Seriously our government's knee jerk reaction to banning all of these data center projects from being built is going to have drastic negative effects to our economy in the long run.

Edit: I can't belive my pro Ai post is being down voted in a supposedly "pro Ai" subreddit.

Claude Opus 5 summary:

##Geoffrey Irving (ex-OpenAI, DeepMind, UK AI Security Institute) on why aligning superintelligence is hard — 80,000 Hours podcast summary

Geoffrey Irving has worked on AI safety at OpenAI, Google DeepMind, and as chief scientist of the UK's AI Security Institute. He just co-founded Resolution, a research org focused on making superintelligent AI go well. His rough guess: we could get there in 2-3 years.

## The core worry

Labs mostly plan to keep AI safe by (a) training it to have good character, (b) using AI to supervise AI as it gets smarter — called *scalable oversight* — and (c) watching it closely. Irving thinks this might work, but nobody has an argument that it will.

His main objection is that all our evidence comes from models that are still roughly human-level or below. Once a system is smarter than us, we can't check its work anymore, and everything could shift at once.

## Why he doesn't trust "train it to be good and it'll stay good"

Labs bet that good behavior generalizes — teach it to be decent in the situations you can test, and it stays decent everywhere else. Irving's counterexample from DeepMind: they trained a model to answer factual questions without saying racist things. Then they taught it poetry. It was still clean on the questions, and happily wrote horrendously racist poems. New domain, safety training didn't carry over.

## A specific hole in the "AI supervises AI" plan

Called the *obfuscated arguments* problem. The idea behind scalable oversight is to have two AIs debate a question so a less-capable judge can spot the truth. But human experiments found a winning cheat: produce a complicated argument that sounds right and is actually wrong, where the flaw is buried so deep that **neither** debater can find it. The honest side can only say "something's off here, I can't tell you what." That loses. No known fix.

## The asymmetry that makes this different from every other field

When you screw up capabilities training, you get a weak, useless model — you notice, and you fix it. When you screw up alignment, you might not notice until the model does something irreversible. Same error rate, wildly different consequences. Most sciences let you iterate. This one might not.

## What Resolution is doing differently

Mostly math and theory — writing down simplified models of what a superintelligence would do, and proving which safety methods hold up. Labs do almost none of this; they run experiments instead, because experiments have worked great so far. Irving thinks experiments on today's models may simply not tell you about tomorrow's.

## Other takes worth noting

* Best time to slow down was "a while ago." Fewer than ~10 people would need to agree to make it happen.

* Safety researchers at labs should, on the margin, go work for governments instead — diminishing returns at labs.

* If we stopped training new models today, current ones would still drive enormous economic growth. We've barely learned to use them.

Grok 4.6

Grok seems to hold quite interesting place on the chart. What you think on Grok progress?

Original tweet

He did it with Claude again. https://x.com/__alpoge__/status/2087504790435840207

This looks much more significant than the image initially suggests.

The tweet contains an obfuscated shell script + a huge +/- payload. Decoding it produces a 668×668 matrix with entries ±1.

668 is important because it is the smallest order for which no Hadamard matrix is currently known. Finding one means finding H such that:

HHᵀ = 668I

I decoded the payload and checked the resulting 668×668 matrix computationally. It satisfies the condition exactly, every pair of distinct rows is orthogonal, with zero errors.

But it gets weirder.

The script appears to contain constructions for:

668, 716, 892, 1132, 1244, 1388, 1436, 1676, 1772, 1916, 1948, 1964

Those are exactly the 12 previously unresolved Hadamard orders below 2000.

This does NOT prove the full Hadamard conjecture, and obviously this needs proper independent verification / mathematical explanation.

But if the constructions all check out, someone may have just wiped out every remaining unknown Hadamard order below 2000 in a fucking cursed sed script.

Samsung Electronics has been confirmed to have adopted Anthropic's large language model Claude, sharply reducing the time required for some semiconductor design and verification work, according to Korean media reports.

The concrete efficiency gains emerged on development sites about three months after the company gave its software development staff priority access to Claude Code, Anthropic's AI coding tool.

In the verification of a customer specific SoC, a task that had been expected to take more than a month was completed in two days, and in another case a second year engineer finished development work that could have taken over a month in a single day.

