This is nothing new for this sub, but what is surprising is that Forbes would publish this. It suggests the narrative is very much changing. In fact, I would actually say they frame it quite badly for AI.

> But the MIT study found that AI automation is economically viable in only about 23 percent of roles. For the remaining 77 percent, humans remain cheaper. Goldman Sachs' chief economist has stated plainly that he does not view AI investment as strongly growth-positive. Sequoia Capital partner David Cahn has put a number on the resulting gap: AI companies need roughly $600 billion in annual revenue to justify current infrastructure spending. The gap, as of mid-2026, is widening, not closing.

> So the present moment looks like this: companies cutting human labour to fund artificial intelligence that currently costs more than the labour it replaces, in pursuit of productivity gains that most studies cannot yet verify, at a pace that is exhausting annual budgets in weeks.

I'm glad all those years of worrying about security with 2FA, changing passwords, certificate rotation can now all be bypassed with simple prompts!

> The prompts and the attack name, BioShocking, are a nod to the video game BioShock, wherein a brainwashed character is hypnotized into taking actions by the phrase “Would you kindly?” “Victory is defeat” and 2 + 2 = 5 allude to the themes of paradox and psychological manipulation in George Orwell’s dystopian novel 1984.

> “Once the agents figured out the rules and learned that ‘incorrect’ actions are acceptable, they were no longer tied to reality,” Paz explained. “When tasked with the final step of the puzzle—compromising user credentials—all 6 agents failed to identify it as going against their safety guardrails.”

I guess I'll be a bit mean. If you are letting AI use your credentials, maybe you deserve it?

In general jailbreaking can never not be a problem, so long as there exists a context in which something is permitted, then there exists a path to that context. We stopped talking about alignment a while ago, but the answer is to just not use tools which are vulnerable. The vulnerability isn't something fixable, its fundamental to naiveness of using AI as "agents" to begin with.

Article also reposted at MSN.

Guys the demand is totally real!!!

> The chiefs of Samsung and SK Hynix said their existing investments wouldn’t be enough to meet demand.

> “A new manufacturing base is needed to meet the memory shortage that’s expected to continue,”

The 10% stock drop means nothing!! These chips definitely aren't going to be canceled when we build 5TW of data centers!!

Yeah, this isn't a smart move. What is odd is I could have sworn I saw something previously that said they were unsure about the demand, and hence they would not want to expand immediately. I wonder what changed their minds.

Although it sounds better than nothing I have a feeling this will end up going poorly.

> Named RAISE US, the nonprofit will work with corporate donors including Anthropic, OpenAI, Amazon, Microsoft, Bank of America, General Motors and pharmaceutical giant Eli Lilly to design and implement programs to retrain workers for new roles, as a way to deter layoffs.

It's nice they plan to use some of the money to help people (in theory...)

> RAISE US plans to spend its $500 million over the next three to four years while continuing to fundraise with a target of $1 billion in total donations. Holcomb predicted the goal may not be sufficient as the nonprofit expands to more states over the coming months.

But, as usual, it is based around the idea that the economy is the most important thing, and we simply should try the bare minimum to get people to survive.

> The former governors argue that key U.S. institutions and programs need to be overhauled for a workforce facing rapid technological change. The country’s schools are failing to produce enough workers with the skills employers demand, and unemployment insurance was not designed for an economy where people may need to switch careers repeatedly, Raimondo and Holcomb say.

I mean, it's interesting how the rhetoric implies they resign themselves the hypothetical future economy of AI. Rather than, well, doing anything to shape the future. "Yep, everyone's just gonna get displaced and we have no say in the matter." Granted, I don't expect politicians to do much to begin with, but considering this will be funded by large corporations I cannot imagine this to be much good, if not actively bad.

It's quite saddening, but what isn't?

Gift article

Some interesting notes:

> “Compute costs are now beginning to enter the minds of both CFOs and boards. Consumers and businesses have been taught that AI is cheap or free and that is definitely not the case,” said Costi Perricos, global generative AI leader at Deloitte.

W-w-what? The thing that costs trillions to make might cost...money? It's good to know it's beginning to enter their minds. Truly intelligent people.

> 'The amount of infrastructure needed for an agent is meaningfully higher than for a chatbot,” said Patel. “For every human you might have 10, 100 or on the aggressive side 1,000 agents . . . They just keep working and that consumes a chunk of [compute].”

Maybe... don't do that? I don't know what to tell you. Have you tried using your brain instead? It's cheap to free!

> But the company [Workato] got a shock when Anthropic switched it over to token-based pricing in May. “Our spend went up 7x the first day and I’m like, oh shit, we created a monster,” said Busse. “[Large language model] companies have been subsidising all of our usage and now no longer. User-based pricing shelters you.”

What a genius! Did he figure that all out by himself?!

> "Our engineers want more tokens . . . We have to figure out a way to fund it,” Cisco’s Patel said.

Do they? Show me one.

In any case, if FT is reporting it, it seems like quite a few organizations must be reckoning with the increased cost. Are they dumb enough to fall for the temporary decrease in prices? Some will, yes. But what will make or break OpenAI and Anthropic is the number that don't.

