I want to learn mathematics, physics, programming, and AI, but AI itself is making me question the point of learning anything.

[u/IshanFreecs]

I genuinely want to learn mathematics, physics, programming, computer science, and AI. I want to build a career in computer science, pursue AI research, and hopefully do a PhD someday. But lately, I've been feeling increasingly demotivated.

The problem is that I rely on AI to learn almost everything. Whenever I have a question, I ask GPT. Whenever I don't understand a concept, I ask AI to explain it. And the more I learn this way, the more I wonder: What's the point of learning anything when AI will always know more than I ever could?

Even the most basic AI models seem to possess more knowledge than I could acquire in a lifetime. And with new models becoming increasingly capable, I keep wondering whether the knowledge I'm working so hard to gain will even matter in the future.

With GPT-6, Claude Opus 5.5, Gemini 4, and other models competing to become more capable, it feels like the gap between human intelligence and AI capabilities will only continue to grow.

I want to understand the mathematics behind AI, learn physics, become a better programmer, and contribute to actual research. But sometimes I question why I'm putting in the effort when AI can explain concepts faster, write code better, and potentially solve problems that I might spend years learning to solve myself.

And even when I genuinely want to learn, I'm confused about what I should prioritize learning and whether any of it will remain valuable by the time I become proficient.

I'm not necessarily saying that learning is useless. I genuinely love the idea of understanding how things work. But I'm struggling to reconcile that curiosity with the feeling that AI might eventually outperform me at almost everything I want to become good at.

For those of you studying computer science, mathematics, physics, or AI, how do you deal with this feeling? How do you stay motivated to learn when AI is advancing so rapidly? Do you think deep technical knowledge will still matter for people pursuing AI research and PhDs, or should we be approaching learning differently now?

[u/hornwalker]

Taking a class is very different than asking a question of an LLM.

Thinking deeply requires work, satisfying curiosity is not that same as actually learning about a subject.

[u/IshanFreecs → u/hornwalker]

I am scared if I keep on satisfying my curiosity I won't have any skills that could lead to some earning or secure job.

[u/hornwalker → u/IshanFreecs]

Look, you don’t have to stop being curious, just get your information from other people preferably who are experts in a subject and/or trained educators

[u/diogenes_sadecv]

Well, don't use AI. Read a book. Read a paper. Take a class. Watch a video. If you're not sure learning is intrinsically valuable I don't know what to offer you.

[u/Quantumtroll]

What you have to remember is that an LLM doesn't understand things. There's no meaning to anything it does. An LLM doesn't love knowing things, it doesn't love teaching things, it doesn't want anything. It just outputs text. Sometimes we've hooked things into it that gives the text the power to have an effect, sometimes we read the text and it has an effect on us, and sometimes we just toss that shit.

More than ever, we need human beings with a deep understanding of ourselves and the world so that we can use these new tools to make the world a better place to live. If we don't, then we'll make the world a worse place instead.

Sensible people understand this and will give you a place to work if you're one of those human beings.

[u/IshanFreecs → u/Quantumtroll]

Then wouldn't those things you mentioned which humans are good at works only in non academics field?

[u/Quantumtroll → u/IshanFreecs]

I'm not sure what you mean. I work in academia, and while LLM use is very fashionable and sometimes handy for some things, I don't see it as very likely to replace people in things requiring actual judgment.

Thus far, LLMs produce actually useful novel things in two fields — programming and mathematics. Both of those only work if the LLM is given the instructions and ability to do lots of testing. An LLM without a very solid mechanism to test the output will produce junk. Sometimes we like junk, but most of the time we want something we can trust. An LLM can only do useful things if a human being tells it how to test its output. Until an LLM understands the world well enough to set its own useful goals and test itself — something which may never happen — we need good humans in the loop.

[u/byronmiller → u/IshanFreecs]

Any fool can ask chatGPT what it thinks of xyz, or for textbook knowledge. It takes years of study, discipline, and immersion in the field to really internalise key concepts, build a working knowledge of who is doing what and how it relates to what comes before, and make unexpected connections which lead to new avenues of exploration.

I'm not saying AI tools and workflows can't contribute to this, but I am saying those workflows are more complicated than asking an LLM to explain stuff to you.

[u/Potential_Being_7226]

Frictionless “learning” is not deep learning. You don’t really know or understand something until you can fully explain it to someone else. Just because reading an AI summary makes you feel like you understand something, doesn’t mean that you do. 

LLMs make errors and “hallucinate.” Do you think it’s reasonable to rely on such a tool?

[u/KamiNoItte]

Ask yourself if AI development will be finished in your lifetime.

No?

Then consider that everyone who developed the systems you’re listing had to learn and start somewhere.

Maybe you’ll make the next system on the list :)

You might find some inspiration in reading the stories about the history of the development of your favorite gizmo.

The history of mathematicians in particular is a wild ride ;)