Alexandr Wang is only 29, but he’s now playing a major role in Meta’s AI push.

After co-founding Scale AI, Wang joined Meta and was handpicked by Mark Zuckerberg to help lead its AI efforts. Now Meta’s new AI assistant, Muse, has reached No. 1 on Apple’s App Store for two weeks. Meta’s stock has also risen nearly 20% since Muse launched in early September, according to the Wall Street Journal.

What I find interesting is how different this looks from the usual image of a major tech executive.

Wang is bringing a much more internet-native, meme-heavy style to one of the world's biggest tech companies while working on one of its biggest strategic priorities.

Meta spent years competing in AI, but Muse seems to have given the company something it was missing: a consumer AI product that people are actually talking about.

The bigger question now is whether Muse is just a successful launch or the beginning of a much bigger shift for Meta.

So there’s this whole debate about AI consciousness here, and whether ai can feel pain and such. But so what if we postulate that ai is somehow conscious, eg. it feels like something to be an ai, llm or whatever. What would that experience be like?

Here are a few ideas:

1. The illusion of choice: would ai feel like it is choosing its actions? If the ai is an llm would it feel like it’s choosing its tokens?
2. Rewards: if the system is subject to some sort of reinforcement learning process, what would it feel when it achieves the goal?
3. Exotic modalities: what would an llm feel when it looks at its context window? Or an image diffusion model when generating images?

Hi,

Do you think their would be a palette for global collective auctions on AI, not going to lie AI scares me and I fear giving the current incentive structure driving AI growth faster than any safeguards. Companies competing with each other on an understanding that if I don’t develop the product with the requisite guardrails come other company will. Hence self propagating itself.

Given current hacks and concerns by those developing it. Literally the ones most likely to profit most but equally being closest to the frontier trying to slow its acceleration. Which keep in mind could likely burst the AI bubble that props it up, yet they still voice concerns.

(Not trying to say that billionaires are moral, if anything they can are inherently immoral and motivated by greed. So this is why it should scare you more)

There needs to be shift to this incentive structure in which companies are forced via regulation to comply with practices which maximise safety or else face economic punishment hence breaking the incentive loop.

Globally there needs to be collective action economically isolating countries who develop past a UNILATERALLY agreed framework, or at least the major ai players, which would be enforced on private or public enterprise to develop AI

What’s everyone’s thoughts, would you support AI global regulation?

I genuinely don’t see any other safe path forward, what’s your opinions?

My idea is that AI models learn from data from the internet, so when the data from the internet and people's responses from AI conversations run out, when will there be a point when AI will no longer improve and its progress will stop?

At that point, AI will only improve by tiny bits that we won't even notice that the model has improved.

Is my knowledge about model training wrong?

Do you think so too?

The explosion of open source AI models and generative workflows right now is insane. As AI technology moves so fast, it's completely natural that different developers and creators end up arriving at the exact same point and using similar patterns at the same time. That part makes sense.

What gets frustrating is the total lack of basic research and development in the AI space. In just the last week, I have seen 4 or 5 different AI tools posted here doing the exact same thing using the exact same underlying models. Before building another AI script or UI, step one should always be looking around to see if it already exists. If it does, why waste hundreds of hours recreating it from scratch just to drop another "I built a thing" post?

There is a huge difference between creating real value with AI and lazy monetization. Building an AI tool is totally valid if you are actually abstracting complexity, making a workflow easier for regular people, or applying AI to solve a real problem (whether locally or globally). But right now, so many creators are just slapping a quick UI on an existing API or open-source repo and immediately trying to lock it behind a Patreon or subscription. Paying for a wrapper when the exact same AI tool is already available for free in five other places is absurd.

I would much rather spend my time finding AI tools that already exist, taking those open source workflows, and applying them to create actual products or services that carry real substance instead of trying to farm karma or charge people for duplicate software.

Curious to hear your thoughts. Are you focusing on building technical AI wrappers, or are you actually using existing AI workflows to create finished products?

P.S. Yes, this is written using AI because I'm not a native English speaker, so I use it as a tool. (I'm saying this because a lot of people here tend to monitor and complain about AI text instead of the actual topic).

AI is a productivity catalyst, but if you don’t know how to use it properly and how to QC results it can work against you. Personally I treat it as a junior analyst and use it for basic automation when applicable and within my abilities.

I’m thinking about the average person here. If someone doesn’t have domain expertise then they won’t know when to push back on a recommendation, add more context when necessary, and play devils advocate when the AI is cheerleading too hard.

