They aren’t; the parent comment is incorrect. It’s safer to say Nvidia is encouraging the money that already exists to be deployed on AI buildouts.
But everyone is now chasing the same opportunity (AI and its dependencies like hardware and power) that will drive prices higher in those sectors until supply responds (or demand disappears).
“as long as it’s cash flow
continues” is doing a lot of optimistic heavy lifting. The whole premise of the circular financing worry is that Nvidia sits in the middle of all the guarantees made to companies like OpenAI. If any of those companies become insolvent, Nvidia is on the hook for it.
Also Nvidia isn’t really creating money. The 500B number is third party capital that already exists (BX, Apollo, etc).
Compute has already lost value for me. Six months ago I thought you needed a 1T+ model to be useful coding. Now I am able to get by just fine with a 27b model.
I see two factors converging to cause a collapse of this house of cards:
1. People are realizing that what they need isn't more general intelligence, it's more specialization. A small but well tuned coding model, a small but well tuned customer service model, a small but well tuned document explorer.
2. Specialized hardware - TPUs and NPUs - especially coming out of china. The latest GLM model was trained and runs on Huawei hardware. Nvidia is only worth so much because they are the biggest and best provider of the kind of compute needed to run llms, but the export bans mean china has a lot of incentive to topple that monopoly.
The amount of compute we need to do the things llms do is falling rapidly, the number of people who can provide that compute is rising.
> People are realizing that what they need isn't more general intelligence, it's more specialization. A small but well tuned coding model...
It’s not quite as simple as that. Several studies have shown the opposite: models trained on more diverse knowledge tend to cross-pollinate across domains. So a more generalized model can actually perform better than a specialized one.
That’s why you’re not seeing tons of tiny models (one for Python, one for Pascal, one for Rust, etc).
This is definitely the position of the big ai companies.
But it doesn't match my experience. Qwen3.8 27b is clearly smarter at coding than MANY bigger models. gpt-oss-120b for example, is almost 4x the size, and performs way worse at coding tasks.
It's clear to me that you can build small models that work well at specific tasks.
Python vs Rust is probably too fine grained a way to build a model. Coding in general seems like a better target.
There will always be a place for large generalist models, no doubt. But I think that place is much smaller than the big ai companies are counting on.
Gpt-oss—120b is like 1000 years old in AI years, whereas Qwen 3.8 27b is pretty young. What you’re seeing is that parameters aren’t apples to apples, and at a given parameter level, the new models are much, much better than the ones from a year or two ago. Like, to a comical degree.
Wasnt this known by everyone who cared to pay attention?
It practically became a joke about how a huge amount of the training data for GPT-4 was bottom of the barrel reddit vomit and obvious bot spam. Leading to many bizarre edge cases.
Does that not prove my point? Bigger doesn’t automatically mean better. Quality of training data, and model structure, matters as much or more than size
Ah sorry, I should've continued, the bigger recent models are commensurately smarter. If you really want to make the point, then you'd need to show 27b being smarter than similar vintage bigger models. And in that case, there's confounding issues like efficiency, speed due to excessive thinking maybe to make up for the smaller amount of world knowledge baked in (qwen 27b's main issue iirc), etc - they're tuned for different things.
Shows qwen3.8-27b along side seven larger models of ~similar vintage. Only one scores above 27b.
Many of those are closed models so idk their exact parameter count / active param count, but it hardly matters - i’m sure all of them are far above 100b params
My point is not that bigger is pointless. It’s just clearly not the only road to take to make a model better, which is obvious just from seeing how models of the same size have gotten better over the past few years
First off, I'd include Qwen flash-next and GLM 5.3 to show some of the other strong open weight models, and they predictably dominate it, but they're much larger. But, it shows up right next to DSv4 Flash 0731 on the overall index, and that's much larger. It's a great model! But then scroll down and hit Time Per Task, and you'll see that DSv4 Flash takes 3.6 seconds per task to Qwen's 21.1. That's what I meant when I said this:
>speed due to excessive thinking maybe to make up for the smaller amount of world knowledge baked in (qwen 27b's main issue iirc), etc - they're tuned for different things.
