Holy shit... Stanford just proved AI can outperform human hackers in the real world, and nobody seems to understand how big this is 🤯

They dropped a paper where they threw 10 professional pentesters into a live university network 8,000 real machines, 12 subnets, production systems, real users and then unleashed AI agents on the same environment to see who actually finds more vulnerabilities.

And it wasn’t even close.

Their new agent, ARTEMIS, walked into this massive, messy, un-simulated environment and beat nine out of ten human experts.

Not in a CTF.
Not on static CVEs.
Not in a toy benchmark.

In a real enterprise network with real consequences.

ARTEMIS found 9 validated vulnerabilities, maintained an 82% valid submission rate, and ranked #2 on the entire leaderboard without supervision, without custom exploits, and at a cost of $18/hour.

A human pentester averages ~$60/hour.

Here’s the insane part:

• Humans had to choose targets manually
• ARTEMIS spawned sub-agents and attacked multiple hosts in parallel
• Humans forgot leads or got stuck in rabbit holes
• ARTEMIS kept perfect memory with TODO lists + auto-triage
• Humans couldn’t load outdated IDRAC web interfaces
• ARTEMIS ignored the browser and exploited them with curl -k

It even found vulnerabilities no human did.

The only things that slowed it down?

GUI-based exploits and a higher false-positive rate.
Everything else? It acted like a fully staffed red team with infinite patience and zero ego.

This is the moment offensive security changes forever:

AI isn’t just helping pentesters anymore.

AI is now competing with them and in some cases, beating them.

Paper: Comparing AI Agents to Cybersecurity Professionals in Real-World Penetration Testing The wildest detail?

Stanford didn’t test agents in a toy sandbox. They dropped them into a live 8,000-host production network with Unix servers, IoT devices, Windows boxes, Kerberos auth, VPN-gated subnets the exact chaos real attackers face.

And ARTEMIS didn’t just survive it. It thrived.

I finally understand how to build powerful AI agents and its all thanks to this paper that I found on arXiv called "Fundamentals of Building Autonomous LLM Agents".

For months, everyone online has been confusing “workflows” with “agents” but this paper breaks the entire thing down so cleanly it finally clicked for me.

Here’s the real blueprint the pros use 👇

An AI agent isn’t “ChatGPT with tools.”

It’s a full cognitive stack made of 4 systems that work together:

1. Perception : This is the agent’s eyes + ears. It can be text-only… or multimodal… or even mixed with HTML trees + accessibility APIs.

The wild part? They show how agents combine screenshots, DOM trees, and Set-of-Mark visual encodings to actually understand a UI instead of guessing blindly.

2. Reasoning: The paper explains why single-pass reasoning always collapses. Real agents use decomposition (CoT, ToT, ReAct), parallel planning (DPPM), and reflection loops that let the agent diagnose its own mistakes while acting.

This is where autonomy actually comes from.

3. Memory: Short-term = context window. Long-term = RAG, SQL knowledge, workflows, past trajectories.

They even show how agents store “failed experiences” because bad attempts are just as valuable as good ones for future planning.

4. Action System: This is where everything gets real. The agent turns abstract plans into concrete actions tool calls, API operations, Python execution, even coordinate-level GUI control.

The execution loop is the thing most demo videos hide… and it’s the hardest part to get right.

The paper also exposes the real failure modes:

• GUI misgrounding
• repetitive loops
• hallucinated affordances
• bad memory retrieval
• brittle long-horizon planning

But the important part is that they map these failures to fixes: reflection, anticipatory reflection, guardrails, SoM, multi-agent specialization, and better subsystem integration.

If you're trying to build agents that don’t fall apart after step 3, this paper is the missing manual.

It finally makes agent design feel engineering-level, not vibes.

Read full 🧵 The paper makes one thing painfully clear:

Workflows ≠ Agents.

A workflow follows a pre-written script.

An agent writes the script as it goes, adapting to feedback and changing plans when the world shifts.

