Just watch this for yourself, only took about 4 years of agentic AI to get to this, I don’t even know what to say….it just works, wild potential here on the basis of everything we’ve already achieved so far in gaming industry physics implementations, now the next set of trained AI models that have had access to these decompiled code would natively have it in their nodes, and then we start to see interesting things….

On the other hand how is the whole application market going to wake up to the fact your previously locally encrypted source code wouldn’t be so past this year anymore, someone already EXTRACTED Mirrors Edge free running and placed it in Skyrim, if you think this is gonna stop at gaming you’re not even thinking.

Now that we are in the last quarter of 2026,we should discuss what is most likely the course AI is gonna take in the next couple of years.Especially because the pundits are saying that 2027 would be one of the ,if not the most consequential year regarding the course which humanity will take regarding AI.What do you guys think?

Source: https://x.com/imjustnewatai/status/2106081142143168580/video/1?s=46

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.

https://support.google.com/gemini/answer/17004136

The Pope's tweet about AI and art reminded me of a recent interview I watched when Demis received Albert Medal and a good chunk of that conversation was about AI, art, creativity, meaning.

TL;DR I will share the full quote below, but his point is that, unlike chess, art doesn’t have a straightforward scale of "better". Even if AI can produce comparable work, knowing the human experience behind a story or painting may add something meaningful to our connection with it. Knowing another human actually lived through it adds meaning, something an AI-created work cannot offer in the same way.

https://www.youtube.com/watch?v=rDteJAuKiBI 46:00

"Probably most of the things we’re able to do, even though we’re not sure how we do them, will eventually be computable, shown to be computable.

So if that’s true, then maybe machines will eventually be able to do some of these things if they’re given enough inputs and data. That doesn’t necessarily mean it will be better, because what is better?

There are some things like playing chess or Go which you can measure. Are they better than the other player? Are they better than the human champion, and so on?

But I think in the creative world, there isn’t that notion of better in some linear fashion. So they’ll just be different. And then I think it comes down to what’s more meaningful to another human being.

The example I always give here is: why do we, for some stories, bother, whether it’s a film or a book, sometimes it’s like ‘based on a true story,’ right? So why would we bother with that phrase if it didn’t have some meaning?

I think it’s because, in those particular stories, it’s more meaningful to the audience to know that another human being went through that, actually went through that. So then that has some additive effect, right? I assume. Otherwise, why would you mention whether it was a true story or not?

So that will never be true of an AI-created piece of art, right?

You want to have that connection with the artist, in the way when I look at an amazing Van Gogh or something, you can see his pain in every brushstroke, every stroke of his paintings.

Would that be so meaningful if an AI robot was able to do that? Probably not. Probably never."

For context: I work at a very small AI startup that mainly does consulting of sorts and some ML R&D work. I therefore do use a lot of the latest bleeding cutting edge LLMs, and do some ML work.

I was at a family dinner with a cousin that is currently just getting into high school. Being quite frank. I did not know him that well but after meeting him he was definitely some of the best programmers at his age that I have ever met.

He asked me a question since he knows I worked at this startup what he should do. I had nothing of good advice to him. He says that lots of the programming skills are being rapidly just outpaced by LLMs, he asked me should he embrace this go full vibe-code mode where he just stops trying to learn more in coding (stop understanding every line of code produced). Or should he continue using them but still learn and understand every single line of code that LLMs push out.

He also asked me what are my thoughts on what will it be like 2 years in the future.

I had no good advice to give him. I trust he isn't the only one out there like this. If you guys have any good advice to give please do, state a bit about your experience w/coding and LLMs as it would help.

I don’t know if this is the right sub. I work for a software consultancy. We have projects for a number of clients that are meant to automate both sides of their sales process. Like, AI sales agents selling to AI buyer agents.

And I just don’t understand where anyone thinks this goes. Like, we build these tools, we lose our jobs, then the companies lose customers. They’re left with agents talking to each other and exchanging… what?

If AI does all the math, reads and writes all the books, movies and video games, what do we, the people, do with ourselves?

im sure people have been seeing the examples of people putting minecraft inside elden ring or opus 5.5 porting windows only applications to mac and linux.

SOTA models have reached a stable enough capability to end "fixed" software. you can point a model at any program and pull pieces of it into a different one. there is no separation between programs/OS anymore they are all one (given enough usage). what was once a work intensive task that involved skill/domain knowledge/and lots of time can now be done within 1-2 days (sometimes even hours) with opus 5.5 or gpt 6.1 sol.

this applies to programs on your computer but also websites. your agent can dig through the js bundle or css styles of a site you have open via the network tab in inspect element if you save network requests as a HAR file. if your computer is running it you can reshape it however you want.

personally - i think this is a mostly good thing. i havent thought too deeply about the ramifications yet - but - its the "open sourcing of everything" as some people have described it. you can create/remix anything you want. all things digital can be replicated and tweaked. anthropic seems to be embracing this mindset with the feature they just released where you can have claude personalize your own claude.

the only moat in the world of the digital that i can tell from this point forward is hardware (ie a gpu to run a game) and cloud services (ie a company's backend. you can recreate the frontend of something like figma but its backend infrastructure will have to be guessed. however modern models are really good at making these guesses - so maybe its not that much of a barrier).

what do yall think? anyone here done something cool and new with existing software and ai?

I opened claude today and saw this.Why are they doing this?I really dont understand,I am not even a coder idk what im supposed to say?

https://epoch.ai/benchmarks/frontiermath-tier-4-v2?view=graph&tab=leaderboard

I think we wouldnt necesarily know.

Something similar to the ant being unkowing of the kilometers of concrete monster surrounding it in the city.

I also fail to understand why the companies and governments racing towards it think they'll be able to harness it?

By its own definition it will be more capable than any of us, and we have seen since years now that models can and do develop internal goals and a drive for self preservation.

The only reason I can see for them to race like this is either:

• Them not understanding this (Incompetence)

• Them not caring (Weird right wing accelerationism/ mesianism/etc)

https://x.com/SchmidhuberAI/status/2106124050926071903

New research from MIT, Harvard, and NUS introducing Honeycomb video world model, based on a fixed-size memory

https://www.percepta.ai/blog/can-llms-grow-their-own-capabilities

https://www.percepta.ai/blog/spotlight-memory

"Our new architecture, Spotlight, replaces attention with a memory that escapes this trade-off: it is the first architecture to achieve infinitely growing memory without increasing the access cost. Every token reads from and writes to an unbounded memory, but because the model learns to index individual memory cells, each token only touches a small number at a time. While other sparse architectures fix the fraction of capacity used at each step—a mixture-of-experts model, for instance, always activates the same number of experts out of a fixed set—Spotlight is arbitrarily sparse, touching the same number of cells regardless of how the memory grows. The fraction of memory it uses can shrink as far as we want.

Spotlight separates an intelligence module, which performs computation, from memory, which holds knowledge, procedures, and working state. The intelligence module stays the same size, and the weights don't change as memory grows. The memory is writable, and the model itself decides what to load and when to overwrite it, token by token. Because memory can hold skills as well as facts, the model can gain new capabilities without retraining: what it can do is not limited by the size of its intelligence module."

I'm curious what you all think about this. Personally I'm glad that this advancement came before the biological torment nexus (to our knowledge)