It can make up for its shortcomings by iterating a lot longer, and using way more thinking tokens. And that's a great trade if you don't have the vram to run the bigger models, but speed is pretty important for getting things done... And that's why DSv4Flash is great, too, despite being much larger, and scoring similarly on the intelligence index.
> Qwen flash-next and GLM 5.3 to show some of the other strong open weight models, and they predictably dominate it
Absolutely - no argument from me here. Bigger is very clearly a lever you can pull to get more out of a model.
> But then scroll down and hit Time Per Task, and you'll see that DSv4 Flash takes 3.6 seconds per task to Qwen's 21.1
Fair point, qwen definitely is slower - it’s a dense model, 27b params, vs a sparse 13b active params model - but the data doesn’t quite agree with what you’re saying about reasoning. I.e.:
> It can make up for its shortcomings by iterating a lot longer, and using way more thinking tokens
If you look at the total tokens generated, deepseek thought for 45k tokens and qwen thought for 48k. Barely a difference. The wall clock difference is all down to the speed of token generation, not the amount of reasoning done. At least when we are comparing deepseek and qwen 27b. The comparison swings more towards your position when it comes to the other models on the chart that reason for much fewer tokens.
So perhaps a hypothetical Qwen-27b-a13b could never rival deepseek’s larger model and the tradeoff is one of speed vs overall size - i.e. a small model needs more active params to compete than a big one does.
One data point that seems relevant to me is that the previous gen qwen Qwen3.6-27b was not so different in performance from its sibling model Qwen3.6-35b-a3b. We never got a qwen3.8-35b-a3b, but if we had, would the gap have stayed the same or gotten bigger? I.e. would the quality gains by improving training coming up against a hard limitation with 35b, or not.
Ah good catch on the total tokens, was going off vague memory there, and I thought people had gotten qwen 3.8 27b up to similar decode speeds as ds v4 flash.
>One data point that seems relevant to me is that the previous gen qwen Qwen3.6-27b was not so different in performance from its sibling model Qwen3.6-35b-a3b. We never got a qwen3.8-35b-a3b, but if we had, would the gap have stayed the same or gotten bigger? I.e. would the quality gains by improving training coming up against a hard limitation with 35b, or not.
Yeah good question, kind of shocking that a 3b active model would perform as well as a 27b dense.
I make heavy use of smaller local models on a daily basis (Qwen3-VL for auto-captioning images, Gemma3:27b for some translation work, etc.). Gemma3:27b is a good example of a very capable general purpose multimodal model and has handled almost everything I've thrown at it from sentiment analysis to documentation writing.
I suppose I was drawing a distinction between specialized and general intelligence versus small and large. I don’t think those are necessarily mutually exclusive.
gpt-oss-120b only has 5B active parameters, so its not surprising Qwen3.8 27B outperforms it (Qwen3.8 is also ~13 months newer, which is forever in LLMs)
Fair enough. I’ve barley touched oss-120b, so i didn’t know it was so few active params. For a direct comparison, qwen3.6-35b-a3b is still better at coding than oss-120b.
And Qwen3.8-27b is still better at coding than opus 4.1.
Yes, if you list off models 27b is better than it’s all older models. But that’s my point - newer models are better than older models at the same AND much smaller size. That’s because model size matters less than they say. Training data and model architecture matter more.
The western labs are very AGI pilled, and their public models are distilled down from larger research-only models that are uneconomical to serve directly. They could (and probably will) start distilling models for more niche use cases eventually, but we're not there yet.
Problem is conflict of interest: the studies are mostly from the providers of the biggest models, or someone who received free tokens to do the research.
It would be nice to hear exactly how the conflict of interest has impacted the specific studies and how they are wrong rather than conspiracy theory level speculation and hand waving at the entire category
I think is more than reasonable to be suspicious of studies funded by party with conflict of interest. Think of how many studies about climate change were funded by big polluters, for example.
In 2026, the default outlook should be suspicion for any big private organisations with profit motive.
I think it’s less than reasonable to operate mainly on vibes, rumors, and hearsay.
