Anyone who tells you they know where Al is headed is lying to you.

[IronMike]

Make sure to buy data center stock. They sure will grow in the future....the sheer mass of them being built in all states is a telling sign. I might buy a few shares since money is not an issue. Only problem is some are private and you gotta know someone to know someone to get in. Rich get richer I guess

I'm currently heavy into semiconductors, memory and uranium (to fuel the data centers), the uranium is a 10 year long

 

[AstroSky]

I'm currently heavy into semiconductors, memory and uranium (to fuel the data centers), the uranium is a 10 year long

Anything that uses resources to build more ai is a good buy right now. Even if its just raw material. Copper bars would legit be a good buy. Its going to take lots of the world's copper supply to build these centers. Start stealing copper from people's house and sit on it then sell later

 

[inyerta]

If some tech influencer claims they have a roadmap for the next five years of AI and machine learning, here's how you can be prepared, they are just trying to sell you a course. I have 2 friends friend who are Al researcher at one of the top frontier labs and I'll tell you an open secret in the Silicon Valley. The very people building these massive models have absolutely no idea where we are actually headed.

One thing you need to realize about AI is AI isn’t a closed engineering problem lilike say building a car. we know everything there is to know about a car but for AI it's this strange living learning developing thing that we still don't fully understand how or why it works at scale. It’s not closed or open in the normal sense.

AI has advanced so rapidly with so much money being poured into it that the timeline for major breakthroughs has collapsed so quickly that the engineering has entirely outpaced the science. Like just 2 years ago we learned about emergent properties. AI learning stuff it wasn't trained for explicitly. AI being fed coding corpus somehow learning French in the process etc. Why this happens still not properly understood. Just theories about information hierarchies.

Much of the tech industry rn is stuck in massive hype cycle. Everyone is moving where ever the investor money blowing, in whichever way the wind turns. There is no master plan or milestones these companies operate on. They are just reacting to what they've built rather than executing a predictable master plan. Like oh wow AI did this. Interesting. Not validation of theory but discovery. No body can tell with 100% certainty that this or this is going to happen. If he does he is either some ultra genius who can see the future or someone who has no idea what he is talking about. We will just have to wait and watch how all this will play out in this decade

Too high IQ for the Zoomers on this forum

 

[lnceIs]

How did it pass the SOC2 compliance then? And how is he running the LLMs? Is it on bedrock or some custom orchestration.

Also, if the whole team just runs off Claude why do they need him a highly paid Head of AI to manage an API key.

Ain't no way you believe this nigga

If this was true I would have kidnapped this nigga a long time ago

 

[lnceIs]

The so-called renowned analysts are always so good at underestimating emerging trends and making terrible predictions lol
View attachment 5078571

Exactly, tech CEOs make ridiculous claims about the capabilities of AI to hype up their products, but that doesn't mean big things are happening just with a more realistic timeline

 

[Jason Voorhees]

Ain't no way you believe this nigga

If this was true I would have kidnapped this nigga a long time ago

I think he is larping. He is dodging the real questions and just making very vague comments

 

[lnceIs]

I think he is larping. He is dodging the real questions and just making very vague comments

This guy claimed his dad owns a 700b market cap business, ofc he is lying

That's JP Morgan lvl

 

[Jason Voorhees]

@Sayori

 

[EvilSatanArseRapist]

Give reps then replies

Sry bhai, i forgor..

 

[Alienmaxxer]

Is your thesis leaning more toward building/optimizing models or toward interpreting it. The black box problem understanding how these massive models actually arrive at their answers in the holy grail rn

Building/optimizing models. Which is better in your opinion?