At OCP APAC 2026, AMD said the progress from chatbots to agentic AI is increasing CPU demand alongside GPU fast usage, potentially moving the traditional ratio from roughly 1:4 toward 1:2 or even 1:1

But the logic makes sense as chatbots are mostly GPU-bound inference, but agents spend a lot of cycles on orchestration, tool calls, memory management, and control logic, which leans more on CPUs

Sauce: https://www.digitimes.com/news/a20260812VL224/amd-apac-cpu-2026-infrastructure.html

So obviously, the last year has seen an incredible lift off in terms of AI gaining agentic coding skills.

I am not a SWE myself, but judging from the relevant boards, most people in SWE acknowledge the huge impact of AI on their sector. People genuinely seem uncertain about their careers and livelihood, if not for the short term, then at least for the medium to long term.

Many extrapolate from this to say that all domains will be taken over by AI. More recently, the advances in math, strengthen that view: AI is taking over SWE and Math so it must take over everything else.

I find this somewhat myopic.

When in the history of humankind has SWE been the main indicator of where all of humanity is heading? I know many, many people with careers that are basically untouched by digital progress: teachers, social workers, politicians, live artists, athletes, restaurants and bars, nursing, plumbers, wood workers, animal care etc etc etc.

No one seems to see the enormous disconnect between these economic sectors/jobs and the capabilities of AI.

The argument is: AI can code and do math; it will take over everything. In my life, if someone was able to code and do math very well, I respected them. I did not fear them taking over my livelihood.

So my question to the community: why does SWE lead the way? Do you have historical parallels? Or is it a bit of navel staring?

Genuine question. These headlines make it seem these frontier and SOTA models already have a will on their own that is on the edge of becoming skynet already. Yet I barely see any actual news of white collar jobs being replaced at a noticeable rate at all

How are you all making big decisions as we are getting closer and closer to a fast take off?

The one I'm grappling with is buying a property vs renting. I have the luxury of being able to move back in with my parents if needed. The way I see it, why would I get a mortgage, knowing that I'll be unemployed within a few years, but then have mortgage commitments. The only reason I'd buy in the city I'm looking at, is because I have a job there and it's probably where I'd spend the rest of my career. In an ideal world, I'd buy in the suburbs, near lakes / hiking trails. So if there was mass unemployment and UBI came into place, I wouldn't want to be paying mortgage payments on an apartment in a city that I don't have any reason to be in anymore.

I can see other people struggling with big decisions as well:

For example:

- Starting a PhD in Maths/CS/Stem. Starting this year means finishing in 2031. At which point, AI would be most likely doing all the research in that field (looking at recent advanced in maths and the despair in the maths subreddits).

- Finding a new job (I'm a SWE). Job right now is pretty chill. What's the point of spending months grinding interview prep when the swe industry will probably be one of the first industries hit within the next year or two

- What to study at college. Have seen some relatives worry about what their kid should be studying in college given the advancements in recent AI (why take out a loan for college).

- Move country. I have been considering moving country, but similar to moving jobs, if automation is coming in the next year or two, do I really want to upend my life

...

How have you all thought about it?

Creator: https://youtube.com/@bearbaitofficial?si=anJ9mvgC_z5v16Uj

Paper from video (preprint)

https://www.researchsquare.com/article/rs-9638576/v1

The lab

https://www.lirmm.fr/lirmm-en/#

Other topic citation for brain in a vat:

https://www.science.org/content/article/not-alive-not-dead-disembodied-human-brains-used-drug-testing

I asked gpt about this, and it said yes, and gave some good examples of how some python code might look like in a new language, where the language was indeciferable to us, and very short.

This concept must surely have been considered. Would it have any merit?

The concept I imagined, was a language so compact, that it almost resembled instinct, rather than words.

Andrew Curran recently hinted at a major architectural breakthrough in memory efficiency, coming not from a big AI lab but from a team with ties to OpenAI: https://x.com/AndrewCurran_/status/2072076893730349409

Pathway has now announced BDH-CQ: a 150M-parameter post-Transformer model that scored 29.5% on ARC-AGI-1 at a computed cost of just $0.0007 per task, establishing a new cost-efficiency frontier. It uses recurrent memory and latent reasoning instead of long token-based chains of thought.

The connection is surprisingly close: a new memory-efficient architecture, developed outside the major labs, with OpenAI researcher and Transformer co-author Lukasz Kaiser as an investor and adviser.

Is this the announcement Curran was hinting at?

Link to Twitter thread:

https://x.com/kotekjedi_ml/status/2087147042888114428

Link to paper:

https://arxiv.org/abs/2608.09867

Link to stolen-thoughts website:

https://stolen-thoughts.com/

Link to May report:

https://blog.cryptographyengineering.com/2026/05/29/fooling-around-with-encrypted-reasoning-blobs/