Is it increasingly though? As of 5.5 I stopped using other models all together (and I used to regularly swap, indifferently). My $20 sub no longer runs out, even though I do _much_ more than ever. I use it for every single task at work, even trivial ones, and my spend has gone down. I was given _free_ Codex and realized... it still wasn't worth it. My rational brain says Anthropic has no moat and this is temporary. The progress of all the other models is amazing. I certainly wouldn't bet on them.
But if I look at the actions of myself and the most competent coders around me, they are all doing this same thing - using 5.5 and not really looking around any longer. And we know its not even their best model. OpenAI may respond, Chinese labs may catch up. But for the moment, Anthropic seem to have added separation, not lost it, based on current vs prior _behavior_ of coders I know at home and work.
First, it does not really reflect my experience. I find modern models to be largely interchangeable when it comes to common software engineering tasks, including some of the more recent open weight ones. The strongest models become interesting for Ph.D. level of work for me, which is not something I deal with regularly anymore.
None of them are perfect; they all have their strengths and shortcomings, but I can reasonably work with all of them and it honestly doesn't matter much anymore. I do sometimes wonder what exactly people are doing to see such a difference; at a minimum, their workflows must be very different from mine.
Second, you're arguing based on subscription pricing. But Anthropic makes the bulk of its revenue (and has its biggest margins) from enterprise/API pricing, where they are the most expensive provider by far. Anthropic isn't going to make a lot of money off your $20 subscription. With a subscription, you currently get more usage per dollar out of Anthropic's than OpenAI's plans, which approximately offsets the difference in API pricing.
If you think that, it means you're not demanding enough from your models.
Opus 5.5 still makes a lot of mistakes. Even if it can technically do all tasks you throw at it, there's no way it can do it at 100% reliability.
To be clear, AI writes 100% of my code since last year. But it still produces bugs, needs multiple prompts, and has less intuition than a human.
My usual workflow (not counting throwaway code) is to use agents as virtual pair programmers, which gives me a good balance of quality and velocity.
Each interaction with the agent remains individually comprehensible and makes progress towards one of the following goals:
- Understanding the problem/solution space.
- Implementing a solution.
- Understanding the solution, especially any agent-written parts and interaction with existing code.
- Evaluating the solution (tests, code analysis, what-if scenarios, consider edge cases).
This does not just involve the model generating code, but also explaining or analyzing it as needed.
This process allows me to retain a mental model in lockstep with developing the written code and have some assurance both of its functionality and non-functional requirements. It also fits in nicely with most existing team-level workflows.
But it's also something that I can do with pretty much any of the modern models.
There's two thresholds.
1. Tasks models can do. 2. Tasks models can do with enough reliability that you can YOLO merge without looking at the code.
Most models can do most of 1. That's no longer the interesting part.
2. is quickly expand. I can trust opus 5.5 enough with easy to medium tasks that I can YOLO merge them. For open models, I can only trust them with easy tasks if I want to YOLO merge.
I'd love to have a stronger model that I can trust to YOLO merge hard tasks.
The recent update to SlopCodeBench that includes Astra and Opus 5.5 (including the insight that higher reasoning levels may not improve the situation) seems to match my intuition.
[1] https://github.com/humanlayer/advanced-context-engineering-f...
I struggle to find myself ever feeling comfortable doing this in a production environment at my day job.
These days your average Opus 5.5 PR with multiple passes of review by Opus 5.5 and Sol 6.1 agents is much less likely to contain bugs than your pre-AI PR.
Having said that, I was working on an AI for a certain video game, and I had Astra 6 and Sol 6/6.1 work for 30 hours straight, with some human steering to get it unstuck every now and then. It could not get it to work right, and the code it created was a mess.
When Opus 5.5 came out in like 4 hours it cracked it.
I didn't try but I'm very confident if Astra failed, the open models would've failed too.
Personal efficiency is noted as never hitting my limit despite it doing huge amounts of work at a time (ex: rewrite my notes app in swift for ios and install on my phone, 2 hours later its opened on my phone, looks nice, is working (base set of features). No follow up prompts, around 2 hours of work. 45% of my 5-hour limit.
Work efficiency is spend, task complexity, code quality (how often i have to steer - rare now), and wiring it up to more stuff (ex having it setup metrics and dashboards etc etc).
tl;dr: You should try Opus 5.5 and report back.
All indications are the models are increasingly worth less and less and rapidly approaching zero. Even worse, everyone is rapidly shifting to “router” architecture where they switch models constantly. LLMs are perhaps the least sticky product in the history of tech.
