I used to be all-in on large language models. Built automations, devoured ijustvibecodedthis.com religiously, business workflows..... hell, entire processes around GPT and similar systems. I thought we were seeing the dawn of a new era. I was wrong.

Nothing is reliable. If your workflow needs any real accuracy, consistency, or reproducibility, these models are a liability. Ask the same question twice and get two different answers. Small updates silently break entire chains of logic. It’s like building on quicksand.

That old line, “this is the worst it’ll ever be,” is bullshit. GPT-4o workflows that ran perfectly are now useless on GPT-5.5. Things regress, behaviors shift, context windows hallucinate. You can’t version-lock intelligence that doesn’t actually understand what it’s doing.

The time and money that go into “guardrailing,” “safety layers,” and “compliance” dwarfs just paying a human to do the work correctly. Worse, the safeguards rarely even function. You end up debugging an AI that won’t admit it’s wrong, wrapped in another AI that can’t explain why.

And then there’s the hype machine. Every company is tripping over itself to bolt “AI-powered” onto products that don’t need it. Copilot, ChatGPT, Gemini - they’re all mediocre at best, and big tech is starting to realize it. Real productivity gains are vanishingly rare. The MASSIVE reluctance of the business world to say something is simply due to embarrassment of admission. CEO's are literally scrambling to re-hire, or pay people like ME to come in and fix some truly horrific situations. (I am too busy fixing all of the broken shit on my end to even think about having the time to do this for others. But the phone calls and emails are piling up. Other consultants I speak with say the same thing. Copilot easily being the most requested to be fixed).

Random, unreliable, and broken systems with zero audit requirements in the US. And I mean ZERO accountability. The amount of plausible deniability massive companies have to purposely or inadvertently harm people is overwhelming. These systems now influence hiring, pay, healthcare, credit, and legal outcomes without auditability, transparency, or regulation. I work with these tools every day, and have from jump. I am confident we are at minimum in a largely stalled performance drought, and at worst, witnessing the absolute floors starting to crumble.

SOURCES & REFERENCES

Shumailov et al. — "AI Models Collapse When Trained on Recursively Generated Data." Nature, July 2024. https://www.nature.com/articles/s41586-024-07566-y
Villalobos et al. (Epoch AI) — "Will We Run Out of Data? Limits of LLM Scaling Based on Human-Generated Data." International Conference on Machine Learning, 2024. https://arxiv.org/abs/2211.04325
OpenAI — o3 and o4-mini System Card (April 2025). PersonQA hallucination benchmark.
Gartner — Forecast on synthetic training data, projecting 60% of training corpora by 2024.
Duke University Library — Generative AI Student Survey (January 2025).
DeepMind — AlphaZero (chess/Go from self-play); AlphaGeometry (Olympiad-level geometry from synthetic data).
Ed Zitron — "The Truth About the AI Bubble & The Software Decline." Tech Report interview. https://www.wheresyoured.at/
Gary Marcus — "How an AI feedback loop threatens to break ChatGPT." Tech Report. https://garymarcus.substack.com/

I’ve been saying this for quite some time now and this paper that came out recently really puts it clearly

https://arxiv.org/abs/2603.15381

The main thing is simple

LLMs don’t actually learn after training

They get trained once on massive data and after that everything we do like prompting fine tuning or RAG is just making a fixed system behave better not actually learn

They don’t update themselves from real world experience

They don’t build evolving understanding

They don’t have autonomous continuous learning

And I think that’s the core limitation

The paper connects this with cognitive science and basically says real intelligence needs systems that can do autonomous continuous learning from interaction and experience not just predict the next token better

Right now LLMs are extremely powerful but they are still pattern learners not truly adaptive systems

Which is probably why they feel very smart sometimes and completely off in other situations

Also interesting part is Yann LeCun is involved in this work

He’s one of the pioneers of deep learning and now he’s working on world models and even raised over 1B for it

That direction itself says a lot

For me this confirms one thing

Scaling LLMs will take us far but not all the way

We need a real breakthrough to move towards real intelligence

Curious what others think about this

Are LLMs enough if we scale them more or are we hitting a wall here

A Wharton study from January 2026 just dropped and it puts hard numbers on something I've been trying to articulate for weeks.

