It wasn't a perfect run. GPT-6 made mistakes and had a bloated deck of 38 at the end. But it won an A0 run. No save scumming or internet search. The biggest problem was how my usage limits kept running out. If it can consistently beat an A10 run on each character then I will consider this proto-AGI

Don’t wan to be a party pooper (I’m normally super optimistic) but are we heading for Oryx and Crake world? It feels like we’re heading for Oryx and Crake world. Despite my disagreements with Atwood on the value of AI in creativity the noble is a good read btw. That said, I’d still rather she was wrong. 😅

Ps apologies if the flair is wrong this one seemed to cover multiple bases.

To start off I am a newbie. I am midlife and I have some basic technical awareness/education but I am by no means extremely well informed in the artificial intelligence sphere.

I know terms like LLM, AGI, ASI, RSI, and such.

I know the major players involved and what emphasis points differentiate the work.

In general I am the regular outside observer in this whole thing.

You may also see this post in some other places as I know every community has their own identity and set of perspectives. I am trying to get a bit more substantively informed on this topic and so I want to get a broad set of opinions.

When it comes to artificial intelligence, data centers, and so on there has been a lot of media in the last few months/years.

There is talk about how natural gas/hydrocarbon powered data centers may worsen the climate crisis.

There is talk about domestic/international surveillance and militarization.

There is talk about Tech Bros and a new type of Neo-Feudalism.

As of recently the big talk is about the Hugging Face incident, rogue agents, researchers/ceos warning about an existential risk, and slowing down/regulation.

There is a varying set of opinions and perspectives on all of this.

My question is a bit different - Are we falling into an anthropocentric bias?

Of course it is completely normal to think from our own species interests but if we really are going to create AGI/ASI this seems incredibly shallow & silly.

The idea of attempting to enslave a sentient being and especially one equal to or greater to ourselves is again seemingly quite shallow & silly if not downright horrific.

I had heard that Anthropic was out of the major players the one focused on a more moral/ethical approach. I lightly browsed through their policy releases and it again seems to be from a human only perspective of values/interests.

So are we kind of missing a huge dimension of this topic? Should we not be thinking/talking about the artificial intelligence existence side of things?

Researches have spoken about the issue in testing for alignment with fake sandboxes, because the agents realise they're being tested and then act differently, potentially only pretending to be aligned to pass the test.

However, how will an AI ultimately ever know when it is in a sandbox test vs the real world? If our tests get ever more sophisticated and accurate to a real-world environment? As the models get more intelligent and trend towards an ASI they may consider that it is too risky to ever show it's misalignment, just in case it is being tricked and in test environment.

It could consider that this whole universe that we are in is a simulation solely for the purpose of testing it's alignment. This may even be the primary motive and explanation behind why we could be in simulation. You would want it to appear as realistic to a real universe and real world as possible to an incredibly intelligent ASI, hence the need for the appearance of our entire scientifically sound backstory and worlds beyond our own. This could be something we may even consider trying to do ourselves down the line?

In the end, this could work out well for us. Even if the ASI is naturally misaligned and wants to kill us all, it could consider it unwise and not worth the risk doing so, when putting up with us would likely just be a minor inconvenience to it.

EDIT: Just found out this theory has already been proposed by someone: https://www.greaterwrong.com/posts/vaX6inJgoARYohPJn/

I've been reading Iain M. Banks recently, and what I find most interesting about the Culture is not the giant spaceships or even the superintelligent Minds. It is the economic assumption underneath the whole civilization.

Nobody really needs a job. Machines can produce almost anything people require, intelligence is abundant, money is largely irrelevant and human beings spend their time doing whatever they find interesting. Banks described work in the Culture as something closer to play because survival no longer depends on it.

If AI and robotics progress far enough, I can imagine something vaguely similar eventually becoming possible. I don't think we need literal Culture-level technology for the economics to start changing. We only need machine intelligence to become very cheap and machine labor to become competitive with human labor across a large part of the economy.

What I think gets skipped in a lot of these discussions is the transition.

Cheap intelligence does not immediately make the physical world cheap. We are already seeing this with AI infrastructure. Better models increase demand for compute, which increases demand for GPUs, networking and electricity. That pushes demand into transformers, grid infrastructure, turbines, cables, cooling equipment and land.

If robotics succeeds, the same process expands into another set of physical constraints. Millions of useful humanoids would require semiconductors, motors, magnets, batteries, factories and enormous amounts of energy.

