After the ACA passed it's really hard for insurers to differentiate on policies because so much is mandated. They're essentially all identical products that are going to have similar prices.
Not really. They're highly regulated in their offerings, and the government mandates how much they spend on care vs. overhead. They can't effectively compete so the only option is to negotiate slightly higher payments to providers and PBMs every year so they can take more profit in absolute terms.
There's also little reason to keep iterating on nukes. What we have now more than serves its purpose. With AI/LLM there's always going to be a push to one up everyone else.
Maybe they have human shields on one end of the island and the billionaire reservation on the other. Also, note that Kushner, like Trump, is always lying. We'll only know what is actually built after it is done.
A few thousand recently minted millionaires might become former-millionaires if they decide to HODL. Construction at these Manhattan-sized data centers might slow or stop. The tech industry might go back to solving problems for real people. I'm not really seeing a substantial downside.
The only assumption I am making is that NVIDIA and others dig thier claws into western governments and make decade long contracts for even greater surveillance. Trillions of dollars worth.
There's no realistic way for the music to stop. The demand for LLMs is staggering and the big providers are charging full freight for inference. They might not make back the money from training but these data centers are definitely going to be fully utilized for at least the next 5 years.
> the big providers are charging full freight for inference.
Except they're not. Anthropic's claims of temporary profitability line up exactly with when SpaceX is giving them discounted compute, OpenAI's such a shitfest they threw the CFO off the glass cliff for daring to push back against the IPO. "Profitable on inference" is an unsubstantiated rumour.
Just look at the copilot changes. Demand switching to other providers immediately when prices rise, and there's not even certainty that the new copilot prices cover costs.
> They might not make back the money from training
This is an understatement. With all the datacenter buildout, they need trillions. For the investors get their money back and the bubble to not implode, they functionally need to unemploy everyone in the US.
Unemploying everyone was what openai described as their success condition when it was founded a decade ago. There was a q&a on their website that said "How will you know when you have reached AGI? When the system performs most or all economically valuable work." Lots of people thought they were joking, or it was marketing, but they were 100% serious from the first.
I think they've since changed that definition, but the reason for it is their agreement with Microsoft which stops granting MSFT IP rights to OpenAIs tech once they reach "AGI". So, it's in OpenAI's best interests to be able to claim "AGI" ASAP.
The pricing on Open router is clear. Anthropic, OpenAI, and Google all garner a massive premium over deepseek and qwen. There's no other realistic explanation except that they're making bank.
Why do you think Chinese companies can do that? It's government subsidising price they do it with literally every ibdustry.
Home grow a bunch discount them federally, let them wipe the foreign markets.
If AI is threatened by china why would US NOT do the same? If they did they're in a much stronger position to do so than china. Cheaper energy, more cash, stronger industries.
Infrastrucure is thr kind of thing that only a foolish US admin would let fall apart to their advesary.
This is just silly. Deepseek has published so much regarding speeding up and making cheaper inference and people are still harping on the government subsidies thing.
So what's all the project Stargate stuff? Subsidies only work when China is doing it?
Deepseek is actively sacrificing performance for cost, which is very clear in their latest model releases. They are not attempting to get to number 1 in benchmarks, and they say it clearly in their own publications.
Furthermore, being open weight, anyone can sell qwen and deepseek compute, not just Ali and deepseek themselves.
> Home grow a bunch discount them federally, let them wipe the foreign markets.
US is doing the same and was doing that for decades now. American companies operate on loss for astonishing amounts of money and consider it completely normal. One gotta love complains about Chinese companies selling under price coming from American tech industry.
And yet they are not profitable on an ongoing basis, and aren’t even claiming to be.
The supply is currently constrained because 50+% of data center plans were cancelled as a result of the impossibility of the buildouts happening in a timely fashion, and subscriptions are charging a small fraction of the actual cost of inference, leading them to all bleed money, hence the rush to IPO to get one last infusion, since many of the past investors have publicly stated they aren’t putting any more money in until they see an ROI.
