npilk

Yes, discussion like this is why I'd love to see a deeper analysis of the full value chain. Right now it's mostly speculation (beyond 'gee, everyone sure is spending a lot of money').

Obviously more detailed data isn't easily available to report on, but I'd like to know:

- What are the labs spending on compute specifically to serve models? Are they really profitable "on inference" of existing models? If so, how long is the payback period to recoup their training costs for those existing models only? If not, how much would prices need to rise to be profitable?

- How much capex have the hyperscalers invested in just the compute being used to serve those existing models? Are they making money "on inference" when accounting for just that amortized capex? If so, what are the margins like?

- What share of hyperscalers' AI revenue is from training vs inference? (Presumably training is more dependent on VC investment and inference is more self-sustaining.)

Thank you, I will bear that in mind. I am mostly familiar with the term from online forums, where I’ve seen it used to refer to other forum posts, blog posts, etc.

Interesting. I wonder what they will move to, and where it would be hosted.

I would say Ed makes three main claims in this post:

1) Companies are spending a ton on capex for future AI compute

2) Current levels of AI revenue are not enough to recoup that capex spend

3) Revenues won’t increase enough in the future to recoup that capex spend

Almost anyone, bubbler or not, would agree with points 1 and 2. But Ed cites dozens of numbers from different sources to repeat and reinforce them. It feels to me like an effort to overwhelm the reader with data to support his overall argument. That’s what I would call a gish gallop.

The third point is a prediction. He cites a lot of facts and numbers here too, but ultimately whether you believe his prediction is going to depend on your assumptions.

The thing is, I really would love to see a detailed analysis of capex spend and amortization. Capex spent on the future is a big unknown. But the big labs have claimed they are profitable on inference. How much capex was invested to create the capacity to serve current models? How much revenue is coming from serving those models? What does the full profitability picture look like? What does that imply for future demand needs?

Yes, fair point, analysts tend not to be too critical. But they are still developing more detailed models to understand and project things like revenue and profit, so there's some rigor - at least more than company leadership just giving takes or stating platitudes.

My personal hunch is that the “diffusion curve” for AI is slower than most people in this space think. Most businesspeople I talk to have only tried a basic Copilot chat and/or free ChatGPT. Many still haven’t used anything “AI” at all. As more use cases become practicable and cost-effective, more software will include AI, and more people will use AI with or without knowing.

Also: note that the Wall Street analyst estimates Ed cites (and then declares impossible targets) are predictions by serious people with a lot of money at stake. Of course they could be wrong, but they’re not made up.

Ctrl-F “amortiz”: Not Found

Microsoft’s total depreciation and amortization in Q2 2027 was $11B - not clear how much of this is AI related. Apparently they had $34B in AI ARR as of May.

So let’s say their AI capex amortization and revenue are about equal. Not amazing, obviously they’re relying on continued growth, but doesn’t seem like the end of the world?

Compare that to Ed’s framing - Microsoft has $34B in revenue but spent $116B in capex last year to “make it”. They’re doomed!

But that capex spend is to make future revenue. Clearly he assumes demand won’t increase in the future, and that future projected revenue is “fake”. And sure, it definitely might not increase enough to make profitability.

But his whole analysis hinges on that one assumption. The entire article, all the numbers he gish gallops at you, could basically be replaced with “I don’t think AI demand and revenue will increase much beyond today.” Yeah, we know.

Here's an analogy (as far as I understand the situation...)

Let's say I own a lemonade stand. I sell you a 20% stake.

My stand loses $10. When I report my financial results, I report losing $10. Then I report that your share of those losses is $2, and my share is $8. This creates transparency.

So OpenAI really did lose $8.8B or whatever, but some of those losses are 'attributed' to other shareholders/owners of their subsidiaries, because they have a complicated corporate structure. So they report both numbers - the total loss, and then the part they 'own'.

So when Ed says "It’s unclear what this means", he's either terribly uninformed or intentionally misleading his readers into thinking something fishy is going on when it isn't.

Either way, it's bad journalism - if you don't know what it means, shouldn't you try to find out or ask an expert or something and then inform your readers? (And the thing is, it would easy enough to dunk on them for losing $8 billion, without adding these weird insinuations!)

One example - he often compares current revenues to capex being spent on future capacity to claim that AI companies aren't profitable. (See this post for example: https://www.wheresyoured.at/am-i-meant-to-be-impressed/ .)

