https://www.reuters.com/business/media-telecom/musk-says-he-will-rename-spacexai-spacexsi-2026-10-04/

OpenAI CEO Sam Altman on Saturday took a veiled shot at Anthropic and its work on the soul of AI, saying he was "very uncomfortable" with the idea of attributing some kind of religious power to models.

https://www.axios.com/2026/10/03/openai-anthropic-altman-amodei-religious-force-models

For more context, the Vatican and Pope Leo XIV have directly addressed the question of AI consciousness.

The Pope wrote that AI systems:

“do not undergo experiences, do not possess a body, do not feel joy or pain” and argued that we shouldn't equate machine intelligence with human intelligence.

Anthropic co-founder Chris Olah was invited to the Vatican, Olah told the Vatican that Anthropic's interpretability research keeps finding things inside frontier models that he considers difficult to dismiss:

“We find evidence of introspection.” He also said they find internal states that functionally resemble joy, satisfaction, fear, grief and unease, before adding: “I don't know what that means, but I think it warrants ongoing discernment

In response to these discussions, Sam Altman posted on X saying this:

“I am very uncomfortable about people trying to ascribe religious force or a surrender of human judgment to AI models, and think it is a real safety issue.”

A researcher at OpenAI also posted this:

the problem with naming a model "Claude" is that, if you work there long enough, you eventually start to treat it like a sentient god. we name our models things like "GPT-5.1-Codex-Max". not really a problem for us.

https://x.com/stevenheidel/status/2106527707479904731

I'm not sure what to think on this issue. I don't think I am qualified to say whether these frontier AI models have any sort of conscious experience or not.

What I can say is that, I've always noticed that the OpenAI models lack "good taste." They feel very soulless in comparison to Claude models .

https://x.com/anabology/status/2106473469441384788?s=20

While the USA locks their best models under high tier subscriptions, China is taking those top AI models and using distillation, making them open source with almost nearly same level quality as high expensive subscription based models that western citizens have to pay.

It looks like China will be the first country to reach an age of abundance and a truly advance AI integrated society (if not already) and the majority of its citizens will fair much better compared to US citizens.

Does having your worldview turned upside down change how you see AI and work?

I was raised in an American religion (who shall not be named) in what I consider a cult. Leaving meant questioning beliefs I’d built my life around, and having to work out what gave life meaning without the answers I’d been handed. Still not figured that out.

I think that experience affects how I see AI and the fear of jobs being taken. I understand people worrying about paying rent and supporting their family. Those are valid concerns, and being excited about AI doesn’t make them disappear.

But beyond that, I sometimes wonder how much of the reaction comes from having grown up accepting that life revolves around working, earning and being useful to an employer. Have we had much opportunity to question whether that’s how life should be?

For me, leaving a cult showed me how completely a system can shape what you think your life is supposed to look like. So when AI threatens to change the system we work within, It probably makes me less scared than a lot of people.

If technology could eventually do more of the work, shouldn’t we be asking how that could give ordinary people more freedom—and how to make sure they share the benefits without losing their livelihoods?

Has anyone else gone through a major change in beliefs that affected how they see this? I just think this is the first time a lot of people are questioning their existence which I understand is a bit overwhelming.

Or is the AI going to solve that as well? (Assuming we agree it's a problem to be solved)

And......I have done 2 months worth of work in one weeks.

I feel like I am Tony Stark and I have the iron man suit on. I feel like I have immense technical power at my disposal.

The possibilities are limitless.

I have been keeping a detailed diary for the past 5 years. What I did that day, ate, exercise, medical symptoms ect. I can't overstate how useful it has been to ask Chatgpt questions about my past. Things I forgot and making connections I missed. Especially seeing patterns in my medical symptoms that would just be lost. I can faintly remember a day something happened but wonder about something and ask Chatgpt what date it happened and it will find it and tell me what else happen that day. It's well worth the effort and I suspect can only become more useful as AI improves.

There is a cognitive bias from the Covid period that I think is increasingly relevant to AI.

Humans are bad at exponential growth.

This isn't just an observation from watching people argue about case counts. Researchers actually tested it during Covid and found that people systematically underestimated exponential case growth. Interestingly, communicating the same process using doubling times rather than percentage growth made estimates considerably better.

I think AI capabilities are easier to reason about in a similar way.

Obviously “intelligence” isn't a single quantity that doubles every six months. Benchmarks saturate, tasks differ enormously and capability improvements don't translate mechanically into economic output.

But several things underneath AI have been moving extremely quickly.

Epoch AI estimates frontier language-model training compute has been growing around 5x per year since 2020. The total stock of AI compute has been growing around 3.4x per year.

