Just finished Mansfield Park. Sort of a strange way to end the book, though I enjoyed it overall and was pretty well sucked in from the “play” onward.
1,654 karma · joined January 9, 2021
The visual design is beautiful and the maps are illuminating for some of the points. But a good number of the map elements feel pretty superfluous, in a 'data for data's sake' kind of way.
Overall I think this would be more impactful if it were ~50% shorter or if there were more navigation - this is a ton of content to scroll through one panel at a time.
I don't disagree with the thesis here, I just don't think costs are coming down quite that quickly.
> The reasons why I believe that ... the highlands were functionally cut off is ...
So maybe the extension is that professional SWEs who work at big corporations will need to use AI to 'keep up', but hobbyists can still get enjoyment out of their own work at hobby scale without using AI (similar to the point in TFA).
IMO knowledge work productivity has gone up a massive amount since 1990, and companies could in fact fire most of their knowledge workers while still getting their most critical work done. (These are the bullshit jobs/email jobs.) But companies mostly haven't done this, which implies other reasons for employing so many knowledge workers.
Even if AI becomes capable of doing everything, I would predict there will be a period of many years/decades where companies still employ a similar number of people to sit around and babysit/watch what the AI does.
Here’s an example: I had asked Claude for some music recommendations in a certain style. Part of its output was:
—
*Long journey tracks*
Clinic — “The Return of Evil Bill”
Guided by Voices — not really, wrong band
Silver Apples — “Oscillations”. Proto-everything, deeply repetitive, hypnotic.
—
So at some point there, the next token produced was “Guided” or “Guide” or whatever, and then because it can’t go back, it had to correct itself after the fact.
Reasoning/CoT have helped a lot, but I feel like small versions of this still happen all the time.
Human writing is like 90% editing.
Presumably you could use the same reasoning trace, run multiple generations, and get different outputs (if the temperature is >0).
But now I’m interested in playing more with Cowork or Claude Code/Codex for prose writing to see if the set of tools there affects outputs at all. I guess you might need a more custom “writing” harness.
On the other hand, LLMs are forced into picking some likely-ish word, and then have to build the rest of their response to retcon that choice into making sense.
Even good human writers would probably struggle with this constraint. It would be like someone interrupting your writing to tell you the next word MUST be such-and-such, and then you have to try and make it work as best you can first try, without going back to edit. The result would probably be a little clunky. (Maybe it’s impressive LLMs write as well as they do.)
Maybe they did? Or maybe they don't realize they're playing against other agents.
Of course, if agents running different models are competing in these 'games', I wonder how much of the theory of mind would translate.
(N.B. - I don't think they're all defecting from the first turn, although it's not clear. It just says 'they all defect at the same time'. So if they're playing for 10 iterations, they might all decide to defect after turn 6, but since they all do it together they don't get the benefits. I would expect these models know that optimal strategies in repeated prisoner's dilemma start with cooperation.)
The real problem for the hyperscalers would be demand stalling out entirely (or maybe small local models getting good enough that people don't use the cloud).
One interesting thing is that it also removed some "internal CTAs" to engage (like "Start your day with our podcast!"). These aren't technically ads, so something like uBlock doesn't remove them. But honestly, I don't really mind having those filtered out too.
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.)
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?
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.
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.
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!)
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.
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.”
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
(Though, I do wish people would use just a few extra prompts to break out of the 'vibe-coded' look.)