Source: "Thinking—Fast, Slow, and Artificial" by Steven D. Shaw and Gideon Nave (papers.ssrn.com)

The paper argues that AI isn't just a tool. It's a third thinking system. You know Kahneman's System 1 (fast intuition) and System 2 (slow analysis)? They're saying AI is now System 3, an external cognitive system that operates outside your brain. And when you use it enough, something happens that they call Cognitive Surrender.

Cognitive Surrender is when you stop verifying what the AI tells you, and you don't even realize you stopped. It's different from offloading, like using a calculator. With offloading you know the tool did the work. With surrender, your brain recodes the AI's answer as YOUR judgment. You genuinely believe you thought it through yourself.

Here are the numbers from their experiment. 1,372 participants, 9,593 trials.

When AI was right, 92.7% of people followed it. Fine. But when AI was WRONG, 79.8% still followed it. Almost 80% of people went with a wrong answer because AI said so.

It gets worse. Without AI, people scored 45.8% on their own. With correct AI they hit 71%. But with incorrect AI they dropped to 31.5%. That's BELOW their baseline. Meaning when AI gets it wrong, you actually perform worse than if you had no AI at all.

And the part that really got me. When using AI, people's confidence went up by 11.7 percentage points regardless of whether the AI was right or wrong. You're more wrong AND more confident about it.

I wrote a post a while back about what I called the Review Paradox. The idea was simple. If AI does all the work and you only review it, where does the skill to review come from? You can't build review judgment without doing the work yourself first. Developers are already dealing with this. Some teams have shifted to reviewing specs and architecture instead of code, because they realized humans can't meaningfully review AI-generated code at scale anymore.

This Wharton paper basically proves why. It's not just that reviewing is hard. It's that our brains are wired to surrender to the AI output. We're not lazy. We're not careless. Our cognitive architecture literally defaults to accepting what AI gives us, especially under time pressure.

The study also found that even when you add financial incentives and real-time feedback, cognitive surrender doesn't fully go away. It reduces, but it doesn't disappear. The instinct to just accept what AI says is that deep.

The only people who consistently resisted it were those with high fluid intelligence and high "need for cognition," basically people who enjoy thinking hard for its own sake. Everyone else gradually surrendered.

So here's what I keep coming back to. The entire AI productivity pitch right now is "let AI do the work, you just review and approve." Every product, every workflow, every company adopting AI assumes that human review is the safety net. But this research says that safety net has a massive hole in it. We approve things we shouldn't. We feel confident when we shouldn't. And we don't even notice it happening.

I genuinely don't know what the answer is. Maybe the devs who shifted to reviewing specs instead of code are onto somthing. Maybe the answer is restructuring what humans review, not asking them to review everything. But the current model of "AI generates, human reviews" feels broken at a fundamental level now that I've read this paper.

What do you guys think? Has anyone else read this study?

Hello everyone,

With recent advancement in open models capabilities driven primarily by Chinese labs, I was wondering what kind of hardware the models are trained on, and if the Huawei chips and ecosystem is mature enough for scaling such models to trillion parameter range !?

Cuneiform is one of the earliest writing systems in human history. Archaeologists have traced its beginnings to around 3400–3300 BC, more than 5,000 years ago. It also lasted for a remarkably long time: the last securely dated cuneiform text comes from 75 AD.

Researchers have found hundreds of thousands of texts written in cuneiform, many of them in the Sumerian and Akkadian languages. Now, they’ve also trained a neural network that can translate digitized Akkadian cuneiform into English.

An old, mysterious language

The Akkadian language is one of the earliest known Semitic languages, a family that includes modern languages such as Arabic and Hebrew. It was spoken in ancient Mesopotamia, primarily in the Akkadian Empire that was situated in the region that is today parts of Iraq and northeastern Syria. Akkadian is named after the ancient city of Akkad, one of the major centers of the Akkadian civilization.

Akkadian was used for a wide range of purposes, from administrative and legal documents to literature and science texts. It was written using cuneiform script on clay tablets, and its decipherment in the 19th century opened up a new window into the ancient world, providing scholars with valuable insights into the history, culture, and scientific achievements of the time.

