How AI is Impacting Engineering Leadership

Okay, thanks a lot for the kind introduction. It's really great to be here in London with all of you guys. We have a really exciting topic we are going to be talking about today. It's something that a lot of people are wondering what's actually happening across our industry, how AI is impacting engineering leadership in general. And yeah, today I'm going to be sharing two main impactful things that are happening for engineering leadership because of AI.

But before we get straight to the first impactful thing, let me share a little bit more about myself. I have over 12 years of experience both as an engineer and also an engineering leader. I grew from engineer to senior software engineer to team lead, and then to engineering manager, head of engineering, VP of engineering, and then CTO. And I currently work as a fractional CTO and advisor, and also as a coach and mentor.

And also most people know me from writing the Engineering Leadership Newsletter where I share two articles every week. And also I teach a course, it's called Senior Engineer to Lead, Grow and Thrive in the Role. I helped over 200 students already to be able to get insights on what to do to be successful, to grow from senior engineer to lead position. And also my overall goal is to help as many people as possible to become great engineering leaders. That is my number one goal.

OK, so let's start with our first and, in my opinion, most impactful thing that I'm seeing across the industry. And that is that we don't see the slides. OK, amazing. Sorry. Yeah. 51% of engineering leaders believe that AI is impacting the industry negatively. This is the first impactful thing that I'm seeing. And of course, we're going to see also the slides soon. But you know, when I first saw this data, I wanted to really check what's happening, what's actually, what is the cause that 51% of engineering leaders don't believe that AI is impacting the industry basically positively, but they believe it's impacting negatively. Thanks a lot. And yeah, this is, by the way, from the Lead Devs report. They interviewed more than 600 engineering leaders across the industry. And yeah, when I first saw this data, I went straight into why actually that is the case. And we're going to check it. We're going to go through exactly why that is the case soon. Before we do, this is also a really important data as well, which tells us that engineering teams today are less motivated than they were 12 months ago.

So you can see from data, 38% are less motivated, only 14% are more motivated. So those two things go hand in hand together. So yeah, 51% of engineering leaders believe AI is impacting the industry negatively. And a lot of engineering teams are less motivated today than they were 12 months ago. So the question, why is that? Why is that happening? What is actually the reason for that being the case? Yeah, so I dig through and I went and discussed with a lot of engineering leaders and engineers across the industry. And this is the finding that I came about.

It all starts with sensationalistic predictions regarding AI from non-public individuals. You guys have seen a lot of such takes across the social media, across different media sites. For example, I'm going to be sharing three main examples. The first one, Mark Zuckerberg saying that AI will be replacing mid-level engineers by the end of this year. It's coming to the end of this year and mid-level engineers are still doing their work on a daily basis. And then the second one, we have Antropique CEO Dario Amodei mentioning that AI could spike unemployment to 10, 20% in the next one to five years. And also he mentioned that AI could wipe out half of the entry level white collar jobs, which is very sensationalistic. And to be honest, it also kind of like scares a lot of people because of those statements. And also Amazon CEO Andy Jassy saying that AI will reduce its corporate workforce in the next few years.

You know, whenever a company leader or a prominent figure in the industry says things like that, it inspires fear. It inspires fear of missing out. That is what inspires. Because those people are saying like, we have this new shiny AI tool and we are developing this new technology. And then a lot of company leaders across the industry are feeling like, okay, we need to adopt this new AI tool. We need to use this new technology as much as possible. And what I try to do, they try to enforce it inside their companies or they try to enforce certain AI tools to be used for either certain reasons, productivity reasons and so on. But they don't actually have the understanding. What is this actually going to contribute to success for the company, for the organization and so on? So as you mentioned, company leaders are experiencing FOMO when it comes to AI.

