Shadowing Practice: Don't choose the wrong tech career in 2026 - Learn English Speaking with Video
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I have just spent hours of my life researching this topic for you.
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Which are the best tech jobs you should look to get into in 2026?
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And this is not a vibes-based video.
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So I've done a lot of research
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and looked at what is the data saying on which jobs are growing and which jobs are declining in tech.
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And I've put it all into one list that you can watch in a video.
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Because this is a big decision.
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So maybe you work in tech already and you're looking to pivot into something.
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Maybe you're a computer science student and you're looking at which careers to choose.
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I'm going to go through each one.
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I'm going to go through like AI proof ones,
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the best paid ones, the most stable, which are growing.
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So by the end of this video,
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I guarantee you, you will have a clearer idea of which tech job or which tech career you want to choose.
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Because I remember when I started,
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I just found it so overwhelming,
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all the different options in tech.
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And now with AI, it's even like worse.
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It's a big decision as well,
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but what if I choose something that's gonna be automated in a few years?
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Which are gonna be growing?
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Which are gonna be paid well?
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Which like suit my personality and what I want to do with my career?
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So we're gonna go through all this by the end of
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this video You should have a way better idea of
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which area of tech is for you
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and at the end of the video I'm gonna share the two best ways
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that you will earn the most amount of money possible in your tech career There's two ways
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so stick around for that get coffee Let's get stuck into it.
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If you're new here.
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My name is Andrew I'm a software engineer and digital nomad.
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I got into tech in my 30s and now travel the world as a digital nomad coding apps,
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making content and yeah sharing the journey with you guys what I've learned and what I'm building.
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So if that sounds like your kind of vibe subscribe if you want.
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One thing to be aware of is that roles in tech evolve
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and the people who are like the smartest and the cleverest they adapt with it.
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A great example is network and system engineers.
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So this area was kind of being commoditized and the ones who saw
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that you could actually shift into cloud computing you could basically ride the wave for what was replacing them
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and these people made a load of money basically by adapting seeing an opportunity
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and jumping on it so roles in tech will evolve the ones who earn the most
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and they basically squeeze the most out of their career are the ones who adapt
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and evolve with these changes and also a quick point on money
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so
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if you're a big driver to earn as much money as
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possible a key focus should be the industry you work in
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rather than the actual job for example you can work in cloud
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and you could work for a charity or a non-for-profit
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or like in the retail sector
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if you instead focus on the industry like for example a big tech company an AI lab
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or anything in AI fintech so working for a massive bank you're just going to earn
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so much more no matter what job you do in the non-for-profit
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or the charity sector you will learn way way more in those sectors.
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So you wanna look at FinTech, AI or Big Tech.
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Okay, job number one is AI and machine learning engineer.
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If you are not sure about what you wanna do and if you like math and programming,
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please just choose this.
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This without doubt has the highest ceiling,
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the highest barrier to entry but also like the biggest ceiling in terms of like opportunity
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and earning potential in tech bar none you will have an incredible career
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but the sacrifice is quite brutal barrier to entry you're looking
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at a phd to get the real top jobs you're looking at a phd you're looking at real deep mathematical
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and statistic knowledge and also the software engineering
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so there's a lot going on here that you got to learn
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but
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so much opportunity we're reaching software 2.0 software one was writing
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code for a program we make step by step software 2.0 is feeding a model loads of data
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which outputs a program but yeah if you're on the fence
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and you like math let me make the decision for you choose this okay next is software engineering
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so i'm a software engineer i have a lot to say
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about this i still recommend it as a job to get into in tech It is changing a lot though.
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Basically, this is the job which AI is really focused on.
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It's really zoomed in on at the moment.
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I think in two years that handwriting code and even system design is just gonna be AI generated.
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And the role of a software engineer is gonna be ideation within the business.
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It's gonna be requirements of a product.
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So you'll speak to the business,
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you'll speak to stakeholders and they'll tell you what they want.
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And your job is to like think about it and then look at what feature or app you wanna make.
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So it's working with LLMs and validating the output.
