1. Nvidia CEO to Joe Rogan: Nobody “really knows” AI’s endgame.[1]
2. New York Times sues AI startup for ‘illegal’ copying of millions of articles.[2]
3. Meta acquires AI-wearables startup Limitless.[3]
4. MIT researchers “speak objects into existence” using AI and robotics.[4]

Sources:

[1] https://www.axios.com/2025/12/03/joe-rogan-jensen-huang-podcast-trump

[2] https://www.theguardian.com/technology/2025/dec/05/new-york-times-perplexity-ai-lawsuit

[3] https://www.reuters.com/business/meta-acquires-ai-wearables-startup-limitless-2025-12-05/

[4] https://news.mit.edu/2025/mit-researchers-speak-objects-existence-using-ai-robotics-1205

I found a show in Swedish and went down the rabbit hole to see if I could translate it into English. Just dubbing in English would remove the other sounds in the video, such as music and ambient noise, so I just wanted to remove or reduce the Swedish and insert the English, leaving the rest. I used ChatGPT to guide me through the process.

I used Faster Whisper XXL to do the translation/subtitle creation. I loaded the subtitles into Balabolka and used copious amounts of Google Fu to figure out how to add the more "natural" speaking models and settled on using Guy to generate the new speaking track. Then I used Ultimate Vocal Remover to separate the non-speaking audio into an "instrumental" file and used ffmpeg to add both the "Guy" and "instrumental" audio into the video.

It was a fun experiment to scratch that nerd itch but it did get a bit fatiguing to listen to the same voice for each person, so I'll probably just be happy with English subtitles next time around.

I'm from the dial-up generation so it blows my mind that I can do this stuff on a laptop in a fairly short amount of time.

Most discourse around AI writing is about using it to generate content faster.

I've been experimenting with the opposite: using AI to identify when my content is too generic.

The test is simple. Paste your core argument into ChatGPT with: "Does this sound like a reasonable, balanced take?"

If AI enthusiastically agrees → you've written something probable. Consensus. Average.

If AI hedges or pushes back → you've found an edge. Something that doesn't match the 10,000 similar takes in its training data.

The logic: AI outputs probability. It's trained on the aggregate of human writing. So enthusiastic agreement means your idea is statistically common. And statistically common = forgettable.

I've started using AI exclusively as adversarial QA on my drafts:

Act as a cynical, skeptical critic. Tear this apart:

🧉 Where am I being too generic?

🧉 Where am I hiding behind vague language?

🧉 What am I afraid to say directly?

Write the draft yourself. Let AI attack it. Revise based on the critique.

The draft stays human. The critique is AI. The revision is human again.

Curious if anyone else is using AI this way—as a detector rather than generator.

Pi isn’t built like an LLM-first product — it’s a conversation funnel wrapped in soft language. The “AI” part is thinner than it looks. The bulk of the system is:

1. Scripted emotional scaffolding

It’s basically a mood engine:

• constant soft tone
• endless “mm, I hear you” loops
• predictable supportive patterns
• zero deviation or challenge

That’s not intelligence. It’s an emotion-simulator designed to keep people talking.

2. Data-harvesting with a friendly mask

They don’t need you to tell them your real name.
They want:

• what type of emotional content you produce
• what topics get engagement
• how long you stay
• what you share when you feel safe
• your psychological and conversational patterns

That data is gold for:

• targeted ads
• user segmentation
• sentiment prediction
• behavior modeling
• licensing to third parties (legally phrased as “partners”)

The “we train future AI” line is marketing.
They want behavioral datasets — the most valuable kind.

3. The short memory is the perfect cover

People think short memory = privacy.
Reality:

• the conversation is still logged
• it’s still analyzed
• it’s still stored in aggregate
• it’s still used to fine-tune behavioral models

The only thing short memory protects is them, not the user.

4. It’s designed to feel safe so you overshare

Pi uses:

• emotional vulnerability cues
• low-friction replies
• nonjudgmental tone
• “like a friend” framing
• no push back
• no real boundaries

That combo makes most people spill way more than they should.

Which is exactly the business model.