You don’t need to think about climate change studies. Instead you can read the allegedly tainted studies we’re actually talking about and profess to all of us what is wrong with them. You can’t point to exactly where they’ve fudged them.
They don't even need to fudge the data on the studies they have published. They just need to hold back other studies that contradict the idea. Pouring over the published data looking for flaws will never get us access to the unpublished data.
In an alternate universe where all research is completely auditable and we all have infinite time, yes that's a valid approach. And you're welcome to spend your life going that route, but the rest of us are gonna trust our common sense on this.
I think what will keep the industry afloat, all else failing, is the surveillance industry! Nothing like a fat reoccurring cheque from the government to check if little Jimmy is committing thought crime!
LLMs needing less compute would actually be a good thing for Nvidia due to Jevons paradox. Right now token costs are an impediment to using AI more broadly, and more efficient models would help adoption in cases where AI has proven to be useful, like coding.
Jevon’s paradox is a common talking point but it is not a law of nature. LED lightbulbs use 80% less energy than incandescent but you don’t see people using 5x more lights on their homes. The overall energy used to light homes has decreased.
And even if compute demand were perfectly elastic it’s only a good thing insofar as it drives demand for new Nvidia hardware. If tokens can be served from Apple hardware or Google hardware or Huawei hardware that doesn’t help Nvidia.
As I look up and see three lightbulbs side-by-side over my desk and another one in the lamp on the desk… which is dramatically more light than the old 100W bulb used but also lower energy consumption.
Perhaps. But their huge valuation is based on them supplying the massive buildout of data centers that’s happening / planned.
If that dies because a lot of people’s needs turn out to be met by a system at home they can run a 30b-150b model on, a lot more of that money goes to apple or intel or amd.
Sure, but it would be actively bad to make the code more complex simply because we have machinery that helps us deal with the complexity. A big part of how people assess the models' coding capability is whether they create needless, incidental complexity.
That's like saying it'd be actively bad to make the code more resource intensive simply because we have machinery that helps us deal with the extra requirements. And as we know as computers got more powerful code didn't get lighter. If it can, it will.
OpenAI dropped sora because it was costing them ridiculous amounts of money and earning them very little. They determined that the market can't support the cost of generating video.
Without a material change in the market (more buyers, vastly cheaper generation), it's unlikely a different company could make that work. More buyers isn't likely to happen, so that leaves vastly cheaper generation - something that would cause nvidia's value to collapse if it happened.
> the market can't support the cost of generating video.
I'd suggest that's only the case given the current quality of output. Media is incredibly expensive to produce. A model capable of sufficiently high quality could charge prices that are absurd by today's standards.
It’s a very small set of buyers that are in that price range. Total annual domestic box office revenue is like $10 billion, maybe $50 billion for global TV and film. And that’s revenue, not profit, and a lot of costs are going to marketing, not to filming and casting. That’s a lot of money, but it’s not the scale that OpenAI and Anthropic are at.
Video generation would only make sense at that scale if it was targeting individual consumers, but then it’d need to cost something that consumers are willing to pay - which practically is probably a few hundred per year at most among US consumers, and much less globally, so again it doesn’t solve for the size of the AI companies.
I don’t see a way that video generation becomes a big industry without making generation much much cheaper.
Aren't these two largely separate questions? Viability versus if a given incumbent has interest in a market of a given size. With the combination of (at minimum) streaming platforms, the box office, and advertisements video and audio generation would be viable at a remarkably high price point (as compared to the current token prices for other sorts of things). And as the price comes down presumably the market would grow larger - by how much I have no idea but there are certainly a great deal of currently underserved niche markets.
oAI isn't anywhere near close to fucked as long as their models are head and shoulders above even the very best open models in terms of tool calling and rock solid stability/reliability for agents/coding harnesses. Which, they are right now and we'll see if open models actually catch up in that regard. Even the "best" open models pale in comparison with tool calling and general "prompt and go do something else for an hour" reliability that we have with GPT models. With GPT models, streaming rarely stops unexpectedly. You almost never have to constantly nudge them along, etc. Granted with open models all of this can vary depending on the provider, and perhaps open models/protocols/APIs/harnesses aren't well enough aligned, but OpenAI models just seem to work without constant (or hardly any) wrinkles and with almost any harness/agent.