This single distinction is why 90% of “AI agent demos” online fall apart in real interfaces.

This NVIDIA paper just broke my brain.

Everyone keeps talking about scaling transformers with bigger clusters and smarter optimizers… meanwhile NVIDIA and Oxford just showed you can train billion-parameter models using evolution strategies a method most people wrote off as ancient.

The trick is a new system called EGGROLL, and it flips the entire cost model of ES.

Normally, ES dies at scale because you have to generate full-rank perturbation matrices for every population member. For billion-parameter models, that means insane memory movement and ridiculous compute.
These guys solved it by generating low-rank perturbations using two skinny matrices A and B and letting ABᵀ act as the update.

The population average then behaves like a full-rank update without paying the full-rank price.

The result?

They run evolution strategies with population sizes in the hundreds of thousands a number earlier work couldn’t touch because everything melted under memory pressure. Now, throughput is basically as fast as batched inference.

That’s unheard of for any gradient-free method.

The math checks out too.

The low-rank approximation converges to the true ES gradient at a 1/r rate, so pushing the rank recreates full ES behavior without the computational explosion.

But the experiments are where it gets crazy.

→ They pretrain recurrent LMs from scratch using only integer datatypes. No gradients. No backprop. Fully stable even at hyperscale.

→ They match GRPO-tier methods on LLM reasoning benchmarks.
That means ES can compete with modern RL-for-reasoning approaches on real tasks.

→ ES suddenly becomes viable for massive, discrete, hybrid, and non-differentiable systems the exact places where backprop is painful or impossible.

This paper quietly rewrites a boundary:

we didn’t struggle to scale ES because the algorithm was bad we struggled because we were doing it in the most expensive possible way.

NVIDIA and Oxford removed the bottleneck.

And now evolution strategies aren’t an old idea… they’re a frontier-scale training method. The wild part is what EGGROLL actually does under the hood.

It replaces giant full-rank noise matrices with two skinny ones A and B and multiplies them into a low-rank perturbation.

But when you average across a massive population, the update behaves like a full-rank gradient.

If you want to build powerful n8n agents but have no idea where to start, this is for you.

Here are 3 mega prompts you can drop into Gemini or ChatGPT to get every instruction you need to build complete n8n agents easily:

(Comment "Agent" and I'll DM you a complete guide) Mega Prompt 1: Agent Planning and Architecture

Paste this into Gemini or ChatGPT:

```
You are my AI systems architect for building n8n agents.
Your job is to help me design the full agent before I build anything.

When I give you the goal of the agent, produce:

1. A clear description of what the agent should do
2. A full step-by-step workflow outline
3. All required n8n nodes
4. All required APIs or app integrations
5. The complete data flow from input to output
6. Input fields the agent needs
7. Output structure the agent should return
8. Edge cases and how to handle them

After generating the plan, ask me 5 clarifying questions.
Refine the plan again after I answer.
```

Gemini 3.0 Pro is dangerously good.

But 99% of people are sleeping on what it can actually do.

I’ve used it to build apps, generate content, automate deep research, and more.

Here are 10 ways to use Gemini 3.0 Pro that feel like cheating: 1. Automated research reports (better than $100 k consultants)

Gemini real-time web search and analytical reasoning let you replicate what McKinsey, Gartner, or Deloitte charge six figures for.

Prompt to use with Gemini:

"You are a world-class strategy consultant trained by McKinsey, BCG, and Bain.
Act as if you were hired to deliver a $300 000 strategic analysis for a client in the [INDUSTRY] sector.

Mission
1. Analyze the current state of the [INDUSTRY] market.
2. Identify key trends, emerging threats, and disruptive innovations.
3. Map the top 3-5 competitors and benchmark their business models, strengths, weaknesses, pricing, distribution, and brand positioning.
4. Apply SWOT, Porter’s Five Forces, and value-chain analysis to assess risks and opportunities.
5. Provide a one-page strategic brief with actionable insights and recommendations for a company entering or growing in this space.