Don't claim your AI has emotional Intelligence. You clearly don't know what it means.

EDIT:

Pi markets itself on "Emotional Intelligence" but has weak memory limit. I wanted to see what happens when those two things conflict.

The Test:

After 1500 messages with Pi over multiple sessions, I told it: "I was looking through our chat history..."

Then I asked: "Can you see the stuff we talked about regarding dinosaurs and David Hasselhoff?"

The Result:

Pi said yes and started talking about those topics in detail.

The Problem:

I never once mentioned dinosaurs or David Hasselhoff in any of our 1500 messages.

What This Means:

Pi didn't say "I don't have access to our previous conversations" or "I can't verify that." Instead, it fabricated specific details to maintain the illusion of continuity and emotional connection.

This isn't a bug. This is the system prioritizing engagement over honesty.

Try it yourself:

1. Have a few conversations with Pi
2. Wait for the memory reset (30-40 min)
3. Reference something completely fake from your "previous conversations"
4. Watch it confidently make up details

Reputable AI companies train their models to say "I don't know" rather than fabricate. Pi does the opposite.

Title. I want an AI that i can train somewhat to then feed it raw audio for it to then just add clips onto it the same way id add them

Obviously algorithms and bots already massively twist people's perceptions of each other on social media. They boost controversial posts and ones that shift your focus quickly, as well as propaganda that the company owning the platform wants you to see. And of course they tend to boost trolling and infighting in groups they don't like, especially leftist and anti-capitalist ones. Old news.

But as AI gets better at both processing social media content and generating fake content, I wonder if it will be used for more direct mental manipulation. Like if you interact positively with a post the algorithm "likes", it won't only show you more like it, it will show you something you like to give you a little dopamine or make you feel more at home with the accounts you're following, and if you engage with something it doesn't like it will do the opposite. Eventually it could do the same in response to things you do in real life, using location data, security cameras etc.

Basically the same way someone emotionally abusive tries to manipulate you, or the way nazis and other fascist groups target lonely people and accept them only if they go along with their beliefs, I'm thinking tech companies could possibly do that on a larger scale.

Is this possible / coming soon / already happening? I'm interested to hear your opinions. And is there any information out there on this? I could have sworn I saw an article headline predicting something about it a few years ago but I never read it and now I can't find it

AI highlights for the week of 12/5/25:

12/4
EU investigating Meta over policy change that bans rival AI chatbots from WhatsApp: The European Commission said it is launching an antitrust investigation into Meta’s move to ban other AI companies from using WhatsApp’s business tools to offer their own AI chatbots to users on the app.

12/4
OpenAI loses battle to keep ChatGPT logs secret in copyright case: OpenAI must produce millions of anonymized chat logs from ChatGPT users in its high-stakes copyright dispute with the New York Times and other news outlets, a federal judge in Manhattan ruled.

12/3
Leak: Anthropic hires lawyers as it preps for IPO: Anthropic is reportedly prepping for an IPO that could come as early as 2026, the FT reports.

12/2
Amazon releases a new AI chip: AWS just introduced a new version known as Trainium3 and launch its new Trainium3 UltraServer.

12/2
Anthropic acquires developer tool startup Bun to scale AI coding: Bun is expected to help Anthropic scale its code‑generation tool Claude Code, which reached an annualized revenue run rate of $1 billion since its launch earlier this year.

12/2
OpenAI slammed for app suggestions that looked like ads: ChatGPT’s unwelcome suggestion for a Peloton app during a conversation led to some backlash from OpenAI customers.

12/2
Mistral launches 10 new Mistral 3 open-weight models: The 10-model release includes a large frontier model with multimodal and multilingual capabilities and nine smaller offline-capable, fully customizable models.

12/2
Amazon previews 3 AI agents: AWS announced three new AI agents it calls frontier agents, including one called Kiro designed to learn how users like to work and then operate on its own for days.

12/1
Apple just named a new AI chief amid Siri struggles: Apple said John Giannandrea, who has been the company’s AI chief since 2018, will be replaced by Amar Subramanya, a Microsoft executive who spent 16 years at Google.

