Both side get to feel like they got more out of the initial investment.

1:3 2:3

Everyone wins inside their heads and its nice to live life where everyone wins...

Also easier to justify charity when you think you are winning..

I feel like it's pretty good for what it is.

But otherwise you are right, this is done because it makes everyone donating feel like a winner other than few cynics like me. :D But that said I do not mind these drives.

They might in the future but future is in the future ofc

Edit: to be clear I think it's ridiculous they don't but from a company's stand point it doesn't make much sense

Unfortunately, the growth potential that software industry generally provides has generally prevented governments for creating and enforcing similar laws for software products and such.

I personally think countries should start sanctioning and international warrants for all large investors and senior executives in a company if something like this happens.

But it won't happen unless US changes their stance on companies being liability shiled.

Sure it should require very exceptional cases but I know companies whose negligence has cost lives of hundreds of people, the reason they and all their investors weren't jailed is the real scam of the democraciez we have gifted ourselves.

For anyone curious the big thing with jj is you can undo. Basically if you were running a rebase and bungled a commit sha or branch name, no worries undo your way out, if you forgot to push but abandoned a commit not issues undo it. Same with all jj commands.

Delta db takes this a step further and everything you edit is a change so your actual edits are also all saved.

I find non destructive-ness for as long as possible approach very nice, since there aren't as many constraints on us not being able to do it nowadays, we had limited storage and stuff in 2000s perhaps but in 2026 even with the killer SSD prices it's fairly cheap to buy more SSD, we can definitely waste 100MB per project storing redundant history we may not ever need.

Same way in photoshop non destructive editing took over, especially useful for humans, but works for AI and LLMs too, they can undo their mistakes as well.

You worked on multiple things, you sync with the upstream, then boom now you have conflicts in one or more of your local branches.

Instead of immediately presenting you a conflict resolution view (usually 3 pane vimdiff) and you'll have to resolve at the sync time, the commits are simply marked as "have conflict", and you can continue working on other things until you have time to deal with it.

And for a chain of commits that all have conflicts, chances are fixing the first conflict automatically resolves all other conflicts in the chain, thanks to the auto-rebasing feature.

What happens if one is unlucky and the conflicts are not automatically resolved from changes to the first commit? Does that potentially imply more work overall than if one was using a VCS that forced conflict resolution earlier, like git?

The main advantages are that you can do the changes piecemeal, in any order, at any time, and by jumping around in the commit history however you need or want to.

Sometimes the best way to solve a conflict is by changing something before the conflict, or by moving pieces from one commit to another. Git doesn’t allow this, you can only do: fully fix commit 1, fully fix commit 2, fully fix commit 3, and so on.

Why is git's "forced conflict resolution" better, assuming the same workflow (local change+sync upstream) would produce the same # of conflicted commits?

2. Yeah submodules aren't supported but since you can use jj in a git repo you can commit git stuff via git like submodules and then enjoy the world of jj. Since submodule interactions should be rare imho. I use it for a bunch of projects with submodules.

For instance, if you are interactively using `git bisect` and you mark commits as good or bad, and you accidentally mark a commit incorrectly, you have to do something like:

    git bisect log > /path/to/file.txt
    git bisect reset
    # modify file.txt to "look correct"
    git bisect replay /path/to/file.txt

Another example is when you do something like screw up an interactive rebase. Let's say you rebase 20 commits and then you get a conflict on commit 8. You fix the file conflicts, and continue. You accidentally solve the merge incorrectly, continue and get another conflict -- but only realize your mistake after you start solving it. The reflog can't save you here. You have to completely abandon the rebase and start over. (This specific example might be handled better these days).

I think the biggest thing about `jj undo` is that it works everywhere. You can undo rebases, merges, conflict resolutions, copies, deletions, whatever. The secret behind it all is that internally, jj is architected in a way where implementing a feature looks like you are working with a transactional database. You actually have `begin_transaction()` and `commit()` methods in the codebase that will make changes to the commit graph visible in an atomic way. When a command like `jj rebase` happens, all of the changes it makes are inside a transaction and committed at once. Every operation in the repo is a transaction, and it all goes into a log, which records the effects of a transaction -- very much like a database system!

So "undo" just means "undo the effects recorded in a transaction" and that is about all. And so it works for everything! And this design is very easy to intuitively understand and program against, as a maintainer, along with our other high level internal APIs. Any developer can easily write code that Just Does The Right Thing and the user can undo it and it's no big deal. When I develop and work on Jujutsu myself -- like I'm actively developing new features or prototyping ideas -- I almost always _use my own jj repository_ as a test repo while testing my builds.

