Shadowing Practice: how AI broke the SWE Career Ladder - Learn English Speaking with Video

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So AI did two opposite things to the junior level, which is number one, in the short term, there are a lot less jobs for juniors.

We have unemployment for serious computer grads at 6 % for the first time in years.

That also has to do with the economics, but on the flip side, it also empowered the juniors a lot more.

So back in the days, if you'd be a junior, you really needed a lot of handholding.

And pretty much any questions had to be answered by a senior engineer

that had to mentor you usually those people never had the time

because they're just churning out features and doing bugs

and you'd be left at your own devices with whatever documentation

companies had usually not a lot nowadays they give you a

code base you can just query it with the llm

and the llm not only answers questions

but it can also guide you to bring a feature to an end

and even do it for you

so all of a sudden juniors can actually deliver features kind

of end -to -end almost features two questions pop into my mind.

Number one, if juniors can deliver end -to -end, or almost can deliver end -to -end, what does this mean for mid -level and senior developers?

And secondly, if juniors with AI can deliver end -to -end, then why aren't there any job openings and it's so hard to find a junior position in the first place?

Yeah, so because juniors are also delivering end -to -end features, the frequency of everything that we shipped changed a lot.

We are shipping a lot more code.

So a lot of the work at the senior level, we'll see it's around building the software factory, building the systems that can sustain all this as a junior they can deliver features

but do they understand the long -term consequences of choosing one strategy for our css

or for our components of our backend rather than the other

probably not they can know it at a theoretical level

but they never had that production experience when things actually break

so yes they can deliver a lot more but you need seniors to enable the platform on which they deliver.

So that's number one.

And about the employment question, the problem is the junior label,

the way the industry recruiters and hiring managers look at juniors is still as someone that needs handholding.

And given the immense pressure it's on the budget right now, because a lot of companies are buying tokens, they're spending on AI and tokens and so on,

they really want someone that just can come into the codebase

and deliver end -to -end just because the economics around it

so what happens is they don't want to bet on someone it's also

because there's so much change they don't really know where to go

so they want the senior people to kind of tell them

they are not ready to absorb junior work all this is to say is

that if you're a junior you have to brand yourself as a software engineer take any work you can

if you can freelance if you can work for free

if you can do volunteering so You can add work experience to your resume

and work with AI and then go to companies and show them how you can deliver end -to -end.

Never market yourself as a junior and don't mention side projects or anything that makes you look junior.

People talk about their portfolio and side projects.

Those are, they scream junior.

You rather show them something that's real.

So if I understood correctly, if I'm a junior engineer right now, I should think of myself as a senior engineer from, let's say, five years ago.

Because with AI, even as a junior, you're way closer

and way more able to deliver almost end -to -end like a senior a few years ago

and companies are still stuck in this old mindset where they

are actually looking for what the senior engineer was a few years ago right

so you are solving two problems in one shot exactly now

moving on to the mid -level bugdon how do i go from junior to mid -level

and what does mid -level means these days with ai

so obviously mid -level engineers also got a lot of benefits from ai

and so back in the days you define mid -level as someone

that can own a feature now they can own a whole application

so if you're a front -end engineer you could probably work across the whole surface of the application

and even make more global decisions and

if you're a back -end engineer it's pretty much the same what changed is

that as a front -end engineer now because

when you're mid -level you're working a lot of times in implementation building the feature now

that will be more or less instantaneous

so you have the chance to go into architecture you'll have the chance to go into the CICD pipeline

if you're a front end you can even touch a bit the back end and

if you're a back end engineer you can get closer to

either the DevOps side of things go deeper in the database just

because your time freed up so not only you do feature delivery

but all of the sudden you can care about architecture a bit of system design

and entry and delivery

when it comes to observability metrics CICD granting uptime all those

things used to be owned by the senior in the team

but now it kind of democratizes in a way one question

that comes to my mind Bogdan is now

that everybody's kind of moving up

and doing more the juniors are doing more the mid -levels

are doing more I guess the seniors are also doing more

a lot of people are worried about conflicting responsibilities you know

if I'm doing more

and you are doing more at some point we are going

to have a conflict we're going to have a collision right yes

if you assume a static mindset where we're building the whole thing

and now you can also do what i used to do then cool

but what happens with ai is that a lot of things

that were not possible are now possible people

that were building young native apps are building now native apps with swift

and ios so the surface

that any of us has to own became much bigger the

expectations on a junior level are not the ones we used

to have the expectations for a normal software engineer are not the ones who have the speed at