Companies are hitting their budgeted limits for AI tokens less than half way through the year and reporting that they aren’t seeing enough benefit to substantially increase that budget, and so they are scaling back use and asking people to be prudent rather than token maxxing.
In the meantime subscriptions still exist in the form of chatbots and it’s easy to exceed the inference cost of the provider by simply using your daily, weekly, and monthly limits.
The reality is that we just don’t seem to be at a point now where people are willing to pay full price for the perceived value. Perhaps we’ll get there within another generation or two of hardware and software improvements.
Can 5% of the population even pay for that? Some kind of huge increase in prices for compute and inference and companies maintaining large bills for AI assistants for key employees or teams (1000-2000$) seems most likely to me.
75$ a month for a cell phone? I pay 18$ monthly for mine. This is Southern Europe where the average monthly wage is 1000$. I dont know who could afford yet another 75$ expense.
Anthropic is a five year old startup, if they can be profitable that quickly in the AI space, even if only temporarily, I'm not really seeing the problem?
These companies are going all in and growing rapidly, because they want to dominate the market and since it is difficult to differentiate between competitors, even being third place is a terrible place to be in the consumer facing AI space.
> the big providers are charging full freight for inference
They're not and it's not clear why you seem to believe that. The immense capex for buildouts, training costs, etc. are not rolled into inference costs. Moreover, companies are already rapidly starting to re-evaluate token spend.
The demand is finite. There is clear evidence that it has limits. When costs become great, the consumers set limits, create budgets and seek alternatives. Consumers are still figuring out where the cost/benefit lines are, and we can all see that the lines at least exist.
* Company valuations around LLMs are not realistic
Both can be true, much like they were during the Dotcom bubble. The internet turned out to be a pretty real thing. A couple examples below might feel familiar in the next couple months/years.
> Blucora (then InfoSpace): Founded by Naveen Jain, at its peak its market cap was $31 billion and was the largest Internet business in the American Northwest. In March 2000, its stock price reached $1,305 per share, but by 2002 the price had declined to $2.
> Broadcast.com: A streaming media website that was acquired by Yahoo! for $5.9 billion in stock, making Mark Cuban and Todd Wagner multi-billionaires. The site is now defunct.
> eToys.com: An online toy retailer whose stock price hit a high of $84.35 per share in October 1999. In February 2001, it filed for bankruptcy with $247 million in debt. It was acquired by KB Toys, which later also filed for bankruptcy.
> GeoCities: Founded by David Bohnett, it was acquired by Yahoo! for $3.57 billion in January 1999[20] and was shut down in 2009.
> MicroStrategy: After rising from $7 to as high as $333 in a year, its shares lost $140, or 62%, on March 20, 2000, following the announcement of a financial restatement for the previous two years by founder Michael J. Saylor.
I am of the same mindset as you, but you also have to look at PE multiples of Cisco in 1999 and Nvidia today. One being the "ammunition" supplier in the battle for the Internet, and the other supplier in the battle for AI.
Cisco was over 400 at one point and Nvidia is around 30. Not quite the same.
Other players today:
- Digital Realty 48x
- Equinix 75x
- CoreWeave (still losing money)
There is likely a bubble of some type here, but I don't think this is the same as the Dotcom bubble.
The circular financing aspects in the current era are really obscuring some of the financials. There are also very legitimate companies offering very real products. The big issue today is that things feel a lot more obscured and interconnected, which makes it hard to discern shit from gold. Does not help when the gold and shit are swimming in the same circles and shaking hands with all the same people.
I was expecting this comment. You know the answer. A scam will keep scamming.
There are also legitimate companies from the dotcom bubble era like amazon, microsoft, and intel. They all were vastly overpriced during the dotcom era. Probably also now lol.