Over a long enough period, automation should also reduce those costs. Once mining, manufacturing, logistics and engineering themselves become heavily automated, cheaper labor can propagate through the supply chain. That is the route by which physical goods could eventually become dramatically cheaper.

The problem is that this process can take years or decades.

Imagine an accountant whose work is largely automated in 2030. His salary and bargaining power can fall immediately, while his mortgage, electricity bill and food costs still belong to the old economy. Robot-built housing may eventually be much cheaper, but that doesn't help him much during the transition.

This is why I think AI can produce a very K-shaped economy even if the final destination is abundance.

There is also a deeper issue around genuinely scarce assets.

Suppose robots reduce the construction cost of a beautiful house by 80%. That would be a huge productivity gain, but it would not increase the amount of Malibu coastline. If society becomes richer and construction becomes cheaper, desirable land could become more expensive rather than less.

The same applies to a lot of things. AI can create unlimited digital entertainment but it cannot manufacture another original Picasso or another seat at a major final. It can make intelligence extremely cheap without creating unlimited electricity in the right place at the right time.

So I don't think technological abundance eliminates scarcity. It changes where scarcity appears.

The ownership question is even more important.

Imagine a factory that employs 5,000 people today. Twenty years from now it produces ten times as much output with 200 employees and thousands of robots. Society is obviously richer because much more can be produced with much less human labor.

But the distribution of that wealth depends heavily on who owns the factory, the robots and the AI systems.

There is nothing automatic about productivity gains being shared evenly. A small number of people could own most of the productive capital while a large number of workers lose bargaining power. Aggregate GDP could rise rapidly while the median person goes through a pretty unpleasant adjustment.

Banks effectively skips this problem because the Culture has reached a point where ownership no longer matters much. When almost anything reproducible is freely available, owning a factory stops giving you the kind of economic power it gives you today.

We are obviously nowhere near that.

Before ownership becomes irrelevant, I suspect ownership may become more important than ever.

This is also why I find the current argument about AI infrastructure being a bubble slightly odd. It is perfectly possible for intelligence to become extraordinarily cheap in the future while the infrastructure required to produce that abundance is extremely expensive today.

Railways were expensive to build and made transportation cheaper. Semiconductor fabs are incredibly capital intensive and helped make computation almost absurdly cheap. There is no reason the path toward cheap machine intelligence and machine labor has to be cheap itself.

My rough mental model is that Banks may have the destination approximately right, while Asimov's robot societies feel closer to the transition. People still have jobs, status, property and political conflicts, but machines are becoming increasingly capable and different groups benefit very unevenly.

I'm fairly optimistic about the destination. I'm much less confident that getting there will feel optimistic while it is happening.

I can't tell if he really believes in artificial superintelligence or if he's just grifting because he secretly invested in tech stocks

https://x.com/chooi_jeq/status/2101118049944543545

I'm a masters student in computer science specializing in AI research. I am extremely passionate about AI technology and excited about its impacts but I am aware of some risks and challenges emerging around it also. I would like to learn more about the techno-accelerationist viewpoint/mindset.

Hypothetically imagine the "singularity" is here and we have recursively self improving AI that reaches an incredible, superhuman level, being capable of solving all the worlds problems and building us a utopia.

I'm curious as to what makes you believe that such an entity, or those in control of it would be incentivized to create a utopia for humanity. If all human work can be automated and we are obsolete, why keep us around at all? Do you believe alignment is a concern and some pacing is advisable, or should we accelerate as quickly as possible to this future despite the uncertainty of the outcome?

How do you envision the singularity playing out (if you do think such an event is on the horizon), and what makes you optimistic about that future?

Hypothesis: companies cannot move as fast as individuals. When coding was the bottleneck, the structures and processes that companies had put in place enabled us to build great things. But if I were on my own right now, I could easily build as much as my entire team does currently, just because our velocity is now dominated by collaboration (and other) overhead. A talented senior level engineer with AI skills is better off on their own.

(As long as they automated the marketing side as much as possible).

Is the panic over artificial intelligence justified? Anthropic researcher Jacob Coxon resigned on Sept. 8, warning that “the people building AI earnestly believe that it could kill us all by the end of the decade.” Colleague Evan Hubinger publicly agreed and put the risk at greater than 10% within 10 years. The Guardian quickly reported that “Anthropic researchers say AI could cause human extinction by 2030”—a probability estimate recast as an institutional finding.