which you need to make decisions in terms of the quality of the code it's much higher

and you need to make a lot of decisions without necessarily

knowing what the code is you need to somehow infer what

this decision will mean for the code base not only that

but what we are seeing is that the focus now it's on token efficiency

so until i think mid of this year it was all

about okay let's adopt the i edit everywhere spend as many tokens as you can token max

but now companies realize hey it takes time to build valuable stuff it takes time for us to monetize this

but our cost increased now we spend on the people

and on ai so what they are doing is okay let's talk to our senior engineers

and see how do we spend our tokens how do we

design the code base how do we design a token efficient code base

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and we call that a zero token architecture

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so things definitely change we are not building the same software we used to build three years ago

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if you're looking at the software from three years ago your typical crowd application

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that would take a lot of manual work to get to

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production then yes you'd feel like oh my god i'm being displaced

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but the reality is yes you are being pushed up

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but there's a lot of space up where you can grow talking about this intuition about technical decisions

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and making technical decisions i guess

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that leads us to the senior engineer senior full stack seems

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like we have there how does the senior engineer level changes with ai

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so one of the biggest difference between all those levels is

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that with ai they could all build the same feature

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but the way you can tell if someone needs to see

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or not it's by the quality they get in their first

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version what we are seeing is a lot of applications are

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just releasing slop there's a lot of production box there's a lot of incidents

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because people don't think too much about what they're doing they don't think about the consequences

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or they don't have the experience to own the surface they they have

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so we are passing this debt to the consumer we are passing the box to the user

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and you can see big applications are breaking and

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so the difference today is not any more the speed back in the days you were a senior

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if you could deliver faster

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and the reason why you could as a senior engineer own end -to -end features is

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because you are reasonably faster across all the stack whereas the mid -level let's take them ages

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so now the difference is quality

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whereas a senior could get the same feature live they would spend less tokens

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and there will be less production defects there will be less bugs basically the other difference is

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that the senior can understand the software factory we are building not only the feature

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that comes out

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but also how should we set up our agent md our

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skills what models should we use how to make the system more stable

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so they talk more about the not only the car

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but the factory

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that produces the car now a lot of people are talking

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about software factories i saw other people comparing it with the kitchen

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because software it's a design process we are not churning out the same car all the time

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so this whole comparison with the factory is not direct people are trying to build a structure

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but the software needs to be a lot more mobile anyways all this is to say

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that see the senior full stack engineer would probably be the person

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that delivers across the stack with ai with the highest quality in the least amount of iterations

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and i would say they don't only deliver software

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but they make the system

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that delivers software the teams the ai the code base the harness

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that the models are working around them better right

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and that's a totally new dimension for senior engineer one thing

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i want to touch upon it's we see a lot of front -end engineers

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which are also lost they feel like the market has been displaced

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and that's why you have this full stack word there bogdan

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so why full stack

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so with ai all of us are required to move towards the full stack a bit more

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so either you're in the back end

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or in the front end companies they want speed they know what the models can do

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and you will be required to work across a full stack

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now this does not mean you have to get into database optimization but

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if you look at front -end engineer positions in the US

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in the last three months we scrapped around 10 ,000 of them

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and 60 % were mentioning REST APIs

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and Node .js as a core skill those are front end positions

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so front end is not anymore adjust the client application you also care about the data layer

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so my advice to everybody in the front end is look

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at your front end skills make sure they are solid and

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if they are your best way to increase your chances in

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today's market it's add full stack skills to your skill set Bogdan, what about the next level?

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I think at senior level, it's where people split between individual contributor and engineering manager.

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And I know at least engineering managers have been highly impacted by AI.

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Yes.

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So when it comes to the senior full stack engineer, this is what we call career level because it's where you could stay for the rest of your career.

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Now, the thing that changed with the senior level is that back in the days, you used to be very attached to your tech stack.