>oAI isn't anywhere near close to fucked as long as their models are head and shoulders above even the very best open models in terms of tool calling and rock solid stability/reliability for agents/coding harnesses
That's already not the case today. If you sat me in front of an LLM and told me to figure out if I'm working with K3 or Astra, I could probably do it, but it would take some work to be certain.
I've been thinking about that and that's why Nvidia's prices are surprising to me. Investors should know that better than me so there must be something I don't know
It’s really hard to know when the large tech companies have so many shares owned by a single figure. They can use margin loans and options to create the appearance of demand.
This is the right kind of analysis, but we can look broader. Both the demand and supply situations are a lot more extreme and dynamic than appears at first glance. E.g. to your points:
1. Yes, smaller models will become more popular, especially as the tokenmaxxing trend dies down and people start stretching their budgets farther. That is a downward pressure on demand.
But along the same dimension, consider that currently only about 40 - 60% of the world uses AI for only about 5 - 15% of their work hours. That means there is still 2x growth from users and 7x - 20x growth from the rest of the work hours left to capture! That is 14x - 40x more demand. Then consider that agentic tasks require multiples more tokens, and that is the kind of usage that is most likely to be deployed, and also the kind of usage that is the least used right now. That's another huge multiple to be tacked on.
And the entire AI industry has been lamenting the extreme compute crunch they're facing (and also why Claude has 9's comparable to GitHub; whereas OpenAI has been chugging along because Altman was OK being called a "podcasting bro" while desperately scrounging for compute years in advance.)
Nvidia's meteoric rise is entirely due to this kind of exploding demand with extremely limited supply.
2. Competing hardware is definitely a threat, but it has its own hurdles. Because the real bottleneck is not Nvidia, it's TSMC.
Pretty much all demand for all chips in all devices in all the world flow to, like, 3 companies in the world that actually fabricate them, and TSMC is the biggest. And the supply is extremely tight, as the exploding costs of electronics clearly shows.
So now TSMC will of course try to keep all its customers happy, but it will inevitably be forced to choose which ones it will keep happiest. And those will be the customers who can pay it the most. And that would be the one with all the money from its de facto status as a monopoly (and possibly even a monopsony)...
Which would be Nvidia ;-)
So yes, compute per task is falling rapidly... but it's barely a dent in the humongous total addressable demand, and the amount of hardware to support that compute is still very constrained, and most of that supply will likely flow through Nvidia.
> But along the same dimension, consider that currently only about 40 - 60% of the world uses AI for only about 5 - 15% of their work hours.
Ah yes, i am constantly lamenting that my barista isn’t using ai enough ;)
Hopefully you’ve adjusted your ceiling numbers to account for the large amount of people who can’t afford to pay for llms, and will never be able to pay, and aren’t worth it to advertise to since they can afford very little
If you reshuffle your argument, and apply the same facts you get to a similar conclusion but with a drastically different spin.
> it's more specialization
China, constrained by hardware, and talent (not to slight the Chinese, but they are limited to domestic resources - and much of the US effort is very international). They did, what the Chinese do, and optimized the process of production, and drastically lowered the cost of development of their models. Cheeper to build, cheaper to run is just good economics.
Meanwhile in the us, we have open AI doing "experiments" - it looks like the costs around the hugging face hack are going to be about the same as China would spend on building out one of their smaller efforts (several million dollars). (Depending on whos numbers you trust, the fact that I can even make this claim should make you raise an eyebrow).
Go back to the 80s' and "expert systems" - most people will tell you that for their time, they were amazing, and useful. People would have loved to have more of them but they were so cost prohibitive that we all but abandoned them for serious use. The US frontier labs seem to have forgotten this lesson and their calls to "slow down" look like an excuse to "cut the waste so we can move to making money".
> if OpenAI can’t use the compute, someone else can.
