neosat

>> The sort of obvious answer is the same reason McKinsey hasn't taken over the Fortune 500

Sorry, this is not even close to being an answer let alone an obvious answer. The main reason is because 'reality has a surprising amount of detail' (read the excellent blog post if you have). There is a huge difference between doing some analysis, framework, and outlining recommendations on a slide vs. generating business value in the real world. The real world is messy, a complex and dynamic adaptive system, with tons of details where small decisions add up.

Put a feature team in a strong company / startup and put them up against a top consultant on a real world scenario to ship something and generate real world value/uusage and you will get the answer why.

Do these benchmarks have any meaning anymore? And do the announcements seem less exciting now? (Not taking anything away from the advances we are making but it seems more incremental now?) The reliable way to tell if you'll like a model is reliable collage/X reviews to gauge a model's capability and then trying it out to see if you like the style.

The last time a model announcement felt like a leap in capability beyond other things out there was Fable - which was promptly taken away. Sol and recently Opus 5.5 were strong because they approach that capability with a lot more efficiency and don't blabber incoherently (looking at you Opus 5.1).

Deepseek is a workhorse for those who prefer open and API usage. Other than that the model announcements all just seem like a blur and quite interchangeable but I wonder if that's just me tuning out or do others feel the same way?

Lots of use cases! I've personally used it for the following:

1. Evals (once you have your rubric defined and tuned using a reasoning model, jev can be great for running periodic evals especially those that run daily.

2. e-commerce catalog classification 3. quick search using anything as context and query mapping to a pre-defined set.

>> "are these companies interested in developing research" judging from the money, resources spent and the value they derive from this the answer is very definitively yes.

What makes you think these companies (and I'm not a fan of all their motives) are not interested in developing research? The motives may be self-serving, but it is undoubtedly and objectively accelerating research.

You can couch it different words but the basic shape of all these is the same, whether it was voice artists earlier, IT outsourcing, or now disciplines like mathematics. A small set of people (relatively) who were the primary source of getting something done, suddenly find that technology has made it accessible for others to do what they specialized in. It's a tough pill to swallow and it is natural to not be comfortable with this for most people.

However, as it has happened in the past, once there is a technological wedge, technological advances will move forward, whether some community likes it or not, and human ingenuity will find ways such that the benefit is greater than the risk.

Can you or someone else from A\ comment on whether the conversation style is coming to Opus 5 or a future 5.1 asap as well? Currently it seems the model has been made unusable by the way it 'speaks' and there is a clear solution where it can speak better but nothing has been done about the flagship model on Pro plans. I've literally had to work on Opus 4.8 which does not have this problem and speaks fine.

Sorry but the authors of this article probably don't fully 'get it' (or at least cannot explain it well) either if this is their conclusion.

"In other words, Stripe did not buy a router. It bought a strategic frontier AI systems security and alignment asset. We believe combining this asset with Stripe’s existing infrastructure strengthens the ecosystem’s independent alignment capabilities in ways that would be difficult for any single lab to accomplish by itself. As such, this acquisition is net positive for the health of the independent frontier ecosystem."

Contrast with the clarity reflected in the letter Stripe reportedly sent to its investors.

"We see capital and intelligence are becoming the two digital flows undergirding every business ..... going forward every developer will also need a straightforward and reliable way to manage their intelligence pipeline" So basically Stripe already enables companies to manage capital. It now also hopes, with the acquisition, to jump to the forefront of managing intelligence / tokens for them. By bundling two core capabilities for AI forward companies they increase their offering attractiveness to businesses.

As the above two comments mentioned this is not true in practice due to batch effects (you can read about some interesting work published by Thinking Machines on this), as well as calculation drift that happens across computations esp. now with inference optimization becoming common.

This may not 'quite a bit behind' those at all. If you look at the benchmark numbers they are very comparable to Fable, but beyond a certain point the benchmark numbers don't tell you much. Opus #5 beats Fable on some benchmarks but given similar cost almost everyone who has used those two models will prefer to use Fable.

At this price range $0.87per 1M they will get a lot of usage of people trying it out. Given the benchmark numbers, for many people and many use cases this will become their primary driver. There are people and use cases where Fable, Sol will work better but those are likely not the target of DeepSeek anyway.

In terms of performance and price pareto curve I don't think any model can beat this today (though openAI is doing some exciting recent work in efficiency) - which is a remarkable feat for the DeepSeek team.

Either way, what a time for consumers of these models :)

Yes I had a similar experience with Opus 5. It is very token efficient, fast, and gets reasonable part of the work right but makes a LOT of mistakes. In a month+ use of Fable completed each task without ANY errors. Opus could not complete a single of ~5 tasks without some issue or the other - either not getting it fully right or actually introducing regressions. To their credit it was able to catch regressions and fix competently. It seems like a pre Opus 4.6 model in terms of reliability with a lot more power and spiky intelligence. When it gets things right it's powerful and efficient but without reliability I had to 'downgrade' to Opus 4.8 forcibly (since it was not a default option on claude code). I really miss Fable on the pro plan and will likely churn to K3.

Definitely not my experience. Fable is better but I'd prefer K3 to Opus based my experience with both.

Good observations. There's definitely a trend in pricing increasing but also balanced by innovations and availability of other models (both open and closed) emerging as alternatives. It's natural for the labs to explore how much they can push pricing, and for competitors to explore how they can treat that margin as their opportunity to grow their business.

Eventually the pricing should be more stable.

I've been using GLM 5.2 recently (company hosted, for non-coding tasks) and it's been strong and reliable. There are areas where GPT 5.5 and Opus 4.x still feel marginally better but only marginally. For most tasks if GLM 5.2 is the only model I have to use I'm productive and happy. This was not true before GLM 5.2. No doubt in my mind that the gap is closing quickly and for most tasks that are not very specialized open models will be usably on par on flagship closed models and have an edge factoring in cost.

For coding I still use 5.5 w/ Codex and prefer that to other models + harness combinations.

Anthropic is really on a tricky path here. When you have had runaway success due to a hit it is easy to believe that it is the natural way of things. However, that happened due to unique convergence of tech paradigm shift, the competitive landscape, and how they were positioned to capture that value through claude code.

They somehow conflate their value with 'safety'. While it's an admirable internal quality for the company to have, their treatment of their user base (developers, users) has been bordering on indifference and their stance bordering on arrogance.

As competition heats up, there is a very real chance of them shooting themselves in the foot with friction such as this (to be fair not completely in their control but also they had their share of responsibility that led to this)

Agree. Audio has strongly temporal so there is almost certainly some positional encoding one way or another.