u/rajzzz_0

Yaar sab mere saath he aise kyu karte h. I sent a message to cousin sister whom i thought we were very close to each other and the message was delivered 3 days ago but she saw it today and replied because she had some other work with me.

Then my bhujai, jo hamesha jab bhi m call krta hu and if she doesnt pick it up or is talking to someone else she never calls me back on her own. Like i hold no priority to her.

I pickup the calls of these people or call back as soon as possible but they do this to me.

Will i be alone like this forever. Where no one like me or values me unless i dont have any money.

And when i will be rich why i will give them importance or talk to them much.

Like ofcourse bhuaji spent a lot of money, gets me things, bulbul also talks to me nicely and tells her that i am her favourite.

I can also do that by just giving them money in future.

Maybe they are wearing a mask because just in case i become rich in fiture and they just need money.

Even with my family i dont talk to them much or like to spend time with them, so i will be alone forever just in my own world.

Ofcourse money will change their behaviour but i will remember things. It just sometimes feel very sad to me that there's no one in the family i can talk to without being judged or pitying me.

My uncle feels like he wont, but i just dont want to take the risk and open to him as in the end he is the member of the same family and has grown with same people.

Another reason i dont want to share is the if i tell them that things are not bad they will say that we knew that or then why you changed neet or we told you that you will suffer.

For the past 3 years, making AI smarter had one rule: bigger is better. More data, more compute, more power. It worked. Then it stopped working.

The dirty secret about scaling laws is that the gains aren't a straight line — they flatten. And at some point the math turns brutal. A 4-8x increase in model size requires a 5x increase in training data just to not get worse. When your model has already consumed the entire internet, where does that data come from?

That wall is here now. Right now, individual server racks the size of a fridge are consuming 11x more power than they did in 2020. By next year that quadruples again. You don't need a bigger server room anymore — you need a dedicated power plant.

So how are models still getting smarter? Two things happening quietly:

1. Synthetic data — AI generating curated training data for itself. The student teaching the student.

2. Test-time compute — Instead of spending the same energy on "hello" as on a hard math problem, models now dynamically scale how much they think before answering. That "please and thank you" token problem that was costing companies billions? Largely solved.

But the next step is weirder. Companies are already working on latent reasoning — models that stop thinking in English entirely. They'll process your prompt through thousands of simultaneous thought directions in raw mathematics, then translate only the final answer back into language. It's not science fiction. It's the next 18 months.

Which brings the uncomfortable question: if AI trains itself, scales its own compute, and thinks in math we can't read — what exactly is the human role?

My answer, which surprised me while making this video: your imperfections.

When intelligence is cheap and abundant, perfection becomes cheap too. The only signal left that something was made by a human is the rough edges — the wrong turns, the inconsistencies, the things that aren't optimal. Those stop being weaknesses. They become the asset.

The research showing that children who use AI from birth never develop certain cognitive skills isn't just a safety concern. It's the real story of what we're trading away.

Made a full video on this if anyone wants the complete picture: [link in comments]

Every week there's a new paper or tweet claiming some model "understands" context, "reasons" about math, or "knows" what it doesn't know.

But when you look closely, there's almost no consensus on what "understanding" even means — philosophically or empirically.

Searle's Chinese Room argument is 40 years old and still hasn't been cleanly resolved. The "stochastic parrot" framing treats token prediction as the ceiling. Integrated Information Theory would say current architectures are near-zero in phi. And yet GPT-4 passes the bar exam.

A few questions I've been sitting with:

1. Is "understanding" even the right frame — or is it a folk-psychology term we're forcing onto a system that operates on completely different principles?

2. Does it matter if a model "truly understands" if the outputs are indistinguishable from someone who does?

3. Are we anthropomorphizing because it's useful shorthand — or because we genuinely don't have better language yet?

I've been going deep on AI + philosophy of mind for a channel I run (@ContextByRaj on YouTube if you're into this space). But genuinely curious what this community thinks — especially people coming from ML or cognitive science backgrounds.

Where do you land on this?