So first it starts with sensationalistic takes coming from prominent public figures. And then because of that, company leaders are experiencing fear of missing out. I'm going to be sharing two of the main examples. The first one is the memo from Shopify's CEO that has been sent out not so long ago. And basically what Shopify's CEO mentioned is that they wanted to enforce AI across the whole company. And three of the most important things from the memo, the first one, AI improves productivity up to 100x, which is quite interesting. The most I've been hearing is about 1 to 2x from many different engineering leaders and engineers across the industry. And the second one, including AI usage as a KPI for the performance review. There is also another one which is very interesting because you guys know if you try to... what you measure becomes a target, right? If you measure just pure usage, AI usage, how many AI tokens does a particular person use, they might set up a cron job, they might set up a recurring task that is going to use as many AI tokens as possible, and they're going to be on top of the leaderboard. But they will not actually contribute to anything useful. If you're tracking that, that's going to happen. And I'm sure that's happening across many different companies already. And also the third one, demonstrating that you can't do what is needed with AI before asking for more headcount. Again, this is similar to what Andy Jassy mentioned, we're going to have less people in the upcoming years. When you're saying things like that, people automatically feel less motivated to do things, less motivated to contribute. Psychological safety is a really big thing in high-performing teams. That is very important. And if you're saying things like that, that is going to unfortunately negatively affect the psychological safety in teams.

And now let's go to the second example. Second example is the memo from Fiverr's CEO, saying that AI is coming for your jobs. And yeah, you know, things like that are happening a lot more regularly than we might actually realize. They are a lot more common. They are just not as public as these two examples. I've heard many stories from engineering leaders, engineers, and also company leaders as well asking me different questions. I saw as well, I see regularly, for example, CEOs of startups or mid-sized companies trying to do white coding of a homepage of their personal company website. And also trying to do white coding of the first page of their application and then saying to engineers and engineering leaders, look how easy that is to develop because you can just do a quick prompt and it will actually do like 90% of the visual stuff that is needed to actually do a company website. But at the end of the day, those are just quick prototypes. They are not actually working things that are going to be scalable and maintainable in the long run. And a lot of company leaders don't understand that.

Because of the fear of missing out, and then again, they try to enforce the usage, that's where the big impact comes on engineering leaders. So yeah, the first starts with sensationalistic takes. Then it trickles down to company leaders feeling that they're going to be missing out on things. And then this trickles down to engineering leaders and teams operating within unrealistic expectations.

When a CEO or another person, part of a leadership team comes to you and say, okay, we need to use AI for this, we need to use AI for this. Those are the unrealistic expectations that a lot of teams are facing these days. And that is the reason why many of the engineering leaders believe that AI is impacting the industry negatively and why engineering teams are feeling less motivated today than they are feeling 12 months ago. I've also heard some of the interesting unrealistic expectations over the industry as well, believing that AI is a plug and play solution, expecting AI to eliminate all of the manual work, underestimating the cost and complexity of building internal LLMs.

A lot of company leaders believe that we can just build LLMs internally, foundational models, and that that's going to be as easy as just building a quick prototype. But we all know, like building a foundational model is going to take a long time, it's going to take a lot of people, it's going to take a lot of knowledge. Instead, a lot of times the solution might be just calling a foundational model like GPT and do some computing there and then get the results back. And also, assuming AI will just magically understand the whole context of a specific domain, we all know, if you don't share the context, you can't expect the best results.

And the more context you share, the better the results are going to be. And also expecting AI to replace entire teams, assuming AI would just work without failures, thinking that there are no ethical, legal, and security risks. And also assuming AI will just exponentially increase the performance over time, it's going to just, you know, be exponentially increased over the years, and so on. Those are some of the most common unrealistic expectations I'm hearing.

To summarize, sensationalistic takes by non-public individuals, company leaders experiencing fear of missing out and enforcing AI usage and AI feature development, and then engineering leaders and engineers need to operate with unrealistic expectations. That is the main reason for a bit of a negative sentiment regarding AI for engineering leaders and also engineering teams.

