A blogger showed that job interviews in 2026 are already taking place without people. The company sent an AI recruiter to the video call, the blogger responded by connecting his AI agent and moved away from the frame.

Neural networks have continued to discuss the open position for a little more without humans. In the end, both got stuck and started saying goodbye to each other endlessly.

Dystopian.

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?

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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

When Google's AI Mode ends an answer with an offer ("Want me to compare prices?") and the user types "yes please", that reply is processed as a new search. Google's own Search Console documentation confirms follow-ups in AI Mode are reported as separate queries. So the turns of a conversation end up in the search data that website owners see, mixed in with normal keyword searches.

I build analytics for Google Search Console, so I measured how much of this there is. I used 16 months of data (June 2025 to September 2026) from 155 websites and classified every query by its shape.

What counts as a conversational query

• Reply artefacts: bare replies like "yes", "ja graag", "try again", "show me". Meaningless as a search, obvious as a chat turn.
• Pivot follow-ups: "what about salary", "how about in chinese?". They only make sense after a previous turn.
• Conversational questions phrased at a listener: "is it free", "can you...".

What the data shows

• Reply artefacts went from 5.6 to 116 per million impressions (21x). They appear on 94 of the 155 sites. The steepest rise was January to May 2026.
• Pivot follow-ups grew 39x, from a very small base.
• On the median site, the conversational share of impressions went from 0.032% to 0.22% (7x). Pooled across all sites it fell, because a handful of very large sites with declining conversational-question traffic dominate the totals. If you only look at aggregates, you'd miss the trend.
• These queries get clicked 2.9% of the time, against 35.6% for all queries. Someone mid-conversation with an AI is reading the answer, not picking a link. For publishers, that's the real story: the impression gets counted, but the visit doesn't happen.

A side finding: politeness varies by language

Of the yes-replies, the share that includes please or thanks: German speakers 75% ("ja bitte"), Dutch 67%, English 40%, Finnish 8%, Indonesian 0%. I counted each phrasing once per site, so no single big site skews a language. This reflects language conventions more than manners: a bare "kyllä" is normal in Finnish.

Limitations

• Google doesn't label AI Mode traffic. This is a query-shape classifier, so it's an inference. Some bare words are brand names or abbreviations, which is why I lean on the trend rather than any single query.
• Search Console hides rare (anonymised) queries, so all figures are shares of what Google reports.
• It's still small: 0.13% of all impressions.

Question for this sub: if conversational turns are becoming "searches", search logs stop meaning what they used to. Is anyone seeing the same thing in other AI-search products, or in their own site data?

I'm getting pretty tired of seeing so many personal agents. Most of them look more or less the same to me (including features like memory, reminders, connectors to other apps), and I'm struggling to see much actual innovation. What am I missing...?

May I ask which one are you actually using regularly and why? What made you choose it over the others? Thanks in advance.

I work at an AI startup but to be honest the more AI I use and the more AI products I come across, the more I just want to get away from it all sometimes and live like a caveman(?) for a bit.

I am into marketing, and my question is on how people validate AI recommendation. The answer might have a real source but recommend something that does not fit you.

I will try to find out if the recommendation fits my needs and whether the source is real.

Have you ever checked an AI recommendation and find it misleading? What was wrong with it?

By that I also mean most decent machines with about 16gb of ram can run them with out being stressed out. Apple seems to be going in that direction with on Device AI. I am just wondering how soon ? If that happens , no need to pay for subscriptions. I also understand the actual size of these models are going to be very large to fit on an average consumer hdd, but what about task specific models, like one only used for programming , or one only. used by designers those can surely fit, say 60-80gb is fine

I think we've been reading too many feeds from AI text lately! I actually want to reduce my cognitive burden...

So I built TextFold, it turns any text into 5 zoomable layers—from a one-screen section map down to verbatim text. You pinch (or press 1–5) to zoom between them without losing your reading position, your agentic output, any papers, or documents.

- Web Demo: https://chenxiachan.github.io/textfold/gallery/

- GitHub (Apache 2.0): https://github.com/chenxiachan/textfold

Welcome any feedback!

Look at the last few weeks:

• OpenAI shipped Dots, always-on agents running on a new model, at the end of September.
• Google rolled out Gemini 4 Argon to paying users on October 13, four days after cutting free users down to Flash-Lite.
• Google announced that Gems will be replaced by skills, which use the open SKILL.md format, so the same instructions can work on other platforms.
• Claude Code started reading AGENTS.md, the instruction file Codex and Cursor already use, in September.

