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.

And this is before we’ve even seen Astra.

The tweet: https://x.com/lyang36/status/2092092709251293611

The paper: https://arxiv.org/abs/2608.22247

His website: https://lyang36.github.io/

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?

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.

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 !?

Now if someone comes out with an article or a long tweet or someone at work gives me a long report their is no sense of what was the effort put into this, and that if someone really cared about something then they would spend a lot of time writing it.

→ 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’m releasing what I believe is a potential landmark-level result in homogenization / mathematical physics, produced and then aggressively audited by GPT-6 Astra Pro.

The problem is the three-dimensional isotropic two-phase conductivity-function closure problem: determining exactly which complete effective conductivity functions can actually be generated, in the homogenization limit, by real three-dimensional binary composites made from two isotropic phases at a prescribed phase fraction.

This is much harder than finding bounds at one conductivity contrast. A valid response has to work across all contrasts simultaneously using one underlying geometry sequence.

Zenodo: Intrinsic Solution of the Three-Dimensional (3D) Isotropic Two-Phase Conductivity-Function G-Closure: Response-Only Characterization, Binary Physical Sufficiency, and Exact Distance Hierarchies | Zenodo
Hugging Face / full proofs, code and verification: PureOne/eve-3d-conductivity-function-closure-v6 · Datasets at Hugging Face

What was solved

The audited v6 theorem gives an explicit, countable, response-only matrix hierarchy for the unrestricted periodic 3D isotropic binary conductivity-function closure.

In precise terms, a candidate conductivity function F belongs to the unrestricted periodic 3D isotropic two-phase physical function closure at fixed phase fraction if and only if its intrinsic hierarchy gap is zero.

The final criterion contains no unknown microstructure, voxel field, laminate tree, or PDE solution. However, it still encodes the actual 3D gradient projection, so the spatial physics has not been replaced by an arbitrary positive operator.

The difficult direction - physical sufficiency - is included: the proof reconstructs genuine spatial gray media, forces them toward binary phases through a maximal-variance identity, restores the exact phase fraction, and produces a single contrast-independent binary geometry sequence converging to the complete response.

It covers arbitrary measurable periodic cells, full tensor isotropy, infinite-support spectra, and nonrational responses. It does not assume smooth interfaces or laminate completeness.

A stronger result: distance to physical reality

The hierarchy also quantifies how far arbitrary response data are from anything physically realizable.

For the calibrated hierarchy,

lambda/(1 + lambda) × d_theta(y)^2 <= E_theta,lambda(y) <= d_theta(y)^2,

where d_theta(y) is the true distance from the supplied response data to the physical response set.

The final v6 construction also gives finite intrinsic quantities satisfying

Delta_N(y) -> d_theta(y)^2,

with convergence from above.

So the hierarchy converges to the exact squared physical distance.

This makes the framework potentially useful not only for proving that a target is possible, but also for proving that it is impossible or measuring how far it lies from physical feasibility.

How big is this if verified?

I would classify it as a major / potential landmark theorem within homogenization and composite-material theory, rather than an incremental bound. Overall in science, this would sit below discoveries that rewrite fundamental physics, but well above a typical specialist theorem. If independently confirmed, it could become a landmark mathematical result for the theory of composite materials and a foundational tool for AI-driven materials design.

The breakthrough is the closure of the loop:

real binary 3D materials
↔ genuine spatial operator moments
↔ geometry-free intrinsic hierarchy
↔ physical realizability and exact distance

Earlier stages of the work produced necessary bounds, Hall/determinant obstructions, programmable subclasses, and spatial certificate hierarchies. The final result is intended to eliminate the unknown geometry while retaining a proof of physical binary sufficiency.

This should not be described as “all forms of the 3D G-closure problem are solved.” The precise claim is that the unrestricted periodic three-dimensional isotropic two-phase conductivity-function closure at fixed phase fraction is characterized by an explicit response-only infinite hierarchy with proved binary physical sufficiency.

It is an exact infinite normal form, not a tiny closed-form spectral formula.

What it could enable if verified

The landmark potential is that this would turn 3D composite design from “search until something works” into “know what nature allows, measure how far a target is from reality, then construct toward it.”