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So you'd be a senior full stack engineer that knows react

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and typescript nowadays it's easier for you to extrapolate that to

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other programming languages or even other platforms so

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when we talk about full stack it's not anymore about your typical stack somebody

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that can really think about the high level the fundamentals of software engineering

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and they can apply

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that to even a different platform like i don't know you

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could do embedding engineering you could build a mobile app

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so this is the most stable level it's also the hardest to get to

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because you need to be able to move very fast from sql optimization to CSS patterns.

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So your fundamentals have to be really strong

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and you need to have this ability to transverse your knowledge from one point to the other.

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Now moving up you could choose to either go on the management track or go to the CTO level.

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What we've seen in the last couple of years is that the management track also became more thin.

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A lot of companies fired a lot of engineer managers and a lot of tech leads.

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A lot of the clarification and alignment can be done with the central LLM.

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Other companies have this central LLM that you can ask things about the company strategy.

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So a lot of the management work, it's also being replaced.

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But again, when it comes to hiring people, making sure the culture is okay, you probably have a lot more conflict now, as you mentioned, between different people because they all overlap.

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It's very hard to set boundaries for responsibility if there's a live bug who did this.

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And so I do think you will need very good engineering managers

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and head of engineering

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but again the the sector is changing a lot of those big companies have been letting people go

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and what amazon and a lot of other companies did is

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that they force their engineering management to become hands -on

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which is closer to the senior full stack goal

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or the lead developer so all in all this is to say

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that the engineering manager also needs to become a bit more full stack

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and i would add bogdan now that everyone is building and building

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and shipping i i feel like alignment of the product alignment

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of the team alignment of the code base it's something

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that someone has to do a lot more intensely than they used to do a few years ago

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because you just have a lot more stuff

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and it's easy for teams probably to go in different directions

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to be like let me build this let me explain everybody has a an experimentation mindset right now

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and i think they forget about the vision right

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and probably an engineering manager role is to align

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that tangle vision just more intensely like before

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because there's more stuff being built align the product managers behind it

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and align the management behind it so

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if i would be an engine manager right now in the

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market i would specifically talk about my my ability to steer teams in the right direction

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when they're building and shipping so much exactly

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so it's easier than ever to just go into 100 different directions

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and you'll see products

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that are not cohesive at all in terms of how they behave i'm not only talking about css

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but also like the interaction patterns how screens look like

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and what happens to the back end it will all look different

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and the one thing that i want to mentioned with AI is that it made easier to build new software companies.

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Now, it's going to take some time, but you do see a lot of AI founding engineers, a lot of positions where they require someone that's pretty much at the senior level

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and has a bit of understanding of the product, and they're building things from ground up.

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So what happened is big tech is getting a bit displaced, and a lot of the new positions, and it's very relevant to your senior full -stack engineer, will be in traditional industries, where you go into finance or healthcare,

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and you are working almost directly with stakeholders and AI to build something from scratch.

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Awesome.

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Then what about the individual contributor path beyond senior?

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We had, you could become a freelancing contract to stay at the senior level.

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Like you said, you could become a lead developer and lead teams, but technically.

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And then there was a principal staff engineer path.

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Exactly.

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So the freelance contracting is pretty much the same dynamics as a senior.

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The only difference is you can usually charge more, but you have less stability.

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Lead developer is pretty much where engineering managers are being forced.

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And the principal staff, this one is weird because those are the people that are actually focusing on actually the AI work.

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So this means kind of building harnesses, doing a lot of context engineering at company level,

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and doing a lot of choosing which models we use, optimizing our tokens.

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So there's a lot of work to be done there.

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And also there's a lot of pressure on all these downstream services.

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People are releasing daily now.

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We used to release every two weeks.

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And so there's a lot of things that are breaking under the hood, and the principal engineers are the ones responsible to get the company there.

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One of the good examples I can give is we had this new model that is JEV, which is basically about adding static.

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the safety to LLM generations, to LLM decision -making, and that's where engineers, principal engineers come in and are like, okay, should we use a reasoning model?

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Can we use a decision -making model?

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Can we use a local model?

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Can we run an open source model?