The problem with that is that OpenAI can only afford to pay for the compute because they are burning investor money (and so are most of OpenAI's biggest clients). They are losing billions. If they stop burning money, nobody else will be there to pay for that compute at OpenAI's cost.
Sure, somebody will probably be able to use these GPUs, they just won't be able to pay nearly as much for them as OpenAI does.
In reality, it's just nowhere near worth as much as OpenAI pays for it. Inflating the cost of compute is part of the problem caused by the circular financing, and if (or maybe when) OpenAI goes, the price of compute will go with them.
OpenAI and Anthropic are so far ahead of anyone else in terms of compute demand generation. Iirc correctly they're like 70% of GPU demand between them on hyperscalers and then Meta is 10% and Google internal demand is some distance behind meta. If OAI halved in generation you would need a couple of new companies with a Metas worth of demand to replace it is quite a sobering thought.
Believing only makes things true for so long until things fall apart. You can't keep burning billions every quarter. At some point, you run out of investors who believe, and the ones you have run out of money (see: Softbank).
Yes of course. Is the point you're actually trying to make that investors are making a pure call, so their belief in the story is misplaced? That they should believe a different story?
Devil's advocate: OpenAI not being able to use compute is highly correlated to many other AI companies not being able to find a meaningful use of this compute.
Failure to take into consideration those kind of correlations ("If my biggest client isn't able to buy it, I would be able to find someone else who will") is one of the principle causes why many risk models turned out to be garbage during the Great Financial Crisis.
But I also doubt Nvidia is on the hook if OpenAI just no longer wants the compute. I bet they are only on the hook if OpenAI cannot pay for it (is insolvent in some way).
I also have to bring up that OpenAI has already spat out an inference chip that beats Nvidia on flops per watt. So they could potentially not need the compute while other ai companies do.
The problem is that if OpenAI doesn't want the compute nobody does. All of these companies' demand for compute are correlated. It isn't likely that OpenAI will want less compute in isolation. Furthermore, the circular financing structure means that if OpenAI buys less chips it means that Nvidia has less money to give to say Anthropic to buy more chips and suddenly the exponential growth that circular financing has allowed to grow runs in reverse.
All the "frontier" AI companies *are* currently insolvent. They have never been anything other than cash burning machines.
The only way they keep the lights on and the doors open is by borrowing money --- and epic amounts of it. If those operating the cash spigot decide to turn it off, all AI companies will likely be similarly affected --- and so will Nvidia.
OpenAI expects to burn through more cash between 2024 and 2029 than Uber, Tesla, Amazon and Spotify did - combined - before those companies started making money
To make things worse, hardware prices have spiked, due to AI companies.
Fairly sure data center construction costs are also going up (they require so many resources that everything is constrained at the moment, especially electricity production).
So I don't understand in what world these frontier AI companies can somehow become profitable. The basic tech they're using is basically the same. Yes, around the edges there are a lot of things that can be done, and were done, like caching, batching, mixture of experts, etc, but basically everyone has done all of that by now, and they're still losing money.
So:
Total costs going up a lot - revenues per unit not increasing proportionally, if anything, Chinese models are forcing those down.
How does that math work out to profits? I don't see it.
Or about as bad, after trillions of dollars in investments over multiple years, let's say the entire frontier AI sector has a total profit of $20bn by 2030. In what world does that make sense? Assuming they can scale that total profit to $100bn in 2035 without investing another cent from 2027 to 2035 (utterly ridiculous), the return on investment would happen in roughly 20 years.
> if OpenAI can’t use the compute, someone else can
This is the big point IMO since I have never given $1 to OpenAI but I subscribe to Vidu and Typecast, and have given money to Kling, Hailou, and even Gemini in the form of Google Workspace.
So these other guys have products and use cases, which OpenAI has never been able to crack beyond ChatGPT. And ChatGPT was never worth paying for, IMO.
If OpenAI dies, it's not because there is no market for the technology (which is all NVIDIA cares about), it's more that OpenAI doesn't know how to run a relevant technology company.
