Been reading about AI voice scams lately and it's honestly made me realize how little I know about online security lol. I always thought if someone called or messaged me from a number I recognized then it was probably them seems like that's not always the case.

I started looking into ways people can check who they're talking to and found out there's technology that lets you scan a QR code to set up some kind of trusted connection with someone. From what I understand you can use that connection to check it's the same person later on. Maybe I'm getting it wrong but I had no idea this stuff existed. Now I'm wondering why we still rely so much on phone numbers and email addresses when accounts can get hacked and voices can be copied. Does this verification work or is it one of those things that sounds better on paper? I'm not much of a tech person and I wanna hear what people think.

Hey "AI safety" cultists: Turns out there's already an IT discipline that covers insider threats. Can you guess what it is?

(it's InfoSec, that thing you keep waving off as unimportant while you push "alignment" on everyone)

In his final book there are six possible futures.

“1. Humanity will lose control of an existential race between multiple actors trapped in a security dilemma.
2. Humanity will suffer the exercise of supreme hegemony by a victor unharnessed by the checks and balances traditionally needed to guarantee a minimum of security for others.
3. There will not be just one supreme AI but rather multiple instantiations of superior intelligence in the world.
4. The companies that own and develop AI may accrue totalizing social, economic, military, and political power.”
5. AI might find the greatest relevance and most widespread and durable expression not in national structures but in religious ones.
6. Uncontrolled, open-source diffusion of the new technology could give rise to smaller gangs or tribes with substandard but still substantial AI capacity.

Excerpt From
Genesis
Henry A. Kissinger, Eric Schmidt, Craig Mundie & Niall Ferguson
https://books.apple.com/us/book/genesis/id6479973847
This material may be protected by copyright.

I've been thinking about the whole "turn off model training" thing, and there's something about it that bothers me.

Let's assume OpenAI actually respects that setting and doesn't use those conversations to train its models.

But training isn't the only way to get value out of user conversations, right?

Most people probably aren't saying anything particularly valuable to an AI. It's mostly everyday questions, homework, coding problems, random conversations, etc.

But among millions of users, there must be scientists discussing unpublished research, engineers working on proprietary technology, and ordinary people coming up with ideas that might actually be worth something. You don't necessarily need to be an expert to have a good idea. Sometimes a random thought from someone outside a field could turn out to be surprisingly useful.

What if you had a separate AI system whose only job was to go through conversations and flag anything potentially interesting?

I guess you could call it "idea mining" or "knowledge mining." Instead of using conversations to train an AI, you'd be using AI to mine conversations for valuable ideas and information.

It wouldn't even need to be particularly expensive. A smaller model could filter out the vast majority of conversations, and only the interesting ones would get passed to more capable models for analysis.

And you wouldn't necessarily need people to tell you who they are. Someone discussing a very specific technical problem might reveal quite a lot about their background without ever mentioning their name or employer.

But the part I find more interesting is what happens when you start connecting information from different users.

Imagine researchers from three different companies working on related problems. They don't know about each other's work, but they all happen to use the same AI assistant.

The company running that assistant could potentially see connections that none of those researchers can see individually.

Now scale that up to millions of conversations.

You'd essentially have a system that could spot emerging technologies, promising research directions, and potentially valuable ideas before any of them become public.

And none of this would actually require training the chatbot on those conversations. The information could just be collected and analyzed separately.

I'm not saying any AI company is doing this right now.

But with how fast AI is advancing and how people are increasingly using it, I feel like this could eventually lead to a new kind of monopoly. Not just by collecting knowledge from experts, but by picking out valuable ideas from millions of ordinary people too.

Instead of controlling a particular industry, a handful of companies could end up having a unique window into what people across the world are thinking about, experimenting with, or simply imagining.

This whole idea just popped into my head, so I figured I'd write it down. Maybe it's something people have already thought about or discussed before, but I just wanted to share it and see what others think.

And of course, I used ChatGPT to help me write a post about AI companies potentially mining our ideas.

I'm so fucked. 💀

A Fudan University team ran the same 120 questions through 10 AI search engines ("From Public Posts to AI-Search Citations", accepted at NDSS 2027): ChatGPT, Google AI, Perplexity and Grok on one side, Doubao, Qwen, DeepSeek, Kimi, Yuanbao and Baidu's Wenxin on the other. Roughly half the questions were in Chinese and half in English. Then they looked at which websites each engine cited, 17,211 citations in total.

