Why on earth would AI labs be bragging about how little the product they sell actually costs them to make? You don't want to do anything that reduces it's perceived value to the user, that might make them less willing to pay for it.

Also, inference costs are bound to go way down with more optimized architectures. GPUs are fundamentally not great at inference. No platform where the weights are streamed from a large pool of memory is. If the models ever quiet down, there will be massive step changes in cost/token, energy/token and tokens/second, as models are etched into silicon ala https://chatjimmy.ai/

Inference has traditionally been far less expensive than training. One public example is the fact that hobbyists can run StableDiffusion ($600k training costs[1]) on their personal computers.

Speaking to your point, inference being dramatically less costly than training would not be seen as a delta from the norm. The model of providing inference for anything near the operational costs (like a utility would), would the delta from the norm if it were true.

[1] https://x.com/emostaque/status/1563870674111832066

There's still a lot of room for the best models to get better at coding .

Your argument rests on the "for marginal gains" part but it's really not clear that the gains are marginal in the foreseeable future.

Open source models that you can run locally are much more than 3 to 6 months behind. 6 months was the November inflection for Claude. No open source model is as good as Claude Opus 4.6.

I am aware of at least a handful of companies doing the latter. I don’t work for them and cannot speak to their setup.

what models are you using on that? My experiences with apple hardware have convinced me that it is not really good enough for coding locally.

> AI for product development and management would be far more impactful than automating rote coding tasks [...]

Yeah, if this stuff actually worked that well already, OpenAI et al. would just run AI CEOs and engineers. Why get some other company to pay you at all when you can automate every other company out of existence and take all the money they make?

The fact of the matter is that while the tech has some uses, it sure as hell isn't a full scale replacement and you almost always actually have to massage the input into LLMs to get anything decent back out in practice. Some CEOs and managers can learn to do this, of course, and some already are... but that quickly turns into a second full time job. A "programmer" is still needed. The job might change from mostly hand-writing C++/JS/Python to prompt engineering + some manual coding to fix all the stupid fuck-ups that the bots can't solve themselves, but you still need someone to actually prompt the bot.

When that changes, it won't just be engineers losing work; there will be no reason to even have a human CEO any more.

I don't know, if you've ever tried to build something at companies of that scale you run into incredibly boring problems "what data table do I need for X" and "who is the right person to reach out to for Y" and "they aren't answering me I guess I'll have to escalate"

I don't think there is any shortage of great ideas at these companies, they are just extremely bloated. And I don't think its something like indecision or bad PMs, it's "we have a finite amount of time and resources so we need to be conservative but also not too conservative"

If you have AI systems that can simply build out POCs in days, backtest on real data, show reliable results and numbers, you get a suite of product options you were never able to get before. If you have coding agents that can speed up implementation, you can build more stuff and choose the things that stick.

It changes the cost/benefit calculus of the entire business. I think you are exactly right in that: PMs/leadership are by their nature orchestration machines. Other roles are as well, but I think PM's are at a particular advantage here in that it will be quite awhile I would expect before core product decisions and creativity can be delegated to an AI, but not quite awhile until virtually everything that they're blocked on (legal approvals, POCs, wire frames, etc etc etc) will become less and less of a blocker

Google's internally developed and sometimes even launched plenty of innovative new products in the past decade. Stadia, Fuchsia, federated learning, and the whole transformer architecture that underlies this AI boom are good examples.

The problem is they get killed by some other executive who is afraid of their department looking bad by comparison.

I think this is fairly illustrative of the challenges in AI becoming as impactful as the Internet. The bottleneck is not making things. There are plenty of people who are really good at making things and can easily be 10x or 100x as productive as the average corporate worker. YCombinator was founded on that premise - small teams of founders and early employees could be orders of magnitudes more productive than the 1000s of corporate employees at their competitors.

The bottleneck is on bringing your product to market. If your innovative new product is built within a corporate environment, it'll get killed unless the executive you work under can get a promotion out of it, and you'll be denied all sorts of help with approvals, launch process, PR, marketing, branding, etc. If it's a startup, they'll try to shut you out with exclusive distribution deals, legal threats, lobbying efforts to change the legal environment, PR campaigns, FUD, etc.