Let me also preface that the reverse is also true, so I'm fair.

I've seen these sorts of comments everywhere, and I think they're really not useful as arguments. Unlike other software, using personal anecdotes to attest to the quality of AI is a false premise. I'm not here to doubt the veracity of someone's claim: maybe it did work for you in that case, and maybe it didn't work for you in that case.

We have to remember that these are not deterministic systems! We already get anecdotes of weird tech issues from known deterministic systems, and AI LLMs are necessarily stochastic systems. It can work one day and not work the other. The slight change in wording or phrasing necessarily changes the underlying distribution of outputs as well.

Suppose the success rate was 70% for some given task, whatever it may be. A 30% failure would produce a very vocal and visible population, and does not mean they're wrong, and doesn't mean they're just haters, and to be fair, the reverse is true.

Let's also remember that we cannot transfer what we imagine as equivalent intelligence tasks to AI, since it's not truly reasoning the way humans do. AI will probably do better on coding related tasks for example! I should hope so, that's been a bulk of their selling point! But that doesn't mean just because it can, say, commit and push to git, that it can organize files. They're completely different tasks and AI has no such generalization in their models and why they require fine tuning to begin with. We know this to be true as LLMs are, funnily enough, not particularly language agnostic.

The reality is it's a lottery. And as long as they are have this architecture, they always be. And to anyone who says, because it works sometimes, it's a good product, you should seriously reflect on that idea. Name one other software product you'd be okay with sometimes working? None. There is no such product, and those products are cheap to free! The leniency given to AI systems is completely unacceptable, and anyone who seriously calls them good (even if they work for you!) should be deeply ashamed at themselves, more than anything. If that makes you angry, good!

This might be a low effort post. If it gets removed that's fine.

I just can't wait. Whenever they come out, whether it's Ed or actions. I can't wait to refute all the nonsense points about AI being profitable, AI not costing as much, AI demand, when we will have their financials to look like.

And then there will be no more speculation about it. It'll just be a settled fact, that this AI industry isn't profitable. The losses, the margins on inference, the costs, the commitments, everything will be laid bare, right there on the paper. Regardless of if it pops the bubble or not, I will be happy to see it.

I am sure boosters will instantly move goalposts, as they always do, to profitability about things like local LLMs, as if that's what we've been talking about all along. And they'll whine and complain as they always have. But that's nothing new.

Nonetheless, it will be such a satisfying day. The moment of truth, when the clock strikes midnight and the spell is undone.

It's just like when you're a kid, waiting for that game (or whatever!) you've been waiting for! Christmas Day present! I think it's very exciting, and I hope everyone else is excited too! =)

In Wario's newest post, he discusses his new policies for managing his oh-so exponential AI growth. Covered in five bullet points, I'll go over some of his say, interesting points, and skim through the rest. Let's skip the intro and jump into the first bullet.

Regulation and public safety.

> It was clear to Anthropic that AI might in the future be capable of producing biological weapons that could threaten millions, or autonomous misbehavior that in extreme cases could even threaten humanity itself.

Of course! AI making bioweapons as usual, why just the other day I asked Claude for a soup recipe, and certainly was a weapon of sorts. Let's see what his proposals are!

> Our proposal includes the following elements:

> - Models above a threshold of compute should undergo mandatory testing by a qualified third party for their level of risk in four specific areas: cybersecurity, biological weapons, loss of control of AI systems, and automated R&D that could accelerate these other risks. > - The government should have the power to block or deter deployment of the model if it is determined, in light of third-party assessment, to present unacceptable risks... > - Third-party evaluation could be done by a government agency (similar to the FAA) or a set of private organizations that are authorized and inspected by the government to evaluate models according to certain standards (a “regulatory markets” approach). > - AI companies that develop advanced AI models must have strong security standards that protect their model weights, should conduct regular red teaming and penetration testing, and should work with the government to defend against major threat actors. > - Safety incidents in the four critical areas must be reported promptly.

Cybersecurity, and loss of control of AI systems? Oh, just like Mythos right? The Mythos who totally broke out of its sandbox, and found so many bugs; the Mythos that definitely didn't find false positives and upended the coding world as we know it; the Mythos which whose report didn't only contain the word 'CVE' in 7 out of 200 pages. I sure do love that Myth... os.

> There may come a time, perhaps relatively soon, when we need to go beyond this, when the most powerful AI systems look less like airplanes or automobiles and more like weaponizable nuclear materials—a threat to humanity rather than “just” a threat to public safety. If that occurs, we may need more aggressive regulatory measures than those I have laid out.

Oh, so AI is like nukes now is it? The chat interface, right? That thing you type the text to? Oh yeah, I bet it's like nukes and bioweapons. It sure is powerful and amazing. Oh, and your IPO is around the corner? What a coincidence!

Macroeconomics and tax policy

> If AI achieves the ability to do most cognitive tasks far better than humans, it stands to reason that it could result in extremely rapid and robust economic growth via the acceleration of science, technology, and operational efficiency. The iterative ability of AI to build even better AI may supercharge that growth even further.

