Once they make a model better than Fable I’ll be switching to Codex. Their priorities in terms of consumers seem to be better. I do think Anthropic has some solid safety viewpoints, but I don’t necessarily think that either is entirely aligned yet with delivering exactly what humanity needs. Maybe the AI will help align the AI companies when it gets smart enough. That’s the real misalignment I’m concerned about.
It feels like 5.6-Sol is already fairly close to Fable, and in some ways exceeds it. Just the other day I had Fable draw up a solution for me, and then I fed it into 5.6-Sol and said how does this look ... it found an oversight and told me about it, and when I then fed that observation back into Claude it acknowledged the miss. I've noticed also that 5.6-Sol is more concise with output than Fable (and let's not talk…
OpenAI: GPT 5.6 Sol price reduction (until at least Nov 21)
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Re: OpenAI: GPT 5.6 Sol price reduction (until at least Nov 21)
#162Earlier quoted context omitted.
OpenAI could still have a significant moat. ChatGPT occupies most consumers’ minds when they think about AI and has become a household name. Google won because search became a habit-forming product people grew accustomed to using. Bing was once effectively indistinguishable from Google Search, yet still failed to achieve mass adoption because users had already become accustomed to “Googling” things. The same could be…
> OpenAI could still have a significant moat. ChatGPT occupies most consumers’ minds when they think about AI and has become a household name. ChatGPT is AI for the average non-techie the world over, but the average non-techie isn't eager to pay for it. The more progress that's made, the less incentive to pay - most people are happy with the total garbage spewed by google AI overview. They'd be happy with google's 30…
Re: OpenAI: GPT 5.6 Sol price reduction (until at least Nov 21)
#163The fact that AI models can be so easily distilled and replicated is such a stroke of luck. 10 or 15 years ago if one had asked me to envision a future where a private company invents artificial intelligence, I'd have thought for sure they'd have a massive moat, be very difficult to catch, and it would create an almost instant monopoly. Rather, it seems that selling intelligence might end up as a race to the bottom.…
OpenAI could still have a significant moat. ChatGPT occupies most consumers’ minds when they think about AI and has become a household name. Google won because search became a habit-forming product people grew accustomed to using. Bing was once effectively indistinguishable from Google Search, yet still failed to achieve mass adoption because users had already become accustomed to “Googling” things. The same could be…
Re: OpenAI: GPT 5.6 Sol price reduction (until at least Nov 21)
#164Earlier quoted context omitted.
if you're picking AI models for long-term sustainability you're doing it wrong. There's really no point in locking in model choice for anything more than a month or two these days.
What about companies purchasing enterprise contracts? Most contracts are minimum 12 months. At a minimum, to secure enteprise requirements like zero-data retention, you'll need to lock into a single provider. These price reductions are mostly targeted towards self-serve customers on individual or small team plans, where individual choice matters and the friction of changing models/providers is low.
enterprise SaaS LLM calls do lock in rates but don't lock in models for similar reasons to the above.
Re: OpenAI: GPT 5.6 Sol price reduction (until at least Nov 21)
#165I think this is a move to get people off the subscription and move to API. The weekly usage is still awful altough it seems they're trying to fix it but I'm not hopeful.
Why do you think they want less people subscribing?
Re: OpenAI: GPT 5.6 Sol price reduction (until at least Nov 21)
#166Earlier quoted context omitted.
Yes, but 10 or 15 years ago, I would have thought that there'd be more to it than just a slight modification on the ideas behind a CNN to get this level of AI. There were somewhat good reasons to think it needed more than just this data-driven ML approach.
There's something startling about how (relatively) simple these networks are and yet how powerful they are. The main ingredient the AI darlings are using is vast amounts of compute and data. I don't want to take away anything from what the researchers came up with, but I suspect even they are surprised at how capable some of these models have become.
Re: OpenAI: GPT 5.6 Sol price reduction (until at least Nov 21)
#167Earlier quoted context omitted.
Where is the actual evidence of distillation? I keep seeing this repeated ad nauseam but I must have somehow missed the evidence.
Distillation a pretty well documented technique that actually pre-dates LLMs https://arxiv.org/pdf/1503.02531 Here is a project that guides you through it if you want to prove to yourself that it works https://github.com/arcee-ai/DistillKit
It's about evidence this is an active force in competition in LLMs.
Re: OpenAI: GPT 5.6 Sol price reduction (until at least Nov 21)
#168Earlier quoted context omitted.
Subscribers already get random rolling resets.
Which is not that great for people using less than 50% every week, because the next reset date moves forward too. In essence, it is redistributing compute from people who haven't used their quota much to those who have. Though I think they gave a banked reset this time.
Re: OpenAI: GPT 5.6 Sol price reduction (until at least Nov 21)
#169Earlier quoted context omitted.
Altman specifically has said in an interview that I listened to once that he envisions AI being as cheap as electricity.
I hope it's a good bit cheaper than that, I pay close to $400/mo for electricity and I'm in no way interested in paying anything like that for AI.
Re: OpenAI: GPT 5.6 Sol price reduction (until at least Nov 21)
#170Earlier quoted context omitted.
This viewpoint doesn't make any sense to me. The weights + inference code are the "source code" for AI. I literally don't know what else you are demanding for the "open source" label.
If you think of LLMs as programs. The weights and inference code are very much a binary. While the training code and data are the true source. Since if you want to robustly modify the LLM that's actually what you need. But since "compilation" (training) is extremely compute intensive this isn't something accessible to anyone without an entire datacenter. Anyway semantics aside having the binary is still infinitely be…
I don't know LLM theory well enough to say if there's some secret sauce they can hold back that makes training ineffective. Less effective I'm sure, we don't have access to their smart training schemes, but post-training should always be possible IIUC.