The Internet was revolutionary because it let millions of people bring products to market without asking permission. Instead of having to bid for retail shelf space among dozens of entrenched competitors that all had sweetheart deals with the retailer, you could just put up a website and sell it to anyone across the globe. Instead of following hundreds of regulations that governed existing commerce, you could just launch something and sort it out later. AI doesn't really have that property - if anything, it makes things more centralized, with more gatekeepers, and so seems more likely to destroy economic value than add to it.

>I would argue that that's been the case for quite some time before AI.

I would agree but it's really minimized the building. More and more time is being spent on pre-coding work.

Google & Meta are illustrative of late-stage capitalism -- it's all about distribution, not innovation. Their job is (mostly) to just acquire the products that have passed the gauntlet, then scale up their monetization through their distribution-focused machine. The same dynamic plays out in virtually every industry (not just tech).

You'll find that most internal "innovation" teams are just lip service. In most cases, the "mothership" will be incapable of reproducing true innovation -- from a statistical perspective, culture perspective (mega corps are anti-scrappy; internal politics), and motivation perspective (startups aren't 9-to-5). It's much easier to have big M&A budgets, a VC arm, and some handwavvy internal innovation group.

Every now and again, you'll get real innovations (Waymo, transistors, GUIs), but even those have a spotty track record of commercialization when created internally.

Outsourcing of knowledge workers didn't work out because at large enough scales, the geographic arbitrage disappeared. Companies mostly always got what they paid for.

The determinant of success was only whether the task needed American-tier labor or could make do with sub-American quality labor.

The more time I spend using agent tools the less I worry about knowledge worker job loss.

It takes a skilled knowledge worker to use these things.

There were no knowledge workers in the middle ages.

> Who pays for that value, and from what, if all knowledge workers lose their jobs?

They do not care unless these companies can get a bailout.

UBI only exists for companies that are too big to fail. Case in point, 2008 and SVB when there was too much money on the line.

One of the AI companies attempted to guarantee themselves a way for the government to bail them out if they were close to defaulting on the debt from the data center build out.

Feelings aren’t fact. A lot of data shows the doomerism is not reflected in the actual numbers and much of it has to do with rapid inflation and continued vibes.

Consumption has risen, inflation adjusted wages have risen for blue collar and white collar alike. Most social mobility has been the middle class moving into the upper middle class, not moving to the lower class.

The main thing holding people back is the housing crisis. This is orthogonal to the value creation of businesses.

Value creation is growth. If it didn’t exist the S&P would still be 42.55$.

Sounds like internet sentiment and not research data.

It's kind of become socially taboo to not be suffering "in this economy", but on paper it's hard to see weakness in places that there isn't always weakness. As long as the 65-95% are doing well, there isn't going to be a collapse.

A literal example is that I can use AI to file my taxes instead of spending a weekend and hundreds of dollars to have an accountant do it for me. It costs me like $5. that 245$ delta is the value of that output to me, as long as I am confident it is correct.

Thats the thing; the "increase in productivity" isn't being felt by the general public, the end user. If your "increase in productivity" just means more money being shifted around at the corporate level then it is meaningless.

100%, a driving factor will likely be how good we can make models that are so small they use almost no compute. Until then it is a race for adoption and moat-building (or screwing people over?) once you have users

> The more AI causes productivity increases, the less and less number of workers will be needed.

Why does this have to be the case with AI but it didn't have to be (and wasn't) the case with the steam engine, electricity, the automobile, or the computer & internet?

Certainly, AI could be different.

It's curious to me why the vast majority of people on here think it must be different.

>The more AI causes productivity increases, the less and less number of workers will be needed. This will heat up the job market even more and bring salaries down.

>Net effect of this productivity increase: less consumption by the masses, even though you may be producing more good and much more efficiently.

Big tech companies can't even create login flows and account recovery flows that work for everyone yet. There are countless stories of folks losing access to business Instagram accounts that get hacked, Google support from a human to fix a problem that is outside of their help articles is non-existent, etc etc. There's still so much "low-hanging fruit" IMO that isn't particularly fun or exciting to fix, but ask your average non-tech friend or family member what they think of the Facebook + Instagram security settings pages / sites / desktop-only settings.

Who is going to pay for all of these subscriptions that will power this GDP increase when average purchasing power of those outside of the top ~10% of earners is decreasing YoY? We're headed toward food and water shortages next to sprawling datacenters, not shared societal prosperity and a healthy middle class.

> The more AI causes productivity increases, the less and less number of workers will be needed.

