Every AI Subscription Is a Ticking Time Bomb for Enterprise

Every AI subscription is a ticking time bomb for the frontier provider; within a few years we will be running local models as good as today’s frontier models with almost no cost burden. The floor will fall out of the enterprise market for all the frontier companies.

I think I'm going to puke if I see one more "It's not X. It's Y." phrase or the word "load-bearing" used metaphorically.

Brad Gerstner confirmed that tokens aren't being sold at a loss. Whatever the formula, API + Subscription split, the companies are making a profit on net token sale.

They maybe running at loss after all the salaries and stock comp, but tokens are in profit now.

Article is mistaken these subs are not available to businesses. Companies are paying much closer to API prices. The strategy is to get you accustomed to infinite tokens on your personal sub and bet that behavior transfers to work.

Not just AI. Every subscription in general can be a time bomb. You grow more dependent on it, and the provider can disappear or take it away at any moment.

Disclaimer: didn't finish tfa, so obviously AI even I could tell.

Perhaps OpenRouter can be used as a benchmark for commodity cost to serve AI. I keep hearing it's better value than Claude, which suggests to me that either Anthropic is especially inefficient for some reason, or they're turning a profit on inference. They could be losing money on training, but I suspect that's just part of the cost of staying a leading lab. If any single one goes under due to debt etc. then companies can just switch?

MSFT, GOOGL, META are spending $60-100B+ annually on AI infra partly to own the cost floor. the moat isnt the model, its the infrastructure.

Since we can't reliably detect AI generated crap, I think it makes sense to penalize their submission. I say this as a generally pro-AI person.

Why does the author assume that enterprises use subscriptions?

Many companies use models deployed on Azure/Bedrock etc are already paying based on usage (often with discounts).

Replacing your workers with AI:

--You lose control over their "salary"

--You lose control over their "schedule"

--Your company becomes reliant on another party that does not share your interests or values, and can stop working for you on a whim for any reason

But AI is definitely good and trade unions are definitely bad, apparently...

Eventually, after the seed funding is spent, you will have to pay the real cost of the coal used to power your queries.

The best course of action is to take advantage of subsidy for awhile, but not integrate is so deeply one can’t retreat. You’ll still have full productivity, just be cognizant of the reality of the situation.

Hopefully the market eventually collapses to where companies are hosting their own inference, and you simply lease a model package to run on your own (or rented ) specialty hardware.

Even if they are momentarily losing money it’s important to note the value add they are providing.

If you increase the price, the value is still astronomical in comparison.

Companies need to find a way to leverage local models in tandem with frontier models to offset the costs.

It’s all about targeting specific workloads with the appropriate AI. These tools are not sentient beings they are tools that need to be properly configured to match the job at hand.

The FED will print to infinity as the US gov can’t stop spending, mostly all of that money will keep going to the only industry that’s growing and provides crazy returns for family offices and VC’s right now which is AI. I don’t agree with the authors opinion here as the “time bomb” timer is simply the entire world buying US debt here, which won’t happen in the short/medium term

> A knowledge worker running a few hours of Claude daily, uploading documents, drafting reports, analyzing data, can easily burn through several million tokens per week. At API rates, that same workload runs somewhere between $200 and $400 a month per seat. Some power users push well beyond that. But on a Pro subscription, the company is paying $20 per head. Anthropic is not the only one eating this cost.

What? Anthropic's costs aren't the API rate. The article never attempts to estimate that cost, which renders its thesis tautology.

Inference is profitable. Companies lose money because:

1. Training is expensive. Not just compute but getting the data, researchers salaries etc 2. You have to keep producing new models to ensure people use your inference and there seems to be no end to this. So they have to pour more billions to keep the cycle going on 3. People salary and other admin cost are not that high compared to 1 and 2.

How do the owners of that site correlate this with their business model, which is to use AI to write articles like this one, so as to get clients in the news?

Isn't EVERY subscription and SaaS a ticking time bomb for enterprise?

Those price increases will increase the pressure to use cheaper / free models (commoditization), thus cutting into the revenue projections of the frontier model vendors. Its going to be exciting to see what happens to these huge investments and valuations.

I’ve said this before on HN, but there are two things that make me optimistic that we won’t see a big rug pull where price-to-capability ratio skyrockets relative to today:

* People keep finding ways of cramming more intelligence into smaller models, meaning that a given hardware spec delivers more model capability over time. I remember not that long ago when cutting edge 70B parameter models could kinda-sorta-sometimes write code that worked. Versus today, when Qwen 27BA3B (1/23 of the active parameters!) is actually *fun* to vibe code with in a good harness. It’s not opus smart, but the point is you don’t need a trillion parameters to do useful things.

* Hardware will continue to improve and supply will catch up to demand, meaning that a dollar will deliver more hardware spec over time. Right now the industry is massively supply constrained, but I don’t see any reason that has to continue forever. Every vendor knows that memory quality and memory bandwidth and the new metrics of note, and I expect to start seeing products that reflect that in a few years.

I hope that one day we’ll look back on the current model of “accessing AI through provider APIs” the same way we now look back on “everyone connecting to the company mainframe.”

every infra wave starts with land-grab pricing and ends with metered billing, AI is just running the cycle in 18 months instead of 10 years

Aside from the obvious fact that this is AI slop, the author (prompter?) doesn’t consider the R&D of AI itself. Efficiency gains, more compute, etc.

We all know every frontier AI lab is heavily subsidizing usage, and so do all of the VCs & CEOs funding them.

TL;DR to save you time:

1. GenAI companies are making a loss in order to gain adoption and later lock-in

2. ???

3. They're going to cash-in soon and start milking you now that business critical systems rely on GenAI

The "???" denotes a complete failure to offer compelling arguments that link 1 and 3.

As a few commenters already pointed out, IME enterprises aren't paying for subscriptions. They're paying per token.

But also... is this shit AI written? I'm so tired of this.

"In 1975, Dr. Joseph Sharp proved that correct modulation of microwave energy can result in wireless and receiverless transmission of audible speech."

> is not a rounding error. It is

Who said it was?

> Pull out the napkin. This matters.

The article wouldn't exist if you didn't think it mattered, just tell us why.

> the question is not whether they got a good deal. The question is

Who said that was the question?

> This Is Not One Company's Problem

Who said it was?

Stop telling us what thing aren't, just speak like a normal human and convey your own thoughts. It's an insult to your audience to throw constant AI slop at them.

> thousands of companies have woven AI subscriptions deep into their operations. Marketing teams draft copy through ChatGPT Plus.

Yea I bet you do..

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It is "bait and switch" --- done on an industrial scale.