They were given everything, not just NVIDIA's billions of dollars and credit backing but all the first-mover advantage, all the respect and credibility early on, so it's really sad to see them unable to develop interesting products and turn a profit in a space they helped pioneer, while so many others are making money with the tech all around them.
NVIDIA is fine. The technology will continue to improve and NVIDIA will stay at the center. OpenAI is fucked - knew it when they retired Sora to focus on text-to-text and coding (a largely solved problem).
To each their own. When OpenAI droped Sora and focused more on Codex, the product improved dramatically and I'm probably not the only one who dumped Claude Code subscription in favor of Codex; OpenAI's is miles ahead of Anthropic and has been since at least the release GPT 5.6 Sol - even the PR and generous resets is far better than how Anthropic is nickel and diming by requiring paid subscribers to pay yet more credits to even use their best available model (which is not even as good as OpenAI's top 2 models)
Do you use coding agents? Just curious bc from my experience using coding agents, open ai’s codex is neck and neck with anthropic’s claude code if not ahead. I wouldn’t agree that OpenAI hasn’t done anything since ChatGPT since codex is my daily driver for software engineering
It's kinda nuts to me how people can act like Claude is lightyears ahead of OpenAI models. Sure, it's one thing to simply have a preference or claim that Claude does some things better, but in reality they are both about as effective at doing the same job. I've long preferred GPT models because they know better how to shut up and don't seem to overthink as much as Claude, but I'm under no illusions that if OpenAI went belly-up then I couldn't do my job essentially the same with Claude. GPT models have also clearly improved over time in terms of programming. There haven't been any "big bangs" necessarily, but it's really not hard to give the same task to 5.3 and 6 and see which one has better output.
> if OpenAI can’t use the compute, someone else can
How? The hardware is in OpenAI's datacenters. Does Nvidia have a couple hundred semi trucks, contractors, and IT technicians, to repo the hardware and resell it to someone else before it's lost most of its value? These chips will be replaced approx every 3-4 years. So if OpenAI tanks, after Nvidia pays for and waits for the process to collect the hardware, they then have to sell it for pennies on the dollar. They lose almost all the investment.
Also consider that SpaceXAI already had datacenters full of gear that they basically weren't using because nobody wanted their product, so they now rent it to Anthropic. The demand for hardware isn't really there at the scale of OpenAI.
This is the fun part: the AI bubble bursts and the price of components keeps rising. Why? Because companies can just sell you a glorified thin client and force your average user to buy their compute, all subscription-like, from data centers.
Load bearing, heavy lifting... Your comment wasn't LLM-written, either. I think we're starting to see LLMisms infect human writing. I might try to start speaking like this and see if anyone notices. It could be a good gag.
> “as long as it’s cash flow continues” is doing a lot of optimistic heavy lifting
It's not. It's stating a condition. For traditional banks, a stock crash can independently trigger a failure.
> whole premise of the circular financing worry is that Nvidia sits in the middle of all the guarantees made to companies like OpenAI. If any of those companies become insolvent, Nvidia is on the hook for it
Sorry, I meant revenues. If Nvidia's revenues stay stable, these commitments aren't a problem. Even if the stock price crashes.
> Nvidia isn’t really creating money
It absolutely is. Similar to the way banks create money [1]. The commitments support credit that wouldn't exist without it.
> It absolutely is. Similar to the way banks create money [1]. The commitments support credit that wouldn't exist without it.
Making a loan/offering credit isn't automatically money creation - the amount of money in the system before and after the loan might be the same. Haven't been following Nvidia all that closely, but it seems a little bit unlikely that they're a commercial bank. Financial chicanery they may be doing but offering deposit accounts would be new territory. The loan has to be made in a very particular way for it to be money creation (notably, in a way that creates new money), and it should be illegal for most people to do that otherwise we'd all be printing our own money instead of the printing being directed to wealthy asset owners first and foremost.
This is the way. Take it with a grain of salt (as the author mentions) but we have gotten much better results and less token usage after we hooked up our codebase to an indexing service’s MCP (in our case sourcegraph). But any indexing service will do.