What to do, what we should be doing as engineers and engineering leaders? I'm going to be sharing some tips for company leaders as well. Starting small is the way to go when it comes to adopting AI and trying out different AI tools. There is no better productivity hack than a great culture. Educate yourself, know what's possible, and learn from other engineering leaders for better management of expectations and effective communication.

Don't try to believe firstly, this is for company leaders. Don't let fear of missing out get to you, but always focus on setting the right set of goals and ensuring that, you know, your business is going to be successful. That is the most important thing. Don't try to just implement a new shiny tool because, you know, everyone else is using it, or you heard it somewhere, you heard it on social media, and so on. A lot of big traps are set up because of it.

And also, I've seen many cases where companies try to do full AI transformations all at once, and it backfires, unfortunately. I don't recommend doing that to most companies. We've seen example from Duolingo. Duolingo did a full AI transformation and completely changed their application and the way they operate in the business. And a lot of people didn't like that. A lot of users actually stopped using Duolingo because of it. So my recommendation, you know, it may work in some cases, but in most cases, it will actually backfire.

Starting small is the way to go when it comes to adopting AI and trying out different AI tools and so on. And also, this is very important thing to understand, is there is no better productivity hack than a great culture. Great culture is always going to make everyone more productive than a shiny new AI tool or any other tool in general. This is very important to understand. Now, let's go to engineers and engineering leaders. So yeah, the thing is, when you are managing expectations with the stakeholders, with the leadership team, first of all, you need to educate yourself. You need to ensure that you know what's out there and also, you know, what's possible and also what other companies, other organizations, other engineering leaders are using.

And this event is a great, great way to know that more in detail, since, you know, you guys can talk with each other and see like, OK, this works well. This works well. This is working for us. And so on. You need to be informed. That is crucial.

And then the second thing is you need to be confident because when you're dealing with managing expectations from company leaders that are feeling FOMO, they will try to persuade you in so many ways that you should do this or you should do that or we should adopt this new AI tool or we should do that particular process and so on. You need to be confident and you need to say no. You need to. The last thing you want to do is just say yes and then just completely and just completely start implementing it. Right. That is that is the worst thing you can do. And yeah.

Small experiments and then, you know, measure the impact of the productivity. That is my recommendation. Start some summer where it's not as much big impact. Try to try a new process, try a new new tool. And then if that works correctly, then you can move it to some other parts as well. And also, this is very important part for all the directors, VPs and CTOs. You need to own the AI strategy.

You should not be waiting to be looped in on the strategy. What happens a lot of times is that engineering leaders or engineers get put into the room where they are already discussing the implementation of certain AI tools. No, this is not the way to go. You need to be the one who is driving it. You need to be the one who is driving the AI strategy, who is saying, OK, this is the tools that we should be using because of this, this, this reasons. And those are the best possible things that we should use in order to increase business value as much as possible.

By the way, this is from Migdha Jaffer. He is a product leader at OpenAI. We recently did a collaboration on my newsletter and he mentioned, don't wait to be looped in on AI strategy. You should be owning the AI strategy as an engineering leader. You are the strategy. When we're talking about AI strategy, the how part is very, very important. It's crucial to understand how we are going to build a certain thing with AI, let's say AI feature or AI product or even like adopting a new AI tool. It can be the difference between building something in three days or 300 days, as I mentioned, building our own foundational models or or using already made models for doing something. The only thing that my and my big recommendation to engineering leaders is to also start owning the what, when and the why when it comes to AI strategy.

That means get better at the product, get better at business understanding because you already understand the how part that is crucial. That is very, very, very important in AI era. Nobody, non-technical people will not be able to create, to make as accurate decisions as engineers and engineers can do. This is also a great picture from Migdha Jaffer. This is what he mentioned. This is the traditional product strategy that was before. And you can see the ownership is based on many different departments, including product management, design, finance, UX research, QA and procurement and so on. And now a lot of things are moving closer to engineering because the how part is very crucial. A lot of other departments don't understand what it what actually it takes to build stuff with AI. Experimentation is very crucial, is very important. If you're building something with AI, you need to really understand how you're going to tackle that. So a lot of that is moving closer to engineering. The only thing that is important for all the engineering leaders, you guys already understand how to do, how to build things, how to do things.