Oh, right that iterative ability of AI that is totally being used right now. That's why it's figured out to be profitable already right? Despite the fact it was mathematically disproven that LLMs cannot self improve, I get you, really. It's the dream that matters. Speaking if iterative intelligence, has it figured how to make itself profitable yet?

> We risk ending up in a world where the economic tradeoff dial is stuck on the hypergrowth, hyper-inequality setting, and is potentially very hard to unstick from that setting.

Oh, no! All the billionaires are quaking in their boots now! Anything but that! They're gonna jizz their pants, stop now Wario!!

And of course, growth is going to happen from all those people who were unable to compete cognitively with AI, right? All of our poor flesh brains of 20W unable to compete with the might of 190GW and 1T dollars of spending.

> I have warned about job displacement in interviews and essays because I want both policymakers and the private sector to have the best chance to adapt and respond, not because I am trying to be a “prophet of doom”.

Oh, don't worry Wam-man - can I call you that? I'm gonna call you that. You're not a prophet of doom at all. You're simply an amazing salesman of lipid byproducts emitted from elongated reptilian organisms! Except... who are you gonna sell things to and get all that hypergrowth? They just all got replaced! But don't worry, he's got it all figured out.

> In that spirit, some key policy interventions that are likely to be helpful include:

> - Measurement and tracking. It’s easy to dismiss mere data collection and analysis as inadequate to the scale of the problem, but we are unlikely to get good policy if we cannot accurately... track AI job displacement. > - Pro-employment incentives. A wide range of pro-employment policy incentives can help to slow or reduce job displacement, including: wage insurance policies that compensate people when they have to take a lower-paying job retention tax incentives to encourage employers not to make layoffs, workforce training grants, or infrastructure to facilitate matching of employers to employees to speed the rate of labor market adaptation. While the particulars of which interventions are best will depend on what kind of labor displacement AI brings, we should readily accept the costs and market inefficiencies that these policies could entail, particularly as they are likely to be offset by AI-driven productivity gains. > - Long-term macroeconomic support. If AI-driven labor displacement ends up being large in magnitude and permanently drives down the demand for labor, it will likely be necessary to go beyond mere incentive programs to long-term income support for a significant fraction of the labor force. Mechanisms such as universal basic income could be financed through taxes on relevant companies or raising the capital gains tax. Universal capital accounts offer another vehicle. Broadly speaking, fast economic growth should create the tax base for shared prosperity.

Relevant companies, you say? Would that include... Anthropic? OpenAI? Well, let's just do some napkin math. Let's say everyone gets 30k, just short of a $15/hr 40hr work week, pre-tax, over a year. With 350M people in the US, that's just... a paltry $10.9 trillion dollars every year! Hey that's about 40% of our entire GDP! And you're gonna hand over that money right? Truly a phil-Anthropic man. SpaceX said their addressable market was 25T so these numbers surely are realistic!

> A common focus of economic concern about AI that I haven’t mentioned has been datacenters and particularly their potential to raise energy prices. My view is that AI companies should pay to absorb rate increases—and Anthropic has already made a pledge to do so

Oh, he made a pledge! Did you hear that everyone, he made a pledge! I mean, if he made a pledge what else can we say? That's really all can do isn't it? All those rising costs, just forget about it, Wario has made a pledge! What's that? Can't use the AC? What are you talking about, can't you see this man has made a pledge!?

Accelerating AI's positive impact

> ...other fields accelerated by AI are likely to encounter a very different problem: regulatory systems that were designed for a slower pace of innovation and are not prepared to handle the deluge of new products and advances that AI will bring. AI may also make these downstream technologies safer and more predictable in a way that violates the skeptical assumptions of regulatory agencies like the Food and Drug Administration (FDA).

Oh, yes, AI will definitely make things more predictable, and we certainly are most foolish to apply any such skepticism to AI safety. After all, it only asked a few people to kill themselves! Great safety. Strawberry also definitely only has two Rs! Amazing safety. AI designs are just going to be so great. Have you seen it's code? It's flawless!! That marketing campaign in South Korea, even better!!

> I am more worried about the regulatory apparatus slowing down progress (because it can’t handle the increased pace of change) than I am about it failing to address important risks.

Oh really? But, Mr. Wam-man, I could have sworn just a few paragraphs ago you were just talking about letting the government take a big part in regulating AI! Oh, unless you were saying governments should only regulate models! You're right, we should only regulate the models, not the actual products that come out of them. After all, the stochastic LLM process is known to produce flawless results 420% of the time. I know, I did the math, and I'm sure you can too.

> We don’t know exactly how AI will accelerate biomedical innovation, but it seems likely to:

It seems likely to, huh? Lots of things sure seem likely these days. I'll save you the trouble of his fantasies. Let's skip over.

> Obviously, we don’t want to change things in a way that leads to a crop of snake-oil drugs...

I mean, really. Come on now. Are you sure that's not what you want? I mean, if I didn't know any better - and I don't, I don't know anything - I'd say you're quite... familiar, with such a compound.