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But also product management
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and I've talked about this in my newsletter where I see
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the role of the software engineer moving more to the human side.
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So yeah when I started in 2020 it was about how do you loop over an object in JavaScript.
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Now it's going to be what is the business problem,
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how do I turn that into a product,
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how do I use LLMs,
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how can I get the best out of an LLM so it outputs code.
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It's also changing a lot in terms of the specialisms.
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So let's take for example front-end development.
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What What companies are looking for is people who are T-shaped developers.
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So they're really good at one particular part of the stack.
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For example, front-end.
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But they also want people who are good at back-end and DevOps.
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So a job which was a front-end developer a year ago is now just a developer job.
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Now, I'm biased, but I think software engineering is one of the best jobs in the world.
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At its worst, it can be frustrating because you don't know how things work.
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At its best, you're being paid to solve problems, listen to music.
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It's very chill.
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And what you can do is learn on the job,
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then try and build your own apps and become a founder.
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Okay, so we talked about software engineering.
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We talked about ML engineers.
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Let's talk about one of the hottest jobs in tech right now.
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That's an AI engineer.
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And there's a bit of confusion about what they actually do.
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So a software engineer will build the systems, the apps, the infrastructure.
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A machine learning engineer will build and train the models.
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The AI engineer, they take the models and turn them into real AI-powered products.
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So they're basically the ones who make AI usable in production.
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So if you want to work in AI,
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but you don't actually like training the models,
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which can be dry for a lot of people,
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and also you don't want to go back to school,
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and maybe you don't like the math,
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but you want to see your products out there in the wild,
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then this is a great mix between a software engineer and an ML engineer.
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The AI bubble might pop,
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but this job is going to grow so much,
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and also it's a great option if you're a developer and you want a new challenge.
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Okay, just a little pause.
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If you're enjoying this content,
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if you're learning anything, if you're getting value from it,
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I would massively appreciate it if you just like the video.
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It's completely free for you,
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but it helps me out a ton.
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And subscribe if you want.
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Back to the video.
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Next one is cybersecurity.
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This is just, in this world of AI vibe-coded apps,
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cybersecurity is going nowhere and it's very AI-proof.
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But it's getting very difficult to get into
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because I think what's happening is a lot of people who studied software engineering
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or computer engineering are getting freaked out by AI and a lot of them are moving into cyber.
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So it's harder now than it was two or three years ago,
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but it's gonna get even harder.
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The best option, which I would do,
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is move into cloud security.
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It's just lots of opportunity there.
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And there's loads of different areas of cyber too.
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There's like offensive stuff, so like ethical hacking,
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which is just probably the coolest part of tech outside of programming.
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Also, you've got defensive stuff.
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And also there's more corporate ones like government and risk and compliance.
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There's just loads of different parts of cyber.
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It's AI proof, really great money.
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But as I say, it's getting harder and harder to get into.
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So if you're more interested in protecting things,
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cybersecurity is a great option.
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Okay, next one is product management.
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And I think this is one of the biggest winners of the AI revolution.
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And product management is like an outlier in tech because a lot of tech jobs,
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they focus on basically solving a technical problem.
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This is more like a lot of soft skills.
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It's a lot of influencing.
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It's a lot of managing different stakeholders,
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working across different departments to turn a business idea into a feature or an app.
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So a lot of it will be figuring out the how and the why of building software.
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So you'll be working across lots of different teams.
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The negatives of this job of product management,
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I think it's just quite exhausting office politics, managing relationships.
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Are you the type of person who would like enjoy that?
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Lots of meetings, your calendar is gonna be full.
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It doesn't pay as well also than usual engineering jobs,
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but if you move into AI or data,
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you'll be fine in terms of pay.
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Some people love solving problems systems and music.
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Some people love meetings, so it's whichever you like.
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All right, the next one I recommend,
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which is AI proof, great option for you,
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is cloud computing or like cloud engineering.
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This is probably like the most stable,
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kind of like unsexy part of tech,
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but it's just every company needs this and it is growing all the time.
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And I think it's hard to describe what kind of person would suit cloud computing.
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Maybe someone who's a bit OCD.