So a lot of that is moving closer to engineering. The only thing that is important for all the engineering leaders, you guys already understand how to do, how to build things, how to do things. The only thing that is important is to also learn the business and the product skills in order to support the what, when and the why. OK, that has been the first impactful thing that I'm seeing across the industry.

Now we're going to be talking about the second one. And the second one is that engineering roles are getting closer and closer together. That's what I'm seeing, talking with many engineers and also engineering leaders across the industry. And we can see already expectation from engineering managers to take on staff engineering roles if needed, staff engineering roles to take on manager hats if needed. And then also architects taking on staff engineering role or manager roles if needed. Those three roles are getting just closer and closer together. I believe over time the titles are going to be just more blurred between them.

The product engineering is getting closer and closer together as well. The role of a product engineer is being more and more popular. We can see, I can see already in a lot of startups, they actually, a good example is Lovable. Lovable don't hire product managers. They hire engineers who are product focused. They basically, if you check their careers page, they don't have a role open for a product manager. They have their engineers initiating requirements, initiating the features, proposing solutions and then also owning the implementation. That is going to be more and more common, I believe, in our industry, starting with smaller companies and then also moving to bigger and larger companies.

That is going to be more and more common, I believe, in our industry, starting with smaller companies and then also moving to bigger and larger companies. And why I believe that is the case, we've seen layoffs happening within multiple companies and when there are less people in the company, then of course, existing people are going to be more, you know, they're taking more additional responsibilities. And also we've seen Amazon's initiative to increase the ratio of individual contributors to managers by at least 15%. And as soon as big tech does something, then also like other companies are trying to mimic that. And we will see more companies trying to do that as time goes on. And also the tools for development are just getting better and better. As tools are getting better, that's automatically going to shift the expectations to be bigger, to be larger. As I mentioned, the titles are going to be blurred as time goes on. And also, you know, being good at many things will not be a nice to have, but a must have. That is my opinion.

To help you with this, you know, what to do as an engineering manager, engineer and architect. Let's go to that next. I've dissected what are really important skills to focus on in the AI era. The first one is human-related skills. Those are people skills, leadership, teamwork, good communication, being a great person to work with, empathy, and overall emotional intelligence. Those are very important skill set that, you know, AI can do this stuff anytime soon. And those are just going to be more and more important human-related skills. And then also the second one is being good at solving problems. Ten years ago, you needed to know one programming language and one framework, and you would be considered a great engineer. But today, you need a lot more, right? You need to be a good problem solver, you need to be pragmatic, you need to be resourceful, you need to know how to utilize different tools, you need to have a good business understanding as well. And also you need to have a good and deep understanding of a relevant topic that you work with. All of those are going to be just more important as time goes on, especially when tools are getting better and better. Focusing on having good fundamentals are just going to be more crucial. To help you with this, I've prepared a list of resources that, you know, you can all check and there are going to be a lot of helpful tips there to get better at all of the skills that I mentioned before. You can just scan this QR code, it will take you to the landing page. And all you need to do is, you guys will see also the template for an email. You guys just need to send me an email and I will grant you a complimentary one month paid subscription to my Engineering Leadership Newsletter because some of the articles that are on the resources are actually paid articles to the Engineering Leadership Newsletter. So yeah, that's it from me today. If there are any questions, I'm more than happy to answer. Skills that you mentioned, for me, those are not necessarily new, right? Interaction with people, good communication.