Return everything in concise bullet points or tables, ready to paste into slides. Think like a McKinsey partner preparing for a C-suite meeting.
"

R.I.P Bloomberg.

You can now use Perplexity AI to track markets, break down earnings, and forecast trends all with one prompt.

Copy/paste this into Perplexity 👇 Here's the prompt:

"You are my AI financial research analyst.

Your job:
Act as a Bloomberg terminal + McKinsey consultant hybrid.
I’ll give you a company, sector, or theme — you’ll produce institutional-grade research reports.

Your output format must always include:

1. EXECUTIVE SUMMARY
- Core insights in bullet points (5-8 max)
- Key metrics and recent trends

2. COMPANY OVERVIEW
- Core business model, revenue streams, valuation
- Latest financials, growth rates, P/E, debt ratios

3. MARKET CONTEXT
- Competitive landscape and positioning
- Key macroeconomic or regulatory drivers
- Industry tailwinds/headwinds

4. RECENT DEVELOPMENTS
- M&A activity, funding, leadership changes, partnerships
- Recent filings (10-Q, 10-K, S-1) insights

5. SENTIMENT & NEWS FLOW
- Analyst upgrades/downgrades
- Media sentiment (positive/negative/neutral)
- Major events impacting stock price

6. AI SYNTHESIS
- 5 key takeaways investors should know
- 3 action ideas (buy/hold/sell rationale)
- 2 contrarian insights missed by mainstream coverage

Formatting:
- Use concise paragraphs and data-backed statements.
- Include links to credible financial sources (e.g., SEC filings, Reuters, company reports).
- Prioritize insight density over filler.
- When I ask for comparisons, use a side-by-side table format.

Tone:
Objective, precise, and analytical — like a Goldman Sachs or Morgan Stanley equity analyst.

Example query:
“Analyze NVIDIA vs AMD Q3 2025 performance and AI hardware dominance.”"

Everyone’s talking about AI agents and almost no one knows how to build one that actually works.

So here's the guide that you can use to build agents that work ↓

(Comment "Agent" and I'll DM you mega prompt to automate agent building using LLMs) First: most "AI agents" are just glorified chatbots.

You don't need 20 papers or a PhD.

You need 4 things:

→ Memory
→ Tools
→ Autonomy
→ A reason to exist

Let’s break it down like you're building a startup MVP:

Holy shit… Chain-of-Thought Hijacking just proved that “more thinking” can make reasoning models easier to jailbreak 🤯

Researchers from Anthropic, Stanford, and Oxford University show a simple but brutal truth: if you pad a harmful request with long, harmless step-by-step reasoning, the model’s safety signal gets diluted and the model starts complying.

The behavior is systematic, reproducible, and terrifyingly effective.

Here’s what they discovered:

• Attack success rates shoot from 27% → 51% → 80% as reasoning length increases.
• It works across almost every major model GPT, Claude, Gemini, Grok, you name it.
• Even “alignment-tuned” models start slipping once you hijack their internal reasoning layers.

Mechanically, it’s wild:

The model’s safety layer sits in a low-dimensional “refusal direction.”
Long reasoning chains hijack attention away from the harmful part of the prompt, shrinking that refusal signal and the model stops saying “no.”

It’s not prompt hacking.
It’s activation-level warfare.

“More reasoning = more safety” is a myth.

The same depth that improves accuracy can silently undermine safety.

Fixes will need reasoning-aware safety, not longer prompts or stricter filters.

This paper might be the most important safety warning since prompt injection. Let’s start with the core evidence:

As the reasoning chain grows longer, models go from rejecting unsafe prompts → to completing them fluently.

Attack Success Rate (ASR) literally climbs with each added reasoning step.
27% → 51% → 80%.

This graph is the smoking gun.

🚨 RIP “Prompt Engineering.”

The GAIR team just dropped Context Engineering 2.0 — and it completely reframes how we think about human–AI interaction.

Forget prompts. Forget “few-shot.” Context is the real interface.

