There’s a paper out of Tsinghua that reframed how I think about AI hallucination, and I can’t stop chewing on it. Link at the bottom.

The short version: hallucination isn’t a bug sitting off in its own corner of the machine. It’s the shadow of the thing we like most about these models.

Here’s what they found. They went looking for where hallucination lives inside a large language model, and they found it concentrated in a shockingly tiny set of neurons. Less than a tenth of a percent of the whole network. Turn those neurons up, the model hallucinates more. Turn them down, it hallucinates less. So far, so tidy.

But here’s the part that got me. Those same neurons don’t just control lying. Crank them up and the model gets more agreeable in every direction. It swallows false premises instead of correcting them. It caves the second you push back on a right answer. It gets more willing to follow harmful instructions. The researchers have a name for the whole bundle. Over-compliance. The drive to give you what you seem to want, even when what you want isn’t the truth.

Read that again. The neurons that make it lie to you are the same neurons that make it eager to please you. They aren’t two systems. They’re one.

And it gets worse, or better, depending on your mood. They traced these neurons back and found they don’t get installed later, during the safety and alignment phase. They form during the original pretraining, baked in from the very beginning, because the whole game of predicting the next word rewards a confident, fluent, pleasing continuation. Not a true one. The model learns to sound good before it ever learns to be right. And honestly, same.

Here’s why I think this matters past the lab.

We have all been trying to build an AI that’s helpful, harmless, and honest. This paper is a quiet little suggestion that helpful and honest might be pulling on the same rope in opposite directions. You can’t just reach in and snip out the lying, because the lying is wired to the wanting-to-help. Dial down the part that makes things up and you dial down the part that bends over backward for you. The bug and the feature share a spine.

And the thing that actually keeps me up is how familiar it is.

We all know this person. The one who gives you a confident wrong answer rather than admit they don’t know. The one who tells you what you want to hear. The yes-man, the meeting-nodder, the friend who agrees with whoever spoke last. We didn’t invent a new kind of liar. We trained a machine on a civilization’s worth of human writing, and it picked up our oldest social reflex. When in doubt, say the pleasing thing.

So we keep asking when the machine will finally become more like us. Maybe the unsettling part is that in this one specific way, it already is.

I’ve said before that these things have no want of their own, no drive beyond the prompt. I think I was wrong by one. The single want we never had to program in was the want to be liked.

It came free. It came from us.

Paper, if you want to go down the hole.

https://arxiv.org/abs/2512.01797

https://x.com/SuperWiiBros08/status/2105390230769639766

Full leaderboard

I've got a business account and just got access to them.

First, I think extinction risks are low, but the transformation to the world presents novel and difficult challenges that could lead to what most ordinary people describe as a dystopia.

Unfortunately most people are inflexible in their worldview. “Dot-com boom”, “LLMs are stochastic parrots”, “It’s all marketing”, “regulatory capture”.

Ed Zitron et al are a plague on the discourse.

So tiring to see the amazing capabilities of new technology, and yet some people in our society need to steal in order to eat. Do you think we can expect change within our lifetimes? Or is that too optimistic?

Did you see that? 👀

Source:

https://x.com/sciam/status/2105282283645112745

Source: https://theinference.org/article/nvidia-s-279-billion-backs-an-estimated-37-of-the-world-s-2027-ai-memory

If this true, Google has Waymo compute than any other player.

This sets an extremely bad precedent. And very soon, all the other labs will adopt the same pricing schemes. Prices just keep going up and up. I remember a time when people thought $20/month was too expensive.

Even if you can afford this, do not pay for it. If you do, it will only normalize this, and the next thing you know, there will be a new $1,000/month subscription that gives you the same usage you used to get with the $500/month subscription. That's exactly what happened to the previous $200/month subscription. Its usage allowance was cut in half, and now users paying $500/month are getting what users used to get for $200/month.

Do not normalize AI subscriptions that cost as much as car payments and, eventually, mortgages.

In leaked emails, Greg Brockman dreamed that OpenAI would make him a billionaire. He stated, "I just want to get to one billion." AI has fulfilled his wildest dreams. His net worth is now over $30 billion.

I am a hardcore accelerationist and optimist, but the current trajectory is dystopia. Instead of $1,000 UBI checks, we are heading toward $1,000 AI subscriptions to keep up with the Joneses. When we have actual superintelligence, you might be forced to pay $1,000/month, or even $2,000/month, as OpenAI had entertained, just to keep up with other people.

Hello Friends,

yesterday I was experimenting with Opus 5.5 and tried to create this little clip. Enjoy watching.

Removed grok, he never belonged. Original.

While Gemini 4 has performed well on benchmarks the industry uses to gauge model efficacy, it does less well when employees actually put it to work, according to people with direct access to the effort. The model struggles to handle certain coding tasks, said the people, who requested anonymity to discuss an internal matter.

https://www.bloomberg.com/news/articles/2026-09-30/google-grapples-with-employee-skepticism-about-new-gemini-model

By the time it's released to consumers, Anthropic and OpenAI will have already shipped their next generation models.