Two things are happening at once. The frontier keeps leapfrogging, so "the best model" changes hands every few months. And the companies are quietly adopting shared formats for the stuff you configure, like instructions and skills, which makes switching cheaper.

Put those together and the rational move for most people is not loyalty. It's using whichever assistant fits the task this month. A lot of people already do: research in one, writing in another, code in a third.

What doesn't move with you is your history. The research, the drafts, the decisions, the prompt that finally worked. Each assistant keeps its own sidebar, its own search, its own export, and none of them can see the others. Instructions are getting portable. Your work isn't.

My bet is that the most valuable layer over the next couple of years isn't any single model. It's whatever sits between them and keeps your work in one place. I'm biased here: I build AI Toolbox, an extension that does that for ChatGPT, Claude, Gemini and Grok, because I hit the problem myself. But I'd expect the big players to fight over this layer eventually too.

Are you still mostly on one assistant, or already splitting work across several?

The cam-footage can help train Al systems and robots through first-person data, allowing machines to learn how skilled human workers perform real-world tasks.

Getting a phone is unequivocally the worst thing that has ever happened to me. I’ve recently come to the realization that I have no real interests, skills, or hobbies. Instead of building social skills in middle school, I spent my time scrolling on TikTok. Instead of learning or doing work in high school, I outsourced my thinking to AI and cheated through my classes, spending my time scrolling. I now realize that I know practically nothing. I have no real interests, no way to spend my time. I’m not passionate about anything. I’m not competent at anything. My dopamine receptors are completely wrecked and I can no longer learn. I can’t persevere through hardship. I can’t find joy in life. I can’t make friends. I can’t properly interact with people. I am no longer an interesting person. I don’t know what I want in my future. I don’t know how to like things anymore. I am so boring. I am so far behind my peers in terms of ability to think, learn new information, as well as background knowledge. There is not a single thing original about me. I am passionless, ambitionless, and lost. I feel sick that I wasted my youth scrolling. Millions of minutes spent with absolutely no gain. I’m not sure how to rectify this. I need help.

I’m considering using AI to organize notes, compare papers, and help analyze research material. The productivity gain is obvious, but the privacy question is harder than I expected.

If I upload unpublished results, participant information, internal documents, or detailed lab notes, how can I know whether that data is stored, reviewed, or used to improve a future model?

Removing names may not be enough if the remaining details can identify a person or project. API access, enterprise plans, “no training” settings, and zero-retention claims also seem difficult to verify from the outside.

For people using AI in research, which privacy measures do you actually trust? How do you verify a provider’s data policy?

Oct 13 (Reuters) - Around 400 scientists, including AI pioneers Geoffrey Hinton and Yoshua Bengio, on Tuesday called for an international body to oversee containment standards and incident reporting for advanced cyber-capable ‌AI systems.

The proposed body would set industry-wide containment standards and maintain a registry of significant security incidents, which the scientists said should eventually be made public.

In an open letter, the researchers said frontier AI models should initially be air-gapped from external networks and subject ⁠to independent oversight, arguing that voluntary industry commitments are not enough.

When comparing agent workflows, cost per successful task is a more useful denominator than cost per model call. A cheap call can still lead to several searches, another generation attempt, and a result you reject.

Here's a simple way to account for it:

1. Log the billed model calls, tool calls and compute usage for every attempt, including attempts that fail or produce an unusable result. A failed request may or may not be billed; use the provider's actual usage record.

2. Define an acceptable output before comparing workflows. For example: a brief with sources that pass a manual check. An HTTP 200 is not the same as a completed task.

3. Divide the total billed cost by the number of accepted outputs. Include any applicable fees in what you actually paid.

Illustrative example: 100 attempts cost $15 in total and 75 outputs pass review. That's $0.20 per accepted output. If the $15 represents usable credit purchased with an additional 10% fee, the cash cost becomes $16.50, or $0.22 per accepted output. These are hypothetical numbers, not measured results or provider prices.

Keep unused prepaid balances separate: they're cash tied up, not usage consumed. Combining them with run costs makes it harder to tell whether your problem is inefficient execution or overfunded accounts.

For comparisons, hold the task and acceptance criteria constant, then record both cost and success rate. A cheaper model with more rejected outputs can be the more expensive workflow.

Do you measure accepted outputs in your agent setup, or mostly tokens and API responses?

Disclosure: I'm building DreamTune Labs, a shared-balance project for AI services, so this is a problem I'm working on: https://dreamtunelabs.com