Here's the language split of the sites each engine cited:

• Qwen: 96% Chinese
• Baidu's Wenxin: 94% Chinese
• Yuanbao: 76% Chinese
• Doubao: 59% Chinese
• Perplexity: 50/50
• DeepSeek: 54% English
• Kimi: 62% English
• ChatGPT, Google AI and Grok: about 75% English

So it isn't simply China vs the West. Even with half the questions in English, 96% of the sites Qwen cited had Chinese titles, while DeepSeek and Kimi leaned more English than Doubao did.

Other things that stood out:

• Reddit was the #1 cited site for Google AI and Grok. YouTube was #1 for Perplexity, Wikipedia for ChatGPT. The Chinese engines rarely cited any of those big English platforms, apart from technical sites like GitHub and arXiv.
• Any two engines shared only about 20% of their top 20 sources on average, and overlap was lower between Chinese and Western engines.
• The Chinese engines lean on their parent company's sites: Doubao sent 25.9% of its citations to ByteDance-owned sites, Wenxin 24.4% to Baidu's, Yuanbao 14.2% to Tencent's.
• One site was cited by all 10 engines, ChatGPT and Grok included: SMZDM, a Chinese shopping-deals site.

A separate Max Planck study of real chat histories found the split runs inside the West too. Reddit and YouTube dominate ChatGPT's and Grok's search results but don't show up in Claude's at all, while Xiaohongshu (RedNote) appears only in DeepSeek's.

Caveats: 120 questions is a small panel, it's one snapshot from March 2026, and "Chinese" or "English" here means the language of the cited page titles. Different questions would shift the numbers.

But the takeaway seems solid: these engines aren't searching the same internet. Ask the same question in a different app and you can get an answer built from a different web.

Has anyone compared the same question across a Western and a Chinese AI and gotten completely different answers?

You've heard this before, and now you're hearing it again. You roll your eyes. The problem with this statement is that AGI has no accepted definition, so it's meaningless.

Fine. I'll give you a concrete definition.

I'm tired of the pointless arguing. Let's discuss upon a single shared definition. In fact, I'll give a concrete definition for the whole trinity: AGI, RSI, and ASI. Definitions follow:

AGI

AGI stands for Artificial General Intelligence. It is any AI that can successfully perform tasks that it was not trained to do. That is, the AI can generalize its specific intelligence (learned from training) to new, novel tasks.

RSI

RSI stands for Recursive Self-Improvement. It is the ability of an AI model to improve itself and create more intelligent versions without any human intelligence input.

ASI

ASI stands for Artificial Super-Intelligence. It is any AI that can outperform every single human on every single task.

---

Okay, beuno? Now I'll explain my definitions, starting with AGI.

AGI is the definition that is most disagreed upon. My definition of AGI is relatively narrow and strictly defined. In fact, I can point to a single test that determines if an AI model is AGI: the ARC-AGI-3 benchmark. (If you don't know what this is, look it up. TL;DR: it tests if AI can learn in novel environments it has never seen before.) GPT-6 Astra is the first AI model to score 99.9% on this benchmark, matching or outperforming humans. Because of this, I do agree with Sam Altman's announcement that claimed GPT-6 Astra was the beginning of the "AGI" era. We also see this generalized intelligence in real-world scenarios: Astra can play Minecraft, beat Portal, operate robotics, and do things that it was not specifically trained to do. Note that this definition does NOT necessitate any level of skill. It does not mean that AGI can replace all human jobs. Astra is not extremely good at playing Minecraft or operating robots—the point is that it can.

RSI is a pretty agreed-upon definition. Once human researchers aren't needed to create new AI models, this ability is reached. If I could add one thing: I don't think RSI is necessarily a single point in time. Some basic level of RSI may be achieved, but it might suck. For example, the self-iteration cycle might take too long or cost too much. Likely the "foom" explosion point will only happen once a highly effective form of RSI is achieved.

ASI is the one that everyone is dreaming about. An AI overlord is ASI. A cure to cancer will be achieved by ASI. Infinite energy will be discovered by ASI. This is the sci-fi goal we are trying to achieve. Note that just because current AI is super-intelligent in some areas (e.g. math) doesn't mean it is ASI—under my definition of ASI, it must be super-intelligent in all areas.

---

Therefore, under these definitions: YES, we have already achieved AGI, and we are on the way to ASI. Probably RSI will be achieved soon.

Written by human.

Noticed everyone building AI influencers and characters try to pass as real is playing it backwards.