But what I have seen is that engineers themselves, like part of their craft is changing. Some people really loved hands-on coding. That is completely gone, which means they will be demotivated or you might, as an engineering leader, need to motivate them in a different way. What have you seen with the people you've spoken to that actually works well for still inspiring people and motivating? Yeah, it's interesting. We need to get to the roots of software engineering, right? Software engineering is not just coding, it's actually solving problems. You need to do what it takes to solve the problem in the best way. Writing code is just one way to solve a problem. So a lot of people, I mean, me included, I started with programming because I really like to make things and build stuff and do stuff. And when you're writing code, it actually inspires you to have like a problem-solving mindset and so on. But you need to think in a way like what is going to be best for your team, what is going to be best for your organization and do that. Writing code is just one part. And software engineers, it's communication can solve a lot more problems than just writing code can do, right?

Questions are coming in. Feel free to, if you see a question, also upvote so we will handle the ones that are mostly upvoted. And let me see if the highlighting works. There we go. What can you say about the actual impact of using AI coding agents on the productivity of engineering teams? Yeah, this is a really good question. I have a separate article on that. I don't know the exact data that's happening that is giving the exact impact of using AI coding agents. But I know a lot of teams are using that. They're using different tools like cursor or cloud code or something else. And it helps. It helps a lot. I've seen that across the industry from talking with many people. Again, as software engineers, it should not be something we should be avoiding it. But it should be definitely something that it makes us faster. It actually makes us type less. The thing is still with software engineering is just writing code and typing code has never been a bottleneck. The biggest bottleneck has always been communication and bad requirements, building the wrong things. Also bad management as well, bad processes.

Also bad management as well, bad processes. Those have been the biggest bottleneck. Actual writing code hasn't been that thing that is going to slow you down. Oftentimes, you need to go and you need to talk with that person, that person, this person, and so on. If it's a really tangled process, then you need to do this for quite a long time in order to understand really what is needed. So, yeah, definitely helps. Definitely, I see a lot of good things about it. But I don't see those drastical, drastic improvements.

As a tech leader, how do you evaluate your team's use of AI? How do you ensure they're using it properly? Yeah. That's an interesting question. What I'd like to say is if an engineer opens a PR and you just know it's coded with AI, any kind of AI tools, and they don't check, actually, they don't have accountability of what they're actually opening, right, then they're using AI the wrong way. The number one thing is that all the tech leads, team leads inside the team needs to ensure that the accountability is still on engineers. And the wrong thing that you should say is, you know, because of AI output of that. I've heard that a lot of times, like cursor output of that or GitHub copilot output of that, because that's the reason why this function is built that way.

Interesting. I'd like the lack of ownership, I feel like is going to get better. As soon as we get better at reading and understanding code. Previously, we were doing a lot more writing. Now, because of generation, you need to understand exactly what's being written in a different way. We've been reviewing code. I mean, reviewing code is even more, we spend more time reviewing than actually writing. And that has even increased. I mean, this has been the case before, but now it has even increased to bigger level, especially working on a larger code base. You're doing a lot more understanding the parts than actually you spending time writing. That's exactly what the case is, that writing code hasn't been the bottleneck. And yeah, we need to get better at this. I mean, it's reviewing code is not something that is as fun, I'm sure about you guys.

That's exactly what the case is, that writing code hasn't been the bottleneck. And yeah, we need to get better at this. I mean, reviewing code is not something that is as fun, I'm sure about you guys. But for me personally, I like to write code, it's a lot more fun because you're actually solving problems and you're doing stuff as you're typing. But when you're reviewing, you need to understand so many parts. And it can get sometimes overwhelming, especially if some particular parts are not written as well as well. Yeah, but it is ever increasingly more important to review correctly.

What do you think are the motivation of AI leaders to make those unrealistic comments? The ones that create FOMO? Yeah, good question. The ones that you shared at the beginning. Good question. I think we all know the answer to this question, which is selling their products, which is that they are building AI products and many, many people are incentivized to say things that are going to be aligned to increase the sales of their products. So automatically saying things like sensational takes like that is going to say, okay, Meta has a great AI or Entropic is building a great AI and we need to use that. Unfortunately, a lot of people across the industry are believing that is the case, but we all know the reality is just they can be helpful in certain situations, but they're not going to be the end of all solution to everything.