But this ignores that the capex spent to build more capacity is expected to generate additional future revenue. You don't need to recoup your capex immediately. A better approach would be to amortize the capex and compare revenues to that.

Clearly he assumes revenue won't increase enough to recoup this level of capex (and it's very possible it won't) but IMO it's either a miscalculation of how the financing works or a deliberately misleading framing to compare current small revenues to a big scary capex number.

I'm sure the above is simplified by the way, but I am confident that people who work at Goldman understand the relevant details extremely well.

So the author tells us there is a crisis of meaning in the workplace, and their proposed solution is… to remember none of the work matters?

The start of the piece: “These conversations inevitably turn to fundamental questions about careers: What the fuck are we actually doing? What the fuck is the point of all of this?”

The end: “For most employees, we owe it to each other to remind one another that this work is not, in the grand scheme of things, all that important. It is illusory.”

GitHub Pages, GitLab Pages, and Cloudflare Pages are all free. If you have a static site, you don't need to worry about lock-in, since your files are easily portable to a different provider.

I made this tool to try and make it easier for beginners to get something up on the public web for free: https://weejur.com

If their goal is to automate scientific discovery, why would they not automate building their website?

(Though, I do wish people would use just a few extra prompts to break out of the 'vibe-coded' look.)

Huh. I used to think that was a Keynes quote but now I've changed my mind.

To play devil's advocate: couldn't people generating AI image/video take the time to consider what they want to generate, decide how to prompt it, ask for edits to have subjects pose certain ways, and specify lighting and color grading (or tweak it afterwards with the same tools digital photographers use)? Wouldn't that give them an 'essence' in the same way?

It turns out people have even mapped individual greens, fairways, bunkers, etc on OpenStreetMap! So you can use that data to generate a yardage book for each course: https://npilk.github.io/openyardage-web/

What if you aren't motivated solely by making money? There will still be new free content created by hobbyists, passionate creators, altruists, etc.

(Agree with you more generally.)

It's cute they think 'retail investors' are reading balance sheets in the first place.

Interesting. I had heard that chess players burn a crazy amount of calories during tournament matches, but when I looked it up just now it seems like that isn't true: https://statmodeling.stat.columbia.edu/2025/06/30/its-sapols...

I appreciate the sentiment but can't quite tell who the author is targeting. Is it people who don't even know what HTML is, or those who are technical enough to have considered tradeoffs between web hosting options?

For complete beginners, I made https://weejur.com , which is designed to make it easy for even a complete beginner to paste in HTML and get it hosted on GitHub Pages for free. (And is also hosted itself on GitHub for free!)

I'm very eager for subvocalization tech to take off - this, AlterEgo, etc.

I have never liked talking aloud to Siri or similar, but I could see using "voice" as an interface for so much more if I could speak silently to my device.

Haha, I guess having a troll posting nonstop racial slurs is just part of that old web feel...

I think it implies they are more likely to hallucinate if they don't know the answer. So a big model will return the correct answer more often than a small one, but in the cases where it doesn't, it will be more likely to make something up instead of saying "I don't know".

This will be even more interesting when AR glasses finally go mainstream in a few years, further increasing this effect.

IMO people in this thread have missed the point. I, too have moved about every two years as an adult (even though I've never had mold problems). Or when I haven't moved, I've made a job change.

Perhaps there is something in my nature that gets complacent after a couple of years and looks to make a change. It seems like that's what the author was trying to grasp towards.

I expect this tendency might go away as I get older, have a family, etc.

I feel like Arc nailed this perfectly with the vertical tabs and multiple "spaces", and since almost everything happens in-browser these days, this was 99% good enough. I can't understand why more power users don't find this setup ideal. I'm hoping Zen Browser can become a solid replacement.

They make custom chips with a model's weights and parameters "hard-coded" which allows for much, much faster inference.

Human-slop is a new one for me. How would you define it? Low quality work written by a human? Human writing that sounds like an LLM? Something else?

Love chatting with an agent via iMessage for the right use case - it feels very natural and human. I hacked my own together with an old laptop and BlueBubbles.

How would you compare your offering with Spectrum (https://photon.codes/)?

I've been using DeepSeek Flash to replace Sonnet once the subscription stopped working. Haven't really noticed a difference, although I don't usually have it doing anything very complicated.

Do you have an example of a 4th grade problem in mind that isn't "American-style"?