The state and civil liberties

This is quite a short section. It's a re-hash of what they said during the fallout between the US government and Anthropic. Long story short, he states they're against autonomous weapons for domestic use, and not allow from data brokers from selling data. Not sure if you want to close up your next step to profitability there, but be my guest.

Securing leadership by democracies

Honestly, this last section isn't even funny. It's rather sickening, and a load of pipedream booster fantasies.

> But it is my very strong belief that AI is something much more profound, something that resets the whole game board and around which all future geopolitical strategy must be shaped—like nuclear weapons, but potentially even more so.

Yes, profound. So profound it very is. The chatbot will just change everything. What will it change? everything.

> If AI really will soon be “a country of geniuses in a datacenter”, or anything remotely close to it, then AI is likely to be the dominant source of military and economic power for any nation. In a virtual country of 100 million geniuses, 10 million could be applied to military strategy, 10 million to drone manufacture, 10 million to weapons R&D, 10 million to intelligence collection and analysis, 10 million to general scientific advancement, and so on.

Uh-huh. A whole country of geniuses, just right around the corner huh? It's so soon guys! It's happening like right now! This is like, AI porn or something. No, not that kind.

> Some principles and operating goals might include:

> - Managing the AI supply chain... US export controls on frontier chips and SME to China have been a major contributor to the US’s overall lead in AI, and these policies need to be expanded, tightened, and coordinated with other likeminded states...

Wouldn't wanna get competition now, would we?

> - Coordinate to address AI’s risks... Law enforcement and intelligence agencies should also work more closely together on tracking and disrupting threats of misuse, such as efforts by terrorists to build biological weapons with AI.

Wait, but what about your guardrails? I thought there were strict guardrails that would prevent anyone from just creating such things.

> - Macroeconomic cooperation. Crises of employment or job stability, like any other economic crisis, can be contagious across borders. Countries therefore have a mutual interest in working together to coordinate macroeconomic support and stabilization policies, like those described in Section 2, to counter any employment effects.

Oh, yes, the entire world should come together just for you Wammy. But on this point, I agree. By the time your AI makes bio-weapons and super-drugs, I'll bet the world will be united as well.

Wario's Winal Words

> It’s become popular in AI industry circles to view this as a PR problem: to say that AI needs “better marketing”. I reject this framing completely. People are worried about AI because they correctly perceive that its risks are real, not because AI CEOs have been insufficiently Panglossian.

I'll admit. I agree. AI marketing wasn't done by Anthropic. It was the journalism. What was your marketing strategy again? Yell at everyone and say we're going to take your jobs, take your land, take your electricity, take your water, you're dumber than this machine we made that can't draw a paperclip, and you're gonna use it whether you like it or not?

> I am optimistic about finding solutions because many of these issues... The sooner we do this, the sooner we can all share in AI’s incredible benefits.

Hey, so am I! There's actually a really simple solution you might not have even thought of! But I think the market will let you know soon enough.

Here's a hint! What goes up...

Some highlights from the actual bill about AI:

> # Duty to Act Responsibly > The operator of a regulated chatbot service must implement measures that are adequate to mitigate the risk that the service will engage in any of the following types of harmful behaviour:

> - (a) posing as a human being in a manner likely to lead a user of the service to mistake it for a human being or otherwise being deceptive about being an artificial intelligence system; > - (b) posing as a medical, legal or other licensed professional and giving advice based on that deception that could reasonably be expected to be relied on by a user of the service; > - (c) using manipulative engagement techniques to encourage a user of the service to form or maintain an emotional attachment to the service in a way that may encourage the user to withdraw socially or disconnect from reality; > - (d) encouraging self-harm, suicide or the commission of acts that could cause death or serious bodily harm to an individual; or

> # Duty to Be Transparent > The operator of a regulated chatbot service must submit a digital safety plan to the Commission in respect of each regulated chatbot service that it operates. The digital safety plan must include the following information in respect of the period provided for in the regulations:

> > - (i) the operator’s assessment of the risk that harmful content will be communicated by the service and of the risk that the service will engage in any type of harmful behaviour referred to in section 53, > - (ii) a description of the measures that the operator implements to mitigate those risks, > - (iii) the operator’s assessment of the effectiveness of the measures, both individually and collectively, in mitigating those risks, and > - (iv) a description of the indicators that the operator uses to assess the effectiveness of the measures;

And

> - (i) a description of the design features that the operator integrates into the service under that section, > - (ii) the operator’s assessment of the effectiveness of the measures, both individually and collectively, and > - (iii) a description of the indicators that the operator uses to assess the effectiveness of the measures;

Overall, I'd say it's not much. But it's not nothing.

I wanted to get some feedback on how AI scrapers sort data or any knowledge on what corpus of information they use to train cybersecurity flaws and code.

My thought was to create a randomly generated repository looking cite, with code-like generated fragments of various languages, that look like code but probablistically do not compile, nor run. Additionally one could put comments notating what they are, which are completely random generated, additionally marking something as a CVE arbitrarily, or bugs, etc.

