why Senior Engineers struggle in the age of AI

There are three types of senior engineers who struggle in this post AI era. This first category of self-thought ex-bootcampers, which are now senior engineers years later, they feel betrayed. They learn coding to live in a dream that no longer exist. They spend time and money and years of training to get better at this craft and now AI is coming and it feels like it's going to eliminate this professional together. On top of that, there is no real solution insight. Everybody's saying, hey, do more lead code, more project, you should learn AI. But nobody has a clear vision. So, what would you tell software engineers in this situation? So, of course, when you invested so much time and then you see AI coding faster than a human and the typical react component or whatever that you take half an hour to think about and then write and carefully craft and it took you three years only to get 20% faster doing that or 50% or whatever. And then you see AI doing that in a fraction of a second and people building things, people with no expertise, but that they didn't make the same investment of time and money. And then they build these things, it feels demotivating also. So, I totally understand that. And from a career perspective, it feels like just because I learn does do that thing that you got so much better off, then you'll be fully replaced. And it's basically the same as saying that you've been starting to be a civil engineer or an aerospace engineer and then they release computer assistant software and you won't have a job. The reality is to build with the tool, the people that will build with the tool, the people that will build healthcare software, the people that will build finance software. They'll be software engineers. Now, everything you learned will help you scale yourself with AI. So, a software engineer vibe coding will be much more effective than vibe coder. And you see the trend on X where people that started vibe coding are now thinking, oh, maybe I should learn the fundamentals when they realize that they are doing bugs. And solving problems that were solved way before, they realize, oh, probably if the code base was modular, I wouldn't spend so many tokens every time I fix a single problem because it wouldn't build the spaghetti that the model has to ingest. Or if I had a stable API, it would be easy to refactor. Or if I had tests on this, it would be easier to write tests. Or maybe I done that.

have to write TDD if I keep changing code. I could just write entry testing, but to make all those decisions, you need to reason like software engineer. You need to know software engineering. So a lot of people are feeling like, whoa, this coding thing, it's worthless. But the thing is that the meta skill, the reasoning that you built while learning how to code, it's extremely valuable, especially when building with AI.

Because the things you can build with AI, if you know fundamentals, if you know computer science, if you're good at coding, are much bigger and of much higher quality than anyone that's vibing. And to answer the question, what should these developers do? Should they grind it code, should they build projects? Of course, there's two types of them, the ones that are employed and they are doing okay. They just feel probably like these skills that they built are not economically valuable, are not irreplaceable, a machine can do them.

All right, that's one category, the employed developers who feel like that. And then there's the unemployed ones who, of course, when they go to the market and they think, hey, a few years ago, I had people reaching out to me on LinkedIn, it was so easy to get interviews and now they can't even get a single interview. And this this creates those insecurities because they went to boot camps and they got into software engineering just to have some economic security and now they're unemployed and they hear all this noise.

And of course, they fall into depression and anxiety probably and they feel like it's all pointless. What would you tell those people? As they say, you cannot fight the storm, but you can outlast it. So we are living to a period where there's a lot of change, there's been a lot of innovation, but things are slowly settling in, companies are realizing we do need developers, even if we use AI. And right now, AI is becoming an optimization problem where they're like, okay, how do we use AI, but don't tell me I can max, we need the budget to hire people that work with AI. And it's just going to become another infrastructure layer, just like cloud became one.

So, number one, it's don't quit. Because if you quit now and you're gone and you abandon all the hard work you've been doing, it's going to be even if you decide two years from now, things bounce off again, you try to get back to it, it's going to be a lot harder. And the second thing is there's a lot of uncertainty in the world and you need to give away those expectations that you had. We had this fancy day in the live of people making 300k and having three Fuchinos a day and doing yoga in the office.