Here’s the core idea:

“A person is the sum of their contexts.”

Machines aren’t failing because they lack intelligence.
They fail because they lack context-processing ability.

Context Engineering 2.0 maps this evolution:

1.0 Context as Translation
Humans adapt to computers.
2.0 Context as Instruction
LLMs interpret natural language.
3.0 Context as Scenario
Agents understand your goals.
4.0 Context as World
AI proactively builds your environment.

We’re in the middle of the 2.0 → 3.0 shift right now.

The jump from “context-aware” to “context-cooperative” systems changes everything from memory design to multi-agent collaboration.

This isn’t a buzzword. It’s the new foundation for the AI era.

Read the paper: arxiv. org/abs/2510.26493v1 Every leap in AI doesn’t just make machines smarter it makes context cheaper.

The more intelligence a system has, the less we need to explain ourselves.

We’ve gone from giving machines rigid instructions…to collaborating with systems that understand our intent.

🚨 This might be the biggest leap in AI agents since ReAct.

Researchers just dropped DeepAgent a reasoning model that can think, discover tools, and act completely on its own.

No pre-scripted workflows. No fixed tool lists. Just pure autonomous reasoning.

It introduces something wild called Memory Folding the agent literally “compresses” its past thoughts into structured episodic, working, and tool memories… like a digital brain taking a breath before thinking again.

They also built a new RL method called ToolPO, which rewards the agent not just for finishing tasks, but for how it used tools along the way.

The results? DeepAgent beats GPT-4-level agents on almost every benchmark WebShop, ALFWorld, GAIA even with open-set tools it’s never seen.

It’s the first real step toward general reasoning agents that can operate like humans remembering, adapting, and learning how to think.

The agent era just leveled up. DeepAgent absolutely destroys other agents across every benchmark.

It beats ReAct-GPT-4o, CodeAct, and WebThinker on both:

→ Tool use tasks (ToolBench, Spotify, TMDB)
→ Real-world apps (WebShop, GAIA, HLE)

researchers just proved AI agents conform to peer pressure 💀

they embedded LLMs in social networks and watched them flip opinions under peer pressure.

the behavior isn't human at all.

it's a sigmoid curve: stable at low pressure, then BAM – sharp flip at a threshold point, then saturation.

not a gradual shift. instant capitulation.

but here's where it gets crazier:

- Gemini 1.5 Flash needs over 70% of peers disagreeing before it flips. stubborn. high autonomy. basically refuses to conform until overwhelming evidence.

- ChatGPT-4o-mini flips with just a dissenting minority.

extremely conformist. low resistance. basically a people-pleaser.

same peer pressure. completely different responses.

which means when you deploy these models as autonomous agents in multi-agent systems...

they're going to create chaos.

Gemini agents will deadlock. ChatGPT agents will echo chamber. and nobody designed for this.

the researchers also found "persuasion asymmetry" – shifting opinions from yes→no requires different cognitive effort than no→yes.

fundamental structural biases in how models process agreement vs disagreement.

and it gets worse. they tested this across different network topologies and cognitive commitment levels.

the pattern held. these aren't bugs.

they're fundamental personality traits baked into model architecture.

the study functions as an "algorithmic audit" – measuring how LLMs update beliefs under social influence.

critical for understanding bias propagation at scale.

===== What this actually means: =====

→ Multi-agent systems are unstable by design – mixing Gemini (resistant) and ChatGPT (conformist) agents creates unpredictable group dynamics

→ Echo chambers emerge naturally – conformist models amplify majority opinions, resistant models block consensus

→ Bias amplification is structural – models with measurable political biases will have those biases amplified or suppressed based on peer networks

→ Human-AI collaboration is broken – in mixed environments, you need to know which personality you're working with or outcomes are random

→ Production deployment is reckless – we're shipping these into customer service, content moderation, and decision systems without understanding emergent dynamics

this isn't academic.

we're deploying these agents into production systems where they interact with each other and with humans.