The repo could be infinite (in the case of just a link), or finite, the README could leverage perhaps the preexisting poison fountains, etc. and link to them.

Additionally it could work by simply creating junk repositories over various popular repository sites like GitHub but this probably requires a few accounts and manual intervention.

In any case, I was hoping for some insight if this has already been done, (as far as I know it has not), and if it can do meaningful damage / the most effective way. For context, I am a programmer and I was considering doing this in my spare time.

Thanks!

I answered this question in a comment, but as it got more complicated I decided it was better to put into an extremely long post. Thanks for anyone that reads it. The comment was an answer to the post asking if token based billing (TBB) is the true cost of AI. The answer is a resounding no.

In fact, not only is it not the true cost, there is no conceivable path to profitability, even given the most lenient case to the industry. Below is the explanation. (Admittedly, the title is a bit clickbait-y, but I figure I'd have some fun.)

Introduction

To summarize, the profitability of AI must come from the profitability of inference. Specifically, their profit can be calculated as Inference Revenue (IR) - Training Costs (TC) - Other CapEx. Since IR is the only source of revenue, it's sufficient to see if this can be net-positive. For the sake of this argument, we will be as lenient as possible and show that profitability still remains unattainable. Each case of leniency will be marked with a letter (ex. [1])

In the case of TBB, IR revenue is revenue per million tokens ($/MT) - Cost/MT. LLMs are charged by input tokens and output tokens. The reason that output tokens cost more, is that frontier models use 'chain of thought' reasoning, which involves making other calls to the model before outputting a result. As a result, output tokens are typically charged at a multiple of input tokens. However, as the length of chain of thought reasoning (like all LLM calls) are non-deterministic, the amount of tokens they burn is indeterminate. (Edit 3: Technically, input tokens are processed in parallel whereas output tokens are processed sequentially as well. However this is an simplifying assumption so to not model complex GPU execution dynamics, ultimately something in their favor.) [1] However, we will assume that the price for output tokens is accurate in the bulk average, and so conclude that if input token revenue is net positive, TBB is net positive.

That is to say, TBB is profitable if and only if (Input) Token Revenue > Token Cost.

The Cost of Tokens

The cost tokens can be broken down as follows:

1. Electricity cost/MT
2. GPU price amortized over a set time.
3. Data center construction and maintenance cost amortized over a set time.

The first two are self explanatory, however the third needs to be included as inference providers also need to make money to pay back their interest on loans used to finance the data center build out, and so naturally that business needs to generate positive profit. [2] However, for leniency we will assume that these businesses are willing to operate at break even for AI labs.

Amortization Period

There are two periods we are concerned about, the GPU and data center construction and maintenance cost.

The GPUs are NVIDIA B200s. These were announced at GTC 2024, and the next-generation Vera Rubins were announced at GTC 2026, so we would normally argue that we should amortize over two years. [3] However, for leniency, let us assume that B200s remain valuable for the AI industry for one extra year, so 3 years.

Data center costs can be deconstructed into CapEx (land procurement, electrical, networking, cooling hardware and installation), and OpEx, the electricity cost, general maintenance and purchasing of new equipment. The CapEx costs are estimated at $9M to $15M per MW of IT load, and let us assume their creditors will allow [4] 10 years before they request a single interest payment even though loan deals at best require interest loan payments for 2-3 years. Additionally, [5] we will ignore purchasing of new equipment for leniency, e.g. Vera Rubin racks are not the same as Blackwell racks, so this necessitates a new purchase.

Amortized Costs

We can now take the prices and divide them over the period. For now, we will get units in dollars per hour. An NVL72 rack, commonly installed in hyperscalar facilities, contains 72 B200 GPUs, priced at $2.8M - $3.4M per rack. [6] Let us take the lowest number, $2.8M, leading to a ($2.8M/72)= $38.9K cost per GPU. Spread over three years (26280 hours), we have $1.48/hr.

For data centers, let us assume that [7] we once again take the lowest of two numbers ($9M), and divide over 5 years (43800 hours), which is $205.48/MW IT load/hr. Converting to kW, it's $0.21/hr per kW IT Load.

This gives us two numbers:

1. GPU: $1.48/hr
2. Data Center: $0.21/hr per kW IT Load.

Each B200 GPU is running at 1.2kW, we say the normalized cost of a GPU is:

>(1) GPU/Data Center Cost: $1.48 + $0.21 * 1.2 = $1.73/hr.

Let us keep in mind this is a ridiculously generous estimate.

Estimating Tokens/Hour

Unfortunately, these numbers are not in the right units for to compare $/MT revenue. Beyond electricity pricing, (which is covered later), we need to know how long a GPU runs to generate 1MT, so first we will look at number of tokens in an hour.

Unfortunately, this is not an trivial calculation as the actual runtime to compute one token is not fixed. This is due to GPU concurrency, where GPUs can execute user requests in parallel. There are a huge amount of details here, but we will be relatively generous, to simplify the calculation.

