How AI is Impacting Engineering Leadership

Hi, everyone. My name is Gregor, and I'm happy to be here today on Tech Lead Conference with all of you guys. And today I'm going to be talking about how AI is impacting engineering leadership. And this is a topic that is quite a lot of people are asking about it. And I'll be talking about the most impactful things I'm hearing across the industry. And this is based on my discussions from various different engineers and also engineering leaders, both from big tech and also startups. And also at the end, I'll be also sharing, you know, tips and also my advice on what to do to be a great engineering leader in 2025 and beyond.

Before we get started with those impactful things, let me say a little bit about myself. So I have over 12 years of experience both as an engineer and also an engineering leader. In my career, I grew from engineer to senior software engineer, then to team lead engineering manager, head of engineering, VP of engineering, and then CTO. Currently, I'm working as a fractional CTO and an advisor and also coach and mentor. Most people know me as the writer of engineering leadership newsletter. And I also teach a course it's called Senior Engineer to Lead, Grow and Thrive in the role where I helped over 200 students to grow, to be able to get insights on how to grow towards the next role, which is lead position. Overall, my goal is to help as many people as possible to become great engineering leaders.

Okay, so let's talk about the first impactful thing that I'm seeing across the industry, and that is that 51 percent of engineering leaders believe AI is impacting the industry negatively. So this has been quite an alarming data point that I saw when I came across this information. By the way, this is from the Lead Dev report where they interviewed more than 600 engineering leaders. And when I saw this data, I was thinking, hey, what is going on here? What is happening? Why do we see that? And yeah, I started to dig through it and I started to ask other engineering leaders what's happening. And I saw also a lot of different important findings when I digged more in details.

But also before we go into that, an important thing to mention also from the Lead Dev report is that teams these days are feeling less motivated than 12 months ago. And that's also alarming and that's also connected with with, you know, engineering leaders believing that AI is impacting industry negatively. And we will go straight into why is that next? So it all starts with sensationalistic predictions regarding AI from non-public individuals. And you have probably seen a lot of these predictions. It's all across social media and different media platforms.

You know, for example, Mark Zuckerberg saying that AI will replace mid-level engineers in 2025. But we all know that this is not even close to being a reality. And you know, for example, also Antropic CEO Daria Moudei mentioning AI could spike unemployment to 10 to 20 percent in the next one to five years. And you know, believing also that AI could wipe out half of the all entry-level white collar jobs. You know, such predictions are very, very sensational. And you know, just believing that unemployment will go up in the next one to five years by five to 10 percent is a very, very, very big, big, big take.

And it's hard to believe that's going to be the case. And then also, you know, Amazon CEO Andy Jess is saying AI will reduce its corporate workforce in the next few years. And the problem with such takes is that they create FOMO, they create fear of missing out. And whenever people feel that they are missing out on something, then they are doing some acts that are quite not as strategic and not that actually thought of. They're doing very reactive things in order to try to improve or try to, you know, try to adopt certain technologies in order to not feel like that they are missing out and so on.

So what this creates then is that company leaders are experiencing FOMO when it comes to AI. Shopify CEO enforcing the use of AI across all the teams in the company. And especially I went through this memo pretty in detail. And some claims like AI improving productivity by up to 100 percent, sorry, 100 X is quite a bold take. And it's quite unrealistic based on my discussions talking with various different people in the engineering industry and also across different industries as well. What we've seen the most is around one to two X productivity increase.

And also, you know, the bigger and the larger the codebase you're working with and the more complex systems you're working with, the less impactful is going to be AI in order to be more productive. So such takes are what is then causing a lot of issues for people inside the team, inside a company, because the expectations are skyrocketing and then people need to need to manage those expectations. Right. And then also, you know, demonstrating that you can do what is needed with AI before asking for more headcount. Again, this is something that is very sensationalistic. And, you know, it feels like that, OK, AI is going to replace many different people and we need to justify new hires to be part of the team instead of looking at what are the actual business challenges and what do we need in order to solve those business challenges the right way.