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Maybe if you don't like building or you don't like the math side,
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then this is a good option.
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You're basically just ensuring these systems are reliable,
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the infrastructure is good, and you're just going to focus on a few of the cloud providers like Azure and AWS,
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go really deep and just learn everything about these platforms.
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Very stable job.
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This is going nowhere.
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All right, as promised, at the start of the video,
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I talked about the best ways to earn the most amount of money in tech.
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And there's two ways.
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Based on 99% of the people watching this video,
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I assume you don't have a PhD from Stanford
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and you're not going to go into like these research roles for like OpenAI.
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I assume that's like most of my audience.
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I'm talking to like the normal people who work in tech.
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There are two ways to earn the most amount of money in tech.
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Number one is people management,
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and number two is sales.
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All right, and this is how you become a people manager.
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So you work in a field for like three to five years,
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for example, cloud computing.
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Get good at it, and then you just ask the company to take on more responsibility.
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So ask for like managerial tasks.
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You're not asking to get paid more,
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you're just asking to take on more responsibility.
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Then you basically just have to prove yourself that you can become a manager before you're a manager.
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And if it's a good company, they'll promote you.
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If it's a bad company,
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just leave and then do the same at the next job.
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Okay, and the next one is sales.
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So if you're someone who's watching this and you're quite good at speaking,
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you've got good communication skills,
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good soft skills, and you're not quite enjoying the technical side of working in technology,
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like you don't like building stuff,
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you don't like coding or like hacking,
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whatever, then I do think sales is just such a great option for you.
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And it is probably the best paid area in tech if you do it well.
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A lot of people think sales is like cold calling or like knocking on doors,
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but sales, there's something called sales engineering,
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which is a lot different.
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For example, I went to IBM last year and I met a guy there who works in sales for IBM.
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He studied quantum computing.
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He has such a good technical knowledge that he just goes into meetings,
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he meets clients and just gives them,
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he just talks about the products of IBM and like their technology offering.
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It's the kind of job where you're not working at 100% all the time,
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but if you do one good presentation for 20 minutes,
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you could make the company million.
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It's a job where if you enjoy technology,
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like talking about it, and you like reaching goals,
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obviously financially, you're very money motivated.
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I really do think sales is a great option for people.
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It's a lot of it's dependent on the environment,
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but if you get a good environment,
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it's a great way to be in tech,
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earn a load of money and still be talking and enjoying the technology space.
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But a key part in all this is what's hot now
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and what's trendy right now and what's not saturated might be different in five years time when you're in the career.
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So I wouldn't focus so much on what's trendy or what's like hot right now and what has loads of vacancies.
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The key thing really is what do you like?
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What do you enjoy?
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Like, are you creative?
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Do you like designing things?
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Well, like UX could be a good option.
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Do you like security and protecting stuff?
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Does that give you satisfaction?
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Cyber security.
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Do you like infrastructure and do you like seeing how things are reliable?
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You want to keep things online?
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Cloud.
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Do you like math and experimenting?
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Machine learning.
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Do you like building things?
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Are you quite logical?
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Programming.
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There's loads.
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So this is a big thing in all this.
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What is the work that you will enjoy spending 40 hours a week doing?
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And then make sure there's a demand and just go for it.
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All right, and that is it.
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If you enjoyed this video,
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I would massively appreciate it if you like it.
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Subscribe if you like this kind of content.
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Comment below what you're thinking of doing.
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I'll try to respond to all the comments and I'll see you next video.
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Happy coding, take care, bye-bye.
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Vocabulario y notas de pronunciación para esta lección
Esta lección de conversación de nivel C1 se basa en el vídeo “Don't choose the wrong tech career in 2026”. Las palabras que más se repiten: tech, software, engineer. Este vídeo tiene 262 frases y 2596 palabras para practicar shadowing. La parte hablada dura 12:04. El hablante habla rápido, unas 215 palabras por minuto, así que espera sonidos enlazados y reducidos. El 85 % de las palabras está entre las 3.000 más comunes del inglés; conviene repasar el resto antes de empezar.
