Investor, L/S tech + consumer
NYC
Joined February 2020
- Tweets1.5K
- Following1.4K
- Followers676
- Likes3.6K
The plan? Enroll at the University of Tehran and befriend a geology professor. Eventually he mentions a weirdly hollow mountain
43
54th Parallel retweeted
hey @pmarca maybe good to reign in your pawns here
Hi Brett, the AI accelerator has some materials but it doesn't offer what you're speaking about here?
1
391
54th Parallel retweeted
It has become fashionable to quote our token index as support for a bearish view on the AI trade. We have pushed back gently a few times, because nothing here is definitive and people should come to their own conclusions. It now seems worth saying a little more.
In June we clarified what our LLM Token Index measures: what API users in our sample actually paid per million tokens. A usage-weighted price. Not token volume, not total spend. We noted it can be read, loosely, as revealed willingness to pay for frontier intelligence.
We should have emphasized the conditions. The binding one is that the intelligence content of a token stays stable. Over a few weeks that is defensible. Across a couple of release cycles it clearly is not. While a token is simply the wrong unit for intelligence, measuring capability itself without bias is also extremely hard. [Btw we will struggle with this measurement problem all over the place as AI proliferates delivering non-market economic value.]
The upshot is that the message from June landed. Maybe a little too well, because it created a new misread: that a falling index is necessarily bearish for the AI trade, since if token prices fall, model-layer margins must follow.
Two things are being conflated.
The deflation in our sample is real, and it is recent. It dates from the end of May. The index peaked at $2.07 on May 28 and sits at $1.00 as of Sept 21, down 52%. But the same index rose 67% from January into that peak, and few read the rise as bullish for lab margins. It is not bearish now. A usage-weighted price moves with the mix, in both directions.
What the mix actually says: more work, at least within our sample, is being routed to cheap, fast models, and labs keep shipping more mid-tier variants. Most everyday tasks never needed a frontier model. That is partial equilibrium for the users we track, and our methodology note is explicit that this index alone cannot separate substitution from efficient agentic routing.
Compute demand is a different question entirely, answered by different data series. Our H200 non-hyperscaler rental index has rerated through the summer: $2.87 average in June, $3.29 now, with a record $3.32 on Sept 19. B200 is $5.76, up 7% over the same stretch and 31% year to date. B300, our newest and thinnest series, is up 44% since inception in late April. H100 is off 7% from its August high, consistent with workloads migrating up the stack. Rents on the parts that are actually scarce are not signaling a demand stall.
A world of mass agentic use is one where cheap tokens are nearly all of the count. Total token usage should keep growing far faster than the price is falling, with most of that growth coming from cheap, fast, and increasingly open models. The usage-weighted price can keep falling anyway. Our view, not a finding from this index: the highest value-add work still routes through frontier models, and that is where most of the economics will accrue. In any event, the implication for compute is more, not less.
An astronomical number of tokens, most of them from cheap flash models, is what economy-wide AI proliferation should look like. It is not a demand stall.
Our LLM Token Expenditure Index should really have been named the “Token Expenditure Price Index” bc it’s an expenditure or usage-weighted average token price index. It tells you how much currently the entire market AI is paying for a million LLM tokens irrespective of models.
The naming might’ve led to some misinterpretations as some seem to have interpreted the index as either the total volume of token used or the average price of tokens. In reality, the index captures something more subtle than either interpretation: it tells us the marginal willingness to pay for LLM models.
Over the course of the year, while model token prices haven’t moved that much, the usage patterns have moved dramatically leading to the token index movement down and then up sharply as AI users moved en masse into using cheap open weight models and then en masse to the much more expensive frontier closed source models. From consumers to enterprises, everyone is Claude-maxxing!
More recently, as can be seen in the chart below, the token index has stagnated, which suggests that usage migration towards frontier models has slowed. Time will tell whether this is just a pause or an inflection in the trend as users move back towards open weights models. In a sense our token index could be roughly interpreted as a “quality premium” of frontier models over the much cheaper open source models (if we assume users and prices are both “rational”).
For more details on what we offer beyond the few indices we’ve listed on the Bloomberg Terminal, check us out at silicondata.com and give us a holler! 😊
Michael, the Market Situation Report from this morning sounded like it was written entirely by Claude. It had an awful rhythmic tune to it. Please stick to writing these yourself. Much more enjoyable to know your true thoughts otherwise you are just a meat proxy!
