What does a Software Engineer do in 2026?

Software engineering has drastically changed over the past few years, and the most important thing that software engineers do is not writing code anymore. If it's your first time here, my name is Sam, and I'm a software engineer at a big tech company in New York City, and I'm here to give you some insights on what software engineers do day-to-day. Before we dive in, I kind of want to define what type of software engineering I'm talking about. Typically, software engineering is divided into two different categories, front-end and back-end. Front-end engineers work on the visual aspect of an app or a website, and back-end engineers work on the business logic or the server stuff, basically. Now that we have that defined, let's jump right in. The first one is product alignment and planning. This is something that's overlooked, but software engineers actually spend a lot of time on aligning on product requirements and planning out their implementation strategy. When working on new projects or features, we spend weeks or even months working on technical design documents, which is basically a document explaining how you want to go about implementing such things. This requires a lot of meetings, a lot of writing docs, and iterating on those docs after getting feedback. I would say this is the most tedious part about the development life cycle, because as engineers, we just want to start coding. But it is pretty crucial to have this part really set in stone, because once you have this strong foundation and you start implementing something, there will be less chances of implementing it incorrectly or forgetting to consider certain product requirements. These days, people have been using a lot of AI to actually write those design documents. You might think that this creates a lot of AI slob, but as long as the document has the crucial information, then it's actually pretty useful, because people use AI to write those docs, and then people will use AI to read those docs and kind of summarize it for them. Number two is writing code. Once we finally get product alignment and we write all of our documents, we actually get to start writing code. Now, this process has changed significantly over the years, as I said earlier in the video. Now, with all these AI tools, code is cheap. You're able to implement one month of work in a few days. Because engineers are able to kind of crank out code at an incredibly fast pace, the bottleneck is usually PR reviews. There's a lot of coding practices and guardrails that actually allow the AI to implement certain things in a surprisingly accurate way. It's 2026, we're rapidly approaching an age where AI code is going to be almost close to perfect in terms of accuracy. But I still think the problem with relying on AI so much to write code is that when you start running into issues with AI or the AI starts hallucinating, if you don't really have the domain expertise and knowledge about the code that you're writing, you're pretty much cooked. And that's why I still spend a lot of time actively learning and sharpening my programming skills. And honestly, one of the best platforms I've been using to do this is Datacamp. I partnered with them for this video, and what I like about Datacamp is that it's really hands-on. Instead of just watching hours of lectures, you're actually coding from day one while you're learning, which makes it so much easier to actually retain information. And right now, despite the increase in developers using AI, companies still value knowledge about coding or programming because coding with AI without the knowledge of actually coding is kind of useless. If you want to get started with coding as a future software engineer, you should check out the Python for Software Engineering track, which covers all the fundamentals you need. If you already have some experience programming and if you want to add AI engineering to your stack, then I recommend checking out the Associate AI Engineer for Developers track, which teaches you how to use tools like the OpenAI API, HuggingFace, LangChain, and Pinecone, plus Prompt Engineering and LMOps in real projects. You can also earn a Statement of Accomplishment and work towards Datacamp's AI Engineer certification. It's useful for your resume and your LinkedIn while you're building your portfolio. So if you're serious about building your career as a software engineer, check out the link in the description below. Now back to the video. Next is QAing and dogfooding. QA stands for Quality Assurance, and dogfooding is the process of using your own product to test out what it's like to be an actual user of your product. So once you finish kind of like a draft or like a prototype of your product or feature, your role is to make sure that you catch any bugs early on or capture any potential edge cases. And this part is kind of something that AI still struggles to do, but that's just for now. And next is debugging. Now, and of course, whether you write your code with hand or with AI, your code will most likely have bugs. Another important role of software engineers is to actually identify those bugs and implement a fix to those various types of bugs or errors. Something that's commonly done at companies is that people hold bug bash sessions where they basically invite a bunch of people to a call and they have everyone use their product at the same time and have a document where everyone can write any bugs or errors that they ran into. And again, I've been seeing a lot of these new debugging tools using AI, and honestly, I've been pretty impressed by them. There's this one tool that I saw, which basically was an AI agent that would crawl through the backlog of all the bug tickets, and then it would read the ticket, analyze it, make the fix, and make a PR that is ready to be reviewed by a human. Now, it's obviously not 100% accurate, but if it's some trivial bugs, then it actually is super helpful and very accurate. And this is pretty game-changing because if it reaches to a point where it's able to fix pretty complicated bugs, then it needs really little human overhead. And so it's pretty crazy. Next is being on-call. Now, a lot of times, big tech companies have on-call rotations. It basically means for a week, you have to keep your phone nearby, and whenever you get paged or mentioned, whether that be during the weekday, during the weekend, or during midday, or even at like 3 a.m. at night, if you get paged or if something happens, you have to be able to jump right in on your laptop and kind of make any fixes or investigate any errors that are happening to your product. And yet again, there has been a lot of AI tools that are useful for investigating these incidents. Something that I did was once I got paged, I would kind of jump on my laptop, and I would use an AI agent to kind of help investigate the issue or incident that's going on. It would give me like a holistic summary and even a potential like root cause of that error. Super helpful. It's not, again, it's not like 100% accurate, but it's a very good starting point for people who don't have much context on a certain error. Next is code reviews. Software engineers are expected to do a lot of code reviewing. When an engineer writes code and wants to make a change to the code base, then typically the other engineers on the same team have to review that code change. And other engineers typically, you know, just leave comments, suggest small fixes. And if it's completely wrong, then the engineer who wrote that code wouldn't actually be able to make that change until other engineers approve it. So a lot of times you're required to just read a lot of code. And you guessed it, there's AI tools for that. There's been pretty sophisticated agents that would give very deep feedback based on the context they have. And that context would be huge, right? They'll be able to have full concept of the entire code base. But still, humans actually reading the code is still important because AI can make mistakes. And finally, we have to learn how to use new AI tools every day. At this rate, there's been a new AI tool being created every single day. And usually those are tools that are actually surprisingly good, right? They actually provide value. And that means you have to pay attention to a lot of the new and up-and-coming AI tools so that you won't fall back, basically. Tech moves incredibly fast. And so you have to make sure that you know all the latest tech trends or tech tools. Almost all of them happen to be AI tools. But yeah, anyways, that's about it. But of course, this doesn't really cover everything that a software engineer does because there's much more detail in everything that an engineer does. And this was just from my perspective working as a software engineer in 2026.