That only holds if companies have a fixed need for "productivity" which is met by their current employees, such that their employees becoming more productive means they need less of them.

Every company I've ever worked for has wanted to achieve way more than they are able to get done with current resources.

But generally yes, the biggest open question about all of this is how the impact will play out on the economy, job opportunities etc. I've not seen anyone come close to a confident prediction about how this will play out.

First of all, common people are not living paycheck to paycheck in the sense that they're at risk of not having money[0]. This is corporate content marketing that has entered the collective memory of people, not anything close to reality.

Secondarily, reducing the cost of making a thing doesn't always mean you get less of a thing. For me, certainly, what happened is that I write way more software than I originally did. When we built compilers, the amount of human engineering effort required to do things plunged, but the amount of software engineering jobs didn't go down.

This is as bad as models will ever be. That part is true. And it's entirely possible we go foom. But it's also possible we don't, and then it depends on where the asymptote lands.

0: https://www.slowboring.com/p/this-economic-myth-needs-to-go-...

This would be a Bladerunner future Pope Leo XIV warned against (https://news.ycombinator.com/item?id=48265206), though in different words.

Literally revenue is collapsing in most sectors. Technology purchasing is declining. Service models are failing to turn a reasonable ROI.

People stopped buying shit.

It's consolidating into fewer, higher value assets. Over 40% of the S&P500 is in companies that are heavily (potentially over) invested in AI.

I'm sitting in one right now and don't see any smoldering...

The company’s gone but the assets just got sold to other commercial real estate firms.

Uber was basically only ever software to help people use their own cars so a very small part of their valuation was physical stuff to upkeep, it was just deals and obligations they had.

Not sure how it shakes out for Anthropic and OpenAI. There’s a lot of physical capacity that needs to be built out and can depreciate. But there’s also a lot of network effects and dependencies being built in with enterprise users.

I don’t know how swappable the tooling is either. I think over the long term the UI, model training and documentation, and infrastructure are going to end up being run by different parties and I’m not sure which leg of that chain ends up in a position to skim most of the profit off. My guess is that Apple and Google end up raking in all the money since they control the OS and app stores while the rest of the stack gets driven down to being generic commodities. At least where mass market consumer adoption is concerned.

I quoted exactly what they said in my piece, under the heading "The AI-failure stories around this are pretty thin": https://simonwillison.net/2026/May/27/product-market-fit/#th...

> But then you sometimes go and talk to your senior engineering leaders and you’re saying, OK, how many projects that were on the cutting room floor got moved above the line because of the productivity gains because 25% of our code commits were via Claude Code last quarter?

> That link is not there yet, right? I think maybe implicitly there’s more that is getting shipped. But it’s very hard to draw a line between one of those stats and, OK, now we’re actually producing like 25% more useful consumer features, right? And that line is hard to draw.

That's pretty weak sauce. I don't think that justifies the headlines that came out of it, personally.

Makes sense if you think about it: if all photons pass through you (invisible) then you can't capture them to get info (blind).

Why? All their money is going to Apple Silicon and the five ecosystems, so far in Apples entire history, the largest acquisition has only been $3 billion dollars, OpenAI is currently getting nothing and they gave Google a measly $1 billion refund per year for the use of Gemini.

If John Ternus wants to spend some money, spend it on bringing memory in house. Apple has the money and the engineering talent to do so, have it fab/made onshore in partnership with TSMC.

Do it Apple because you have to not because you want to the Chinese probably will be taking over the memory industry, worldwide, by taking advantage of the greed from three memory companies and their AI overlords.

I agree but it's even worse than that.

Data centers come down to performance-per-Watt. Electricity accounts for 20-30% of a data center's operating cost [1]. I don't know the exact breakdown but the GPU part of that is probably the majority given how power hungry GPUs are. The B200 is upwards of 1200 Watts [2]. The B200 is rated at ~4.5PFLOPS of dense FP8. So you're getting 3.75PFLOPS/W. We don't know what the next generation will look like. The A200 (Hopper architecture card that preceded the B200) had ~4PFLOPS apparently but also lower power consumption. Obviously this changes depending on whether you're looking at dense or spare and FP8 vs INT8 vs INT4 vs FP4, etc so we're just using FP8 as a yardstick.

Imagine a fictional B200 successor, the T200 that has 8PFLOPS of dense FP8 at 1000 Watts. Well then a DC built on that where the T200 will likely cost similar to what the B200 does now, you'll get nearly double PPW so the same size DC and same electricity load is going to be like 2 of your old DCs in operating costs. That's a big deal when you've laid out a trillion dollars.