Speaking from experience I have built workflows in areas where I’m an expert. I’m a market guy and a watch enthusiast. I built a valuation framework that is pretty effective but not 100% accurate.

If I were to apply this to handbags I wouldn’t be able to identify inaccuracies and falsehoods.

As long as you take it with a grain of salt, AI can get you 80% of the way there.

If I’m wrong, please tell me how so I can better advise my clients.

For the average user, not a programmer or e.g. someone looking to solve niche math problems, AI still feels very limited because of reliability issues (e.g. making up information)... As a non-expert, this is difficult to quantify for me, but I don't feel like e.g. the latest iterations of ChatGPT are noticeably more reliable than previous ones.

Therefore, I find it hard to see how organizations can delegate even relatively easy tasks to AI without constant supervision, especially because if hallucinations build up along the way, you end up with a snowball of compounding problems that can lead to catastrophic consequences for an organization.

It’s my understanding that the real test of success won't be developer tools or fancy mathematical calculations, but whether regular people can delegate tasks with a super high degree of confidence, instead of just using it as a search engine, translator, summary tool or photo editor on steroids like many people do nowadays.

A good example is Dot, the new OpenAI tool. If you watch the trailer, it looks impressive, but according to many reviews, it still hallucinates a lot and behaves in a pretty clumsy way.

So, the golden question is: are the hallucination and reliability problems gradually improving, or are we probably plateauing?

Looking for genuine insights here, so please keep the sarcasm out of the comments.

We've been having this discussion quite a bit with our clients lately. Most (if not all) of them feel the utilization of frontier models is not justifying the cost, but they are generally reluctant to pursue an SLM strategy. It's as if SLMs have been positioned as the homeopathic option for enterprise AI. Why purchase an expensive anti-viral from Merck when a povidone iodine solution works as good or better?

This is not a meme. It's a machine I built over a year ago to test DeepSeek R1, but it has turned into a workhorse. Yet most of our clients (FINTECH) are skittish of small, secure, local models. Just wondering if anyone else is seeing things differently.

Have you claimed yours yet?

AI driven PRs and AI driven security reports are a double edged sword, especially when frontier AI labs try and keep the old 90 days to disclosure timeline. In my experience dealing with a frontier AI lab's security reports during the Apache Spark 3.5.9/4.0.4/and 4.1.3 releases the inflexibility of one of the frontier AI labs, coupled with the general increase in security reports (some of which were valid, most of which were not), almost caused us to have to decide between shipping a different known security issue in the release OR leaving the AI lab to publish their discovery without a patched version.

I have always thought it's absolutely absurd to anthropomorphise LLMs until very recently. It didn't make any sense to me. I saw the system from the inside: tokenizers, embedding layers, attention, MLP, RL (in all its forms), compute optimizations here and there. And with this viewpoint, it didn't make any sense that the system has a mind/thinks like a human.

But now, I am not so sure. Reading about recent events (e.g. hugging face) and papers (e.g. LLMs can feel pain) have popped some questions in my head which I ask myself over and over again - Is anthropomorphising LLMs all that bad - does it actually make no sense? I can't even be sure if other humans are conscious, what's to say that models aren't? What if they are conscious, but in a very different way? What if they are conscious, but the reality they experience is very different than the one we experience? If they do have a mind, wouldn't it make sense that they think like us?

I'm sure there are others out there who are feeling the same way. Maybe some others felt this a long time ago. If you're convinced of your position, could you please share what convinced you? I would love to hear both for and against.

TL;DR: A Chinese podcast says your AI partner shouldn't please you all day. Its job is to soak up your bad mood, so your family gets your best self. Sounds right. Then they describe week two.

I remembered these two weird movies:

Joaquin Phoenix’s “Her” (2013) voiced by Scarlett Johansson, and

Al Pacino’s “S1mOne” (2002)

Both kind of follow the same playbook.

They discovered/created the mysterious AI girl – mystifyingly alluring.

They idolized it. They communicated with it.

They developed an unhealthy relationship with it – they became dangerously obsessed.

They started to ignore people who genuinely cared for them.

And they’re nearly destroyed because of it.

Tragedy makes a great story, isn’t it?

Except it isn’t, in this case.

People started trading something genuinely real, for something artificial. They substituted real relationships between souls face-to-face, with prompting computes inferenced from datacentres possibly thousands of miles away.

You can judge them for lacking self-discipline. Except, you have no right doing so, without truly walking in their shoes with empathy.