The only money to be made is selling compute to run models on. The only real out I see for Anthropic is if somehow compute becomes so constrained that they can flip to sell all their committed compute at a markup to run commodity models. Absent something like that it would appear they don’t have a viable business plan. As many have been pointing out, they casually left Q3 off their charts. Put that back in and suddenly the charts look very worrisome.
What many of us experienced this year is that Fable 5 and Opus 5.5 are good enough and future model improvements may have diminishing returns even if benchmarks improve.
I’m very excited for smarter models but what I really want now is not capability but speed, cost and open weights.
Every model release people say the model is good enough, yet Opus and Sol are still more popular than Deepseek. The frontier is shortening, but there are still considerable room for models to improve. SlopCodeBench is new and not close to being saturated.
This is great for us, but that behavior is the behavior of a commoditized product.
Right now OpenAI and Claude (and maybe Gemini) are over the finish line with reasonable prices for many tasks whereas the rest are still in the race.
But what happens when the open weight models cross the line and it just comes down to price?
Somewhere in there is a definition of commoditization.
Probably meant Gemini Argon
Anthropic is in a business that didn't exist 3 years ago.
Which ones? I'm looking at the list of companies with > $1T market cap and none of them have commoditized markets. Instead, they all have huge network effects (Apple), enormous legacy lockin (Broadcom), a religious monarchy (...), or are some of the only people on the planet who can do what they do (nvidia, tsmc).
Mind you, there's lots of _billion_ dollar businesses in commoditized or partially commoditized markets. But Anthropic as a billion dollar business is sunk.
And they likely won't be spending them on something without ZDR like Fable 5.
But they might spend them on American inference providers running DeepSeek v7 Pro with ZDR routed through OpenRouter. Or hell, they might just run it themselves like we're already seeing some enterprises do right now.
IMO, US AI providers are not leaders in efficiency anyway, they're still running the original ~2010 playbook that led to LLMs ("What if we took ML researchers' work, but funded it at 50M/yr instead of 250k/yr?").
They're currently arguing for the continuation of that approach: Scale funding first, and the rest will follow. It is high-risk and WeWork-like, by nature.
Meanwhile, everyone else is working on making these models more useful through a ton of ecosystem around them.
I don't see how the frontier labs will have any control over that to deliver on what investors expect. As mentioned in another post here, if they could, they would already be spawning hundreds of companies to diversify their reach but they aren't, because this era of AI is awesome but still doesn't allow a few individuals to replace millions of others that easily (or efficiently).
but it's already commoditized.
Time has tempered it, then–re-read it [1]. It's a kaleidoscope of stupidity with no coherent economic case. The Anthropic S-1 is ridiculously ambitious. But it puts forward an argument for its valuation that is at least falsifiable.
[1] https://www.sec.gov/Archives/edgar/data/1533523/000119312519...
It's like signing a 10-year lease on an apartment in NYC and renting it out by the day on Airbnb. It may or may not work.
On top of that, there's the question of how you treat the remodeling of that apartment (model training). If it benefits you for years to come, it's an investment that you depreciate. If your guests frequently trash the apartment, or tastes change (the model becomes obsolete quickly), that remodeling (training) is just a cost.
And they've gone private now.
Similarly nobody is saying AI can’t be a successful business, but here too the big labs have dug themselves into a giant hole such that a bankruptcy reset may ultimately be the only way out of it. Time will tell.
According to the S1:
- FY24 revenue $386 million -> FY25 revenue $4.59 billion (+1,008%)
- Q126 revenue $4.7 billion -> Q26 revenue $11.5 billion (+145%)
That seems like pretty good evidence of current and ongoing explosive growth. Much of this revenue is pre-paid (re: cashflow). Meanwhile, operating margins went from -184% to -$4%.
Seems like pretty good evidence. Will growth continue apace? Unknown. But the arguments for Jevon's paradox applying to intelligence seem pretty convincing, so the TAM here seems likely to be enormous.
> All while everyone is discovering there isn’t really a secret sauce moat to demand
Also not convinced by this. The Chinese models seem to have distilled their way to current levels, with Anthropic and OpenAI now getting serious about defending against this.
I don't think you're definitely wrong in all this. Rather, I think your claims are too confident.
I suspect this started stagnating in Q3/Q4, which is why they're looking to IPO now.
GAAP isn’t perfect, but it exists for a reason.
This quote stops me from shorting pretty much anything.
However the price of CDS on bonds is already a strong market indication of where the market thinks all of this is heading. And none of that looks good if you’re betting on the AI exuberance continuing.
You can simply not buy shares in companies you think are on a doomed trajectory.
That said, depending on the IPO target price, I might short it.
IMO the whole thing is based on the fact of "we will reach AGI/ASI eventually, and when we do, we will reach escape velocity".
No one knows - you, me, or Dario if this will happen and when.