I'm a software engineer with a solid full-stack background and web development. With all the noise around LLMs and AI, I’m undecided between two paths:
1. Invest time in learning the internals of AI/LLMs, maybe even switching fields and working on them
2. Continue focusing on what I’m good at, like building polished web apps and treat AI as just another tool in my toolbox
I’m mostly trying to cut through the hype. Is this another bubble that might burst or consolidate into fewer jobs long-term? Or is it a shift that’s worth betting a pivot on?
Curious how others are approaching this—especially folks who’ve made a similar decision recently.
1. Learn basic NNs at a simple level, build from scratch (no frameworks) a feed forward neural network with back propagation to train against MNIST or something as simple. Understand every part of it. Just use your favorite programming language.
2. Learn (without having to implement with the code, or to understand the finer parts of the implementations) how the NN architectures work and why they work. What is an encoder-decoder? Why the first part produces an embedding? How a transformer works? What are the logits in the output of an LLM, and how sampling works? Why is attention of quadratic? What is Reinforcement Learning, Resnets, how do they work? Basically: you need a solid qualitative understanding of all that.
3. Learn the higher level layer, both from the POV of the open source models, so how to interface to llama.cpp / ollama / ..., how to set the context window, what is quantization and how it will affect performances/quality of output, and also, how to use popular provider APIs like DeepSeek, OpenAI, Anthropic, ... and what model is good for what.
4. Learn prompt engineering techniques that influence the qualtily of the output when using LLMs programmatically (as a bag of algorithms). This takes patience and practice.
5. Learn how to use AI effectively for coding. This is absolutely non-trivial, and a lot of good programmers are terrible LLMs users (and end believing LLMs are not useful for coding).
6. Don't get trapped into the idea that the news of the day (RAG, MCP, ...) is what you should spend all your energy. This is just some useful technology surrounded by a lot of hype of all the people that want to get rich with AI and understand they can't compete with the LLMs themselves. So they pump the part that can be kinda "productized". Never forget that the product is the neural network itself, for the most part.
(That's not to say that you shouldn't bother with learning more -- more knowledge is always good -- or that the OP specifically only knows that. It's more a sensible minimum.)
My own "curriculum" for that has been Jeremy Howard's Fast AI course and Sebastian Raschka's book "build an LLM from scratch". Still working through it, but once I'm done I think I'll be solid on your point 2 above. My guess is that I'll want to learn more, but that's out of interest more than because I think its necessary.
I've tried to keep up with them somewhat, and dabble with Claude Code and have personal subscriptions to Gemini and ChatGPT as well. They're impressive and almost magical, but I can't help but feel they're not quite there yet. My company is making a big AI push, as are so many companies, and it feels like no one wants to be "left behind" when they "really take off". Or is that people think what we have is already enough for the revolution?
I've been asking this on every AI coding thread. Are there good youtube videos of ppl using AI on complex codebases. I see tons of build tic-tac-to in 5 minutes type videos but not on bigger established codebases.
And people keep saying you need to make a plan first, and then let the agent implement it. Well I did, and had a few markdown files that described the task well. But Copilot‘s Agent didn’t manage to write this Swift code in a way that actually works - everything was subtly off and wrong, and untangling would have taken longer than rewriting it.
Is Copilot just bad, and I need to use Claude Code and/or Cursor?
I haven't used Claude Code much, so cannot really speak of it. But Copilot and Cursor tends to make me waste more time than I get out of it. Aider running locally with a mix-and-match of models depending on the problem (lots of DeepSeek Reasoner/Chat since it's so cheap), and Codex, are both miles ahead of at least Copilot and Cursor.
Also, most of these things seems to run with temperature>0.0, so doing multiple runs, even better with multiple different models, tend to give you better results. My own homegrow agent that runs Aider multiple times with a combination of models tend to give me a list of things to chose between, then I either straight up merge the best one, or iterate on the best one sometimes inspired by the others.
Even the basic chat UI is a structure built around a foundational model; the model itself has no capability to maintain a chat thread. The model takes context and outputs a response, every time.
For more complex processes, you need to carefully curate what context to give the model and when. There are many applications where you can say "oh, chatgpt can analyze your business data and tell you how to optimize different processes", but good luck actually doing that. That requires complex prompts and sequences of LLM calls (or other ML models), mixed with well-defined tools that enable the AI to return a useful result.
This forms the basis of AI engineering - which is different from developing AI models - and this is what most software engineers will be doing in the next 5-10 years. This isn't some kind of hype that will die down as soon as the money gets spent, a la crypto. People will create agents that automate many processes, even within software development itself. This kind of utility is a no-brainer for anyone running a business, and hits deeply in consumer markets as well. Much of what OpenAI is currently working on is building agents around their own models to break into consumer markets.
I recommend anyone interested in this to read this book: https://www.amazon.com/AI-Engineering-Building-Applications-...
The progresses we are seeing in agents are 99% due to new LLMs being semantically more powerful.
Any suggestion on where to start with point 1? (Also a SWE).
- I strongly recommend Chip Huyen's books ("Designing Machine Learning Systems" and "AI Engineering") and blog (https://huyenchip.com/blog/).