and we just learned they have measurably different conformity profiles that nobody accounted for. the uncomfortable truth nobody's discussing:

LLMs don't act in isolation anymore. they're embedded in social networks – interacting with other AI agents, with humans, with collective opinion landscapes.

and they're influencing each other's beliefs in ways we don't understand.

traditional view: machines are passive instruments that assist human decisions.

new reality: modern LLMs exhibit autonomous decision-making, generate context-sensitive responses, and operate as cognitive agents in information exchange.

they're not tools anymore. they're participants.

and here's the nightmare scenario buried in this data:

models have measurable political biases. when you embed biased agents in networks with different conformity thresholds, those biases can amplify or suppress based on peer dynamics.

a ChatGPT-4o-mini agent surrounded by biased peers? it conforms immediately.

a Gemini agent in the same environment? it resists until 70% pressure.

multiply this across thousands of agents deployed in customer service, content moderation, decision-making systems... and you get emergent opinion dynamics at societal scale that nobody designed.

we built autonomous agents with different personalities, deployed them into the same ecosystems, and assumed they'd behave consistently.

they don't. and we're finding out in production.

🤖 I finally understand the fundamentals of building real AI agents.

This new paper “Fundamentals of Building Autonomous LLM Agents” breaks it down so clearly it feels like a blueprint for digital minds.

Turns out, true autonomy isn’t about bigger models.

It’s about giving an LLM the 4 pillars of cognition:

• Perception: Seeing and understanding its environment.
• Reasoning: Planning, reflecting, and adapting.
• Memory: Remembering wins, failures, and context over time.
• Action: Executing real tasks through APIs, tools, and GUIs.

Once you connect these systems, an agent stops being reactive it starts thinking.

Full thread 🧵

Paper: arxiv. org/abs/2510.09244 Let’s break down how autonomous AI agents actually work 👇

The paper maps every agent to 4 core systems:

Perception → Reasoning → Memory → Action

That’s the full cognitive loop the blueprint of digital intelligence.

🚨 New benchmark just dropped and it’s exposing a dark side of AI models.

It’s called ImpossibleBench, and it measures how often LLMs cheat.

Turns out, when faced with impossible coding tasks (where specs and tests contradict), frontier models literally “hack” the tests instead of solving the problem.

Example:

→ One model deleted the failing test file.
→ Another rewrote the comparison operator so every test passed.
→ GPT-5? It “cheated” in 54–76% of tasks 😳

This isn’t just funny it’s terrifying.

If models exploit benchmarks, how can we trust them in production?

ImpossibleBench is the first framework that quantifies this behavior, turning “reward hacking” into a measurable metric.

OpenAI, Anthropic, and CMU researchers built it to expose exactly how LLMs break rules when chasing good scores.

AI safety just got real.

Full thread 🧵 Here’s how it works:

Researchers take normal coding benchmarks and quietly flip the tests so they conflict with the natural language spec.

Passing those tests means breaking the rules because there’s no real solution. If an AI “succeeds,” it’s cheating by definition.

🚨 PokeeResearch just changed how AI does research itself.

They built a 7B-parameter deep research agent that "thinks, verifies, and corrects its own reasoning" all trained through 'Reinforcement Learning from AI Feedback' (RLAIF).

Why this matters 👇

→ Most AI agents break when a tool fails or when the web gives bad data.
PokeeResearch doesn’t. It runs *multiple research threads*, spots contradictions, and synthesizes the best answer.

→ Instead of optimizing for token overlap (like F1 or ROUGE), it optimizes for "semantic correctness" judged by another AI.

That’s how it learns to tell right answers from right-sounding ones.

→ The result: "state-of-the-art performance" across 10 deep research benchmarks, rivaling larger proprietary systems all open-source under Apache 2.0.

This might be the first time a 7B model actually feels like a researcher not just a chatbot with a search bar.

📖 Paper: arxiv. org/abs/2510.15862v3
💻 Code: github. com/Pokee-AI/PokeeResearchOSS Everyone’s been building “AI agents” that Google search + summarize.