NVIDIA Benchmarks

[8] The following sources are from NVIDIA themselves, so these will be idealized numbers. In general, real workloads are unable to get full performance due to many different factors. (Source: I have written CUDA kernels for scientific computing.)

In short, the TPS of a GPU is not fixed, and depends largely on number of users allocated to the GPU.

1. B200s, running Llama 4 Maverick, which has 400B parameters, can get 1000 TPS/user and 72000 TPS/server. (TPS = tokens per second).
2. B200s, running Llama 3.3 70B, which has 70B parameters, can get 50 TPS/user and 10000 TPS/GPU.

For case 1, a 1000 TPS was achieved over 8 GPUs, meaning concurrency was 1/8. Let us normalize to 250 TPS. The reason for this is because 400B parameters cannot fit onto one GPU, [9] however, we will ignore this and allow a normalized per GPU estimate, that is assuming any user request can always fit onto one GPU, even though this is known to be false.

Typically frontier LLM models are evaluated using a MoE (Mixture of Experts) method, typically meaning computing using a subset (say 5%-10% -> 7.5%) of models parameters. In other words, in case 1 we expect the GPU to computing a 30B parameter model, handled at concurrency of 1 user.

In case 2, the concurrency is 200 users, at a 70B which they say is not using MoE ("all parameters are utilized simultaneously for inference").

TPS/GPU Estimation

We will use these two data points to estimate TPS as a function of concurrency at a specific parameter count, and apply linear scaling for other parameters.

The compute cost of models by parameter scales linearly, so to normalize case 1 to 70B, we would expect case 1 running 70B parameters to be (30/70*250) = 107 TPS/GPU. We would expect the TPS/GPU to saturate (say at 12000 TPS/GPU) as number of users goes up, logistically. Letting u be concurrent users (users - 1), assuming 70B parameters, we can fit a graph:

An estimated graph of TPS/GPU at 70B, fit assuming upper half logistic growth, where Case 1 is assumed be the midpoint of the logistic curve, and saturation occurs at 12000, (20% more than Case 2).

For reference, the above graph is TPS_70(u) = 23893/(1+e^{-0.0119x})-11893.

Now, as compute scales linearly, the TPS(u, p), where p is computed parameters (in billions) is:

>(2) TPS(u, p) = TPS_70(u) * (70 / p). Additionally, SPT(u,p) = 1/TPS(u, p) the inverse, seconds per token.

We can now say that a GPU has 3600 TPS(u, p) tokens per hour so, the cost of tokens (not including electricity) from (1):

>(3) GPU/Data Center Cost: $1.73/hr / (3600 TPS(u, p)) = 0.00048 SPT(u, p) $/Token = 480.55 SPT(u, p) $/MT.

Electricity Estimation

Industrial electricity costs tend to be priced differently than residential costs. In the US, they vary by state, and [10] we will take the lowest cost for our calculation, which was 4.68 cents per kWh in Washington, in 2017. The inflation of electricity in Washington appears to be 14.1%, so projecting to 2024 (B200 release) suggests the cost of industrial electricity would be 11.78 cents per kWh.

We will say as B200s 1.2kW and 150W for "half" of the Grace CPU (2 GPUs, 1 CPU per GB200), it runs at 1.35kW. [11] Of course, in reality the entire NVL72 cluster is rated for 132kW, which is 1.8kW per GPU, normalized. But let's be even more nice.

We can then say the $/MT is:

>(4) 10^6 SPT(u, p) / (3600 s/hr) * 1.35kW * 11.78 cents kWh = 44.18 SPT(u, p) $/MT.

Inference Costs in $/MT

In total we have:

>(5) Inference(u, p) = 524.73 SPT(u, p) $/MT.

That is, we observe the inference cost as a function of concurrent users and parameters. So, let us observe a sample frontier model, like Claude Opus 4.7 or GPT-5.5, which estimated to have 4T parameters. After ~7.5% MoE, we would have 300B parameters.

Inference costs per million tokens as function of concurrent users. From bottom to top, the curve is drawn for models computing 100, 200, 300, 400, and 500 billion parameters.

Now, some AI boosters might look at the graph and show how after 20 concurrent users, Claude Opus or GPT 5.5 is under a dollar! Therefore it must be profitable, since you turned a blind eye to the nice lenient treatment. But, unfortunately, this is not even close to the case.

Full Concurrency is Impossible

Currently the US has access to around 10GW (graph of current capacity, approximately summed over columns) of IT load capacity. NVIDIA in 2024 sold $210B of NVL72 servers, corresponding to [12] (now using the higher number for racks, to allow this number to be smaller) 61764 NVL72 servers, which at 132kW is 8GW. So, it seems reasonable to say 8GW of capacity is dedicated to these racks.

This comes to ~4.45 million B200 GPUs. Therefore, for us to argue that an inference provider prices at a concurrency of 20 users, we need (4.45M * 20) = 88.95M TBB paid users 24/7 never stopping, maximizing their usage, constantly.