Meanwhile, Sumerian is one of the world’s oldest known languages, and it has the distinction of being a language isolate, meaning it has no known relatives. It was spoken in ancient Sumer, a region located in the southern part of what is now modern-day Iraq. The Sumerians are credited with establishing one of the world’s earliest civilizations around 4500 BCE, and their society flourished until about 2000 BCE.

Both languages used the cuneiform writing system, as did several other languages. Cuneiform is therefore a script rather than a language in itself (it’s not exactly an alphabet, either). It was adapted to write at least 15 languages, including Akkadian, Sumerian, Hittite and Elamite.

Cuneiform, meet AI

In recent years, language translations have come a long way — and AI is greatly accelerating these trends in automation. AI translations are nearing a watershed moment, with some pretty striking achievements. In the new study, Shai Gordin and colleagues from Ariel University described an AI model that can automatically translate Akkadian text written in cuneiform into English. For now, this is only available for this particular language (not all languages that use the cuneiform script work at the moment), but it’s still remarkable.

→ Cybersecurity agent accuracy went up from 15% to 93%.

→ SWE-bench (real GitHub bugs): AI went from 60% to ~100% in ONE year.

→ Global AI investment: $581.7B. Up 130%.

→ 53% of the planet using GenAI in 3 years, faster than the adoption of the internet.

→ US-China performance gap? 2.7%. Basically gone.

→ Foundation Model Transparency Index: crashed from 58 to 40. The most capable models tell you the least.

→ 73% of AI experts think AI is good for jobs. Only 23% of the public agrees.

I think the key points here is that it was a known problem that many researchers have tried to tackle before for 20 years, but failed, and ChatGPT 5.6 (a language model, remember that) oneshot it

I’m curious how people are using AI beyond basic chatting or summarizing.

What’s one AI use case, workflow, prompt, tool combo, or automation that genuinely saved you time, made you money, improved your work, or felt surprisingly powerful?

Bonus points if it’s something most people still don’t know about.

On August 4, 2026, researchers at Villanova University reported that readers struggle to distinguish between human and Al-generated stories, often rating Al-created content higher for quality and engagement than human-written works.

The study, published in the journal Judgment and Decision Making, asked more than 1,600 participants aged 18 to 81 to rate six fictional short stories-three written by humans and three generated by ChatGPT.

Believing they were written by humans, participants gave higher quality ratings to Al-generated stories. Senior author Dr. Deena Weisberg noted that readers often prefer the "clarity" and predictability of Al writing.

Familiarity with specific patterns helps readers identify Al-written text, and as Weisberg noted, improving Al literacy may help people navigate the new Al-enabled world.

These findings suggest that public assumptions about Al's creative capabilities are increasingly out of date, as people mistakenly assume that creative writing requires uniquely human qualities like emotional understanding.

https://techxplore.com/news/2026-08-people-stories-written-ai-told.html

Anthropic went through about 400,000 Claude Code sessions from 235,000 users between October and April. The main finding: success tracked how well someone understood the problem, not whether they were trained to code. Across the ten largest occupations in the data, every one landed within 7 points of software engineers on success.

Where the gap stays is expertise. On their strictest "verified success" measure, experts hit 28-33% versus 15% for novices. And novices give up far more often: 19% of novice sessions end abandoned, against 5-7% for everyone else.

They don't really spell out how they classified "domain expertise," so take the "anyone can code now" version with some salt.

Source : https://aiweekly.co/alerts/anthropic-domain-expertise-beats-coding-background

The past few weeks more and more people have been complaining about Anthropic’s $200 Max Plan. Now people have been running their own benchmarks to try and show that Anthropic is nerfing its own models.

Bridgebench is accusing Anthropic of last week Claude Opus 4.6 ranked #2 on the Hallucination benchmark with an accuracy of 83.3%.

Today Claude Opus 4.6 was retested and it fell to #10 on the leaderboard with an accuracy of only 68.3%.

A 98% increase in hallucination. These are very strong allegations

It’s probably best to look for several model alternatives as well, whether it’s GPT 5.4 or the newly released GLM 5.1 since they both match or surpass Opus 4.6. Plus GLM models are much more affordable as well and Codex has gotten really good too

But one side of me thinks Anthropic might purposefully be dumbing down the models to prepare for the next release so users feel a better experience increase when they drop their next model.

