Staying Human in the Age of AI: What Tech Leaders Need to Preserve

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While AI tools excel at generating code, they can't navigate the human complexities that make or break technical decisions. As senior engineers, our value increasingly lies not in what we code, but in how we lead teams through technical choices that reflect our values and culture.

Through real examples of how values conflicts disguise themselves as technical disagreements, this session explores what tech leaders need to preserve and strengthen as AI reshapes our industry. You'll discover practical tools for uncovering team values and learn to make technical decisions that account for both code and culture in an AI-driven world.

This talk has been presented at TechLead Conference 2025: AI in Orgs, check out the latest edition of this Tech Conference.

FAQ

The first AI-related episode of the Compressed FM podcast was episode 130, titled 'Chat GPT and AI, initial reactions,' which aired on April 12th, 2023.

AI has commoditized developers and democratized the process of building web applications, making coding more accessible to a broader population and allowing more people to code than ever before.

The paradox is that while AI makes coding tasks more straightforward and commoditized, the complexity of technical decisions has increased because AI enables teams to adopt almost any technology.

Human values are crucial because they guide decision-making beyond technical solutions, addressing the human complexities and dynamics that AI cannot navigate, such as team dynamics, values alignment, and cultural decision-making.

The author resolved value conflicts by discussing individual and team values openly, understanding different perspectives, and aligning decisions with these values during team retrospectives and decision-making processes.

The author found 'Engineering Leadership for the Rest of Us' by Sarah Drossner influential, particularly its insights on understanding and working with team values.

The Redwood JS core team addressed GDPR compliance concerns by shifting from GraphQL to SQLite databases running in durable objects to ensure data processing within specific geographical regions, aligning with their values of inclusivity and global accessibility.

Understanding team values leads to faster decision-making, reduced friction in implementation, better adaptation to changes, and improved teamwork by aligning technical decisions with the team's shared values.

Amy Dutton
Amy Dutton
20 min
18 Sep, 2025

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Video Summary and Transcription
The speaker reflects on the evolution of AI, its impact on coding accessibility, and the value of understanding human complexity in decision-making. Leadership challenges with junior developers highlight the importance of human dynamics and autonomy. Teamwork involves exploring values and navigating conflicts, emphasizing the significance of aligning team values in decision-making processes. Implementing human-centered technical decisions and fostering value alignment within teams are key strategies for successful adaptation and community building.

1. Reflections on AI Evolution

Short description:

The speaker reflects on their first encounter with AI on their podcast episode 130. AI has rapidly become commonplace and efficient in handling tasks. The introduction of tools like GitHub Copilot has streamlined development processes and increased accessibility to coding, widening the pool of potential coders.

Now, I was thinking the other day, I don't know when the first time I used AI was. I don't remember the prompt. I don't even particularly remember having this, aha, this is the future moment. I co-host a podcast called Compressed FM. I went back and looked, and episode 130, which aired April 12th, 2023, we did our first AI episode called Chat GPT and AI, initial reactions. Funny to go back and listen to it. We spent part of the episode explaining the difference between OpenAI and Chat GPT and what 3.7 turbo versus GPT format. And it makes me laugh now because these terms are common knowledge, and they almost seem obvious. But since recording episode 130, now it seems like we talk about AI almost every week.

My point is for something that has become so ubiquitous, it had a modest-ish start, but it took over, and it took over quickly. Like most people, I think my favorite use case is asking it to do a task I really don't want to do. And a lot of those small menial tasks, AI has gotten so good. It can do a lot of that work just as good, if not better than I can. It can definitely do it faster.

So two weeks ago, Raycast announced their GitHub Copilot extension. I quickly installed it and fired off several tasks from my backlog. I was able to ask it to do five different things all in parallel on my family, and I sat down to eat dinner. That's kind of the dream, right? Well, at least some form of the dream anyway. In some ways, it's commoditized developers. It's definitely made things like building web applications easier, democratizing the whole process. Did you know there's only 0.2 or 0.5 percent, that's half percent, 1 percent of the world's population that can code.

2. AI's Influence on Coding Decisions

Short description:

AI's impact on widening coding accessibility and the paradox of code commoditization. Technical decisions grow more complex despite AI's simplification of coding tasks and technologies. The value lies in understanding human complexities that AI lacks, shaping team dynamics and decision-making in coding culture.

That's about 20 million people. But if you look at some of these AI services like Repl.it, they're reporting 34 million users. Granted, there's probably some overlap here of people that can code and people using Repl.it, or just staying honest, Gemini does say that that 20 million is professional coders. But the point is that Repl.it has bigger numbers. The pool is widening and more people can code now than ever before. AI makes it more accessible and the numbers are only going to continue to trend in that direction.

So if more people can code, if more robots can do what we do, what makes us so special? And I feel like that's kind of the fear that everyone is facing right now. It's this weird paradox. On one hand, AI is making code itself more commoditized. The actual writing of functions, the crafting of algorithms, even complex refactoring, all of it becomes literally point and click easy. But on the other hand, and this is a paradox, our technical decisions are becoming more complex, not less.

So think about it. When you could only choose from technologies your team already knew, decisions were simpler. When implementing a feature meant weeks of careful coding. You thought twice about scope creep. When debugging required deep system knowledge, you built teams around expertise. But now, now AI can help your team adopt almost any technology. Now features can be built in hours instead of weeks. And now debugging often means asking AI to interpret error logs. The question isn't what technology should we use anymore? The question is, what kind of team do we want to become? And AI can't answer that for us. I think the answer is simpler than we make it out to be. Our value isn't in the code we write anymore. It's an understanding the human complexities that AI completely misses. Things like team dynamics, values alignment, and how we make decisions as a culture. These aren't soft skills. These are the hardest skills. Because it turns out, while AI is incredibly good at generating code, it's terrible at reading the room. So let me give you an example of what I mean. And I'll be honest. This one took me a while to figure out.

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