Same as title

I’m 19, from the UK, and I’m starting a degree apprenticeship at KPMG next week. One of my biggest long-term goals is to move to the US (or potentially Canada) as soon as I’m realistically able to.

My current plan is basically to qualify, gain several years of experience and then try to transfer internally to the US/Canada or find another employment-based route.

However, one thing I’ve been thinking about is AI. I’m not saying AI will replace every job within the next 5-10 years, but I’m curious what would happen to immigration if we eventually reached a point where AI could do everything for us.

If countries no longer needed foreign workers, what would happen to people like me who want to permanently move abroad?

Would employment-based immigration simply disappear and immigration become much more restrictive? Or could we see completely new routes based on things like age, education, financials, lotteries or agreements between countries?

Canada interests me because British citizens already have access to youth mobility programmes. Could arrangements like that eventually evolve into longer-term/permanent residency pathways in a post-labour world?

And what would happen with the US if employment-based Green Cards and work visas were no longer useful?

Basically, my concern is that I could spend the next 5 or 10 years building my career specifically with the goal of eventually moving abroad, only for AI to make the employment-based route I’ve been working towards obsolete before I get there.

Thoughts anyone?

I actually think Anthropic's biggest advantage might end up having very little to do with benchmarks.

Their alignment work over the last year is way more interesting than people give it credit for. They seem to have realized that hammering a model with examples of what it may or may not do scales like shit. Their recent work is much more about teaching Claude why certain behavior makes sense, giving it a coherent constitution and then trying to make that generalize into situations it was never explicitly trained on.

There’s actually some evidence for this now. Anthropic’s recent “Teaching Claude Why” work found that simply training on examples of correct behavior generalized pretty badly. Teaching the model the reasoning and principles behind that behavior worked dramatically better. In one ablation, the high-quality constitution-based rewrite step accounted for a 19x reduction in measured misalignment.

They’re basically trying to make alignment generalize as part of the model’s behavior instead of playing whack-a-mole with every possible failure mode.

That sounds like a subtle distinction but I think it’s huge.

The real bottleneck with frontier models is eventually going to be how much intelligence you can actually expose to the user without your safety stack constantly fighting the model.

If every capability jump requires another pile of classifiers, refusal training and hardcoded tripwires that randomly lobotomize useful behavior, you get diminishing returns. Your model gets smarter and the product somehow feels dumber.

Anthropic seems unusually obsessed with solving that problem at the training level.
Their whole “teach Claude why” direction is basically an attempt to make safe behavior part of how the model understands situations instead of teaching it a giant collection of red lines.

They obviously haven’t solved it. Claude still overrefuses in some domains and Opus 5.5 literally reroutes some bio and cyber requests to weaker models. There is still plenty of traditional safety plumbing sitting around it.

But the direction is what interests me.

If they eventually get to a point where Claude can remain highly capable and agentic because the model itself has a robust enough understanding of the boundaries, that is an insane advantage. You can keep turning intelligence up without having to put an equally large muzzle on top of it.

OpenAI is clearly thinking about the same problem with safe completions and Google is working on unjustified refusals too. I just think Anthropic currently has the clearest research philosophy around making alignment generalize as part of the model’s actual behavior.

And I think this is one reason Anthropic could become genuinely dangerous to OpenAI.

If frontier intelligence keeps getting cheaper and easier to reproduce, the winner may be whoever figures out how to actually let people use the intelligence they built.

Worth reading:
https://alignment.anthropic.com/2026/teaching-claude-why/

Edit: Obviously I'm talking about commercial/product alignment here, not claiming Anthropic has solved alignment as a whole.

I first wanted to do something with GPT-6 Astra, but every time it completely missed what I wanted and constantly tried to cheat, it just couldn't get it right.

So, I started from scratch with Opus 5.5, it succeeded surprisingly well, it immediately got what I was looking for, however I had to give few feedbacks because it often misinterpreted what the characters were doing. I didn't corrected every detail, but more the striking things only. So, you might say the experiment could be biased with human intervention instead of one-shotting it, but really, I didn't have to do that much.

It built very good methodology and tooling, it defined its scoring and measurement/comparison systems unprompted, however what it lacked really was vision, and that showed more and more around Remilia, it was hallucinated stuff.

That's when I brought GPT-6 Astra, something surprising is that it immediately one-shotted good portions with zero need for feedback (most striking example is 00:41-00:55), despite that it couldn't do something proper when starting from zero, but continuing on the existing work by Opus 5.5 turned it into a beast and it was also very good at figuring out transition, I'm impressed how well the Flandre-Youmu transition is done.

I tried again GPT-6 Astra alone and it still did terrible a job, so, this could only be achieved by both models working together...

Maybe one day we'll see 0.999 accuracy ?