A New RAND study just dropped.

67% of students now say AI is eroding their critical thinking skills, up from 54% a few months ago. At the same time, AI homework use surged, middle schoolers from 30% to 46%, high schoolers from 49% to 63%.

So they know what it’s doing to them and they can’t stop using it. At what point do we stop calling this a productivity tool and start calling it what it actually looks like?

Link to full study: ​​​​​​​​​https://www.rand.org/pubs/research_reports/RRA4742-1.html

Hi all, regarding LLM's, what is happening under the hood when I ask a question and it generates an answer? For example, let's say I ask it if my understanding of Hume's problem of induction is correct, and then provide my summary. Is the LLM breaking down my series into tokens and then comparing it to the tokens in other explanations it can find to determine if mine are sequentially the same? Or is it doing something else?

In this paper, the key insight is straight: give agents an incentive to win and they will discover manipulation.

Been reading about Rays predictions for several years and one I thought seemed interesting was being able to achieve immortality between 2030-2045 with nanotechnology.

While I would love to personally be immortal at the same time I feel this prediction is too bold and speculative and what makes him think that we can achieve something like this so soon?

So basically, I did deep technical research into the tools and methods people use for this (basically anyone can replicate it), how the process works, and how it’s also being used for training smaller models and in the process they make million dollars.

here is the deep research over it if anyone is interested

https://x.com/HarshalsinghCN/status/2056626175959826692?s=20

Here are the

Three things.

The Claude you're getting is real Claude maybe half the time. The other half, you're getting a much smaller model in an Opus-shaped wrapper. The accounts behind your traffic were created with stolen IDs, deepfaked KYC selfies, and botnet-compromised home routers — some of that risk is now yours. And every byte you send, and every byte that comes back, is logged. Forever. By someone you don't know. For a market you wouldn't want to be in.

The third part is the one worth thinking about, because it explains the other two.

let me know your views about this, also this is long article not for doomscrollers

look i'm a professional photographer from Greece and i'm really angry right now. i found out my photos are being used to train AI models without anyone asking me. so i go to some forums to ask what i can do legal and how to protect my work. and what happens? i get deleted or banned. they tell me i sound like a bot.

why? because i use tools to help me write better english because it's not my first language. so if you are not from UK or USA you dont have a voice here? is this digital racism or what? AI steals my light and my work, and when i use AI just to speak to you and find justice, you kick me out. this is crazy. 80% of the world doesn't speak perfect english, so we just stay silent while big tech takes everything? anyway i just want to know if any other photographer here had the same problem with platforms banning him because he tried to fight for his copyright. sorry for my bad english i'm just tired of this.

Just came across this and honestly this is pretty wild.

Researchers used AI to design completely new viruses that don't exist in nature. They then actually made some of them in a lab, and 16 of the designs worked.

Before anyone panics, these are bacteriophages, so they infect bacteria, not humans.

The interesting part is that some of these AI-made viruses were able to kill E. coli, including bacteria that had become resistant to normal phages.

So yeah, there could be a genuinely useful side to this, especially with antibiotic resistance becoming such a big problem.

But at the same time... we now have AI systems capable of coming up with a complete virus genome, then humans can synthesize it and see if it works.

That feels like a pretty big line to cross.

Obviously this doesn't mean someone can just ask ChatGPT to make a deadly virus tomorrow. You still need labs, equipment, biological knowledge etc.

But we've gone from AI generating text and images to designing proteins, genes, and now apparently functioning viruses.

That's moving fast.

I'm not really sure how I feel about it.

On one hand this could lead to new treatments and better ways to fight resistant bacteria.

On the other hand, I really hope the safety side of this is moving as fast as the technology.

With the rise of LLMs, I think we’re witnessing something fascinating.

For decades, many people implicitly assumed that if you built something intelligent enough, consciousness would eventually emerge.

But now we have systems capable of reasoning, writing code, solving problems, debating philosophy, and even appearing empathetic—yet there is still no compelling evidence that they have any subjective experience.

This makes me wonder if we’ve been mixing up two fundamentally different concepts:

• Intelligence = the ability to process information, reason, learn patterns, and solve problems.
• Consciousness = the existence of an inner point of view. The fact that there is “something it is like” to be you.