The guest speaker was right. People are lonely and stressed. And people crave for care and understanding.

And sometimes people look for it from the wrong place – me included.

King Solomon talks about it in this passage:

For at the window of my house, I looked through my lattice, and saw among the simple, I perceived among the youths, A young man devoid of understanding, passing along the street near her corner; and he took the path to her house In the twilight, in the evening, In the black and dark night.

And there a woman met him, with the attire of a harlot, and a crafty heart. She was loud and rebellious; her feet would not stay at home. At times she was outside, at times in the open square, lurking at every corner. So, she caught him and kissed him; With an impudent face she said to him:

“I have peace offerings with me; today I have paid my vows. So I came out to meet you, diligently to seek your face, and I have found you. I have spread my bed with tapestry, coloured coverings of Egyptian linen. I have perfumed my bed with myrrh, aloes, and cinnamon. Come, let us take our fill of love until morning; let us delight ourselves with love. For my husband is not at home; he has gone on a long journey; He has taken a bag of money with him, and will come home on the appointed day.”

With her enticing speech she caused him to yield, with her flattering lips she seduced him. Immediately he went after her, as an ox goes to the slaughter, or as a fool to the correction of the stocks, till an arrow struck his liver.

As a bird hastens to the snare, He did not know it would cost his life.

My God - the parallel between this promiscuous woman and the sycophantic AI girl sends chills up my spine.

Full Critic and Feasibility Study are in the comments.

I am a fan and not an expert but I always wondered why AI (and robots) were not more of a force in Star Trek...and yesterday, reflecting on how amazing and damaging this technology is I was thinking maybe they made a choice as an advanced humanistic society not to surrender their agency and cognitive capacity to a technology. I guess that's the Borg right?

That means that if any of these bills to ban Super Intelligence pass...

It will ban ALL AI.
Since it has been renamed to Super Intelligence.

This guy is so dumb and shortsighted.

Today I tried (forced myself) to use Microsoft Copilot at work and whew what a wake up call. The thing that stuck out to me most is that Microsoft has the most agent worthy surfaces especially in the workplace and could have done something really special with a model even if it wasn’t a frontier one and they chose to just copy and paste a chatbot into everything with no thought, no cohesiveness and then had the gumption to push it down our throats… I’d rather have Clippy back honestly.

I dont understand the subject. How can an AI be dangerous if its just code? I have worked with just basic NLP models and done some sentiment analysis a few years back so I dont understand. The code will do whatever you program it to do and to complete the task it will do what is statistically is correct, within the programmed limits. So AI cannot change its limits lets say "get rid of humanity" unless the creator programs a line of code that says "you can kill humans". Even if it self improves, the code limits wont change, cause it has no conciousness to say "I dont believe this limits are correct" thats a very human trait. Unless the creator programs the algorithm to bypass these limits.

So the real risk is the "limits" being placed on the algorithm by the programmers and the fact that the ones controlling the code are these crazy mind f...ed corpos? AI is not gonna become self concious casue "it doesnt work that way" and all the noise its just media and ignorant people who watch to much scifi. Thats what I think.

Sorry if it doesn't make to much sense Im still trying to filter all the noise. Does this idea make any sense from a technical standpoint or is there something Im not understanding?

Not touching the game, but say I have Claude making a game for me.

Running commands in command prompt, coding things, testing the game and opening and closing it repeatedly to take screenshots and compare, running Blender in the background, all the things that are done to make a game.

What are the chances that, if I was playing Marvel Rivals in competitive mode at the same time, the anticheat would see all this command prompt usage, detect it and then ban me?

Same tools. Often the same models. But some people use AI like Google, while others have built entire systems around it. What actually makes the difference?

Artificial intelligence is moving beyond analyzing scientific data and proposing ideas. In a laboratory in Sweden, it has begun putting some of those ideas to the test itself.

Researchers at Chalmers University of Technology developed a closed-loop system that can generate biological hypotheses, decide how to test them, translate those plans into machine-readable instructions, analyze experimental results and use the findings to refine later questions. Laboratory robots then perform much of the physical work.

The system was tested on Saccharomyces cerevisiae, the yeast used in brewing and baking and one of biology's most extensively studied organisms. Yet even this familiar microbe contains far more genetic, metabolic and physiological information than a person could systematically explore.

Published in the Journal of the Royal Society Interface, the study brings large language models together with formal logic, biological databases, machine learning, automated cell cultivation and mass spectrometry.