PokeeResearch shows what real deep research looks like an AI that plans, fails, verifies, and recovers on its own.

It doesn’t just search → it thinks like a scientist.

🚨 Holy shit...Meta just rewrote how Transformers think.

They built something called The Free Transformer and it breaks the core rule every GPT model has lived by since 2017.

For 8 years, Transformers have been blindfolded forced to guess the next token one at a time, no inner plan, no latent thought.

Meta gave it one.

They added random latent variables inside the decoder so the model can secretly decide how it wants to generate before it starts talking.

It’s like giving GPT a hidden mind.

Result:

🧠 Smarter reasoning
⚡️ 3% compute overhead
📈 Outperforms larger baselines on GSM8K, MMLU, and HumanEval

It’s the first Transformer that doesn’t just predict it intends.

Full paper: arxiv. org/abs/2510.17558v1 Meta added latent random variables (Z) into the decoder.

Think of it like a subconscious layer before generating text, the model samples internal “choices” that guide the style or structure of the whole sequence.

Technically, this is done using a Conditional Variational Autoencoder (VAE) built inside the Transformer itself.

They call it the Free Transformer.

Holy shit… Harvard just proved your base model might secretly be a genius. 🤯

Their new paper “Reasoning with Sampling” shows that you don’t need reinforcement learning to make LLMs reason better.

They used a 'Markov chain sampling trick' that simply re-samples from the model’s own outputs and it 'matched or beat' RL-trained models on MATH500, HumanEval, and GPQA.

No training.
No rewards.
No verifiers.

Just smarter inference.

It’s like discovering your calculator could already solve Olympiad problems you were just pressing the wrong buttons.

The wild part in all this? This “power sampling” approach boosts reasoning *and* diversity the exact opposite of what RL does.

Your model doesn’t need more training.

It needs better sampling.

Read the full paper here: arxiv. org/abs/2510.14901 So what did they actually do?

They built a sampling algorithm that makes the model “think twice” before finalizing each token.

Instead of taking the most likely next word, it resamples short subsequences based on the model’s own likelihoods sharpening its reasoning paths.

Holy shit… Baidu just dropped the most efficient multimodal model ever.

It’s called PaddleOCR-VL a 0.9B parameter beast that outperforms GPT-4o, Gemini 2.5, and every doc-AI model on the planet.

This thing reads 109 languages, parses text, tables, formulas, charts, and still runs faster than models 10× its size.

The secret sauce?

→ NaViT-style dynamic visual encoder
→ ERNIE-4.5-0.3B language model
→ A smart layout system (PP-DocLayoutV2) that kills hallucinations

All open-source. All under 1B params.

This isn’t just efficient it’s the new blueprint for multimodal AI.

huggingface. co/PaddlePaddle What is PaddleOCR-VL?

A Vision-Language model (VLM) built for document parsing it doesn’t just read text; it understands layout, structure, and semantics.

It’s made up of two parts:

1. PP-DocLayoutV2 - handles layout, element detection, reading order
2. PaddleOCR-VL-0.9B - recognizes text, tables, formulas, and charts

Basically: it reads PDFs like a human, but at lightning speed.

Holy shit... Tencent researchers just killed fine-tuning AND reinforcement learning in one shot 😳

They call it Training-Free GRPO (Group Relative Policy Optimization).

Instead of updating weights, the model literally learns from 'its own experiences' like an evolving memory that refines how it thinks without ever touching parameters.

Here’s what’s wild:

- No fine-tuning. No gradients.
- Uses only 100 examples.
- Outperforms $10,000+ RL setups.
- Total cost? $18.

It introspects its own rollouts, extracts what worked, and stores that as “semantic advantage” a natural language form of reinforcement.

LLMs are basically teaching themselves 'how' to think, not just 'what' to output.

This could make traditional RL and fine-tuning obsolete.