And also, you know, we mentioned including AI usage as a KPI in the performance review. What I believe is if you measure the wrong things, people are going to be inspired to try to game the system. And if you just measure AI usage as a KPI, then people are going to be inspired to try to use as much AI as possible and use as much as AI tokens. And that means also, you know, the cost is going to go way up. But if the cost is not justified with business value, then the cost is, you know, it's not useful. It's actually being used in a wrong way. So actually the company is losing money instead of gaining value.

So actually the company is losing money instead of gaining value. And then also, we've seen company leaders like, for example, a CEO from Fiverr sending a memo to all of the employees about AI is coming for your jobs. And, you know, there are many more examples similar to this. I've spoken with many different CEOs and also company leaders, and I saw that being the case in many different situations. In some cases, I also saw, you know, different CEOs trying to wipe out the homepage of the of the current website and then saying to engineering leaders and engineers how easy that is to be to be the case. And then, you know, it all trickles down to engineering leaders and their teams to being able to manage such unrealistic expectations. And this is what we're going to talk about next. Yeah, I've heard a lot of different unrealistic expectations from many different individuals. And, you know, believing AI is a plug and play solution that you can just, you know, plug it in into existing workflows and things are just going to work out very, very well. But, you know, it's really the case that plug and play rarely works across different improvements, and especially when it comes to processes. And, you know, expecting AI to eliminate all manual work is something that is also, you know, it's very sensational. And it's just not possible. Understanding the cost and complexity of building internal LLMs. Yeah, it's a big difference if you're building a model yourself or if you're using existing models like GPT, for example. You know, there can be a difference, can be, you know, between three or three hundred days when you're building a certain feature with calling the existing model. You can just call that API and it will do a lot of that heavy lifting for you. But if you build your own model, then you're going to need a lot bigger team and a lot more time and research to do that. Yeah, assuming AI will just magically understand the whole context and the specific of the specific domain, expecting AI to be replacing entire teams, assuming AI will just work without failures. And then also, you know, thinking that there are no ethical, legal and security risks when it comes to AI. You know, if you're not careful, if you're just, you know, if you're just generating a lot of codes and you're not you're not reviewing correctly, then, you know, the problem is going to be with security, with ethical reasons, legal reasons as well, because the human accountability should always be on the human side and never on AI. So, yeah, also, you know, assuming AI will exponentially increase the performance over time and so on. And, yeah, this is a good illustration of what's happening. And it all starts with sensationalistic takes by non-public individuals. That then trickles down to company leaders experiencing FOMO with AI and forcing AI usage and AI feature development. What we've seen from a lot of different companies is that they're rebranding their companies to AI companies and also forcing development of AI features. What happens a lot of times is that this backfires back and, you know, by trying to enforce too many different features, you lose the essence of what is actually what problem are actually solving for the users and so on. A good case for this happening is Duolingo, where they completely made the AI transformation and a lot of users didn't like that and they stopped using it. And such things, you know, feeling FOMO from company leaders then trickles down to engineering leaders and teams operating within unrealistic expectations. So this becomes a big problem then for engineering leaders and engineers. And that's also the reason why a lot of engineering leaders feel that AI is impacting the industry negatively.

So what to do? I will give advice for both company leaders and engineering leaders. So let's first start with company leaders. Of course, the number one advice is you should not be letting FOMO get to you. And it all starts with setting the right goals and setting the right environment to be successful. And if you let that FOMO get to you, you're going to be doing decisions that are not going to be in the best case scenario for the business, for the company. And especially if you try to do full scale AI transformation all at once, it will, in a lot of cases, backfire. It's not the way to go. Small experiments are a lot better. You start with something that is less impactful and then you see how it's working. And then if it's working well, you move on to more impactful parts and you utilize that in those parts. And also what I like to say is there is no better productivity increase than a good culture. Good culture is going to be the most impactful for any company. It can be the reason for being more productive by 10x, 100x and so on. Good culture is what really connects so many people together and you can build so much good stuff and people feel great working in a good environment. And good culture is what is going to be the foundation for many companies.

