Honestly just give it time. This stuff moves so fast next month the conversation will be different. For folks who don’t like the ID privacy issues, use Deepseek et al and it should be able to get the job done even if the experience takes a bit more wrangling.
The problem with the ID verification is that they can pair introspective conversations with ID. Either that bothers people or it doesn’t.
Main point: we can’t fret about current state models because the ID verification has future implications. Models will change and competition will catch up. Do what feels right in the long run not whether TODAYS model is better at Anthropic.
I agree with this, my disagreement was strictly with saying that the current open models are as good as Opus.
They're not. And by the time they are Open AI and Anthropic will probably be onto the next thing.
Not sure what happened to Google in all this. They're falling out of the frontier race.
Both Anthropic and OpenAI don't want to continue training models indefinitely.
Anthropic CEO has expressed potentially slowing down on model training. There is little return for billions of dollars burnt for 1-2% increase on various benchmarks. These companies profit via inference.
Not to mention, the whole Fable being banned by the US Gov is a scary prospect for future models. What is the point of spending billions if its going to get blocked?
Of course this can't go on forever. Especially not on LLMs. But are we really close to the limits of what these LLMs can do? I'm not sure we are.
The difference between GPT-5/Opus 4 and GPT-5.5/Opus 4.8 is striking. For software development anyway, there's no comparison. And all this has happened in a year.
My assumption is there will be another 2-3 years of improvements ahead of us on LLMs alone. Through hardware upgrades, larger training runs, better data quality, better algorithms, etc.
Of course, by then these models will be quite expensive. Will my company pay for it? I don't know. I'm sure some people will though.
some people will, but they will have to bear the costs being the only users of llms sustained through billions of dollars of funding.