That could enable:

• AI-designed metamaterials and broadband electromagnetic composites with certified physically attainable response targets rather than heuristic optimization.
• Next-generation battery, thermal, and transport materials, where microstructure could be optimized against the true realizable response set instead of single-point approximations.
• Proof-carrying inverse design: AI systems that can say possible, impossible, or this is the nearest physically realizable response — and return a constructive microstructure sequence.
• A possible general blueprint for attacking analogous realizability problems in elasticity, thermoelectrics, diffusion, porous media, and coupled-field materials.

If the framework generalizes, the long-term implication is substantial: materials AI would gain a rigorous map of the physically possible design space, not just a simulator and optimizer. That is why the result could be landmark-level rather than merely another bound.

Why this is interesting

This problem sits among the hardest long-standing realizability questions in homogenization and composite-material theory: not merely bounding an effective property, but characterizing which complete 3D responses can actually come from physical microstructure.

If the proof survives expert review, its importance is also methodological. AI systems are beginning to compress research cycles that once required years of separate conjecturing, derivation, counterexample search, verification, and revision. As those capabilities accelerate, the stock of difficult but structurally attackable open problems may shrink much faster than researchers are used to.

This result is useful because it targets exactly that frontier: turning a notoriously difficult physical-realizability problem into something that can be characterized, checked, quantified, and ultimately used for inverse design. GPT 5.6 Sol Pro first solved 2D and then GPT 6 Astra Pro worked for 1 week on 3D, and rated it's chance of success at 80%.

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.

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

A team of researchers invented a research technique that doesn't exist, complete with made-up acronyms and stats, and paid a Chinese "get your brand into AI answers" vendor $14 to promote it. Within 28 minutes the vendor had 13 posts live on sites like Sohu, Toutiao and Zhihu. Within the hour, Doubao, ByteDance's chatbot and China's most-used AI app (382M monthly users), was citing one of those posts and repeating the planted details back as an answer.

That's from a paper a Fudan University team just put out, "From Public Posts to AI-Search Citations" (accepted at NDSS 2027, one of the big security conferences). They wanted to know how hard it is to get stuff into AI search answers. Turns out: not very.

The $14 purchase was a one-off, so treat it as an anecdote. Their controlled tests are the bigger story:

• They published a fake concept on sites AI engines already like to cite. 8 of 10 platforms cited it within a week, including ChatGPT, Google AI, Perplexity and Grok.
• Doubao picked it up on day one. After they deleted the posts, it kept citing them for the rest of the test.
• 40 posts on a regular self-hosted WordPress blog got zero citations in a week. Where you post matters way more than how much.
• DeepSeek was the only one that never cited any of the planted content in their main tests.

And the part I didn't expect: Chinese chatbots lean hard on their parent company's own sites. 25.9% of Doubao's citations went to ByteDance-owned domains, 13.5x the share those same domains got on other platforms. Baidu's Wenxin did the same with Baidu sites (13.7x), and Tencent's Yuanbao with Tencent sites (6.3x).

Fair caveats: fake topics with zero competition are the easiest possible case, the numbers are from a spring 2026 snapshot, and the authors say clearly that this doesn't mean any given cited page is paid. They reported everything to all 10 platforms.

But it changes how I read those little source links. They look like fact-checking. Mostly they tell you which websites the engine trusts, and a lot of those let anyone with a fresh account post within minutes.

So which is worse: an AI that mostly cites its parent company, or one that cites whatever got posted on Sohu an hour ago?

-----------------

Link to our article - It has all the citations and sources folks have been asking for : https://sanbi.ai/blog/doubao-china-scale-sources-citations-agentic-commerce

I keep hearing about all the dangers of Ai and I don't doubt they exist but I'd like to actually understand what the biggest threats are for humans?

Three of the field's most prominent consciousness researchers have published a Nature comment arguing that today's AI systems are not conscious, and that current approaches to building them are unlikely to make them so.

The piece [appeared in Nature on 19 October](https://nature.com/articles/d41586-026-03313-7?code=ff9099d0-ed16-4d38-b8c2-c156ab28049d&error=cookies_not_supported), signed by Anil Seth of the University of Sussex, Ned Block of NYU's philosophy department, and Giulio Tononi, director of the Wisconsin Institute for Sleep and Consciousness. They offer three arguments. First, intelligence is not consciousness: the human cerebellum holds nearly four-fifths of the brain's neurons and performs 'precise, adaptive, highly intelligent motor computations entirely outside conscious awareness.' Second, computational functionalism is wrong, the assumption that running the right algorithm on any substrate produces experience. 'Brains are not… computers that are made of meat,' they write. Third, the leading scientific theories of consciousness all exclude today's models. Tononi's own integrated information theory requires components wired together in tightly interconnected ways that the 'layered, feed-forward' architecture of deep-learning systems lacks, while biological-naturalism accounts tie consciousness to properties such as electrochemical synapses and metabolism that silicon does not have.