[1]: https://iaeimagazine.org/electrical-fundamentals/how-much-el...

[2]: https://www.trgdatacenters.com/resource/h200-power-consumpti...

I thought my post was pretty free of hype. I said that this new revenue "Maybe even enough to start covering their costs!"

At a certain point, I genuinely feel like the best way this hype is being sold is by making people genuinely believe in it.

and in that sense, if Anthropic and OpenAI are able to create the projection that they can-be profitable despite finances seeming bubbly at best, I think that what happens is that these companies spew so much amount of content that people like Simon get into it too.

There is a deeper problem of people falling into AI psychosis too, in general, I am not sure if Simon has fallen into it or not

I think that the greatest point which can be made here is to not offload your thinking to others and to think about the situation yourself. Sounds familiar (looks like we are all off-loading our thinking itself to machines)

Side-note: As humans, we have a tendency to quickly judge or make quick decisions which stems from our times foraging and scavenging in jungles.

Another Side-note: at a certain point, I am unsure of how much to think about AI or not, certainly discussions about it that were happening 2 years ago weren't helpful in contexts that they are used now (well not in any way or form that a person discussing and getting into the weeds of AI 2 years ago is better than a person just getting into it say 2-3 months ago)

With the industry (moving so fast) [but that doesn't mean that you can't catch up with it, I feel like the fast word has made people think that they are falling behind which is imo wrong i suppose]*, It is basically unsure to me of any FOMO or anything if you aren't using AI already, I find this notion naive.

People might be making strong opinions (AI psychosis) and skills on the tools available at the moment the same done 2 years ago. We don't quite know about the tech as these are still black-boxes and how they progress and what these "AI skills" might survive or not in future. Heck, we aren't even sure if these tools might survive or not or wouldn't be made magnitudes more expensive simply to break even as they are given to us for the first time at percentages of the price.

I don't know if I should form (strong) opinions yet and also a question of its worth so much thinking efforts in the first place, probably just gonna do my own thing (the way I want to) which includes learning C at the moment. because learning is fun.

The high-risk side of that bet would need to win more like a lifetime supply of Twix. But in a post-scarcity nirvana, everyone already has that. So sure, you're on at even money. See you in two years.

I’ve seen the same dashboard for a dozen custom web applications now, including a couple I had it make for me.

It really does have a particular lane for each chore, and it’s reproducible.

It produces the "most average" web design unless you really prompt your way out, isn't it? If you don't care enough to prompt, Claude does not care to be individual.

Those numbers don't even track even in the same sentence. If it is $2T/year by the end of 2029, it would be something < $6T cumulative in 3 years.

The numbers are made up political correctness anyway.

Everyone's agency is 100% captured by belief in Wall Street. Too few <50 have any meaningful labor skills to blink.

We'll continue to have consent manufactured via media platforms and in 3 years no one will bat an eye at these companies being worth $12 trillion as Altman and Musk climb two ladders holding a "mission accomplished" banner.

I think the underestimation is how many people want a personal knowledge worker in their pocket, and are willing to pay ~$65/mo for it.

Well around 40% of people work. I dont think its crazy to say around a third of jobs are knowledge jobs, but what do I know

A lot of those ‘edge cases’ in the definition of “knowledge worker” are probably the stuff that’s most likely to have significant parts of the work augmented or replaced by AI agents. Like, call-centers are almost certainly going to get turned over in a big way. It’s not like the median tier-1 support operator just reading off a script is much better than an LLM anyway.

Yeah, just looked into this. Knowledge workers is a big group and probably much larger than you think it is.

Basically if you're not doing manual labor, it's probably knowledge work.

Roughly 1/3rd of the working population.

Some data tucked in here: https://gist.github.com/danielmiessler/2dc039762a202b083753b...

> At 200M you’re already including a lot of people that stretch the definition of knowledge worker.

How do you know this? Im certainly open to recalibrating my numbers which is why I asked for the source

There’s a lot relationship and culture management overhead involved when adding 2 more people to a 10 person company. I think any business leader would take the productivity speed up from buying a tool over hiring more people and integrating personalities/habits/viewpoints to an existing established culture any day of the week.

Ah yes, December 2025...such a long, long time ago...