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And all those kinds of things that are not, you don't need to be a machine learning engineer for this, it's traditional engineering, and that's where they would work.

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Whereas back in the days, they would be like, oh, should we split this microservice or not, which is now the kind of work that even seniors can grasp.

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Now, I feel like we went very fast around the freelance contracting.

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That's a whole world by itself, because now with AI, companies feel very capable and there's a lot less freelance work going around in the first place.

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And the work that's left for the folks watching us, the expectations are crazy because people are like, hey, I'm paying you a thousand bucks a day.

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Then just can you just use AI and ship it?

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So if you want to go into freelancing right now, number one, you're getting into a much more unstable market.

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And number two, you have to be a master of managing expectations because what AI did,

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it's totally distorted expectations over employees in the first place.

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But I can't even imagine the pressure freelancers are under right now.

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If you want Bogdan Knight to do a video specifically about the freelance dynamics with AI coding, then just let us know in the comments.

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Now, moving on to the last ladder, Bogdan's CTO, very interesting for people who have high aspirations in the audience.

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How did the CTO job change with AI?

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so i think cto was one of the most stable just

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because a lot of the skills at the cto level are

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dealing with very incomplete information it's a lot about hiring

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and yes you can ask how to do those things

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but it will be a recipe for disaster you still need

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experience what we see a lot is this position called founding engineer which is kind of a cto founding engineer

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And you see a lot of small companies, people that were doing HR or sales, that all of a sudden, they see the opportunity to build their own software.

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And they need a founding engineer, which is someone that kind of does product management and development.

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And I think there's a lot of opportunities there too.

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Now, you need to make sure that you have very sound fundamentals.

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So you deliver with quality because you are the last person.

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Something breaks, they'll come to you.

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And you need the product skills.

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So it's a combination between product management, moving fast with AI, and traditional full -stack engineering.

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So I think that's a new one.

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And it's pretty much the same about the, what we call the forward deploy engineer, which is the equivalent of a founding engineer,

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but in a established company where you get in and you're doing a lot of consultancy around the product.

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And then once the solution is defined, then you can execute on it with AI.

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The million dollar question now, in the past, seniority being the difference between a junior and mid -level senior, what was all about technical ability, right?

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But I feel like AI changed that.

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And my question is, what makes us CMEA now?

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And what is the thing that developers who want to level up should focus on?

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What's the criteria there?

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So if I were to summarize it in one word, it would be quality.

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And so quantity is not a differentiator anymore.

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Just like blog posts and social media posts, everything can be done in seconds with AI.

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But the quality will be very poor.

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And the problem with quality is that you'll get

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that feedback after you deploy that to production and you have less users or more bugs.

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And what we will see is that is the people that can reasonable software quality is the people that ask themselves, okay, we're having all these bugs.

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Is it because the architecture has to be changed?

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Is it because we need to use something different in the way we do our state management

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or do we have to change our database?

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And so that kind of questions and having that wisdom only comes from experience.

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The problem I see with AI and cloud codes is

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that they can definitely do great work if you are able to ask them to do great work.

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And the only way for you to do that is if you understand software engineering, you understand how computers work, you understand HTTP, you understand HTTPS, and you really can reason about those things.

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Now, you won't have to write as much code by hand as you used to, but the decision, the quality of your decision -making matters more than ever.

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So yeah, somebody can come in as a junior and talk about how they can ship a lot of features, but nobody will care because everybody, even a product manager can ship a feature on the code base right now.

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So it has nothing to do anymore with engineering.

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And that's something I want to underline because people say, hey, you know, everybody can answer these questions.

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You know, you just give them access to cloud code.

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And we heard stories of developers and companies where everything is being done by AI.

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Everything has been, the whole business logic has been fed into models

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that tickets are being written by AI and they are being implemented by AI.

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And it's all kind of this virtual dimension where nobody really knows what, they're just following what the LLM says.

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And what people have to be aware of is that, yes, everybody can find an answer if they have access to cloud code, but what if they don't?

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So the question is, how can you reason and what can you do without AI first?

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Because everyone has access to these models.

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They are a commodity.

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The one thing I would close with is that everybody can buy a camera and everybody can buy a phone, but that doesn't make your photographer.