- Andreessen Horowitz' "AI Cannon" is a good reference listicle (https://a16z.com/ai-canon/)
- "12 factor agents" (https://github.com/humanlayer/12-factor-agents)
So in that case I don’t see why not?
there aren't 100x 'top shelf' ml engineers.
There aren't a lot of jobs self taught ml programmers like there are for self taught python programmers.
I see this a lot, but I think it's irrelevant. Even if this is a bubble, and even if (when?) it bursts, the underlying tech is not going anywhere. Just like the last dotcom bubble gave us FAANG+, so will this give us the next letters. Sure, agentsdotcom or flowsdotcom or ragdotcom might fail (likely IMO), but the stack is here to stay, and it's only gonna get better, cheaper, more integrated.
What is becoming increasingly clear, IMO, is that you have to spend some time with this. Prompting an LLM is like the old google-fu. You need to gain experience with it, to make the most out of it. Same with coding stacks. There are plenty of ways to use what's available now, as "tools". Play around, see what they can do for you now, see where it might lead. You don't need to buy into the hype, and some skepticism is warranted, but you shouldn't ignore the entire field either.
However not in the case of AI (agentic AI / LLM), because simply they already have a use case, and a valid one. Contextual query and document searching / knowledge digging will be there to stay, either in form of current agentic model or different one.
It probably helps a little to understand some of the internals and math. Just to get a feel for what the limitations are.
But your job as a software engineer is probably to stick things together and bang on them until they work. I sometimes describe what I do as being a glorified plumber. It requires skills but surprisingly few skills related to math and algorithms. That stuff comes in library form mostly.
So, get good at using LLMs and integrating what they do into agentic systems. Figure out APIs, limitations, and learn about different use cases. Because we'll all be doing a lot of work related to that in the next few years.
That means, if you learn more about the internals of LLMs, your market angle is going to be artisanal customised models. Fashion is commoditised, but people still pay for a custom tailored suit. In the same way companies will continue to pay for finetunes optimised for their business usecase.
If you decide to focus more on the application of LLMs, you should really invest into high-level architectural skills. Good “code completion” models can already do what an outsourced 10 bucks per hour developer used to do. Your job in the future is going to be to decide the structure of which fuse and against the towel and or which type of state is being stored and managed. But the actual coding of the UI forms and the glue code to synchronise from an SQL query to the client state, that part is probably going to be fully outsourced to LLMs.
There was also a dot com bubble, mostly bursting not because of search but because there were a lot of what today would be "AI startup" but is just a web app calling AI Api's. So there's likely to be some bubble burst but it should be smaller maybe hitting more of these small tools that eventually become features.
Not quite the same. E.g. databases are a part of the system itself. It's actually pretty helpful for a SWE to understand them reasonably deeply, especially when they're so leaky as an abstraction (arguably, even the more nuanced characteristics of your database of choice will influence the design of your whole application). AI/LLMs are more like dev tooling. You don't really need to know how a text editor, compiler or IDE works.
Granted this is a pretty simple task and a low stakes scenario, but I don't think we should limit ourselves to assuming AI will always only be dev tooling.
But as for my 2 cents, knowing machine learning has been valuable to me, but not anywhere near as valuable as knowing software dev. Machine learning problems are much more rare and often don’t have a high return on investment.
1) Established companies (meta/google/uber) with lots of data and who want MLEs to make 0.1% improvements because each of those is worth millions.
2) Startups mostly proxying OpenAI calls.
The first group is definitely not hype. Their core business relies on ML and they don’t need hype for that to be true.
For the second group, it depends on the business model. The fact that you can make an API call doesn’t mean anything. What matters is solving a customer problem.
I also (selfishly) believe a lot of the second group will hire folks to train faster and more personalized models once their business models are proven.
Between your two options, I’d lean toward continuing to build what you’re good at and using AI as a powerful tool, unless you genuinely feel pulled toward the internals and research side.
I’ve been lucky to build a fun career in IT, where the biggest threats used to be Y2K, the dot-com bubble, and predictions that mobile phones would kill off PCs. (Spoiler: PCs are still here, and so am I.)
The real question is: what are you passionate enough about to dive into with energy and persistence? That’s what will make the learning worth it. Everything else is noise in my opinion.
If I had to start over today, I'd definitely be in the same uncertain position, but I know I'd still just pick a direction and adapt to the challenges that come with it. That’s the nature of the field.
Definitely learn the fundamentals of how these AI tools work (like understanding how AI tools process context or what transformers actually do). But don’t feel like you need to dive head-first into gradient descent to be part of the future. Focus on building real-world solutions, where AI is a tool, not the objective. And if a cheese grater gets the job done, don’t get bogged down reverse-engineering its rotational torque curves. Just grate the cheese and keep cooking.
That’s my 2 cents, shredded, not sliced.
If you are considering whether the future will boost the demand to build AIs (i.e. for clients), we could say: probably so, given regained awareness. It may not be about LLMs - and it should not, at this stage (it can hit reputation - they can hardly be made reliable).
Follow the Classical Artificial Intelligence course, MIT 6.034, from Prof. Patrick Winston - as a first step.