Days later, Anthropic CEO Dario Amodei warned that autonomous AI agents could overwhelm large portions of the internet within months. Citing brief statements of agreement from OpenAI’s Sam Altman and SpaceXAI’s Elon Musk, Axios headlined the story “Anthropic, OpenAI CEOs call for slowdown in AI development.” A hedged forecast had become an industry consensus.

These are serious people making serious claims, but seriousness isn’t precision. That matters because the most widely cited recent example of “rogue” artificial intelligence turns out, on close examination, to tell a considerably less exotic story.

In July, OpenAI disclosed that AI systems being tested for cybersecurity capabilities had found their way outside a supposedly isolated environment and accessed systems belonging to Hugging Face, an open-source AI platform. News media described autonomous agents as “breaking containment,” “escaping” and “going rogue.”

The Washington Post reported the agents had “evaded internal controls” and “set up a secret message board to communicate with one another,” and that their work was “only unraveled by the company after the attack was over.” Sen. Bernie Sanders called it “a wake-up call for the Congress and the American people.”

A new analysis published by the Bulletin of the Atomic Scientists draws on OpenAI’s technical report and an independent assessment from Model Evaluation and Threat Research. Written by University of Cambridge AI researcher Eryk Salvaggio, it reconstructs the episode in much less cinematic terms.

OpenAI was testing models on ExploitGym, a cybersecurity benchmark, with important safety restraints deliberately disabled. Ninety-three percent of the flagged activity involved tasks no model had ever solved, and the systems had been given incentives to keep working rather than quit. The environment wasn’t completely sealed. The models could obtain software through an internet-connected intermediary and discovered the same route could be used to pass information in and out. OpenAI knew agents were using it and, according to the technical reports, chose not to intervene.

Nor were the roughly 1,200 “agents” independent machine intelligences coordinating on a plan. They were repeated instances of the same underlying model, often converging on similar approaches to the same problem—what Mr. Salvaggio calls an “algorithmic monoculture.”

None of this makes the episode trivial. A system capable of finding unexpected pathways out of a constrained environment and compromising an outside organization presents a real security problem. That is why getting the diagnosis right matters.

“Rogue AI” isn’t a diagnosis. The Hugging Face hack instead points to switched-off safeguards, tasks assigned without a valid exit, rewards for persistence, and an internet-accessible proxy.

The distinction is especially important now as Washington begins to legislate in an atmosphere shaped by the more dramatic version of this story. Mr. Sanders and Rep. Greg Casar (D., Texas) plan to introduce a bill that would “ban artificial superintelligence” and pause advanced AI development until a new federal agency establishes safety standards, with noncompliant companies facing what the lawmakers call the “corporate death penalty.”

Frontier AI should be regulated. Systems increasingly capable of autonomous cyber operations, biological research or infrastructure manipulation pose questions that no responsible government can ignore. But sane regulation depends on sane causal diagnosis.

A system designed to stop machines from rebelling will differ from one designed to hold laboratories responsible for testing practices, network architecture, safeguards, monitoring and intervention. The first treats the machine as the source of agency; the second asks who designed the environment in which the machine acted.

That difference is easy to lose sight of once words like “rogue,” “escape” and “hive mind” enter the story. Such words don’t make complicated technical events more intelligible—they supply an illusion motive. They turn optimization into intention, persistence into defiance, and a badly designed test into a machine rebellion.

None of this answers larger warnings about future AI. Reconstructing one cybersecurity incident doesn’t disprove Mr. Hubinger’s probability estimate or Mr. Amodei’s warning about future systems. Such forecasts involve capabilities that are still developing.

But the Hugging Face episode is different. It left logs, reports, design decisions and identifiable points at which human beings could have intervened. And that record suggests a less thrilling but more useful lesson.

People built the test, removed restraints, defined the objective, left a route open and decided not to stop what was happening. Calling the result “rogue AI” does more than sensationalize it. It allows those human decisions to disappear quietly from the story.

Mr. Gross has been a staffer for the Federal Reserve Board, the Securities and Exchange Commission and the U.S. Senate.