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With that being said, folks, if you are trying to understand where you're at in the software engineering ladder, are you a mid -level, junior, senior, then check the link in the comments.

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There is a free Tangle assessment that's very much focused on fundamentals.

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It's about 10 minutes and it will give you a full overview of what your Tangle gaps.

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Also, if you're looking to get hired in the next 90 days, check the link in the comments.

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We have a specific program for that and

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if you're looking to become an AI native senior full stack engineer

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and work with Bogdan and I personally to help you do

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that check the link in comments and see if you're a fit with

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that being said Bogdan thank you so much and we will see you folks in the next one

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Vocabulario y notas de pronunciación para esta lección

Este vídeo tiene 315 frases y 4251 palabras para practicar shadowing. La parte hablada dura 20:33. El hablante habla rápido, unas 207 palabras por minuto, así que espera sonidos enlazados y reducidos. El 87 % de las palabras está entre las 3.000 más comunes del inglés; conviene repasar el resto antes de empezar.

Vocabulario clave de este vídeo

15 palabras del vídeo que vale la pena aprender, con su pronunciación y significado:

Palabra	Pronunciación	Significado
stack sustantivo	/stæk/	pila, montón
token sustantivo	/ˈtoʊkən/	seña
freelance adjetivo	/ˈfɹiˌlæns/	freelance, free-lance
developer sustantivo	/dɪˈvɛləpɚ/	desarrollador
expectation sustantivo	/ˌɛk.spɛkˈteɪ.ʃən/	expectación
architecture sustantivo	/ˈɑː.kɪˌtɛk.t͡ʃə/	arquitectura
hire verbo	/ˈhaɪɹ/	contratar, emplear
fundamental adjetivo	/ˌfʌn.dəˈmɛn.təl/	fundamental, cimental
bug sustantivo	/bʌɡ/	chinche
alignment sustantivo	/əˈlaɪn.mənt/	alineación
stable sustantivo	/ˈsteɪ.bəl/	establo, cuadra
cloud sustantivo	/ˈklaʊ̯d/	nube, nimbo
whereas	/(h)wɛɹˈæz/	mientras que (locution)
sudden adjetivo	/ˈsʌd.ən/	repentino, súbito
database sustantivo	/ˈdeɪtəˌbeɪs/	base de datos, banco de datos

Phrasal verbs que vas a escuchar

Palabra	Pronunciación	Significado
come out verbo	/ˌkʌm ˈaʊt/	revelarse, salir a la luz
work around verbo		baipasear, circunvalar

Gramática en este vídeo

Las estructuras que más usa el hablante, con las palabras exactas del vídeo:

Estructura	En el vídeo
“Used to” used to + verbo — un hábito o estado pasado que ya no es cierto	used to be · used to do · used to build
Voz pasiva be + participio pasado — importa lo que ocurre, no quién lo hace	be answered · be left · be owned
Oraciones de relativo who / which + oración — información extra sobre una persona o cosa	level, which is · ones who have · JEV, which is
Present perfect have/has + participio pasado — una acción pasada que sigue importando ahora	has been displaced · have been · we've seen

Pronunciación a tener en cuenta

El hablante usa 54 contracciones y formas reducidas, como you're, don't, they're. Dilas en su forma corta, tal como las oyes.

Los sonidos de “th”: thin /ˈθɪn/, theoretical /ˌθi.əˈɹɛt.ɪ.kəl/
Los sonidos de “sh” y “zh”: expectation /ˌɛk.spɛkˈteɪ.ʃən/, dimension /daɪˈmɛn.ʃən/, optimization /ˌɑptəmaɪˈzeɪʃən/, efficient /ɪˈfɪʃənt/, efficiency /ɪˈfɪʃ.ən.si/
Palabras largas — cuida el acento: developer /dɪˈvɛləpɚ/, expectation /ˌɛk.spɛkˈteɪ.ʃən/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, fundamental /ˌfʌn.dəˈmɛn.təl/, economics /ˌiːkəˈnɒmɪks/

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 stack, token, freelance.

¿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.

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Shadowing Practice: how AI broke the SWE Career Ladder - Learn English Speaking with Video

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