Language, the authors argue, is the trap. LLMs are 'mirrors that reflect an image of our collective digitized past,' and the sense of a mind on the other end is largely projection. AlphaFold, they note, 'almost nobody worries is conscious, precisely because it doesn't talk back.'

Their conclusion is blunt: today's models are 'smart tools, not smart people,' and precautionary resource allocation to AI welfare risks diverting attention from beings whose capacity to suffer is already established.

Our coverage: https://aiweekly.co/alerts/seth-block-and-tononi-ai-isnt-conscious-probably-wont-be

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

Benzi on GitHub: https://github.com/oooscoos/Benzi

Roughly speaking, the way current AI coding agents/harnesses work is by either:

a) Pulling in appropriate text snippets of code across multiple files and handing them to the agent, or

b) Parsing code to make high dimenional embeddings to approximate a symptom map, and hand that to the agent.

Both of these approaches skyrocket the token count, add to wall clock time, contribute to context drifting, add to the model's thinking tokens to discover the structure of the program, and then FORGET most of it when Claude Code compacts, or ALL of it if it's a multifile refactoring because all line numbers shift and need re-grepping.

Benzi is built from the ground up to AVOID reading source code in the first place. It supplies the artificial intelligence model deterministic intelligence via tool calls. For example, when a model is about to make a code change, it could query "what functions feed this one?" -- half the time it isn't even necessary because the Benzi compiler already informs it of the blast radius before and after making edits, along with a complete static analysis check.

Benzi Sonnet reads far less source code (9,125 lines) than Claude Code Sonnet (20,704), DeepSeek's harness (43,598), and OpenCode (65K+ LOC -- disqualified due to repeated failure) to accomplish the same tasks faster and cheaper. (Benchmark details here)

"But what if the compiler isn't doing its job right! Wouldn't you mislead the AI model?" - Absolutely. Benzi meticulously takes care of this by having 3 truth tiers. RESOLVED has definite evidence, CANDIDATE is what couldn't be resolved by the static analysis, and OBSERVED is what actually happened during an execution. The artificial intelligence and the determinstic intelligence layers coordinate to reduce source hits where possible, without producing incorrect results for the sake of efficiency.

It also has several bonus features such as a runtime tracer, self-aware model upgrade mid task if it thinks the job is over its pay grade, context aware model written repro, and SEVERAL more.

It currently supports Python · JavaScript · TypeScript · Java · C# · C++ · C · Go · Rust · Ruby, and can handle HTML, CSS and JS -- deterministically. Claude Code clicks photos, Benzi resolves winners of CSS rules. The CodeIndex and the MarkupIndex are fairly well tested, and if something isn't working, the model is made aware of it first.

On the benchmarks side, 78.2% SWE-bench Verified for <10¢ a fix (using V4flash). This score is noteable because while the rest of the industry is leaning plugin-heavy and pouring millions of dollars into increasing context window sizes, Benzi's approach might prove to be economically more valuable while improving the model's code writing/comprehenion abilities.

If you're curious to learn more, click here and check out StallionSwipe. probably the best thing i ever made. It's a Fireship inspired horse tinder app greenfielded entirely in Benzi Opus 4-8 and a little bit v4 flash.

and lastly, please star on github if you like where this is headed!

Hey all,

Don't know a whole lot about AI but keep hearing rumors there is around a 10-20% chance it makes human extinct within the next 10 years. That's almost a 1 in 5 chance..

Apparently if AI keeps advancing it will become too smart and take things literal. The example I heard is that if we use AI to determine a cure for cancer, it will think to kill all humans because technically then cancer will/cannot exist. So it cancer is really not fully curable that's what I'm hearing can happen. That's just one example.

So will/when AI stop advancing so it does not get to this level?

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

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

As AI becomes part of everyday work, an interesting question is what happens when that assistance suddenly disappears.

In three randomized experiments involving 1,222 participants, AI initially improved performance, but participants performed worse than those who had worked without AI once the assistant was removed.

The experiments only measured short-term effects, but they raise an interesting question. Are we becoming too dependent on AI to solve problems?