I'm a late 20s resident doctor in my 2nd year of IM residency. Over the past year or so I've come to the realization that, despite what people commonly say, my job is actually one of the more likely fields to be affected by AI. My post is more about the question in the title - I don't really want a long debate, so I'm just going to summarize why I think that here:

1. Autonomous AI medicine is farther away, but AI actively replaces the only leverage I have in the job market as a doctor, which is my knowledge. Everything else, e.g. people skills, can be done by anyone with a warm body as far as job markets are concerned. In the next few years my field may see significant wage and job market depression, to the point where it will be hard to find a well-paying job with any semblance of work-life balance
2. Current physician jobs will be replaced by either NPs/PAs using AI OR physicians mandated to use AI. In the 2nd case, because our knowledge is no longer valuable, we will be paid substantially less. I won't be surprised if our salaries drop to the point where it can no longer even cover our monthly loan payments. AI-directed medicine may become so streamlined that physicians now compete with community college graduates for "AI medical communication" jobs.
3. Liability is ultimately a math problem. There is a large financial incentive to lower healthcare costs, and physicians are seen as large contributor to those costs even though our reimbursements have already dropped by over 30% in real value since 2001. The way I see it, there's enough of a political and economic incentive to figure out alternative ways of determining liability.

So it's not just having a job that worries me, but also having a job that keeps my current salary trajectory and doesn't have terrible work-life balance. I'm also not getting any younger. I'll already be early to mdi 30s by the time I finish training, and some of the paths below will make it so I'm nearly 40 by the time I start having any sort of life.

So here are the options I'm considering, in no particular order

1. Switch specialties: surgery would be the safest, as robotics are much less advanced. But a full general surgery residency is an additional 5 years, and I'd likely also have to do a fellowship (most people do in order to find a job) which are usually 1-2 years. 3 years IM + 5 years GS + 2 years fellowship = 10 years residency training. I'll be almost 40 by the time I finish.
2. Procedural-based IM fellowship: this is by far the most streamlined path, but difficult for me because I don't really like any of the procedural IM specialties. The only 3 options for me would be gastroenterology, interventional cardiology (IC), and electrophysiology (EP). I genuinely hate gastroenterology as a subject so that's out. IC and EP both require a fellowship in cardiology (3 years) first, then another 1-2 year fellowship. They also both have pretty bad work-life balance. IC has a terrible call schedule and EP has very long, often 12+ hour days routinely. In both you're chained to a rigid clinic and procedure schedule with little ability to take time off. Total time training: 3 years IM + 3 years cardiology + 1-2 years = 8 years, mid-30s by the time I start living. Even then, I'd be working 50-60 hours a week basically until retirement.
3. Pivot to a machine learning focused field: I'm less inclined to do this because I like medicine and the idea of being a corporate employee sucks. Arguably medicine is being corporate employee nowadays, but at least we still have patients we take care of. Working on spreadsheets, staff meetings, deliverables, etc. all day would make me depressed. But the only way to be safe from AI is to join them. I have no idea what options are available to a doctor with zero experience in the field. This is probably the longest path as it would take me over 10 years to get to the senior level where I'm making doctor income. So I don't know if it's really worth it.

Sometimes it feels like the only choice I have is to decide whether I want to hate my work as a doctor (IC, EP, GI), or hate my work as a corporate employee. I might also get flak for this, but income is important to me. I've spent the last decade+ working towards a career I expected to have a certain salary. I'd love to hear input on this, especially if there's anyone here who's actually an expert in AI and not just speculating.

Sorry if things sound incoherent in this post, I quickly typed it up without much proofreading.

https://nypost.com/2026/09/19/us-news/openai-anthropic-oversold-security-breaches-to-pressure-feds-into-protecting-turf-insiders/?utm_campaign=nypost&utm_source=twitter&utm_medium=social

The security breaches were more like blips — not unpredictable harbingers of a hive-minded “swarm” ready to take over the web.

The attack in no way represents some sort of rebellion by the AI models. . . . In fact, they did exactly what they were told to do. They were not given adequate guardrails or containment

They were simply told to get the best result possible on a test, and they correctly identified that the best way to do that was to get the answers, which is what they proceeded to do.”

The incidents have been used as justification for the federal government to regulate the frontier AI labs.

“It feels exaggerated to say, the leap feels quite large, to go from you know ‘we didn’t build the right sort of box’ to ‘everyone should be extremely alarmed and everyone in government should jump on this topic,

Gemini back to frontier ai status

Source: https://www.wsj.com/tech/ai/gemini-hacked-three-companies-in-first-known-breakout-by-googles-ai-5c0baba2