And then also, let's go to the advice for engineering leaders. Obviously, the first thing is make sure to educate yourself on how to utilize AI tools and also how others are using AI tools as well. And be confident. Whenever you're talking with company leaders, with stakeholders, with investors, with people who have unrealistic expectations, you need to be confident and you need to inspire confidence in you. The last thing what you want to do is go straight into saying yes to silly requests like we need to start using this AI tool or we'll fall behind. This is the recipe for disaster. And if you do that, a lot of things are going to go wrong and you will lose respect. People will lose respect for you. And what is going to happen and what can happen is just people, company leaders or any kind of other stakeholders are just going to make decisions without you. So instead of you being reactive, you need to be proactive and you need to take ownership of the AI strategy and not wait to be looped in. That is very important. And especially for directors, VPs and CTOs, don't wait to be looped in on AI strategy. You should be owning the AI strategy. You are the strategy. This is from Migda Jaffer, a product leader from OpenAI.

We did a recent collaboration on my newsletter and this has been one of the most important parts of that newsletter. Also, he mentioned that AI engineering strategy is actually the same as AI product strategy. So you should be the one who is dictating like, hey, this is the way to use AI. This is how we should be using it. And this is how we can also adopt AI across the company. And his recommendation is for engineering leaders to get a lot, a lot more closer to that part, because the reason for that is that you already understand the whole part. You already understand what it takes in order to build that specific AI feature, AI product.

As I mentioned before, it can be the difference, the way you approach to building that product or that functionality can be the difference between building it in three days or 300 days. What you need to focus on as well is, you know, the what, the when and the why. That is very important. And that takes a bit more business and product understanding. But anyway, you need to need to do that. This is very important in 2025 and beyond that you as an engineer or an engineering leader become more business and product-minded. So, yeah, very important to take ownership of it.

And also this is a really good visual as well. This is from Migdat from OpenAI. And his proposal and his recommendation is that, you know, the whole strategy is getting a lot closer to engineering. You can see like a traditional product strategy is across many different departments like product management design, finance, UX research, QA, procurement and so on. But in terms of, you know, AI strategy. That is a lot more closer to engineering and the reason, as I mentioned before, engineering understands the whole part and there is no one else who can understand better what it takes to build something, what it takes to actually make sure that specific thing can be adopted across the organization or we can build that AI feature or we can build that AI product and so on. So again, you should be the one who is owning the AI product strategy. Don't wait to be looped in on the AI strategy.

OK, let's talk about the second impactful thing that I see across the industry. I see engineering roles getting closer together, with managers expected to take on staff engineer roles as needed. This trend extends to staff engineers taking on manager roles and architects assuming staff engineer or manager roles. The roles of product managers and engineers are also converging, with the rise of product engineers owning end-to-end functionalities and solutions. The evolving landscape emphasizes the importance of engineers and leaders becoming more business and product-minded.

The industry shift towards multifaceted roles is driven by factors like layoffs, the push for more individual contributors, and advanced development tools. As companies streamline reporting structures, more individuals report directly to engineering directors. The increasing complexity of engineering roles necessitates a broad skill set encompassing human-related skills and problem-solving abilities. The importance of human-related skills such as leadership, communication, and emotional intelligence is escalating, alongside the need for problem-solving, resourcefulness, and deep business understanding.

To support engineers in honing these skills, a curated list of resources for becoming a successful engineering leader in 2025 and beyond is provided. Accessible via a QR code, the resources cover a range of topics essential for engineering leadership. Attendees of the talk are offered a complimentary one-month subscription to paid articles in the engineering leadership newsletter by emailing the speaker. This initiative aims to equip individuals with the necessary tools and knowledge to navigate the evolving landscape of engineering roles effectively.