Junotomy @Junotomy
What I have learned today. | Just notes to myself | Opinions my own | No financial advice | Sweden Joined January 2026-
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Hot take: The endless rumours about Fable 5.5 and its delayed release is because Anthropic is using it as a deterrence. They are signalling to OpenAI that if you match Opus 5.5, we will release a much better model you can't match, so you ought not to.
Anthropic is ready to release Fable 5.5 the moment OpenAI matches Opus 5.5. However, OpenAI don't seem to be able to compete at the moment. Unfortunately, consumers lose out.
@TokenGremlin It's probably not that they don't want to, but more like they can't match Opus 5.5 right now. Even if they match Opus 5.5, Anthropic is ready to release Fable 5.5. OpenAI gave all they got with Astra and then Anthropic released Opus 5.5 and made Astra obsolete. Anthropic has won.
@TokenGremlin But Bel is not going to be released anytime soon. Fable 5.5 is probably getting released soon. So it's not a fair comparison. By the time Bel is going to be released we might as well have Fable 6 ...
@GlennLuk What's lacking in the table is revenue per hour/worker. I can imagine consumers are more willing to pay a premium for german quality products. That in turn can explain higher sustainable costs per employee and vehicle in Germany.
@TokenGremlin What is the proof of your claim? Many believe Anthropic is ahead. Why would OpenAI not release comparable models if it is devastating for them economically? Because they can't, at least not this soon. In coding, where it matters the most, Anthropic seems to be ahead.
If AI takes over the world it will be aided by disadvantaged humans who think they are using AI for their own good. AI will exploit dissatisfaction with hierarchies, both hierarcies beween countries and within countries, to find human collaborators.
Google seemingly made a good model again, Gemini Argon 4. But it has to be released to the public soon and independently tested before we know. And for Google to be on the frontier it's not sufficient to release 1-2 good models per year.
OpenAI is not able to produce a frontier model anymore. Anthropic is totally dominating the frontier. The duel is over.
I'm calling it, Anthropic has won. Whatever OpenAI releases, Anthropic will release something better. It was the other way around earlier, but Anthropics bet on coding early on paid off. Now they lead.
Conclusions on Opus 5.5 vs GPT-6 Sol: Anthropic is winning.
Next Sol and Opus releases are overhyped. What is really interesting is next Astra and Fable models.
@TokenGremlin I don't think this is new. The internal models have just not undergone (as much) post-training yet and aren't as restricted/aligned yet. Probably all previous models that hadn't been restricted/aligned/trained said very worrisome things.
The number of LLM labs and models will explode. It will be cheaper and easier for small labs with limited resources to create powerful models. At some point actors will release LLMs that have not been restricted or aligned at all. This needs international regulation.
An international AI organization supported by governments, including the US and Chinese governments, needs to be created, whch defines ethical guidelines we can all agree on. Models complying should get certified. There are some things everyone serious agrees on.
Everyone agrees AI is potentially dangerous. LLM companies (especially American and Chinese) should create an International organization and shape best practices for the industry with the intent to minimize the risks. Models complying with best practices should be certified.
@Mr_Salio Flash models can be subpar. They can release whatever is better than the previous Flash model. It's just supposed to be efficient, not necessarily competing with the best models. Pro models are supposed to be SOTA. They can't release their subpar pro models.
@haider1 Google didn't slow down. Anthropic and OpenAI released new models faster. Google has not been able to catch up. Releasing a couple of pro models each year is way too little. They are too slow, but not because they became slower, but because the competition got faster.
I'm getting less bullish on LLMs in the short-term. No breakthrough on continual learning (in context and sample efficient). Still many hallucinations. Don't get my wrong, they are getting dramatically better, but fundamental problems persist despite performance gains.
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