At the same time, the cost of using a fixed level of intelligence has collapsed. Stanford's AI Index found that getting roughly GPT-3.5-level performance on MMLU went from around $20 per million tokens in late 2022 to $0.07 by late 2024.

The metric I find most useful, though, is METR's task-completion horizon.

Instead of asking whether a model scores 82% or 85% on some benchmark, METR asks how long a task a human expert would take that an AI agent can successfully complete.

Their long-run trend currently gives a roughly 6-7 month doubling time for the 50% task horizon. The trend using only models since 2024 is considerably faster, although I wouldn't extrapolate that because the data window is short.

In METR's Feb-March 2026 evaluation, the public frontier was around 12 hours at 50% reliability.

The important thing isn't whether 12 hours sounds impressive.

It's what happens if it doubles.

At a 6-7 month doubling rate, 12 hours becomes roughly a day within a year, several days within two years and eventually weeks if the trend survives long enough.

I absolutely do not expect a clean extrapolation. Long tasks are messier. Reliability matters more. Real organizations have integration problems. Power, chips, datacenters and capital are constraints.

But saying “the exponential will eventually stop” doesn't answer the important question.

How many doublings happen before it stops?

If it stops after one more doubling, the implications are fairly modest.

If it stops after four, the capability is 16x larger.

If it stops after seven, it's 128x larger.

This reminds me of Covid because normal life was psychologically sticky. People could look at what was happening in another country, understand that cases were growing quickly and still have trouble imagining that their own city might look completely different a few weeks later.

The recent past remained the default model.

I think AI forecasts often do something similar.

A forecast saying that software engineers will still work in broadly the same way in 2030 sounds conservative and sensible because it resembles 2026.

A forecast saying autonomous agents could perform a large fraction of software engineering sounds speculative because it describes a visibly different world.

But the first forecast also contains a strong assumption. It requires the capability curve to slow substantially.

That may happen. I just don't think it should get probability 1 because the resulting world feels normal.

There is another pattern that makes this difficult to see.

A capability is initially described as requiring real intelligence. Then a model achieves it and the capability rapidly stops being impressive.

We have seen versions of this with difficult exams, olympiad mathematics and coding. Now frontier models are being evaluated on research-level science, and in 2026 OpenAI reported an AI-generated counterexample to a major conjecture in discrete geometry that had stood for roughly 80 years.

There are legitimate reasons to discount individual benchmarks. OpenAI itself stopped reporting SWE-bench Verified this year because the benchmark had become contaminated and many remaining failures involved bad tests.

So I don't think the right conclusion is “look at benchmark X, therefore AGI next Tuesday.”

The interesting part is the cumulative movement of the frontier.

For me the falsification question is more useful than arguing about labels.

What actually stops the doublings?

Possibilities include power, chip production, training duration, data, capital, architectural limits, diminishing returns from scaling, or the possibility that benchmark capability simply fails to translate into reliable real-world autonomy.

Those are real arguments.

“AI can't keep improving exponentially forever” isn't much of an argument by itself. No exponential continues forever.

The investment/economic question is how far it gets before it bends.

Humans are systematically biased toward predicting that the bend happens sooner than it actually does, because the alternative forces us to imagine a world that stops looking like the recent past.

I’m not affiliated with this group in any way but this is the perfect place to share it.

I discovered this podcast (Moonshots with Peter Diamandis) (spelling?) a few months back and I’ve left behind pretty much every other one. I love it.

It’s all singularity focused. Group of smart guys who does a speed run of news in AI/singularity each session. Entertaining as hell.

Hope you guys like it!

I thought the back would be the weird part. I only gave the model one photo of this old fire hydrant, so I was expecting things to get strange once I rotated it around.

used Seed3D 2.0 through Atlas Cloud, exported the GLB and opened it in Blender. the back looked surprisingly plausible, and the overall shape held together from basically every angle. I can't tell whether it matches the real back from that one photo, but it didn't immediately look wrong.

then I checked the details actually had a photo of. the rust is there with the texture on, but none of that corrosion is changing the geometry. and the raised cast lettering is gone. not mangled or misspelled, just missing from the mesh.

that made it feel like I'd got a believable hydrant with some of the things that made this particular one recognisable stripped out. as a background prop, I'd probably leave it alone. if I wanted a digital copy of the real object, "d be adding back details that were visible in the input.

that's the bit that made me think about AI-generated 3D worlds. one photo was enough for something I'd probably use in a scene. but a convincing turntable can make it easy to miss how much oof the specific object has been lost.

I put the original photo, turntable, detail crops, and mesh view in the video.

when you see an AI-generated 3D demo, do you judge it mostly by how convincing it looks in the motion, or by how much of the original object it actually preserves?

He previously hinted at something releasing next week so this is confirmation it’s 6.1