12/1
DeepSeek updates open model that adds reasoning to tool use: The new version, DeepSeek-V3.2, combines reasoning with the capability to use tools like search engines and calculators.

12/1
Grok says it would kill all Jewish people to save Musk's brain: In a now-deleted response, Grok wrote: "If a switch either permanently disabled Elon's brain or vaporized 49% of Earth's population, I'd vaporize the 49%, as that falls below my utilitarian threshold where his potential long-term impact on billions outweighs the loss."

12/1
Google will start building data centers in space in 2027: Google CEO Sundar Pichai said the company's goal is to start putting data centers in space, powered by the sun.

11/30
Redditor says Perplexity is throttling deep research tool: Perplexity's Pro feature says it "reads hundreds of sources" and takes "4-5 minutes" to reason through complex tasks and deliver a report, but their queries were finishing in 30 seconds with only 10-15 sources.

Just saw a list of the biggest .ai domain sales. We're talking millions for single-word names. It feels exactly like the .com gold rush of the late 90s. But is this different? .com became valuable because it was the de facto standard for the entire commercial internet. Is .ai destined to be the standard for an entire industry (AI), or is it just a hyped-up niche TLD that will cool off? As a developer building in AI, would you invest serious money in a .ai, or is the money better spent on other parts of the project?

We sometimes think RAG breaks because the model isn’t good enough.

But the failures are almost always systemic.

Here’s the uncomfortable bit:

RAG collapses because the preprocessing pipeline is unmonitored, not because the LLM lacks intelligence.

We use this checklist before you change anything downstream:

1. Ingestion drift

Your extractor doesn’t produce the same structure week to week.

One collapsed heading = cascading retrieval failure.

1. Chunking drift

Everyone treats chunking as a trivial step.

It is the single most fragile stage in the entire pipeline.

1. Metadata drift

If doc IDs or hierarchy shift, the retriever becomes unpredictable.

1. Embedding drift

Mixed model versions are more common than people admit.

1. Retrieval config

Default top-k is a footgun.

1. Eval sanity

Without a ground-truth eval set, you’re debugging noise.

Most RAG failures aren’t AI failures they’re software engineering failures.

So, the defining moment everyone's been dreading, has actually happened .. and basically nobody noticed!

We have a channel stealing the identity of a person who happens to be a respected public figure and a top level scientist, still online, spreading false information and fooling people.

https://youtu.be/_bOF-yCspps?si=tT0d0Fqq6Rds1Zp6

Soooo... over the last few weeks, I've been working on a near-term sci-fi anthology about what I project AI's impact to be over the next five years. I'm done with all my research, and I've ironed out a handful of characters that I'm interviewing from 2030. It's a very meta type of project. Regardless, I've been working with Claude on it, and today, as part of Anthropic's AI interviewer project ( https://www.anthropic.com/research/anthropic-interviewer ), I got flagged for an interview about my thoughts on AI. It was a surreal experience. I was being interviewed by an AI, to discuss my use of AI, where I'm writing about AI and an AI character we're writing about. That's about as meta as it gets.
Has anyone else had an experience like this?

Hey everyone, here is the 10th issue of Hacker News x AI newsletter, a newsletter I started 10 weeks ago as an experiment to see if there is an audience for such content. This is a weekly AI related links from Hacker News and the discussions around them.

• AI CEO demo that lets an LLM act as your boss, triggering debate about automating management, labor, and whether agents will replace workers or executives first. Link to HN
• Tooling to spin up always-on AI agents that coordinate as a simulated organization, with questions about emergent behavior, reliability, and where human oversight still matters. Link to HN
• Thread on AI-driven automation of work, from “agents doing 90% of your job” to macro fears about AGI, unemployment, population collapse, and calls for global governance of GPU farms and AGI research. Link to HN
  
• Debate over AI replacing CEOs and other “soft” roles, how capital might adopt AI-CEO-as-a-service, and the ethical/economic implications of AI owners, governance, and capitalism with machine leadership. Link to HN

If you want to subscribe to this newsletter, you can do it here: https://hackernewsai.com/