That was never you? Okay, that's made up. That's just social media. Okay. Now, you can you can you make a living as a software engineer and a good one? Of course you can. In the long run, you will make all your investment back. But was like when you hold a stock, if you if it starts falling and you need the money and you sell, you're done. You're going to lose everything. So you don't want to sell yet, okay? You just want to hold it, you're going to take a bit of losses and you're going to keep getting better. At, you know, you definitely algorithms and data structures, they help, building projects help, but you got if you're out to the market, you got to get yourself back in the market. It doesn't have to be a coding job, you can get a low code job, you can get a job where you use them, automate something with AI. Don't get hold on into how the world used to be. Be open and then have the same expectations. The reward will come, but it's just might take it might take a bit longer. So given you've been self-taught or you come from a boot camp, the one thing I would press on is transferable knowledge. And in software engineering that tends to be very language agnostic. So going from whatever you're doing hands on with the React to learning a bit more about algorithms and data structures, or understanding creativity at a high level. So it'll be like, okay, what when we talk about state, let's say, and I give a lot of front end examples, but you got to think off, okay, what was the data data structure I was using and how can I transfer that to the backend? Because that will help you own a bigger surface, own a big feature that maybe has a backend component that you're not fully aware of the line by line code that it's there. But if you have strong reasoning, you can still make high quality decisions about it. So you got to expand yourself, both by going deeper, uh, but in this case, usually with bootcamp people and self-taught, they have the ability to get things done. They can push things over, but they need to professionalize their knowledge a bit. They need to get better at traditional CS concepts and algorithms and data structure and understanding how computer works. Understanding HTTP in depth, understanding JavaScript, not just the the React layer, and that will give them a lot more confidence and make them able to own a bit more, to own bigger things. And lastly, just to touch upon the software engineers which are unemployed right now, which are looking, they are in the market, they are looking for a position. It's a very competitive market.

I'm seeing Bogdan because we have this 90 days program where we have people coming to us. Some of them they've been six months, other have been 12 months or even 18 months out of work in the market. Right? And they come to us and in a matter of four to six weeks, they start getting interviews and they start passing them. Like and the big difference for the people watching us, it's structure and discipline. You cannot be all over the place in this market. You can't just ask AI for a resume and send it and hope it will come back. You have to be very methodical about your applications, right? And if it's one advice I could give you, it's add more structure, more accountability, right? And more tracking to your job search. Now, we could talk about this for hours, so if you want Bogdan and I to make a specific video about how to stand out in the current market and get tangled interviews, then let us know in the comments. The second category of senior engineers who are starting in this market is senior engineers with a computer science background. Some of them have a bachelor, others even have a masters. And and the reason why they are struggling is because they feel like they are drowning in AI slop. The companies are pushing AI and they feel like they've been trained in a deterministic way. And they've been trained for a world that does no longer exist. There's no best practices, you just push code and use the agent to do it. AI is being pushed top down from the management, it's being forced, like respect without any regard sometimes to engineering principles. And they have no choice, they either sing along or they just quit or get laid off. What would you tell these people, Bogdan? Right now, we are in a transition period where people are still learning what the technology is. There's a lot of marketing telling you how great AI is at coding. And also a lot of people are just abusing it at work. Your coworkers will drown you with a PR that is fully written with AI and the thing with AI is that it puts the weight on whoever will consume that. So, if you write a PR with AI, you don't pay the price and I wouldn't say you didn't even wrote a PR. You generated, okay, you just instructed the the AI to generate it. And the weight is on whoever is reviewing it. And we see a lot of companies, for example, they put the weight of product development, which is usually Q&A, on the users because they release crappy software. Then you find

about you. He pointed and they like, oh, we fixed it the same day. I'm like, yeah, this is not licable yet. You're just pushing it out there and now I'm doing the testing for you. What will happen in the long run is that products lose quality, it's easier to build competitors, and companies that are smart about how they use AI, they can actually use it, but still have people building the decisions, building quality in the product, will grow.

So you see a lot of companies that are at the leadership level, they're not so AI peeled. A good example would be linear, for example, where their CTO has a really good grass on AI and they always talk about this human first approach where they're like engineers are using AI and they are doing more with AI, but it's an engineer centric approach to adopting AI. Not hey, and if you don't use it, we're going to lay off. And so there's companies all over the place right now.

There's people that went full mold, there's a lot of AI hysteria. And so again, for the moment, you're just going to wait and see a little bit. You're going to use AI, of course you cannot review things by hand if nobody does. You got to adapt to how your company is working, but make no mistake. If you look at the latest releases, both in Java as a type safe model, we are introducing now languages for formal verification, which go back to deterministic solutions in the software and approaches that they mirror traditional software engineering, I think there will be a huge turning point.

And when this huge subsidized subscriptions are also going to be over, you see a lot of companies now they try to optimize their token spend, and when you optimize your token spend, you talk about engineering because all of a sudden you need to change your software architecture to be able to develop that with less tokens, which is very similar to the changes you are making as a human. Oh, let's split into microservices, oh, let's make this more maintainable.