In contrast, Git does not have one unified "transactional" layer for things like this. But not all is lost, there has been work on 'git undo' and it was implemented by... Someone who is now a Jujutsu maintainer[2]. :)

[1] Technically we do not yet have "step by step" bisect with good/bad yet (only "automatic" bisect that is one-shot), so that is something Git can do we can't do at all right now, but bear with me. :')

[2] https://blog.waleedkhan.name/git-undo/

Since jj snapshots the commit you're editing a lot, you can often even jj undo an accidental edit to a just-created file that in git would have still been uncommitted.

¹ https://drive.google.com/file/d/1dVzug1lHoOxdbFu8gcCJCu-G_uV...

Points of no return.

I think this misses the actual limits here.

The problem isn't demand it's, "how much people are willing to spend on it".

Cheap AI has to be served on cheap compute, and if inference gets cheap enough to unlock massive usage numbers, by definition it also doesn't require anywhere near as much infrastructure per unit of demand.

Take DeepSeek serving ~100T tokens/day, depending on workload and utilization, you're potentially talking about only a few thousand last-gen GPUs. With current-gen GPUs maybe closer to ~1,000, and with Rubin even fewer I will be damned if I could get my hands on one.

That's the part I think people are missing when they extrapolate token demand into enormous infrastructure or AI revenue.

Yes usage will explode. But if the cost per unit collapses, the revenue doesn't necessarily go up with it.

You can't simultaneously argue that intelligence becomes so cheap that everyone uses enormous amounts of it, while also assuming customers will somehow spend trillions of dollars a year consuming it.

There is no obvious $1T customer-facing AI revenue number at the end of this rainbow in the short/medium term.

The average person isn't going to spend anything remotely comparable to what they spend on a car every year for an AI service. Even businesses have budgets now, huge demand doesn't matter if the willingness to pay isn't there.

The only path I can see to numbers like that is AI consuming existing business domains, even then it's very thin.

Say SaaS + legal + consulting + BPO + various other service industries collectively represent something like $10-20T globally.

Even if AI eventually replaces an enormous portion of that, it's probably not doing so at the same price. Why would customers switch otherwise?

Either the AI product has to be dramatically better, which is difficult for mature workflows, or dramatically cheaper which is much more plausible.

If it replaces $10-20T of existing services at roughly 1/10th or 1/100th (more likely) the cost, then you're looking at maybe a ~$1T AI revenue opportunity after replacing an absurdly large fraction of the existing service economy.

Who are now unemployed and can't pay for shit.

And that's before competition.

I think it's crazy to assume AI companies won't compete aggressively on price. As capabilities diffuse, smaller models catch up, inference hits pareto frontier the open-source alternatives have already improved and caught up, margins on routine intelligence should compress "hard" (emphasis on "hard").

We've already seen how difficult adoption can be even when the technology looks impressive on paper. Cheap here means 100x cheaper for 10x more demand that's a net 10x loss before any software or hardware optimizations.

So yes, I completely agree that cheap intelligence can bring an enormous amount of new usage.

"I just don't think usage means revenue." (you can plaster it on a wall if you want to, "usage doesn't mean revenue", if you want to find that out I have foss software bridge to sell)

The PC analogy actually reinforces this if you really think about it. Compute became "vastly more useful" while the cost per unit of compute collapsed. Society captured enormous value, but all computer companies are literal failing giants without the AI hype. Value got caught by people who provided productionization.

Now if people expect AI to self productize itself I am happy to tell your try it. We all saw how OpenAI fell behind Anthropic because they thought that would work...

Google couldn't productize the search, instead they sold the eye balls and web-real-estate. Maybe that's the AI business model, but that's not $1T worth given you need to unglue people from other stuff.

Unless we get something approaching genuine ASI producing so much additional economic value that entirely new trillions, I don't see a path to $1-2T in direct AI revenue from customers.

The market simply can't absorb that level of spending.

Demand can be effectively infinite at the right price. But I think people are delusional on HN and SF if they think that number is in Trillions like the investments seem to suggest.

I am not saying Nvidia will fall tomorrow but someone will have to pull the breaks before this car goes to hell.

Lol its not even that - its what can I do with it? Which eventually has to show up somehow in the financials - from a macroeconomic stand point. Software production is microeconomic.

