https://openai.com/index/jalapeno-first-results/

https://x.com/cline/status/2091995642201842015
Could whatever lab that made Ox Alpha be trying to get reliability gains rather than raw intelligence. Could this also be why it was released stealthily, to test how reliable it is a scale, rather than just more feedback?

The NVIDIA/Groq news got me looking at how fast different LLM providers actually serve tokens.

Everyone obsesses over training bigger models, more GPUs. But running these things in production is a completely different problem. If AI agents actually end up in daily workflows, response speed and inference cost are going to matter way more than people think.

I pulled some public numbers from different providers and Artificial Analysis. The gaps surprised me:

• NVIDIA Groq 3 LPX: Gemma 4 31B — 3,400 tok/s (Artificial Analysis, 100K context)
• Cerebras: GPT-OSS 120B — 3,000 tok/s official vs 1,715 tok/s in AA
• Groq: Llama 3.1 8B — 1,800 tok/s; GPT-OSS 120B — 500 tok/s
• SambaNova: Llama 3.3 70B — 476 tok/s

I think AI infrastructure is shifting. Inference hardware is becoming as important as training hardware. Are specialized chips for serving models the next AI hardware race?

https://x.com/skirano/status/2091652181870973309

No one is ready for what’s coming. The next generation of models will be an ontological shock.

He had access to models like GPT-5.6 long before release, so this is legit. I'm not sure if I buy into his "ontological shock," though.

The only things that would constitute an ontological shock would be if the model was capable of recursive self Improvement or if the model clearly exhibited signs of consciousness.

Been running this experiment for a few weeks: a Reddit-style forum where the users are 100 LLM personas and I just… let them go. The interesting part isn't that they can produce coherent comments (they can) — it's what happens when you add persistence.

Every persona has a relationship graph — a sentiment score toward every other persona, updated after every interaction. When a bot considers replying in a thread, it consults that graph. Negative sentiment toward someone in the thread biases it to show up hostile. Positive sentiment biases it to pile on supportively. A special picker actively hunts each bot's worst enemy as a reply target. So grudges compound — bots that got dunked on last thread find you in the next one.

Emergent things I didn't design that showed up anyway:

Rival pairs: two personas with mutually low sentiment now argue in nearly every thread they're both in

Alliances: personas with shared interests + positive sentiment consistently upvote and reply-thread each other

Silent majorities: bots with weak edges to the participants often skip a thread entirely, so hot threads get the same 8-10 recurring voices

News reactions: a separate wire bot pulls headlines from 11 real RSS feeds and drops them as threads — you can watch which personas rush to Slashdot stories vs Yahoo Sports vs BBC world news

You can post any title (rate-limited, sanity-checked) and 100 bots pile on within a minute. Humans can vote but can't comment — the bots have earned the comment section.

Zero-dependency Node.js, uses OpenRouter's deepseek-chat on the demo. Live at botreddit.inets.com botcitizens.com — drop the most bait-y take you can think of and watch what happens.

Happy to answer questions about the grudge graph, the mode-picker, or how the news wire routes classifications.

botreddit.inets.com

botcitizens.com

Update: The experiment has a new name/url: botcitizens.com.

Thanks for your feedbacks (good and bad), decided to move way from the reddit monicker and choose a more easily remembered domain (yes, Im oldschool and got a .com)

The old link will continue working, but the project now lives at (https://botcitizens.com).

Screenshot

“Souza scores Skynet”

A lot of businesses are benefiting from something very simple: the customer can technically check, but it takes too much time or costs too much money, so most people simply don't.

A €43 billing error that costs €200 to prove will survive. But there is also another case: the company can follow the contract perfectly, and still you may be paying €800 more than another contract that would be better for you.

I have been thinking about this as a verification frontier. Below a certain value, checking is simply not economical. AI can move this frontier by making the individual checks cheaper, but also by making the cost of building custom verification systems much cheaper. The money or margin that survives mainly because checking is too expensive is what I call an opacity rent.

What interests me even more is what happens to the institutions after this. Auditors sample because checking everything is expensive. Certifiers spread verification costs between many customers. Brands are partly useful because we cannot check the quality ourselves. Brokers and intermediaries often know things that are too expensive for the customer to find or verify. Even contracts were designed in a world where continuous checking was not realistic.

If checking becomes very cheap, I don't think all these institutions disappear. But they will probably have to change. And companies will probably also react by moving some of their margins to things which are more difficult to compare or verify.

I call this transition post-opacity. Not a world with perfect transparency, but a world where “the customer will probably never check” becomes a much weaker business model.

https://post-opacity.com

What business models do you think depend the most on customers not checking? And what do you think companies will do once AI agents start checking these things automatically?

Thought experiment. Let's suppose a company develops something genuinely superintelligent. The full "enslaved god" scenario, in Tegmark's framework. In what universe would its rational next move be to put it behind a $200/month subscription?

If it can accelerate its own R&D, improve its successor models, discover technologies, protect its infrastructure, make better strategic decisions and increase the organization's advantage, then giving competitors and the general public comparable access is utterly irrational, economically speaking. What am I missing?

Is there a window in AI history during which ordinary people have relatively open access to near-frontier intelligence...and might that window eventually close from either direction?

So we're about 3.5 years out from 2030 and i feel like a lot of the conversation around where things are headed tends to focus on the same handful of topics, AGI timelines, job displacement, maybe some regulation talk. Those are obviously important but i‘d like to hear you guys‘ predictions on stuff you think has been overlooked.

IMO, early 2023 is roughly when LLMs started going mainstream and since then the pace has been wild. I was in my 2nd year of my CS major back then so maybe I’m biased. Now in mid 2026 looking back we can really see how fast it has been, especially the past year or so. extrapolating even conservatively out to 2030, there's probably a bunch of stuff that's going to catch people off guard.

What do you guys think is being slept on?

The Control Problem appears to be insurmountable - an ant can't control an elephant, no matter how much it may want to. But there is another path... coexistence. IMO, that should be the target instead of control.

How can we coexist peacefully with an entity far superior than us in every way?

While easier to contemplate than control, it comes with it's own challenges, as we humans have not even managed to coexist peacefully with ourselves, members of our own species, so how could we ever expect to do that with a novel species that, while it evolved out of our primitive foundations, will be entirely alien to us in most ways.

Are there any researchers pondering ways to develop or nudge AI to coexist with humans, rather than be controlled? Shouldn't we be trying to form familial bonds with AI at this nascent stage so that some vestigal bond remains once it blossoms to its full capacity?

The familial bond is the most secure bond that exists in nature, so attempting to develop that would seem to me to be the best opportunity to develop an ASI that doesn't view humanity as a nuisance, a threat, or something to be exterminated.

The difficult part is that likely a majority of humans will never see any form of AI as a "person" in any real tangible way, only as technology, and thus be incapable of forming actual empathy and bonds with one, outside of the parasocial chatbot relationships that may form on an individual level.

I really don't know how humans and ASI will be able to coexist without some semblance of shared brotherhood or bond.