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I think Anthropic and OpenAI have found product-market fit

simonwillison.net

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Re: I think Anthropic and OpenAI have found product-market fit

#641

Post by CuriouslyC »

grttq wrote: post #572
CuriouslyC wrote: post #408

Earlier quoted context omitted.

There's no change in tone, I'm still very bullish on the tech, Claude in particular just isn't worth the API price, which I've always felt was too damn high. I have paid for Gemini 2.5 Pro, Deepseek 3.2/4 and GLM 5 tokens happily though.

Lmao you won’t admit it will you? You financially benefit from stuff like agents. Of course you will be the last to admit publicly when things aren’t quite heading in the right direction. The gap between hype and reality is ever increasing.

You misread my comment because of your personal bias, and now you're acting like it's some sort of own? Come on bro.

The models are putting in work, that's indisputable, the thing I'm calling out is that Anthropic's price and hype are running ahead of the value it's delivering. Agents are a great boost at <=5% of SWE salary, a mediocre tradeoff/risk at 20%+, and insane at SV tokenmaxxing levels.

Re: I think Anthropic and OpenAI have found product-market fit

#642

Post by signatoremo »

airstrike wrote: post #621
timmg wrote: post #591

Earlier quoted context omitted.

> Pmf is this weirdly defined thing where "if you're not sure you have it then you don't". I'm not sure if this runs counter to your point or not, but: I don't see any future where LLMs aren't a core part of Software Engineering. The horse is out of the barn. There is no going back.

True but that is maybe 5% of what is being promised by the average booster

Give examples of boosters (average or not) and what they've promised?

Re: I think Anthropic and OpenAI have found product-market fit

#643

Post by lukeasrodgers »

doug_durham wrote: post #356
simplyluke wrote: post #305

Earlier quoted context omitted.

Yeah, that's the part that just seems to be wildly under-discussed to me. If open source models are ~3-6 months behind SOTA, and ~opus4.6 capabilities are good-enough for product market fit, do the frontier labs have half a decade to catch up on their prior burn? AI cost ballooning faster than companies can afford is becoming a very common topic in my circles right now. The era of "I'll pay infinitely more for margin…

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.

This project argues that with appropriate harness, the performance gap between frontier and much smaller open weight models shrinks dramatically: https://github.com/antoinezambelli/forge. I haven't kicked the tires yet.

Re: I think Anthropic and OpenAI have found product-market fit

#644

Post by aspenmartin »

binary0010 wrote: post #28
mesmertech wrote: post #15

Earlier quoted context omitted.

For coding you always want to go with the best model in the category, not something that would be the best model if we went 1 year back which GLM 5.1 is, and I'm saying that as a big fan of GLM cause I run a translation site where GLM is good enough for the price. Most of the money right now is in coding. Openai and Anthropic just have to be 6 months ahead of SOTA open source models and they'll capture most of the en…

Yes I'm an engineer (20 years most in games/graphics industry) and only use it for code. I've been using glm 5.1 this week a lot. I went in expecting another "decent" but not really "up to standard" open source model. I highly doubt I'll ever use Claude again. I think you are wrong about Claude being any significant level better

Well I think there are a multitude of harder measurements that would disagree with you, but ultimately there is absolutely a use case for cheaper open models (or even cheaper tiers of proprietary models) and in fact the unsolved optimization everyone is trying to get to is how much spend to use for a given task. But there will always be a market, especially in enterprise, for the best performance there is to offer

Re: I think Anthropic and OpenAI have found product-market fit

#646

Post by what »

simonw wrote: post #529
root-parent wrote: post #526

Earlier quoted context omitted.

Author seems strangely unwilling to distinguish usage from profitable product market fit. And from his own numbers: Anthropic Max: $100/month OpenAI Pro: $100/month Total paid: $200/month API equivalent usage: $2,180.16 in 30 days So paid only 9.17% of API-priced value a 90.83% discount, or about $10.90 of API priced usage for every $1 paid... That proves heavy usage but not sustainable unit economics. Anthropic repo…

My point with the $2,180.16 thing is that the price for consumers like myself is heavily discounted... but the price for enterprise companies is not discounted. My usage is therefore a useful indicator of quite how much those enterprise companies may be spending on tokens, given the new pricing scheme. If enterprise companies were still getting the same discounts that I get myself I would not have written this articl…

When you have to train a new model every few months to stay competitive, discounting that cost is rather dubious.