This is the right way to look at it, but a few of your estimates are a bit off. AI is being sold as an accelerator (or, if you're in a dystopian mood, total replacement) of knowledge workers. Currently knowledge worker salaries are $50 - 70 trillion a year globally, $10 - 11T in the US alone: https://gist.github.com/danielmiessler/2dc039762a202b083753b...

> Even if AI eventually replaces an enormous portion of that, it's probably not doing so at the same price. Why would customers switch otherwise?

AI is wayyyyyyy easier to wrangle than humans; no sick leaves, health insurance, perks, HR issues... heck they don't even sleep! If companies could replace us with robots, they would do so in a heartbeat. Capitalism!

So in a "what the market will bear" sense, we have an upper bound on the TAM. Indeed, I expect this is where Anthropic's ridiculous "$30 trillion" number is coming from... except now we see how they came to it.

If AI makes workers even 1% more efficient, that's a $500 - 700 billion value annually. In reality AI makes workers way more efficient (studies from the ancient era of 2024 showed about a 30% boost) so AI companies could realistically charge that much more. But then all the other factors you mentioned -- smaller models, competition, self-hosting, etc -- come into play, which put a downward pressure on revenues.

It's impossible to predict how these dynamics will play out, but the numbers involved are astronomical. This is why everyone from the frontier labs to Big Tech to VCs to nation states are scrambling to get in on it.

And which company profits the most from exponential growth in token generation?

Here we go, I just drew a "circle" for these naysayers.

You will get around 2-4 concurrent streams of aggregate tokens at best for such a model and around 0.5B output tokens per month assuming you use loops and run it when you are sleeping. That's 500 (per mill) * 0.5$ = 250$ only at most.

Then there is maintanence and efficiency costs due to electricity usage and such, any down time, etc.

You will be lucky if you can squeeze more than 200$ of value out of it in a month.

I don't think people should buy local hardware for money reasons, by the time you will pay off a 10K USD machine, 2-3K USD machine will catch up and beat it by a significant margin.

Unless your expectation is that we will be in hardware winter for the next 10+ years. At 200$ per month it will take around 200 * 50 = 10k, that is, 50 months, so around 4-5 years.

Again assuming you are making the most of your hardware somehow, very hard to do in practice.

I don't recommend people to use compute as investment or payoff thing, but if you have the money to burn and can afford it why not, maybe with some software optimizations it will be cheaper but then again Z.ai is currently offering 50% discount and providers will offer cheaper rates for sure.

But either way you will never be able to burn more than 200$ worth of token on a cheap hardware device, because inference becomes more profitable the more you scale it up, you have separate prefill and decode engines/systems, and a lot of nuance, but assume for every 10x increase in infra you increase margins by 5-10%.

So from 10K to 100K to 1M to 10M to 100M.. I don't think this curve continues beyond 100M but I have no idea about that scale unless some AI lab is interested in hiring me lol.

So a 100M infra will have ~30% better margins than you at 10K, then there is software optimizations but that's cheap enough, though some of it is only viable at scale.

Either way assume 10K is the price of privacy if you really want to buy it. Don't worry about making the most out of the usage, you will always be in a net loss but I would assume for you 10K doesn't matter.

But whether you make a loss or not depends on how hardware prices and resell values go though.

I have spent ~$50K on local AI hardware. The market value of that hardware is about ~$80K right now.

So the maths is working out for me so far. I see it as a call option on compute.

That's easy because no matter what IT hardware you bought, it's worth more now than it was two years ago. That's something that's unprecedented, never happened before, and as soon as we get flood gates open on ram manufacturing OR when the AI bubble pops, all IT HW deprecation norms will return and making a profit by buying something IT will vanish.

I had a GPU server four years ago. Had I not sold it like three years ago with 2x price I bought it, it would be likely something like 5x the price nowadays.

I really really miss filling my home rack with old enterprise stuff. All I want is this hardware winter to end.

But for a heavy user who has enough work to be done so that the box runs almost 24/7 at say 50tok/sec, the math gets interesting against API prices.

And it can be interesting compared to subscription in the sense that you don't have the quota anymore. That means there's probably a lot of things you're not doing because of the quotas that you could do now.

It depends heavily on the tok/sec obviously and the very best solution financially remains subscriptions. But the idea remains entertaining and not that disconnected from reality

That's less than what 40$ at current API rates... So if you are willing to pay 200$ per month you will get much better limits paying API rates.

You can't run large Kimi K3 models on 10K worth of hardware either way, you need to spend like 50K USD minimum.

