That analysis is pretty brutal. It's very disconcerting that they can sell access to a high quality model then just stealthily degrade it over time, effectively pulling the rug from under their customers.
I do wonder how much all the engineering put into these coding tools may actually in some cases degrade coding performance relative to simpler instructions and terminal access. Not to mention that the monthly subscription pricing structure incentivizes building the harness to reduce token use. How much of that token efficiency is to the benefit of the user? Someone needs to be doing research comparing e.g. Claude Code vs generic code assist via API access with some minimal tooling and instructions.
I tend to agree about the legacy workarounds being actively harmful though. I tried out Zed agent for a while and I was SHOCKED at how bad its edit tool is compared to the search-and-replace tool in pi. I didn't find a single frontier model capable of using it reliably. By forking, it completely decouples models' thinking from their edits and then erases the evidence from their context. Agents ended up believing that a less capable subagent was making editing mistakes.
Well, according to this story, instructions refined by trial and error over months might be good for one LLM on Tuesday, and then be bad for the same LLM on Wednesday.
just call it something like "[month][year]edition" and work on next release
users spend effort arriving to narrow peak of performace, but every change keeps moving the peak sideways
The constraints of (b) limit them from raising the price, so that means meeting (a) by making it worse, and maybe eventually doing a price discrimination play with premium tiers that are faster and smarter for 10x the cost. But anything done now that erodes the market's trust in their delivery makes that eventual premium tier a harder sell.
And idk about the pricing thing. Right now I waste multiple dollars on a 40 minute response that is useless. Why would I ever use this product?
afaiui they're still losing money on basically every query
There's this one source on Reddit which calculated that Anthropic has been subsidizing their costs by 32x
I look at the output of Kimi and the costs of running inference on it that i can replicate, and it isn't that bad, although admittedly i don't have to worry anywhere near as much about scaling it and about having to dedicate large amounts of compute to research and distillation on the back end. It's true that it's perhaps a step behind SotA vs January's Opus or current Codex, depending on what you do. But not by a lot. In fact it's leaps and bounds superior to the current subscription API experience. Together with GLM, Qwen and Minimax they are an amazing backstop just the way they are right now.
With all the layers of obfuscation it's hard to even know roughly how many i/o Opus tokens do Claude subscriptions pay for. They'll give you some flippant arguments like "people were not looking at thinking so we're not showing you anymore" with a straight face. However podcasts still insist Anthropic are "winning the AI war" (??) it really makes me wonder because in no metric I can see them as providing neither best value nor best quality, and let's not get started about consumer experience.
My intuition is that things must be really bad so they're willing to pull the kind of moves they're pulling right now. They're speedrunning people into understanding how important it is to be able to run your own generative AI infrastructure for reliability, thus becoming a very fancy but trustless throwaway solution factory.
I wonder if OpenAI will turn the screws similarly if/when their pockets start to dry up at a certain pace.
tldr: they are trying hard to change S&P500 inclusion rules so that they dont have to wait 12months after going public so they can list mega-ipo asap in force index funds to buy a portion (presumably before revenue exponential growth settles and profits start tanking due to opensource catching up). They know something that we dont.
btw if they are public and part of S&P500 then potentially they'll be a candidate for a bailout.
The background being that we scrapped working on a feature and then started again a sprint later.
In my cynicism I find it more likely that a massively unprofitable LLM company tries to reduce costs at any price than everyone else suffering from a collective delusion.
This is the whole point of AI. Its a black box that they can completely control.
And I hope we will eventually reach a point where models become "good enough" for certain tasks, and we won't have to replace them every 6 months.
(That would be similar to the evolution of other technologies like personal computers and smartphones.)