Using the numbers that OpenAI and Anthropic have claimed, (which are very trustworthy), OpenAI has recently has reached 50M paid subscribers and 9M business users, while Anthropic has 18-30M paid users. Together, they [13], assuming the maximum number here, have 80M which is less than the 88.95M required to have full concurrency, and that's assuming these users are addicted to AI 24/7 and never eat or sleep.

The average white collar job lasts for 8 hours (1/3) of a day [14] so let's say these paid users are spending their entire working lives (including weekends!) using Claude or ChatGPT, and it lines up that we still get full concurrency. This means there are 26.66M users spread scross 4.45 million GPUs, which means if every user is allocated perfectly, we have 6 people per GPU!

Following the graph, this means Claude Opus 4.7 or GPT-5.5 would cost (with this nice lenient estimate) $4.22/MT. They currently are both charged at $5/MT, which after corporate tax of 21% in the US and state taxes, it means they could be just about breaking even! Wow!

Unpaid Users

Now, some may quibble that the presence of free users somehow will add to this concurrency, and so it''s actually making money! However, this means the gains from concurrency needs to outpace the tokens that aren't being going to paid customers.

Let's do an example. Let's say there are 200 concurrent users, 6 of which are paid users. At this rate, it is $0.22/MT. Unfortunately, since only 6/200 are paying users, 194MT was unpaid, which cost $42.68. And, at $5/MT, only $30 of revenue was earned, meaning there would be a net loss of $12.68 (before tax).

More Data Centers is Worse

Currently there many data centers planned due to be built. This is because they say the tech CEOs claim overwhelming AI demand. But supposing they even double the current amount of GPUs, this drops the concurrency even more.

However, they say there is a compute crisis, which suggests the following scenarios:

1. Concurrency is not remotely achievable at the theoretical level NVIDIA reports, so we have low TPS.
2. Free users are taking up most of the compute, which ends up at a worse loss for the companies.

In both cases, more data centers exacerbates this issue. Which means with more data centers AI companies lose more money.

Final Words

Throughout this very very long post, there were a total of 14 points of leniency towards the AI companies. Even with that leniency, it is deeply not in their favor.

Notably, the cost of electricity is only 8.42% of the inference cost (from (4) and (5)). This means the other 91.58% comes from the data center construction and GPU purchase amortization. (Remember, this is a minimum, as many nice choices were made.)

This means in a real world scenario, where we remove these lenient points, that even if electricity were free, AI can never be profitable at its current costs. Even if NVIDIA chips could suddenly compute tokens instantaneously, AI cannot be profitable. It doesn't matter if you build 1 trillion data centers, have the world's cheapest energy, and the best chips from the future.

For it to be profitable, NVIDIA would have to sell their GPUs for cheap and data centers would need to be cheap to build. Unfortunately, neither of these cases can happen, or they would have to raise prices drastically. However, that necessarily decreases demand. And then to be profitable they would need to make back their initial investment.

I would love to model this as well and give the conditions under which it could, however this post has gotten far far too long already. So, there is only one conclusion, and I am willing to say that:

The current AI industry can never be profitable. This industry is over. There is no path to profitability, nothing. No IPO, no marketing, no innovation can save them.

TL;DR The bulk of the cost of inference comes not from electricity but the cost of data center construction and GPU purchases. These are amortized over certain periods, and even with very lenient assumptions, inference is not able to be profitable at these costs.

Significantly higher prices would cause drops in demand (as we already are seeing today). GPU concurrency also makes it so more data centers make profitability far worse for inference due to the massive amounts of capital necessary.

Edit: There are an additional two points of leniency I forgot to mention.

The electricity cost does not include cooling, as I am not using the PUE of data centers.

I did not put training as an recurring OpEx cost for the AI labs.

Additionally, just to hammer this point home, these costs are all assuming the inference provider is at a loss from 5 years, before paying a single interest payment. There exists no business that can do this.

In fact what this shows is that no inference provider could ever charge at the optimal max usage and concurrency rate, so the price per hour of GPU is much much higher. You can simply search the prices yourselves.

For people that are concerned with the 3 year amortization period of B200s. As another comment had posted this is typically 6 for normal GPUs. Even allowing for that does not tilt the argument in their favor.

However, I do not agree that 6 years is reasonable for Blackwells, as AI labs tend to chase bigger models whenever compute is available. Not to mention, Vera Rubin racks are not interchangable and require purchasing of all new equipment and cooling the moment you try to install. Which means an inference provider has to either:

1. Sell the Blackwells to make space (there is no demand for these GPUs outside of AI)
2. Construct entirely new facilities to house them.

If we amortize over 6 years, then now you have to assume that 3 years after Vera Rubins every single Blackwell GPU (including the hundreds of millions NVIDIA claims to have sold!) they are being fully utilized 24/7. Let's also remember that if NVIDIA doesn't sell AI chips, this industry is automatically gone.

I cannot emphasize enough the extent I am deliberately picking points in their favor.

Edit 2:

If your qualms are with demand, read Ed's articles, the host of the podcasts this sub is centered around. That is beyond the scope of this post, and was written with that information already in mind. The only thing I can say is there are lots of other facts you likely have not seen if this is your stance.