Just pay for the API rates or get a low cost provider that uses higher batching, you can get shittier tps but much better prices, probably go as low as 20$ for as much usage as you can ever get from a 10K USD machine from GLM 5.3 Flash...

The issue is nothing expensive runs on these devices and cheap stuff isn't worth running locally, eletricity costs ~12cents/kwh in us iirc, so at 330W M5 Ultra will burn around 8 * 0.12 = ~1$ per day extra in electricity so the electricity is going to cost you the same as the API rates(30$ per month).

I truly don't think you are accounting for the costs here properly. But again if money truly doesn't matter it's much better for privacy and better than paying one of the shady AI labs who are doing god knows what with your data.

1) Rates are theoretically discounted for GLM 5.3 Flash right now, by 50%.

2) Hardware costs have continued ascending with no sign of letting off, so it's unlikely that a DGX Spark depreciates to zero in one year.

3) Compare performance in terms of difficult tasks/$ over the last 6 months, 3 months, etc. Open weights are a ratchet. In terms of intelligence per $, a Spark is never going to be a worse deal tomorrow than it is today, at least until the entire platform is replaced or obsoleted.

71 days ago the best model you could run on two Sparks was an aggressive Q3 quant of Qwen 3.5 397B (AA 34). 70 days ago it was a mixed-quant of GLM 5.2 (AA 53). 30 days ago it was full fat DeepSeek 4 Flash (AA 53). Today it's GLM 5.3 Flash (AA57) and/or Qwen 3.8 Next (Unknown). Sometime this week it will likely become mixed-quant GLM 5.3 (AA 60).

So in < 80 days we have almost doubled the benchmark score. And that curve is still accelerating. If you view it as "cost per token of model vs API" then yes it's a bad deal. If you view it as "cost of task per $" then it has almost doubled in value in less than 3 months. All of this, imo, API and hardware, is still massively underpriced.

If someone told me that costs for X will keep increasing because they have been increasing rapidly in the last 1.5 years, but they have a history of continuously decreasing for decades before that.

I am not sure if I will take anything they say serious, I am not sure if it's HN or AI but people are delusional if they think compute costs will keep increasing from now on...

Either AI will be really good, hence compute and everything will materially depreciate or it won't be much better than it is today and token volumes will plateau compared to compute.

For instance the amount of token compute that's to come online in 6-12 months is several times what we have today...

Second 3) Compare performance in terms of difficult tasks/$ over the last 6 months, 3 months, etc. Open weights are a ratchet. In terms of intelligence per $, a Spark is never going to be a worse deal tomorrow than it is today, at least until the entire platform is replaced or obsoleted.

This is a bad take because again this assumes DGX Spark will not depreciate in price, we will have something better for far cheaper surely in the next couple years. M5 Max & Ultra are already arguably it, but will have to see.

> 71 days ago the best model you could run on two Sparks was an aggressive Q3 quant of Qwen 3.5 397B (AA 34). 70 days ago it was a mixed-quant of GLM 5.2 (AA 53). 30 days ago it was full fat DeepSeek 4 Flash (AA 53). Today it's GLM 5.3 Flash (AA57) and/or Qwen 3.8 Next (Unknown). Sometime this week it will likely become mixed-quant GLM 5.3 (AA 60).

This has nothing to do with DGX Spark's value, if models get cheaper the API costs also go down, this is not a defensible argument to cost to value.

Are people on HN really not thinking straight?

Tldr; no matter how you do the math compute is only getting more valuable because of a temporary crunch, don't expect this to continue permanently, sure you maybe able to time it and make money but so could you in stocks this is not for investments. Further second hand hardware sells for cheaper than sticker price, outside of a bubble..

And models getting cheaper == APIs getting cheaper == your hardware becoming worse value as your electricity & maintanence costs still remain.

I am not saying local models don't have their place but if someone is trying to use this logic to justify their purchase then I wish them all the best, as someone who is actively working on AI compute/inference/hardware stuff I personally don't have this level of courage.

But this is not a sound investment strategy that if something is going up and seems like it might keep going up, especially when investing in heavily depreciating assets like compute.

https://eshop.asus.com/us/ascent-gx10.html

Otherwise I think they just meant chinese models not mainstream models.

Like Hy3, Mimo, etc. Also gemini models but not sure if they are mainstream anymore.

Save for GLM 5.3 - coming soon once it percolates through our providers.

That's how all engineering leaders I have worked with have worked.