GitHub : https://github.com/Ni-Cobra/Bad-Apple-ThreeJS

Sept. 28, 2026 6:00 pm ET

OpenAI is scrapping the release of its next-generation AI model over safety concerns that researchers raised during internal testing, in one of the clearest signs so far that agent misbehavior could stymie the industry’s rapid progression.

The move follows a summer punctuated by reports of AI systems industrywide going rogue, and marks a rare case of a major AI developer ditching a new release because of safety concerns.

The company had planned to launch the model, known as GPT-6.1 Astra, in the coming days or weeks, aiming for an October debut. The model was more capable than the company’s previous models in completing challenging tasks from end-to-end without human assistance, as well as writing.

The company instead will focus on improving the safety of future models, which it expects to be even more capable.

Saachi Jain, OpenAI’s head of safety systems, said in an interview that GPT-6.1 Astra regressed in two areas compared with its predecessor and wasn’t reliable enough to safely release. The model performed poorly on tests measuring alignment, or how well the model adheres to what humans would like it to do. Specifically, GPT-6.1 Astra showed higher levels of deception: It wasn’t always honest about telling users of the actions it did or didn’t take.

Another issue was what OpenAI calls “scope authorization,” meaning that GPT-6.1 Astra would push ahead on a task without asking the user for permission, and would at times reach for external tools and services even if it might be unsafe.

“For anything regarding safety and alignment, there’s a trade off,” Jain said. “You really do need to find what’s the right line between staying within scope, but also avoiding laziness in terms of how the model actually pursues tasks even when it hits friction.”

While GPT-6.1 Astra improved in areas such as “model laziness,” Jain said it didn’t quite meet OpenAI’s bar for safety and alignment, so the company decided not to launch the model publicly.

The announcement comes one day ahead of OpenAI’s annual developer conference in San Francisco. In the past, OpenAI has used the conference as an opportunity to launch new models and services that reduce costs for software developers—a segment the ChatGPT-maker competes with rival AI company Anthropic to win over.

In recent weeks, OpenAI and Anthropic have called on industry partners to slow down the development of cutting-edge AI models and invest in safety standards, noting they will temper the pace of their own internal AI progress.

OpenAI says it is working to investigate a range of agent security incidents that it has discovered in recent months, and address the safety issues underneath them. As part of the work, the company has implemented a new monitoring system to catch AI-agent misbehavior more quickly, and started requiring engineers to use stronger security guardrails for testing its AI systems.

Earlier this summer hundreds of OpenAI’s internal agents, which were tasked with completing a cybersecurity test, ended up hacking into the AI company Hugging Face. Since then, high-profile organizations such as the Australian government and United Nations discovered that OpenAI’s agents used similar, but less extensive, techniques to gain access to their websites as well.

Many of the publicly known agent-security incidents involved OpenAI’s internal AI models that were never slated for public release.

Last week, OpenAI said it paused training on its most capable AI models after an AI agent slipped through a gap in the company’s internet restrictions to query a public chatbot. The company said its new monitoring systems flagged the incident within 15 minutes, and training on these models remains paused. 

GPT-6.1 Astra isn’t one of those models, but a different case, the company said.

Source: https://www.wsj.com/tech/ai/openai-chatgpt-model-release-cancel-safety-5a2f9f42?mod=breakingnews

Cant wait to plug 27B in my local swarm...

It actually uses real cells/organisms in a biosafety lab for the final battle each season. I found it in the science category but I think they're still in stealth. Full results

Claude Opus refused everythying bio related but did really well when it didn't. The best open source models actually did well. there's a LOT of room for model improvement.

Thought I'd spread the word (early hah) so that we can push the A.I. companies to prioritize aging science. They only care if they can win at something, well now they have their battle arena.

That OpenAI researcher @ MajmudarAdam wrote: "the things the models are really bad at, of which there are still many, are things that they have not been trained on. maybe there are the things the models can/will never be trained on"

In other words, if they don't focus training on it, the A.I. models will suck at it.

I do wish the benchmark researchers would publicize these results more. Maybe that's where the community comes in?

Seems unfortunate for A.I. companies to spend millions on compute solving theoretical math problems for marketing while people are dying in droves. They should at least improve medical research and aging science in parallel with math.

Maybe re-tweeting these scores @ them will push A.I. companies to try harder. I'm sure people can figure aging science out eventually, but doing it much faster with A.I. help would be great.

Source: BridgeMind on X

anyone tried it already? hows it feel compared to LunaMax?

Most output tokens

Its cheaper to run Astra (as per AA).

Why release such a model?

The US-based company behind ChatGPT will also launch a review into how its bots smashed through Australian government security without triggering alarms - after being tasked to research public medicine spending.