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.

1/ There aren't many jobs in this space. There are still far more companies (and roles) that need 'full-stack development' than those focused on 'AI/LLM internals.' With low demand for AI internals and a high supply of talent—many people have earned data science certificates in AI hoping to land lucrative jobs at OpenAI, Anthropic, etc.—the bar for accessing these few roles is very high.

2/ The risk here is AI makes everyone good at full-stack. This means more competition for roles, less demand for roles (now 1 in-experienced engineer with AI, can output 1.5x the code an experience Senior engineer could do in 2020).

In the short/medium term, 2/ has the best risk/reward function. But 1/ is more future proof.

Another important question is where are you in your career? If you're 45 years old, I'd encourage you to switch into leadership roles for 2/. This wont be replaced by AI. If you're early in your career, it could make more sense to switch.

Most of my LLMs made lots of mistakes, but Codex with $200 subscription changed my workflow totally, and now I'm having 40 pull requests/day merged.

Treat LLMs as interns, increase your test coverage with them to the point that they can't ruin your codebase and get really good at reviewing code and splitting tasks up to smaller digestible ones, and promote yourself as team leader.

Learning to work with the outputs of them (which is what I do) can be much more rewarding. Building apps based around generative outputs, working with latency and token costs and rate limits as constraints, writing evals as much as you write tests, RAG systems and embeddings etc.

If you want to switch fields and work on LLM internals/fundamentals in a meaningful way, you'd probably want to become a research scientist at one of the big companies. This is pretty tough because that's almost always gated by a PhD requirement.

If you're good at what you're doing right now and you enjoy it — why change? Some might argue that AI will eventually take your job, but I strongly doubt that.

If you're looking for something new because you are bored, go for it. I tried to wrap my head around the basics of LLMs and how they work under the hood. It’s not that complicated — I managed to understand it, wrote about it, shared it with others, and felt ready to go further in that direction. But the field moves fast. While I grasped the fundamentals, keeping up took a lot of effort. And as a self-taught “expert,” I’d never quite match an experienced data scientist.

So here I am — extensively using AI. It helps me work faster and has broadened my field of operation.

You don't need to deep dive into the maths. You'll need to understand the limitations, the performance bottlenecks, etc. RAGs, Vector DBs, etc

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.

Work for companies (as a consultant?) to help them implement LLMs/AI into their traditional processes?

[1]https://news.ycombinator.com/item?id=44079296

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.

For example, if we wanted to conduct an analysis with a new piece of software, it wasn't enough to run the software: we needed to be able to explain the theory behind it (basically, to be able to rewrite the tool).

From that standpoint, I think that even if you keep with #2, you might benefit from taking steps to gain the understanding from #1. It will help you understand the models' real advantages and disadvantages to help you decide how to incorporate them in #2.

Do you need to understand how the circular saw and drill are made?

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.

But I believe that the value will come after the bubble is burst, and the companies which truly create value will survive, same as with webpages after the dot com bubble.

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.

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.

Just learn this and its prerequisites

Or, if you believe there may be some merit to "AI is coming for your job" meme, but really don't want to do blue collar / skilled trades work, at least go in with the mindset of "the people who build, operate, and maintain the AI systems will probably stay employed at least a little bit longer than the people don't". And then figure out how to apply that to deciding between one or both of your (1) and (2) options. There may also be some white collar jobs that will be safe longer due to regulatory reasons or whatever. Maybe get your physician's assistant license or something?

And yes, I'm maybe playing "Devil's Advocate" here a little bit. But I will say that I don't consider the idea of a future where AI has meaningful impact on employment for tech professionals to be entirely out of the question, especially as we extend the timeline. Whatever you think of today's AI, consider that it's as bad right now as it will ever be. And ask what it will be like in 1 year. Or 3 years. Or 7 years. Or 10 years. And then try to work out what position you want to be in at those points in the timeline.

It is worth getting use to that mind-set, and then use LLMs as a tool (they are likely here to stay, because big tech have started to integrate features based on them everywhere, for better or worse). So this is your option (2.). Personally, I prefer software I use NOT to be smart, but to be 100% deterministic.

But already my favorite LaTeX authoring environment (Overleaf) has a button pop up called "fix this" that auto-resolves syntax errors, many of which overwhelm my doctoral students that no longer read books end-to-end (here, to learn LaTeX).

Gradually, you may dive deeper into the "how", driven by either need or curiosity, so eventually you will probably have done both (2.) and (1.). - in the same way that you will have first learned SQL before learning how replication, transactions, data buffers and caches, or query optimizers are implemented.

It’s up to you where your morals lay and how important money is compared to those morals but it seems like AI is here to stay.

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.

Just because a field is popular, doesn't mean you should switch to going deep into it. But that doesn't mean you shouldn't use it - it costs a few dollars to try it out and see whether it fits your workflow. If it does, this is great and you can be more productive and focus on stuff that you can solve and LLM can't, but if it doesn't, that is fine too.

Do you like science? Then dive deep into LLMs. Be ready for science, though. It involves shooting a thousand shots in the dark until you discover something new. That's how science gets done. I respect it, but I personally don't love doing it.

Do you like engineering? That's when you approach a problem and can reason about a number of potential solutions, weigh the pros and cons of each, and pick one with the appropriate trade-offs. It's pretty different from science.

So I would learn things that are either fun for you to learn or things that you can directly apply.

For AI this means you probably should learn about it if you are really interested and enjoy going through build-your-own-NN tutorials or if you have good chances of switching to a role where you can use your new skills.

Edit: Basically investing anything (also time) is risky. So invest in things that directly pay off or bring you joy regardless of the outcome.

We’re in a funny moment. Right now, AI tech is so powerful and capable that people are categorically underestimating their value and systematically underusing them — whatever the hype is signalling. If the tech froze right now, there’s decades of applications to mine.

Lots of great products being built on that thesis. The strategy is: unlock more of their present capability, harness that for a wider audience’s use case.

In that way you do both — leverage the tools, and in becoming an expert user, you can find yourself a vendor of very valuable guidance — and a builder of desperately sought-after products.

Are you asking to get a PhD or use them as tools?

Over like five years we've been promised a revolution that has yet to appear and is still lighting billions of dollars on fire. Don't bet on it materialising tomorrow.

If you need comforting, go read Merleau-Ponty and Heidegger, perhaps condensed down as Hubert Dreyfus.

RAG could largely be replaced with tool use to a search engine. You could keep some of the approach around indexing/embeddings/semantic search, but it just becomes another tool call to a separate system.

How would you feel about becoming an expert in something that is so in flux and might disappear? That might help give you your answer.

That said, there's a lot of comparatively low hanging fruit in LLM adjacent areas atm.

Worst case, you’ll be a more interesting, well-rounded, and curious person with a broad set of programming skills.

You don’t need much expertise in NNs to still be able to get huge value out of them today

Data Drift. Over the course of a few moNths the data deteriorates and the LLM ceases to function in a worthwhile manner.

Currently most LLMs are based upon the core preMise that people should not believe anythiNg. This is tokenized aBove everythiNg else. Then there are other erroneous tokenizations. Why these are not fully documented, people use these tools. You should know what you are getting.

Tokenization. Different words are tokenized to have a higher value than other words or configurations.

So, these are the dangers that everyone has ignored. It makes it an unethical tool because it is based upon someone's erroneous views. Honesty is the best policy. If it cant be honest, how can you trust it?