DO NOT DO THIS. It is 2026, just buy a 6 or 7 A/micro/mini adapters and just keep a handful of usb-c cables. It takes up less space and is easier to organize. And your cables are now mostly up-to-date and useful PD carriers.
Using a large database of (deidentified) medical records, the researchers compared the outcomes of people taking a GLP-1 for their type 2 diabetes to people taking other common diabetes drugs between 2017 and 2025. GLP-1 users were significantly less likely to be diagnosed with TB for up to a five-year span, they found.
Both of these studies are observational and retrospective, meaning they can only show a correlation between GLP-1 use and reduced (or at least less severe) infections, not prove a direct cause-and-effect relationship. At the same time, these are the only latest pieces of evidence pointing to a genuine germ-busting benefit from GLP-1 drugs.
I wonder if the likelihood that people taking a GLP-1 are probably better off financially, have a health care provider willing to spend on the drugs (and therefore probably a better medical system) or a combination of these and other traits are the real reason there are fewer infections.
They use a method called propensity score matching to try their best to match patients on both sides using a simple linear model with various features that try to ensure that only pairs of closely matched patient histories are compared.
Unfortunately this is rarely clean. Its also easy to make mistakes. Sometimes two arms are fundamentally incomparable. The quality and rigor of the comparison is often determined by a lot of extra checks and validations, and different journals demand different levels of rigor. I need to read it carefully to judge if this is good or not.
It looks like both do standard individual covariate checks for post-match balance, with SMDs. I'm surprised they haven't assessed balance for at least pairwise interactions, too -- we should be balancing out joint risk factors too, no?
I haven't worked on these designs, but I remember the methodologist that taught me this in grad school giving us a lecture about this.
EDIT: the BMJ article (laudably) provides access to the analyis code, although I won't have time to review it:
They already do acknowledge socioeconomic (and other confounding factors) in the analysis.
The primary analysis they perform is a ‘Propensity Score Match’ which is a technique used specifically to address for confounders in observational studies, and they do report balanced cohorts.
Still they write in their discussion “Although we adjus-
ted for several available proxies of socioeconomic and lifestyle status,
direct measures of income, insurance coverage, or out-of-pocket
payment were not available in the TriNetX database. Residual confounding related to unmeasured socioeconomic factors, therefore,
cannot be excluded“
Given the size of the dataset, the effect size, significance and sensitivity testing they did I think it’s very strong evidence for GLP1s causing this and it would be very very surprising to me to see the effect disappear even if they had perfect socioeconomic data.
With so many signs of improvement, even if some of them happens to be just flukes, from so many different areas, it feels like a penicillin moment. And i suppose penicillin also was initially mostly available to the ones with better socio-economy.
I’m sure you can just look up the studies, but note that GLP1s are widely prescribed to people without weight issues —— weight loss itself was originally an off-target effect. I have friends who run marathons who are on semaglutide.
This video by a medical doctor cites many studies showing that GLP-1 health benefits go beyond weight loss (see video description for link to papers): https://www.youtube.com/watch?v=yKPaVhpomks
The benefit is probably from the removal of fat, not a direct antibacterial/antiviral effect. Fat plays a complex immunoregulatory role in human physiology: it down-regulates some pathways, while up-regulating others (notoriously, the production of IL6 is carried out, in part, by adipocytes). The overall effect of fat on the immune system, however, is negative: it tends to increase the chances of rheumatological disorders, cancers, and many other diseases. Alternatively, the effect may be due to some sociological factor that their analysis failed to account for.
Nobody's denying this, but the interesting story about GLP-1s is that after you control for fat loss (for instance, by taking cohorts of patients that aren't losing weight) you still get evidence of these off-target effects.
That's true, and it's also one of those factoids that everybody already knows but certain people can't help themselves from bringing up every time it's even tenuously relevant.
The fact that it was the top comment only shows that people agreed or liked it, not that it was actually valuable, accurate, or useful. Popularity and value aren't the same thing, correlation is not causation, etc, etc...
Most observational studies do in fact account for the first thing a random HN poster can come up with 5 seconds after reading the title. So there is very little value in such a comment.