>Actually he provides sources when he analyses stuff and imho much better than the usual corporate

You said it was likely an internal leak to the WSJ "according to Ed Zitron". Did Ed have a source for that, or was it just vibes?

> "in 6 months" that AI will be doing all of SWE work

I assume this is the quote you're referring to from Davos?

"I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it. I do the things around it… we might be six to twelve months away from when the model is doing most, maybe all of what SWEs do end to end."

that was in Jan, he said "might" and he said 6-12 months. Yes! Let's hold him accountable for saying reasonable things!

I work in big tech and probably 90% of code over the last month has been written by AI. And I suspect it's probably higher within Anthropic, which is probably what he's basing his opinion on.

So, he's closer to correct than not.

That said, your recollection is also flawed. It was in mid-March, and here's the relevant quotes:

>I think we’ll be there in three to six months—where AI is writing 90 percent of the code. And then in twelve months, we may be in a world where AI is writing essentially all of the code.

[...]

>But the programmer still needs to specify, you know, what are—what are the conditions of what you’re doing, what—you know, what is the overall app you’re trying to make, what’s the overall design decision? How do we collaborate with other code that’s been written? You know, how do we have some common sense on whether this is a secure design or an insecure design?

[...]

>So as long as there are these small pieces that a programmer, a human programmer, needs to do, the AI isn’t good at, I think human productivity will actually be enhanced. But on the other hand, I think that eventually all those little islands will get picked off by AI systems.

With another 3-4 months left on the clock, his prediction seems remarkably on point for at least certain organizations and domains.

I welcome you to also hold yourself accountable in the coming months if this trend continues. ;)

Elon playbook

Yes, when he made that claim back in 2024 they were spending like $100M to train a model.

Depreciation is part of the cost of inference. Inference happens in GPUs that have a relatively short lifespan.

Those GPUs are very expensive.

Inference is expensive because a GPU can only process a certain amount of requests in a given timeframe. Remember that Anthropic is constrained in compute.

If they are constrained, it means that those GPUs are not idle. If they have more customers, they will need more GPUs.

If they have to play silly games using EBITDA to be "profitable", then it means that they need to ramp up prices a lot more than they already did.

Which is why in these discussions I always say that inference is also extremely expensive. Too many people like to pretend without any evidence that inference is cheap.

In the meanwhile, Google AI search still says the next year after 2026 will be 2028.

The thing that matters most to me is if reading what I wrote teaches you some new things and gives you something useful to think about.

If I make an argument and you disagree that's fine with me, provided I didn't use misinformation or sloppy thinking in making that argument.

I think you should highlight your exemplary pre-AI writing too.

All of these are about AI misuse, not skepticism of AI. By skepticism I mean doubting whether AI actually delivers on its promises which, based on this last post, sounds like something you think we're already past.

Many people still think AI coding agents are slop on steroids despite all the current hype around AI actually shipping functional products.

As I commented elsewhere, I'm still bad at making money from my open source work: https://news.ycombinator.com/item?id=48296794#48298909

(I have a feeling if I could say "and I closed $2m in sales with the software I wrote!" people would find a way to say that didn't mean anything anyway, because how can I prove I wouldn't have made those sales writing it by hand?)

He's saying he's getting a great deal...a token from Opus on Claude code is the same as a token from Opus on the API. I remain as confused as Simon. He's not talking about "here's the ROI I got from my $100 subscription" it's "here's how much I saved from getting the monthly subscription instead of sending things through an API".

> Just because API pricing would've been $2180.16 doesn't mean that's the value of those tokens.

You seem to be suggesting the price of tokens is entirely disconnected to the cost of providing the service? I don't see much basis for that assumption.

That doesn't seem to be the case. From what I've seen enterprise deals get API pricing now. Have you seen evidence that's not true?

And "large" just means that AWS will assign an account manager to talk with you. I was at a start-up who spent $300k/year on AWS and that was enough to get special attention and discounts. Enterprise pricing is confusing.

The point is that those a real prices real people are paying for real API usage. it's not made up.

your point is large players won't pay those prices at massive volume. ok

Claude is so in demand at the moment that there aren't really volume discounts. Anthropic sets the terms and you either accept them or get lost they have that much of a lead (mindshare/desirability wise).

> So how do you accurately price these tokens at all

Like anything else in the economy: at the point where enough customers can pay you, and not enough will go to the cheaper competition.