Here's my theory about the dotcom bubble. The market correctly identified the internet as hugely valuable and correctly identified search engines as being able to capture a large share of this value. Consequently early search engines, chiefly Yahoo!, obtained (merited) high valuations. What Yahoo! did with their stock (they IPOd in 1996) was go on a big startup buying spree. This is what actually started the bubble: invest in some random dotcom crap company in the hopes that Yahoo! swoops in, buys and you get a big payday.
What caused the crash was Yahoo! being unable to do anything with their acquisitions and Google coming out with a better search engine, undermining Yahoo!'s core product. Google basically pulled the rug from under the dot com bubble.
The situation we're in now with LLMs is different, if I'm right we're actually pre-bubble, the bubble hasn't even started yet.
Data center operators are in the business of selling electricity. They do not command large PE multiples. This is an even worse business, because xAI decided to also be the bagholder for the NVIDIA graphic cards. Not to mention they finance an unreasonable number of 20-somethings on way too large salaries with shitty opinions and no AGI delivered.
This take clearly has a bone to pick. But ignoring that, the first sentence is just not reflective of the reality here—xAI is making a killing on renting out its GPUs, way more than "just power". The dynamics that normally make infrastructure providers have slim margins don't apply when demand far outstrips supply; the situation right now is closer to monopoly pricing power.
It will likely take a few years for supply to fully catch up, which means xAI will eat well for a while.
I can see a world where a few data centers come on line this year and reduce margins a bit, but it's crazy to think the margins will go to "cost of electricity plus a few percent" anytime soon.
In the article, it states that the two deals will cover the entire cost of SpaceX's AI buildout in 18 months. OpenAI and Anthropic would kill for that kind of cashflow.
xAI is a failure of an AI company from a consumer perspective. They invested a large amount of money into owning their own infrastructure, while driving away consumers with their right-wing or "alt right"-ish branding and having a reputation of X users abusing the AI services.
Turns out there was another company with a much better reputation for which the compute is a better fit. Now that the data centers are being put to use, they actually make them a little bit of money instead of losing money.
That story roughly tracks the one I hold. One piece that's missing is that grok's / X's image also made it radioactive to the best researchers. 'Aligned AGI' is an easier sell to the best engineers than 'abusive neo-Nazi chatbot with a porn problem'.
Datacenter operators who rent space are selling electricity. SpaceX is selling a fully built datacenter with compute designed for a specific purpose. They’re operating at a higher level of the value chain and can charge accordingly.
What's their novelty or moat to maintain the value chain? And why do we only see google, who already owns it, raising their hand to rent at these prices?
I’m not sure they need novelty or moat. AI compute resources are so scarce that inference providers will buy whatever is available. SpaceX sells inference hardware in bulk, with a proven track record of running inference and training workloads at scale.
What? They make money from their own inference and models too, which they can train effectively for free by funding their operations with rental income from their last gen datacenter.
SpaceX and Tesla used aggressive vertical integration, manufacturing simplification, and reuse to radically lower the cost of building rockets and EVs. It's not unreasonable to speculate they might be able to do the same for hyperscale compute.
They're not any sort of bag holder. They're going to make back what they spent on these data centers in a year.
It's a fairly sweet deal for everyone involved. Anthropic/Google get to sell more tokens and xAI gets a war chest for another bite at the apple. I don't have much confidence that they'll do anything with it but that doesn't mean these deals don't make sense for them.
There were a ton of programs after WWII to improve the nutrition of the country. This largely meant raising calories to prevent malnutrition. And the 80s are as good a point as any other to where that succeeded.
An amusing thought but I mean the potato salad deli stuff. Anything prepared on site (in a store or restaurant) doesn't need to disclose ingredients. There's no special exception for bread.
Personally, I prefer my bread to not have plastic labels on it. Besides on the bread itself, where would you even put such a label? The sleve-bags for bread are used for more than just one specific bread, so can't put them there either...
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