Re: I think Anthropic and OpenAI have found product-market fit

#647

Post by aspenmartin »

solomatov wrote: post #613
mesmertech wrote: post #15

Earlier quoted context omitted.

For coding you always want to go with the best model in the category, not something that would be the best model if we went 1 year back which GLM 5.1 is, and I'm saying that as a big fan of GLM cause I run a translation site where GLM is good enough for the price. Most of the money right now is in coding. Openai and Anthropic just have to be 6 months ahead of SOTA open source models and they'll capture most of the en…

>For coding you always want to go with the best model in the category, not something that would be the best model if we went 1 year back which GLM 5.1 is, and I'm saying that as a big fan of GLM cause I run a translation site where GLM is good enough for the price. Currently, the difference is substantial, but what happens if capabilities saturate?

Then the house of cards comes crumbling down, but there is so much evidence to point to this not happening that it requires a bit of a theory for how that may happen

Re: I think Anthropic and OpenAI have found product-market fit

#648

Post by aspenmartin »

lunar_mycroft wrote: post #574
mesmertech wrote: post #15

Earlier quoted context omitted.

For coding you always want to go with the best model in the category, not something that would be the best model if we went 1 year back which GLM 5.1 is, and I'm saying that as a big fan of GLM cause I run a translation site where GLM is good enough for the price. Most of the money right now is in coding. Openai and Anthropic just have to be 6 months ahead of SOTA open source models and they'll capture most of the en…

> For coding you always want to go with the best model in the category This is transparently false, because the best "model" is still competent human developers. They're just more expensive. If you're willing to use current LLMs at all, it means you're willing to sacrifice quality for a better price, and your disagreement with the comment you were replying to is entirely about what the optimum tradeoff is.

Well it may be false that you always want the best model, but the point is performance of you+ is far more cost effective than you+someone else

Re: I think Anthropic and OpenAI have found product-market fit

#649

Post by stevenally »

whatshisface wrote: post #173
trjordan wrote: post #11

They've got, ballpark, $5t to $10t to make back in the next 5 years, or the hardware buildouts will start getting written down. This means we're going to need $1t+ per year in spending, per year, on tokens. 200m knowledge workers in the world, 30m developers. We're talking about a world where you need 5% of every knowledge workers salary to go into tokens. 20% if you're a developer. That's a _huge_ shift. Most people…

Here are a few thoughts: - The publicly available information about how inference costs compare to training costs is conflicted. EEs involved in datacenters talk about power usage spikes during training runs as if they were a major factor in the designs, but academic papers discussing cost-optimal scaling confidently treat inference-time compute as a major factor. - On the side of the balance indicating that training…

> If we don't even know the ratio between amortized capital expenses and operational costs, outside investor analysis is impossible.

And yet we surely need this data for the IPO? Or are they relying on rule changes on the indexes to force ETFs to buy shares?

Re: I think Anthropic and OpenAI have found product-market fit

#650

Post by simonw »

bradleyjg wrote: post #640
trjordan wrote: post #11

They've got, ballpark, $5t to $10t to make back in the next 5 years, or the hardware buildouts will start getting written down. This means we're going to need $1t+ per year in spending, per year, on tokens. 200m knowledge workers in the world, 30m developers. We're talking about a world where you need 5% of every knowledge workers salary to go into tokens. 20% if you're a developer. That's a _huge_ shift. Most people…

> That's a _huge_ shift. Most people I know cite +20%-40% velocity with these tools, against the actual work their company cares about doing. We all have our own observations and mine don’t significantly diverge. But that’s bottom up. At this point shouldn’t we be seeing it top down? If we are beyond potential and into significant productivity gains, why isn’t that showing up for the customers? Why didn’t delta airli…

> Why didn’t delta airlines get significantly more operationally efficient in the last 3 months due to the introduction of better software?

The coding agents got good in November. Most individual engineers didn't fully clock this until January/February. This means that companies didn't really figure it out until March/April.

Assuming companies like Delta have adopted coding agents (which would be pretty fast) it still takes months from adopting a new tool to the code results of that tool rolling out to production.

I expect (and would hope) Delta's software development culture is very conservative. Since nobody can confidently tell Delta "here are proven practices for using this tech to produce high quality, more secure code" yet it would be surprising if they were blasting full-steam ahead.

I expect that even companies that got on board with coding agents in January will only just be starting to ship user-facing features that benefited from those new tools. Shipping software takes a long time, no matter how much faster the "typing the code in" bit gets!

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