So those fundamentals that you studied, those are the governing forces of building software at high scale, at enterprise level. So don't get frustrated, tell yourself, this should also pass. This is a short term. You're seeing a transient face and you're seeing a lot of disruptions, but things will settle. And building computer software will always require a deep knowledge of how computers work. At the same time, there is a change in mental models.

So and right now we see patterns in mental models like the software factory, right where the engineers are building the factory and the agents are the ones building software. Yes, we see a lot of people experimenting with the idea of software factories, there's definitely more focus on that. You will have agents building small features, building, fixing bugs, completely autonomously. And you have agents that fix bugs autonomously and unless it's a very critical bug, you don't even review it. And I think that's definitely a good direction and definitely there's will be automation, but the engineer will take a second stance, which is okay, I'm looking at the software factories. If I look at the last 10 production incidents we had, they all resonate about this one feature. Is there an architectural mistake that we made in this feature? Do we need to reorganize something? So you'll always have the need for the engineer. Even if you have agents solving the bug or even product managers are now pushing code with AI, they're like, okay, let me change a button. That's okay. Anyways, it's not the kind of work that you enjoy doing, like spending three days on a weekend fixing a bug that was purely mechanical. Nobody enjoyed doing that. So there's also some things we just kind of give away. Some things were fun, but a lot of work was also very dull and very boring. And by the way, folks, if you want us to make a more in-depth video about the software factory mental model and how a software factory should be built, then let us know in the comments. And if you want to become that AI native software engineer who goes from building software themselves to actually using AI to build software, then check the linked comments. We have a free training and a mentorship program that can help you get there faster. And now let's move on to the third type of senior engineer who is struggling in this AI world. And it's funny because these people they don't think they are struggling until they are struggling. And these are the software engineers who actually learn how to code or got into professional software development after 2022 when Chat GPT was released. And the problem these engineers are facing is they use AI for everything, they cannot live without it. They don't know any other way of building software. So they have a huge imposter syndrome. Some of them tell me, hey, look, I can do anything when I have AI, when I have access to cloud code, I can get everything done.

pool me into a meeting and they ask me why you did this and how you you would you expand it? I I feel like I can't even contribute, so I stay silent. Right? And I'm afraid of technical interviews where this kind of setup can happen. But some of them can still memorize things and pass technical interviews, is just they're walking around with this thing where they feel like deep inside, they don't know much and they need AI and they cannot get anything done without AI. So, the problem here is that at the end of the day, you're not the product person, you're not the vibe coder, you're a software engineer. So, you'll be always evaluated on your technical knowledge. Now, nobody cares if you will build the application with AI or not, you're the one guaranteeing for the quality. And, and I see a lot of interviews moving towards that direction where they'll make you build something. And then they'll go in and be like, okay, what state management philosophy you chose? Why are we writing CSS in this way? And if you cannot defend that, then they will completely unvalidate all your skills because they assume that you are not in a position where you can make things decision. So, again, the scarcity after we have large language models and text generation and the scale we're doing it in code generation, is not anymore quantity or speed. It's always quality. And so what will happen is that a software engineer that has a product mindset, that has good fundamentals, their version one of the product will be much better than the vibe coded version number 20 of somebody that's still figuring out. And what you'll see a lot is that in the everyday meetings, you're going to and uh there will be standups and people will say, why already released? And then it will come back with a bug. There will be production bugs attached to their releases and the senior person, they will be a bit more quiet, but they will release things that have quality. And product, they're quite fearful, they don't fully understand the systems, they need trust and they'll end up going to the person that really has strong fundamentals. So, for these people, they're just learning, hey, look, it's great that you can use AI and it's great that you can deliver, take that as an invitation to free up space and go deeper into the craft. Because you need to get up to speed as

basing you go file by file, module by module, you tend to understand what is going on. You can use AI for that, it doesn't really matter, but do make sure that you are always building your judgment and your decision-making. with good old software fundamentals. And by the way, if you are one of those software engineers which is trying to get better at the fundamentals and make sure it's not only AI that gives them superpowers, but they actually have superpowers, then check out the free think color assessment we've got in the comments. It's about 10 minutes and it will give you an exact overview

of the things you are lacking for you to become that senior AI native software engineer. With that being said, thank you so much, Bogdan, and we will see you, folks, in the next one.