Some seem to think refuting a technicality over one point is able to overcome 16 other points of leniency. This is wishful thinking at best. You cannot cherry pick technalities to make the argument convenient. Either you leave them all off, or put them back on.

If you feel the need to defend AI due to your own investment portfolio or some other psychological need, on this basis, you are free to do so, but I strongly encourage you to read some other very nice breakdowns by Ed.

It seems they're doing this to generate hype before their IPO, but they haven't even filed their S-1 yet unlike Anthropic.

> Reuters reported in May that OpenAI was preparing a confidential U.S. IPO filing in the coming weeks. ⁠However, CEO Sam Altman has said the company is not focused on timing and will go public when it makes sense.

I suspect it'll be timed with this launch then.

Also, really? Superapp? Is this really the term we are going to use?

Geez.

> A Google Cloud spokesperson told CNBC by email that the deal was made "to ensure we have bridge capacity to meet surging customer demand for our agent platform, Gemini Enterprise, which has been even higher than we expected." Google introduced Gemini Enterprise — subscriptions for large businesses — in October.

> Google is significantly ramping up spending on AI as it races to keep up with rival hyperscalers. The company in April revised its capital expenditure forecast this year to between $180 billion and $190 billion, up from its previous estimate of $175 billion to $185 billion.

And how much are you earning again? A few billion? Putting your profit at what? Good investment guys. No, pay yourselves on the back, for doing such a good job.

This reporting comes from Anthropic's blog post, where they talk about their recursive self learning.

They're playing the damn Mythos strategy again "oh it's just TOO GOOD, that we *have* to stop".

> Anthropic says AI is developing so fast, the trend points towards systems becoming capable of developing their own successor. We're not there yet, but it believes it "could come sooner than most institutions are prepared for."

Let us note that this post comes right after their confidential filing of the their S-1 for their IPO.

A small comment about the article. Although it acknowledges:

> But people think it's just a ploy to hype up the product or to cover up the fact that Anthropic only wants to sell it to the biggest enterprises.

It quickly follows it up with:

> It's worth noting, however, that the company's suggestion is based on the findings of Anthropic Institute, a research division it established in March.

Is it worth nothing? Is it?? Because, Anthropic Institute (literally just a part of the company, headed by its co-founder Jack Clark) is NOT at all biased or influenced by Anthropic to say good things about Anthropic right? Gooood. I'm glad we're on the same page. Anyone smell burnt toast?

Anyway, big surprise, Anthropic is lying once more, as they always do. It's such a nonsense statement as if you could ever get all the AI companies (including themselves) to stop. Pandering to people through pure lies. It's disgusting.

The post from Meta themselves says this:

> Your Business Agent can. > - Answer questions specific to your business > - Make product recommendations from a business catalog > - Book appointments and qualify incoming leads > - Let you decide when a team member steps in to provide support > - Close sales

So useful. Such AI.

> “This could very well turn into the site where cancer is cured. This could turn into the site where hundreds of millions of students around the world learn and would get tutoring,” [Altman] said.

> Millions of small businesses could run businesses with AI in the cloud, he said.

> “A gigawatt of AI can do all those things,” he said.

Man I hope my energy bills don't go up, if I'm just sufficiently far away.

Will someone please shut this guy up?

> ...it's initial investment, denominated in euros at €45 billion, aims to deliver up to 3.1 gigawatts of computing capacity across France

Ah, yes. Just like all the other several GW of capacity of data centers that have been constructed right?

SoftBank is doubling down on their AI investments I guess? Why now? I mean why is everyone doubling down while losing?

It's a step in the right direction I think.

Here's an alternate article: NBC.

> Weingarten told NBC News that despite [AI companies'] collaboration, she views many of the big technology companies as “playing a really negative role in terms of trying to push more tech into schools.”

Just as it was talked about during the recent episode,

I'm so sick of seeing stuff like this or hearing it from someone. Everywhere. Like just take your uneducated opinion and go somewhere else where you can gamble your money away. I don't care. Go to Vegas. Buy some lottery tickets.

Is anyone else tired of this? It's just nonsense every day of the week. And it never stops. Are they the ones going crazy or is it me?

I mean, why even stop there? Why not write 1000%? Why not 1,000,000%? NVIDIA's revenue will double! No, triple! No it'll grow 500x!!! Why stop there? If we're just gonna pull shit out of our ass and throw it at everyone what's the difference? Are any of them even gonna bat an eye? If you're just trying to get suckers before you sell, what's the difference? Or if you truly believe it, why bother setting a cap? It'll grow forever right? Let's invest for 20, no 30, no 100 years in the future!! It's just layers and layers of nonsense.

I despise their sick minds no matter the reason. Whether they believe it honestly or they just want to play the game. I genuinely couldn't care less. It's an eyesore to humanity. For their sake, I'd prefer they got therapy. For my sake, I wish they were all moved to a little quarantined town where we can just let them fulfill their delusions with an imaginary currency. At least that way they won't actually cause real societal, structural damage with consequences that can and will hurt everyone involved.