This isn't true at the moment, though. So far there hasn't been the negotiating power. What happens is you end up capping usage for employees at a fixed amount. I think eventually, prices will come down and there will be discounts, but for enterprise accounts at least of our size (<5000), we're paying almost 100% retail, which kind of sucks, because it's expensive, and pretty easy to burn $50 to $100+ in a day, if you're not careful. In fact we got pushed off the former plan to the token-utility one at the last contract negotiation.

Going to be interesting to determing the metrics we give to engineers for determining whether the spend on this is worth it. Measuring PRs, lines of code committed, commits fully generated by agentic workflows, etc.....

> API pricing drops DRAMATICALLY in enterprise agreements

Do you have any numbers or reports to back that up?

Claude Teams and Claude Enterprise are 2 distinct plans. Simon is right that Enterprise seats have no included usage (and so all usage is charged at API billing rates), whereas Teams seats do.

No one is claiming that OS is as good. They are saying it isn't that far behind SOTA commercial products. So why pay exorbitantly just to get something only a few percent better than the free option?

But there have been very good open source office apps for decades and few enterprises use them, so perhaps this is just the nature of B2B purchasing committees and 'nobody getting fired for buying IBM.'

+1 .. just wanted to reiterate that this is the answer. The open models work great if you just do a little more of the design/architectural work up front and organize your work appropriately.

But if the closed-source models can do this without the additional effort, that's a significant gap, no?

I've been mostly coding with GLM-5.1 as well and I agree with you. DeepSeek V4 Flash is another very good surprise. Incredibly cheap, fast and effective.

Cost for the value delivered. Like if you offered the current SOTA open source models at $0.1/M, I still think I'd be using Opus or 5.5 at $30/M. Or say GPT 5 which was released Aug 25, I don't think I'd use it for coding for even $0.1. I'd def find other uses for it(translations, agentic workflows, prompt guards etc), but for coding I don't think I'd ever completely switch to a SOTA open model

Unless ofc there was an actual speed difference, only reason I'd be willing to go with a worse model couple of percent worse than current best model is if the speed was at least 5x higher. Looking forward to kimi k2.6 offered publicly by Cerebras

Would love to hear more about your thought about the RAG.

Depending on RAG is a workflow problem, not an AI problem

Yeah, it’s called supply and demand. Demand for memory went way up suddenly. Now supply is going up rapidly as companies try to cash in on that demand.

Supply will eventually catch up with demand. Then the prices will come back down.

Algorithms are also improving. I believe it's very unlikely for these two improvements together to not result in one to two orders of magnitude cheaper cost per "intelligence". Of course, that might just make use cases that are too expensive today viable and thereby increase usage further.

Winch Design [0], which have built some of the world's largest superyachts [1], seem to be using AutoCad. [2] Afaik it's also the same with Lürssen (but don't quote me on that)

[0] https://winchdesign.com/ [1] https://www.superyachts.com/directory/1516/winch-design/flee... [2] https://www.autodesk.com/design-make/articles/naval-architec...

Except LLM's even with Vision are still useless at AutoCAD let alone Revit (please dont quote SCAD LLM's at me, useless). Knowledge based approaches still win.

I might agree "AutoCAD" is the current level LLM's are at, but wait until your design departments discovers "Revit", its another ballpark (in wasted cots, engineers on site still get "clashes").

Revit costs are high, and the end results are marginally better - but local LLM's tokens are cheaper 24/7 at "AutoCAD" level - "Revit" level tokens will make Ubers CTO/COO weep harder than they already do. While producing results no better than "Revit" does (engineers still face "clashes").

As someone completely outside the 3D design world who always thought of AutoCAD as the gold standard - really? What program would be used instead? Please enlighten me.

Insane Clown Posse, though given the context here probably Ideal Customer Profile.

Insider tips from Google and AWS telling me that they run inference at a profit (though that was over a year ago now).

Dario telling Dwarkesh three months ago that they have a margin on inference: https://www.dwarkesh.com/p/dario-amodei-2?timestamp=3528.0

Not yet, but that's because it was almost all open source and I'm really bad at generating revenue from that.

When I account for the amount of time it saved me there's no question $2,000 was worth it.

> Do we not care about code quality, maintainability, performance, extensibility, or understandability anymore?

Yes, we should care. I've been writing a whole book about that: https://simonwillison.net/guides/agentic-engineering-pattern...