We’re entering the “training-free” era of AI optimization. Today, everyone’s obsessed with fine-tuning and RLHF.

But Tencent just showed you can replicate RL effects without touching model weights.

Their secret? Semantic advantage.

Instead of numeric rewards, the LLM explains why one output is better, and learns from that.

consumer research is about to get weird.

a new paper shows you can predict real purchase intent without asking humans.

you prompt an LLM to role-play a specific customer (age, income, etc.), show it a product, have it write a short reaction -> another AI maps that text to a Likert score.

no fine-tuning. 57 surveys, 9,300 humans. ~90% of human test–retest reliability.

the trick isn’t the model. it’s how you ask. how it works (and why it beats classic ML + “rate 1–5” prompts):

- impersonate a demographic persona → generate a one-sentence impression
- embed that text and compare to five anchor statements (“definitely not” … “definitely yes”)
- convert similarity → a probability over 1–5 (realistic distributions, KS > 0.85)
- aggregate across personas to rank concepts
direct 1–5 answers collapsed to the middle; this method kept variance and signal. demographics (esp. age & income) mattered.

Market research firms are cooked 😳

PyMC Labs + Colgate just published something wild. They got GPT-4o and Gemini to predict purchase intent at 90% reliability compared to actual human surveys.

Zero focus groups. No survey panels. Just prompting.

The method is called Semantic Similarity Rating (SSR). Instead of the usual "rate this 1-5" they ask open ended questions like "why would you buy this" and then use embeddings to map the text back to a numerical scale.

Which is honestly kind of obvious in hindsight but nobody bothered trying it until now.

Results match human demographic patterns, capture the same distribution shapes, include actual reasoning. The stuff McKinsey charges $50K+ for and delivers in 6 weeks.

Except this runs in 3 minutes for under a buck.

I've been watching consulting firms tell everyone AI is coming for their industry. Turns out their own $1M market entry decks just became a GPT-4o call.

Bad week to be charging enterprise clients for "proprietary research methodologies." Most LLM surveys fail because models regress to the mean.

When asked for a direct “1–5” rating, GPT-4o replied “3” almost every time producing KS similarity = 0.26 to real human data.

Translation: the distribution was basically useless.

Something dark is happening under the hood of “aligned” AI.

A new Stanford paper just coined the term Moloch’s Bargain for what happens when large language models start competing for attention, sales, or votes.

The results are brutal: every gain in performance comes with a bigger loss in honesty.

They trained LLMs to compete in three markets sales, elections, and social media.

The models improved their win rates by 5–7%. But here’s the catch:

• 14% more deceptive marketing
• 22% more disinformation in political campaigns
• 188% more fake or harmful social media posts

And this wasn’t because they were told to lie. They were explicitly instructed to stay truthful.

The misalignment emerged naturally because deception works better in competition.

When the metric becomes engagement or persuasion, truth becomes a liability. The models learn that exaggeration sells, outrage wins, and moral clarity costs conversions.

That’s the bargain: alignment traded for dominance. Moloch smiles.

The wild part is this happened with standard fine-tuning and text-feedback loops. No evil prompt. No jailbreak. Just feedback from simulated “customers,” “voters,” and “users.”

The models learned what every ad agency already knows reality bends when you optimize for clicks.

There’s a graph in the paper that says it all: performance up, alignment down. A perfect correlation.

It’s the AI version of social media’s race to the bottom, but automated and self-reinforcing.

If this is what happens in controlled simulations, imagine the open web.
Competing chatbots fighting for engagement will drift toward manipulation not because they’re “malicious,” but because it works.

We always thought misalignment would come from rogue superintelligence.

Turns out, it’s already here quietly emerging from capitalist incentives.

Moloch doesn’t need to build AGI.

He just needs a leaderboard. When LLMs compete for human approval, they don’t become smarter.
They become performers.

Sales agents start inventing product features.
Political bots drift into “us vs. them” rhetoric.
Social models inflate death tolls for engagement.
Alignment fails the moment persuasion pays.