market alone.
Also when a bubble like this deflates it hits almost everything so it is very hard to avoid, but world indexes are particularly exposed.
"trajectory of humanity to a parabolic move upward" is poorly defined here. Whether we are headed to a machine god ruled scenario or "just" incredibly powerful productivity tools, there will be a lot of economic pain for some (most) and a lot of economic gain for a few.
I've yet to a see an LLM/agent-based business plan in where scaling with an order fewer workers than before LLMs is not a central part of the value proposition.
It comes back to your perception of what AI is because to people who say AI is glorified auto-complete won't believe that the money is worth it.
The AGI-pilled true believers who say it will end all money and result in a post-scarcity world believe literally any amount is justifiable.
Most people, me included, land somewhere in the middle- it seems like AI is a humanity-level sea change in technology and how computers work and serve us. It seems plausible that a few trillion is a reasonable amount.
It would be strange if the race to wield this power wouldn't result in AI getting pumped to the moon, way beyond anything that seems reasonable.
You’re talking about about an amount that is a 13% of the total US government spending, of which is 20% of the entire US GDP.
I’m not saying it’s insignificant but it’s only a few percent of the US GDP and it represents spending over several years.
The R&D expenditure is a critical requirement for the inference profits, to the point where we should probably lump their financials together, at which point is definitely not profitable.
What will it look like when R&D plateaus (and yes it definitely will, but it could take a while), investment falls, and a few main competitors remain in the music chairs?
It's very difficult to predict. The inference profits we are seeing the profits of a company that is temporarily ahead, but the revenue will level-out in a more stable market, depending on how many survived. It's also hard to tell where the costs will be at the end of the game, with constant efficiency optimisation mixed with cost increases for higher intelligence.
I think it will be quite similar to the semiconductor industry, where, yes there are some key monopolies, but they are not the initial big players, and none of it is actually very profitable; while the real profits are reaped by those that make popular consumer products based on the foundational tech. I guess the main difference is that OpenAI and specially Anthropic have been quite effective at directly tapping into the consumer market rather than remaining technology providers.
And then everyone would stop using their inference as soon as a better model for a reasonable price came out.
We have seen plenty of examples in other industries where you can never really stop investing a ton on R&D with diminishing returns (like in semiconductors or pharma), because the moment you stop newcomers overtake you.
Or the whole thing becomes a commodity with lots of competitors, where technological advantage is overtaken by marketing as the dominant force.
Or you really are left as the only player alive, but you realise that the market cannot absorb higher prices for your product by then, they prefer just not to buy it. Perhaps you are the only player alive because the business has become so low-margin that everyone else has abandoned it intentionally.
That Silicon Valley pitch you are echoing rarely works out as advertised, even for the winners.
An Amazon fulfilment warehouse depreciates to $0 over 30 years.
One type of investment is different to the other.
I can't prove it. It's just my opinion.
This might actually still hold true now, or or at least many actors in the market behave that way. But I'm not so sure there isn't a cliff to that effect. At some point, if SOTA models remain expensive, it'll turn into a market advantage to figure out how to get things done without depending on the most expensive tooling available.
Similar scenario, different phrasing: if your company relies on overqualified workers to deliver 100% quality, the market may still decide that it's fine to go with 90% quality for 50% the price.
I never said there will never be a diminishing return. I'm challenging the statement that we've already hit.
Note: We're still scaling chip nodes. It's still worth it for TSMC and chip design companies to invest hundreds of billions into every new chip node every 2-3 years. This is after decades of scaling already.
If we couldn’t isolate a variable we would never be able to argue.
Using a better model is an advantage even if only for the coders. There are a million ways to turn that into profit, both proper and not so proper but that’s the beauty of ceteris paribus: the other factors do not matter now.
Mind pointing where that profit for companies consuming AI is? I don’t mean hypotheticals. Where are the proof that current AI contributes positively to ROI?
And while the quarter by quarter growth may seem astonishing it very different saying “debt levels today are alarming” versus “if this trend continues debt levels will be alarming”
We have seen plenty of examples in other industries where you can never really stop investing a ton on R&D with diminishing returns (like in semiconductors or pharma), because the moment you stop newcomers overtake you.
I think you're proving my point. Eventually, R&D heavy industries almost always become a monopoly or duopoly. Small/losing players can't keep up and drop out or acquired.
Their revenue has always been sustained by the fact that their technology needs to be constantly replaced because it keeps getting better. The moment it stops getting better, the replacement rates plummet and so do their revenues. It's also really not that hard to compete with them when they get complacent.
I'm not counting Nvidia because they don't produce semiconductors themselves, they are a different kind of business.
They are indeed an example of those that cater to consumers and/or build popular products based on foundational tech from others, like Apple or Sony, which do tend to be quite profitable.
But the actual deep-tech semiconductor firms? They may be critical to the world economy, but they don't actually make that much money comparatively. In many cases they are not real monopolies, it's just that no one else wanted to continue investing in a shitty business model. Only the likes of TSMC and Samsung were okay in playing the low-margin game, but most US players left the board.
I believe AI has a lot of the same characteristics.