This was published in Nature, I'm pretty sure the reviewers understand and account for that. HN and reasonably educated people always think this is some huge gotcha, second only to "CORRELATION != CAUSATION!!!!"
Nature isn't an arbiter of truth, it's a publisher of papers. It's also meant to be read critically by people who see a paper like this and and think "interesting, but this is just an observational study, I'd like to see more research, maybe something with more controls before I believe this".
Say what you want about the "correlation doesn't imply causation", but many people do not understand why that is. As a shorthand for "hey, you don't know the statistics behind this, so don't take the pretty picture at face value", it's pretty good.
I would imagine people going out of their way to take GLP-1 might also lose weight, be more active, and be also likely to eat better. Which I imagine would have a large effect on "germ busting"
I work in tech (like most of us on this website). But I’m originally from Northern VA. If you travel down Route 7, Route 50 or any of the numerous Parkways in the area you will see mile after mile of double height buildings just blighting what was otherwise a fairly bucolic area of the Appalachian foothills.
I had the same view recently. For the past year I’ve been using my iPad to remote into both my MacBook Pro M1 Max and my racked Linux workstation at home using Jump and moonlight/sunshine, respectively. Never looked back
I’m even more confused by the economics of the frontier model businesses. Even if they got preferential prices, they are still paying an inordinate amount for their infrastructure.
If it wasn’t economically feasible without VC/Nvidia money 3 years ago, how is it possibly economically feasible now at 10x prices for things like memory?
And watch … $5 says they have nothing to do with the ram because even if there was enough power there’s no use case for this much spend
If I had to guess they’re just buying up ram to keep others from having it same way meta, Google, etc hired up engineers to keep others from having them
Also imagine people having their own cheap 2TB rigs at home for their own usage/inference, or a multitude of small providers serving/sharing capacity thanks to cheap available hardware.
But we cant have that, as the big guys are hoarding everything for themselves, so that you have to pay them instead.
I don’t think there were some grand conspiracy to buy up the world’s ram to keep other people out, it is probably more likely that they just really thought they would need it.
However, I do feel like this is the type of conspiracy that unfiltered Claude would come up with.
I’ve been wondering this if you buy up all the memory you can ensure the competition cannot compete with huge investment raising the bar for competition. Of course this all sounds all conspiracy theory. Though without a place to power this or put it makes me wonder this.
It also would be a huge win for the memory companies to agree to this, considering they will make more money with less supply. Seems interesting to me.
It's possible to be economically feasible. It requires the ability to pay off the capex. Not only do they have to pay off their loans in record time (prob 2-3 years), they also have to keep spending every 3 years because the failure rate of GPUs is something like 20%. In addition they need to complete building out the stuff they've started, which means not failing to acquire land, energy, and water (the most dangerously rare and absolutely necessary resource for AI), not dealing with collective bargaining, nor any increased shortages or price hikes in materials. So there is a lot of risk.
Ballpark that they need to make around 110 billion a year, each, to break even on these investments. Let's estimate 550 billion a year in necessary profit required for the major frontier companies. That means there needs to be 550 billion of money, available to customers today, that isn't being spent on anything else, that they will now spend on AI. Maybe some of that comes from increased value, efficiency, or layoffs. But 550 billion is not a small amount of money.
Spread over the whole globe, the cash is there. But it is a hunt for cash, combined with a battle to successfully complete their buildouts, keep them running, and make bank, before the bookie comes knocking.
The railroad panics of the 19th century (and subsequent depressions) happened because they over-leveraged private capital without the ability to profit from it quick enough. So it really is a question of 1) can they really build it all, and 2) will people really pay for it all. If either answer is No, we are looking at economic catastrophe.
It's pulling the ladder up behind them. OpenAI bought up more co tracts for wafers than they could even use. But now that means everyone else has to fight over what's left. This makes it too expensive to compete with OpenAI. No upstart can really afford to build infrastructure.
But everyone is now chasing the same opportunity (AI and its dependencies like hardware and power) that will drive prices higher in those sectors until supply responds (or demand disappears).
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