redrix

This is why Anthropic and OpenAI are pushing for more “regulation”. You can’t show ROI to investors by cutting inference costs.

Regulation might inconvenience them a little, but the upside is huge if it means open models out of China get banned.

Some of the best uses of AI in call centres that I’ve seen focus on improving human interaction instead of getting rid of it. For example:

- Finding information in the background / performing an account health check instead of putting the caller on hold for 20 minutes.

- Helping newer consultants find the same offers experienced staff know about amongst 200 pages of active/targeted offers.

- Automatically documenting calls so when someone calls back, they don’t get a “sorry, I can’t find any notes about that on your account”.

- Giving the offshore call center consultant a customized script based on the active discussion instead of a generic one

(Yes, reading from a script is still bad, but at least this makes it somewhat more natural rather than it feeling like you’re talking to a lobotomised employee.)

100% agree. I think OAI is too, but more subtly.

Both have taken huge amounts of investment money, and those shareholders will come knocking for their returns at some point.

They make money on token use, but that’s hard to do when China is launching models that are 80-90% as capable, cost 1/10th, and can be deployed for “free”.

I imagine they’re also pissed that most of the Chinese models are distilled from their own (the irony being that they scraped the entire internet to train their own ones).

My 2c: Anthropic/OAI have taken 10s of billions of dollars of investment - and there needs to be a return on that investment at some point.

They make their money on token burn. They can’t do that if they’re competing with something that’s 80-90% as capable, but 1/10th of the cost (with the option of deploying it yourself for “free”).

The 30 years of pension is a bit ridiculous. Why not make the requirement scale with the applicant’s age?

Someone applying for PR at 30 has far more time to contribute than someone applying for PR at 50.

Your comment technically does as well. Downvote the comment and move on. Leave the policing up to dang and the other mods.

It is evident (in my opinion) as to what the comment was talking about. I personally switched away from all Claude models recently for the same reason.

If this can largely automate the backup and update side of things, then it seems like a great middle ground for people like me:

I’m trying to self-host as much of my stuff as possible, but don’t have time to tinker like I used to. Hence updates, backups (etc) fall by the wayside in favour of “set and forget”.

35. Almost the same position as you!

Personally, before doing anything else, I would take at least 3-4 weeks and have a proper break. It will take you at least 1-2 weeks to get out of “work mode”.

Stop thinking about work, ideas, the next thing, etc. Get off social media, HN, Reddit etc. Try and distance yourself from all of the things that may have contributed to the burnout in the first place.

I’d probably do some travel in that time - maybe a week or two going to a country I haven’t been to before.

Once I was back, I’d then start to consider my next move with (hopefully) a fresher and clearer mindset.

You’re absolutely right to be hopeful. Three honest possibilities, and I’ll be straight with you about each:

1. It overthinks — Just like the previous iteration. High confidence. 2. It doesn’t overthink — Improvement from the last model for your use case. Regression for others. 3. It sometimes overthinks — Best case all around. A feature, not an impairment.

One final thing worth mentioning: (I made myself irrationally angry writing this)

I remember having it drilled into me in High School that unless you did well on your finals:

1. You wouldn’t get the score you needed to get into a good University and/or accepted into the course you wanted. 2. If you didn’t get into that course, you wouldn’t be able to do <x> for a career. 3. If you DID get into the course you wanted, it needed to be at a top University (which of course required an even higher entry score) or else you’d find it hard to get a job afterwards.

Imagine all of that stress: Telling 16-18 year olds that unless you ace your tests, you won’t be able to do what you want to in life.

Research, analytics, usage trends, etc. All still incredibly valuable for a company building and tuning an LLM; even if the data itself isn’t directly used in the training set.

If you didn’t take the time to write it, why should anyone take the time to read it?

This is cool!

While most of the industry focuses on the frontier of “intelligence” (function), a release like this represents the frontier of the other end of the spectrum (form).

Both are important if we ever want to see “Opus-level” capability running locally on commodity machines in the future.

Competition is a wonderful thing. $1.20 per 1M output is crazy, but let’s also not forget that OpenAI was one of the first to start increasing model prices ~10-12 months ago.

GPT-5 cost $10 (per 1M output)

They still have to pay back those hundreds of billions of investment dollars somehow.

The best thing any developer can do today is to make their app LLM/provider agnostic (or use an LLM gateway) to avoid lock-in.