Vocabulario clave de este vídeo
Las 15 palabras más avanzadas del vídeo, con su pronunciación y significado:
| Palabra | Pronunciación | Significado |
|---|---|---|
| compute verbo | /kəmˈpjuːt/ | computar, calcular |
| cybersecurity sustantivo | /saɪbəsɪˈkjʊəɹɪtɪ/ | ciberseguridad |
| evolve verbo | /ɪˈvɑlv/ | desarrollar |
| vibe sustantivo | /vaɪb/ | onda, vibra |
| adapt verbo | /əˈdæpt/ | adaptar, ajustar |
| subscribe verbo | /səbˈskɹaɪb/ | suscribirse, subscribirse |
| nomad sustantivo | /ˈnoʊmæd/ | nómada |
| stakeholder sustantivo | /ˈsteɪkˌhəʊl.də/ | apostador, baratero (chapas, juego de apuestas) |
| trendy adjetivo | /ˈtɹɛndi/ | de moda, canchero |
| automate verbo | /ˈɔ.təˌmeɪt/ | automatizar |
| frustrate verbo | /ˈfɹʌsˌtɹeɪt/ | frustrar |
| outlier sustantivo | /ˈaʊtˌlaɪə(ɹ)/ | cerro testigo |
| overwhelm verbo | /ˌoʊ.vɚˈwɛlm/ | apabullar, arrollar |
| validate verbo | /ˈvæl.ɪ.deɪt/ | validar |
| usable adjetivo | /ˈjuː.zə.bəl/ | utilizable, usable |
Phrasal verbs que vas a escuchar
| Palabra | Significado |
|---|---|
| go through verbo | atravesar |
| figure out verbo | averiguar, captar |
Frases que vale la pena repetir
Frases cortas y completas del vídeo que puedes reutilizar en la conversación diaria:
- The key thing really is what do you like?
- Do you like designing things?
- Do you like security and protecting stuff?
- Does that give you satisfaction?
- You want to keep things online?
Gramática en este vídeo
Las estructuras que más usa el hablante, con las palabras exactas del vídeo:
| Estructura | En el vídeo |
|---|---|
| Oraciones de relativo who / which + oración — información extra sobre una persona o cosa | stable, which are · people who are · ones who saw |
| Present perfect have/has + participio pasado — una acción pasada que sigue importando ahora | have just spent · I've done · I've put |
| Voz pasiva be + participio pasado — importa lo que ocurre, no quién lo hace | be automated · be paid · being commoditized |
Pronunciación a tener en cuenta
El hablante usa 79 contracciones y formas reducidas, como you're, gonna, don't. Dilas en su forma corta, tal como las oyes.
- Los sonidos de “th”: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, ethical /ˈɛθɪkəl/
- Palabras largas — cuida el acento: cybersecurity /saɪbəsɪˈkjʊəɹɪtɪ/, ideation /ˌaɪ.diˈeɪ.ʃən/, managerial /ˌmænɪˈd͡ʒɪəɹi.əl/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/
Sonidos difíciles para hispanohablantes:
- s + consonante al inicio — sin añadir una “e” delante: stakeholder /ˈsteɪkˌhəʊl.də/, squeeze /skwiːz/, stack /stæk/
- /v/ — no es /b/: los dientes tocan el labio inferior: evolve /ɪˈvɑlv/, vibe /vaɪb/, massively /ˈmæs.ɪv.li/, overwhelm /ˌoʊ.vɚˈwɛlm/, validate /ˈvæl.ɪ.deɪt/
- /z/ — sonora, no /s/: usable /ˈjuː.zə.bəl/, newsletter /ˈn(j)uzˌlɛtɚ/, exhaust /ɪɡˈzɔst/, zoom /zuːm/, squeeze /skwiːz/
Cómo practicar con este vídeo
- Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
- Empieza a velocidad 0,75×, haz shadowing frase por frase y vuelve a la velocidad normal cuando te resulte fácil.
- Grábate y compara con el original, prestando atención a palabras como compute, cybersecurity, evolve.
¿Qué es la Técnica de Shadowing?
Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.