Meri is an experienced CTO who has led and scaled technology organizations in a range of sectors including medtech, fintech, government, e-commerce, telco and manufacturing.
They excel at scaling up teams, transforming processes and organizations to go from good to great. Passionate about technology and building smart multi-functional teams and organizations. Published author, international speaker and chair (co-curator & host) of The Lead Developer conference.
We're going to see some organizations give up on the idea of managers completely and then slowly realize that they were wrong. Some companies are going to continue to hire or grow engineering managers.
And there's a middle ground, which is where a lot of companies are at, where they were scaling so fast in the last couple of years that they’ve probably made some people managers who should never have become managers. And they’re going to start rethinking that positions.
In the next 10-15 years I'm either going to be a CTO going around cleaning up after AI, or maybe they're not going to need people like me anymore because we're just going to write the specs and generate the whole app from scratch every time.
It's going to be really fascinating to find out which way it goes.
We’re seeing smaller teams. That “growth at any cost” mindset, both for teams and companies, just isn’t the flavor of the month anymore. The end of zero interest rates has shifted a lot in the VC-backed world.
Two big things are happening at once:
The end of the halcyon days of endless funding.
The arrival of AI tools, which some believe means they can have much smaller teams and deliver more with fewer engineers.
There is also a lot of flattening, organizations thinking they don’t need middle managers or pushing them back into coding roles. It’s been a fascinating experiment, which I think has failed. Many of those companies are now quietly rehiring managers because they’ve realized the invisible work managers do, like developing people, maintaining cohesion, and ensuring collaboration, really matters.
Organizations can be OK without managers for a short time. After six months, cracks start to appear. In the next six months, people start to disappear.
Many engineers get promoted because they’re good senior engineers, an they have not been trained for people leadership. Those who succeed in the engineering manager role have the growth mindset, have that ability to recognize that they’re a novice again. They’re ready to be bad at what they do for a while because they’re learning new skills they need to be a good manager. They see it as a career change, not a promotion.
Those who have only experienced bad managers will be very excited about this new world of smaller teams and fewer managers. Those who have had great managers - someone who helps them be the best engineer, who helps them figure out how to get better at what they’re not good at, but also become excellent at what they are good at, those will miss having managers if they go away.
Don’t be a manager if you fundamentally do not care about other people or their success. That’s necessary for the position and the only thing that's not teachable,.Because at the core of management, you have to want to get the best out of the people that you're looking after. You have to want to look after them to some extent.
And that doesn't mean being super soft on them. It doesn't mean being over empathetic. But it means caring about them as more than just resources. I tell engineers that if their manager refers to them as a resource, they should call their manager overhead until they stop it.
Almost everything else can be taught. As long as you accept that it's a new career that you're starting over as a novice in and you need to learn. The only people who struggle to be good managers are the ones who want that daily dopamine hit.
I’m not a fan of saying all managers need to code, but right now things are changing so fast that we need to experience the new reality ourselves, including the frustrations of using AI coding tools.
We also need to use AI to improve our own work as managers. I’m not sure anyone should be handwriting performance reviews anymore.
Today, EMs need strength in three areas:
Developing people – helping engineers be their best.
Developing teams – making teams more effective and cohesive.
Delivery – getting the right things done the right way, often without dedicated scrum masters or agile coaches.
There are a lot of AI-native companies talking about how fast they’re building and how much revenue they are generating with AI and just 30 or 50 engineers. Nobody really knows how they’re going to scale it.
Greenfield projects are easier, and AI can help there. But once your codebase is mostly AI-generated, maintainability and scalability become real challenges, and those are not problems LLMs are great at solving.
LLMs are pattern matchers. A lot of the code they’re trained on isn’t great, and there’s little evidence that they produce consistently better or more secure code. Some believe we’ll just rewrite apps from scratch instead of maintaining them. I’m not sure how realistic that is.
I’ve worked on systems so old they were “vintage.” The real messiness comes from generations of people adding to a system over years, that’s when maintainability, scalability, and tech debt really bite.
There are three key vectors of technical debt:
Developer experience & maintainability – How safely and easily can you make changes? How good is your testing?
Cognitive load – How much complexity does a developer have to hold in their head to work on the system?
Understandability – How well is the system documented?
AI can help with documentation and testing, but it’s not good at reducing complexity. In fact, right now it tends to produce more complex code. In terms of keeping out of trouble, the things that matter are the things that have always mattered, like how well documented is it? How well tested is it? How easy is it to load it into your brain and work on it? If people keep an eye on those three things and use AI to prevent those risks rather than just to rush further, faster and more headlong into those risks, then it's very possible to avoid those problems.
In the next 10-15 years, I'm either going to be a CTO cleaning up after AI, or there won't be people like me anymore because we're just going to write the specs and generate the whole app from scratch every time.
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00:00 The Changing Landscape of Engineering Management
10:20 Navigating Technical Debt in Software Development
19:52 The Future of Engineering Management
35:58 Adapting to AI in Engineering Management