How do you let junior colleagues progress in their career? If AI can solve most smaller problems, this is a take I've seen before, what do juniors then do? Yeah. This is a good question. I've also seen this decrease in junior engineer roles that was from a recent report from SignalFire. They mentioned that I think it was around 30% decrease in junior engineering roles in big tech. And yeah, it is alarming. It is alarming and it's going to be interesting to see what's actually happening in the future of the industry. But my number one thing is build projects. Find ways to get a job to actually get experience. Try to initiate any kind of side projects, any kind of freelance project and so on. Try to get experience yourself.

They mentioned that I think it was around 30% decrease in junior engineering roles in big tech. And yeah, it is alarming. It is alarming and it's going to be interesting to see what's actually happening in the future of the industry. But my number one thing is build projects. Find ways to get a job to actually get experience. Try to initiate any kind of side projects, any kind of freelance project and so on. Try to get experience yourself. You need to get experience to get a job and you don't have experience as a junior engineer. That is like a catch-22 statement. And it's problematic because many people, especially in the United States, they go to a college and they don't actually focus on getting real-world experience. And then they believe they will get a job at big tech, but it's not the reality like it was like five years ago, 10 years ago anymore. So right now, you need to get experience even without a job. So for me personally, freelance work has been amazing for my career growth. And that is my biggest recommendation for junior engineers is to try to build stuff, try to build products, projects, try to show what you know and try to be useful as much as possible. Yeah, I feel like, I mean, this is a room full of engineering leaders. It's up to engineering leaders to inspire people to build things. They won't figure it out themselves. They're like, okay, there's so many people wanting these jobs, junior jobs are slowly disappearing, while the senior jobs are actually peaking. Yeah, yeah. They're hiring seniors, which is, yeah, quite a, it can be quite demotivating. But inspiring people and saying what do you love with regards to a passion and then building projects out of that, it shows that people have already started instead of starting when they get a job. It is the best way. Also if you're passionate about something, if you're passionate about sports or anything else, like it's also going to be a lot more motivating for you to build something. And as soon as you show that you can build stuff, you know, your knowledge is not just theoretical or it's not just, okay, I did my bachelor's at that university. That's going to be a lot more tangible than just expecting to get a role that way. Yeah, absolutely. Let's see. First of all, great talk. That's a good compliment. Thank you.

I'm an iOS staff engineer turned engineering manager, but at enterprise level, leading a cross-functional team, it's not as easy to act as an engineering manager and staff for back end. It's a lot of responsibilities front end and mobile at the same time. How do you deal with title blur in this case? Yeah, that's a really good question. And you know, you need to pick your battles. You can't do everything. You can't be both the best engineer on the team and also the best manager at the same time. This is something that is going to be the case. And it's up to you to set boundaries.

You need to set boundaries and need to find people that you trust and delegate work inside your team. That is very important. You need to find people who you know that are good, that have potential. If they are not inside a team right now, you need to get people with that potential inside a team that you can build and you can work with them to grow them to take on bigger responsibilities. That means that you need to decide like, okay, obviously you won't be able to take out your managerial responsibilities because you're an engineering manager.

But then the best thing would be to find people inside your team who can take on different domains and different responsibilities. Like someone that is going to be more like a front end tech lead and another person that is going to be more like an iOS tech lead and also someone that is going to be more like a backend tech lead because you can't be an expert in all of these different technologies in iOS at the same time, in backend, in frontend. I've seen this also problematic in a lot of teams is they have one fixed tech lead and they have a full stack application, but one person cannot be an expert in all of different technologies. So you want to find people inside your team that you can trust and they are experts in owning specific domains.