101
54th Parallel retweeted
There are so many things that are wrong in this post that it's hard to know where to begin. This honestly is starting to sound like the incoherent ramblings of a mad man. Is there really such a chasm of beliefs and information btwn the fully AGI-pilled technoptimists living in SF and everyone else?
In the scenario, where AI is driving risk free interest rates up and developing countries into default and the world is collapsing around us, the US GDP wouldn't be growing 10% a year. GDP is currently calculated as the final sales of goods and services. The USSR used to calculate GDP by summing up output bc of the lack of markets to price output properly, which also led to extreme misallocation of capital and poorly made top-down decisions on what to produce. That's just not the US economy.
If the (US) govt's rates go up, the hyperscalers' rates will be higher still bc default risk premium isn't negative. The development of AI will have long halted before such scenarios fully developed.
And where are they seeing data or evidence for these "little money turned into lots of money" or "$1 turned into $20" investments? Even the hyperscaler bulls are not claiming such high ROICs.
This is honestly what I had said about "techoptimists". They are genuinely nice, curious, and well-intentioned people, but they really don't seem to understand basic economics or the concept of equilibrium. 🤦🏻♂️
I'm a big fan of Dwarkesh' podcasts but this is honestly pretty bad.
Could AI investment start a global sovereign debt crisis?
If you look at the SpaceX-Google deal, it is already the case that the entire capex for the data center can be paid off by leasing it out for roughly 1 year.
As we get closer to AGI, the returns on compute will be massive and obvious, many-fold the principal within a few years. And as AIs get more capable, the demand for the compute to serve them will be as large as the demand for white collar labor - aka 10s of trillions of dollars a year.
In this world, why would anyone invest in anything but datacenters/ semiconductors/ energy/ robotics, which will have astronomical returns?
There may be many upsides to this world, but one of the downsides would be many sovereign debt crises around the world
In 1980, Fed Chair Paul Volcker raised interest rates about 10 pp in order to fight inflation - and this drove some 40 odd countries, mostly in Latin America, to default. A similar thing might happen again - what @BasilHalperin calls the second Volcker shock.
To put it in very plain terms, investors would say, "Hey, why am I going to lend Egypt money at 5% when I could just buy relatively safer investment-grade hyperscaler debt in America at 10%?" It’s even worse, because the investor also correctly notices that rising rates make Egypt more likely to default, so they demand even higher rates to account for the risk. This makes it even harder for Egypt to continue servicing their debt, which accelerates the default.
Most equities would get pummeled too. Anything valued for stable cash flows craters in price as the discount rate increases, resulting in a barbell of market equity returns. The market overall may be up because of AI stocks, but almost every other stock will be down.
The U.S. fiscal situation may also be troubling because short-duration U.S. debt would get refurbished to the new interest rate just as the main source of federal revenue (labor) is decreasing in relative share.
At the end of the day, I think the U.S. government will be mostly fine, because the data centers are on American soil, and Congress can always invent new taxes to capture some of the token revenue, or, less efficiently, the investment itself.
All this being said, if the economy is growing tens of percent a year, this would also be a world of great abundance!
I think this is a bunch of nerd speak for the very basic fact that in a world with explosive growth as a result of AI, there's just a lot of opportunities to invest and grow money. The higher interest rate reflects the fact that the opportunity cost for government spending just shoots up extremely high. You're paying a ton of opportunity cost to give people pensions now rather than building a new datacenter.
And yet, net-net, most people might still be significantly better off.
Generally true that we can call the Fed's bill purchases reserve management and Treasury's long-bond purchases debt management, but if one arm of government creates reserves while another removes duration from the market, the combined balance sheet effect looks a lot like QE
151
Would add that Transocean doesn't have durable pricing power nor is it a demand aggregator. It's a supply aggregator.
1
6
1,324
54th Parallel retweeted
I think web browsers are going to die way sooner than people think.
They are already archaic for heavy AI users like me.
Everything is going to be done within the AI agent itself.
Your AI will have its own identity, you'll define the permissions for what it can and cannot do, and that'll be it.
There will be an equivalent to Google My Business, whereby every business will create their Claude account and have a Claude AI that represents them with its own unique name, identity, contact details, etc.
Then, the final piece of the puzzle is the coordination layer: both individuals and businesses will be able to opt in to a massive multiplayer online network of human and AI hybrids, where the AI goes off and finds exactly what it is you need by talking to other AIs and humans in the background.
Once you have this, it's all over. Browsers die, websites die, most SaaS dies - the AI subsumes everything.