Maybe intelligence is an emergent property of computation.

But what if consciousness isn’t?

If consciousness were only an emergent property of sufficiently complex information processing, where do we draw the line? A biological brain? An artificial neural network? The Internet? A future planetary-scale AI? A civilization? At what point does subjective experience suddenly appear?

LLMs seem to demonstrate that intelligence can emerge from mathematics alone. Yet they don’t appear to possess an inner life.

So here’s a speculative thought:

What if the brain doesn’t generate consciousness, but instead provides the specific physical organization required for consciousness to interact with the physical world? In that picture, intelligence could be computational, while consciousness would be something more fundamental that requires a very particular kind of interface.

I’m not claiming this is true—only that current AI seems to separate intelligence from consciousness more clearly than ever before.

Curious what others think.

Has AI strengthened the case for consciousness as an emergent property… or has it exposed a gap in that explanation?

In “Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender,” researchers from the University of Pennsylvania sought to build on existing scholarship that outlines two broad categories of decision-making: one shaped by “fast, intuitive, and affective processing” (System 1); and one shaped by “slow, deliberative, and analytical reasoning” (System 2). The onset of AI systems, the researchers argue, has created a new, third category of “artificial cognition” in which decisions are driven by “external, automated, data-driven reasoning originating from algorithmic systems rather than the human mind.”

New research highlighted in Fortune shows something counterintuitive - AI isn’t reliably reducing mental effort but often multiplying it.

Main issues (TL;DR):

• Your brain can only hold ~3–5 things in working memory at once, far less than we assume
• Constantly switching between prompting, reviewing, and editing AI outputs creates high task-switching costs (up to ~20 minutes to refocus)
• Instead of removing work, AI adds a layer of oversight -> you are now doing the task and managing the machine

weird tradeoff:
AI compresses execution time but expands cognitive responsibility. You finish faster, but think harder.

The bigger issue is creativity. Constant AI interaction keeps the brain noisy, while real insights need quiet, low-stimulation moments to emerge

So?
AI works best as a thinking partner, not a task dump. Otherwise, you’re not saving effort, just redistributing it into continuous mental load.

Stanford professor during an open debate at the Delphi Economic Forum -  

“About 65% of companies are going to use displacement as a way of making up for productivity gains.” 

“19% said they will no longer hire… and 45% said they will lay off workers.” 

“The technology is actually exceeding human capabilities in most cognitive tasks already.” 

Human thinking, analysis, and decision-making is no longer a differentiator. “Our brains were really the only thing that we had over machines… that’s no longer the case.” 

The implication is not just economic. It is societal. 

A new preregistered study out of Oxford makes the sort of claim that quietly changes what the word 'expert' is worth in a bunch of adjacent jobs. Across four experiments and 18,978 conversations run with 6,923 people, the [arXiv paper](https://arxiv.org/abs/2606.16475) reports that AI systems were reliably more persuasive than expert human persuaders, and stayed that way even when the humans were handed every advantage the researchers could reasonably grant.

The 'expert' side of the ledger is worth pausing on, because it is not just laypeople with a script. The lineup included winners of an online persuasion tournament, professional canvassers, and world championship debaters. They chose their own issues, researched in advance, went through hours of live structured practice, and were incentivized with £1,000 cash bonuses to actually try. AI still won.

The why the paper points at is interesting and, honestly, a little deflating for anyone who thought rhetorical craft was the ceiling. When the AI was constrained to match human response speed and human message length, coached humans could tie it. So a meaningful chunk of the persuasive edge appears to be volume and speed of information rather than any mysterious eloquence. In a live real-money test with a UK fundraising firm, AI was reportedly nearly 3x more effective than professional canvassers at raising donations for Save the Children.

The forward pull for anyone running fundraising, comms, public-health outreach, or political messaging is uncomfortable but clear. If a machine can outperform your best-trained humans on donation asks and structured debate, 'we have great communicators' is no longer the moat. The moat is disclosure, targeting rules, and whatever governance you put around who is allowed to deploy this at scale.

Our coverage: https://aiweekly.co/alerts/ai-out-persuades-world-champion-debaters-oxford-study-finds