The ones that build a real audience long-term will be openly artificial. The audience knows it's a character, the character performs like it's real, and everybody's in on it. Pro wrestling figured this out decades ago — old kayfabe meant pretending it was real. Modern kayfabe is the crowd knowing and playing along anyway, and the attachment got stronger, not weaker.

Meanwhile the "totally a real person" AI accounts all die the same way: one slip, one uncanny detail, and it turns into a scandal instead of a story.

Am I wrong? Is the honesty actually the product — or do people only care when there's a chance they're being fooled?

I own an online store. Daily I get emails where a customer explains, with complete certainty, how my own product works, what my terms say, or what the law requires. Beautifully written, full of jargon, and often completely wrong.

I use AI every day myself, so this isn't an anti-AI post. But people stopped separating "AI told me" from "this is true".

Is this just a phase until people learn how to use it, or are we watching people outsource thinking at scale?

My six-month prediction is that frontier-model providers could see slower revenue growth without any slowdown in overall AI adoption.

The reason is task economics.

For difficult coding or research, a stronger model might justify a substantial premium. For extracting fields from a document or drafting a routine email, the extra capability may not change the outcome enough to justify the extra cost.

An application developer has an incentive to route the easy work elsewhere and reserve the expensive model for exceptions. If that happens at scale, usage can grow while average spending per task falls.

This is a hypothesis, not a claim that provider revenue is already slowing.

There are strong reasons it could be wrong. Better models might unlock entirely new paid workloads. More reliable execution could also reduce retries and human review enough to make a higher token price economical.

The question is whether new high-value demand expands faster than ordinary tasks move toward cheaper alternatives.

Over the next six months, what would convince you that frontier pricing is sustainable: widespread premium usage, or a few categories where the strongest model produces much better economic results?

What goes wrong when you actually try it?

Customize your AI to help map ALL OF EARTH'S NATURAL PROCESSES and look for novel connections & hypotheses.

Yes - you can make a scientific breakthrough with your chatgpt subscription and a little bit of ingenuity.

http://forks.world

In a September survey of 100 U.S. respondents, 65% said they always or frequently read Google’s AI summary before the traditional results. But only 24% said an AI Overview very often removes their need to visit a website. The biggest group, 44%, said they get the gist from the summary and still click for details.

The chatbot results followed a similar pattern. For complex research, 40% reported using traditional search and chatbots equally. Just 9% said chatbots were their primary starting point.

Half preferred a future search experience combining summaries with prominent links to human-written articles. Another 26% wanted a website directory without AI.

It's predictable that an anxious culture will predict doom.

James Lewis argues AI existential risk is the fifth media-driven moral panic since 2000, and that the case against it rests on two tests. Probability, where he notes no extreme prediction of the last seventy years has come true, from peak oil to global famine by 2000. And process, where he argues a hypothetical risk only becomes real once you can name the steps, and that nobody has connected superintelligence to mass casualties without routing through a weapon that already exists. His comparison is nuclear winter: a real underlying danger extended into an improbable scenario, which he suspects is also what is happening with AI. He does not argue the risk is zero, and accepts frontier systems are error-prone in the way early cars and aircraft were, with grid or infrastructure failures plausible but not mass-casualty events.

The models are getting smarter everyday and pretty soon any frontier model will be great at any task we throw at them. By any, I mean the most common enterprise use-cases today like coding etc. And these companies have no moat. I can use Claude today and move to OpenAI tomorrow without any impact.

So, at that point, it is just a matter of cost and these companies have to fight to be the cheapest. And their Capex is not going to let them do that. So, how are these companies going to adjust and be profitable?

GitHub's Agentic Autofix is now generally available for GitHub Code Security and GitHub Advanced Security customers on Enterprise Cloud. When a user assigns a code-scanning alert to Copilot, it explores relevant files, proposes a fix, reruns CodeQL, and opens a draft pull request for review.

The useful limit is that this is still a review workflow, not an automatic merge. Enterprise admins must enable the policy, and usage consumes AI Credits from the Code Security budget plus GitHub Actions minutes. It supports alerts on the repository default branch, not alerts found only on pull requests or other branches.

The practical question is less whether an agent can write a patch and more how teams budget, review, and measure fixes before they merge.

Sources: GitHub Changelog: https://github.blog/changelog/2026-10-15-agentic-autofix-for-code-scanning-alerts-general-availability/ GitHub Community: https://github.com/orgs/community/discussions/209845