Leadership in Tech
Leadership in Tech
AI can now match experts at processing data, shifting what makes leaders valuable. The real edge lies in creativity, human connection, and ethical judgment. Also: why real low-cost strategies mean bold, unique choices—not just trimming budgets.
Hope outranks trust as what employees need most from leaders. Competitors will fix their own weaknesses, so study them deeply and often. And why hasn't AI changed everything yet? The same reason electricity took decades — the real work is rewiring how people actually work.
Unwritten team standards for AI tools can be versioned into shared instruction files, turning senior engineers' instincts into shared infrastructure. Meanwhile, cheap AI-generated code is spawning personal, sprawling software projects — shifting the real bottleneck from writing code to managing attention.
A team of 8 engineers can cost more than most orgs can justify — yet few track the numbers. Also worth reading: how the 2003 blackout shows that silence from your monitoring tools isn't safety. And a simple nudge to just say what you want at work.
Amazon hiring decisions often hinge on storytelling, not just skills. After 1,000 interviews, one recruiter says a weekend rehearsing personal stories beats dozens of extra coding hours. Also: why nobody has AI adoption figured out yet, and what that means for engineers.
Your career growth is your responsibility — managers won't push it for you. A slow engineering team usually signals a messy codebase, not bad people. And "good taste" isn't a gift — it's just pattern recognition built through practice.
AI brings big benefits but also serious risks — and simple safety rules won't be enough. Meanwhile, startup survival rates haven't improved in 30 years despite popular frameworks, because when everyone follows the same playbook, everyone builds the same thing.
Slow down before you code — AI makes it easy to ship fast in the wrong direction. Also, most company wikis fail not from bad tools, but bad structure. Treat docs like a web of linked pages, not folders. And sleeping rats may hold the secret to better org design.
Good managers step in for serious, hard-to-reverse mistakes but leave room for growth otherwise. Combative colleagues? Build on their ideas instead of challenging them. And experienced engineers may be giving outdated advice as AI changes what's hard — time to build again.
Technical interviews need a rethink. Banning AI misses the point — better to test whether candidates can review AI-generated code critically or navigate a real codebase. Separately, earning trust as an engineering leader has nothing to do with coding skill and everything to do with fixing broken systems.
AI makes building faster but raises the bar for managers—they must build too, set higher standards, and watch usage-based costs. Meanwhile, most people explain things bottom-up when listeners need the conclusion first. Three simple fixes can make you sound far clearer.
Empty buzzwords signal weak thinking — workers most inspired by vague "visionary" language score lower on analysis and spread the cycle upward. Also: good goals need a single owner, explicit priority order, and stated non-goals. Without that, they're just intentions.
Resistance to AI tools follows the same pattern as every past scientific revolution — denial, clashing worldviews, paradigm defense. Also: mimicking skilled people can instantly raise your performance, even imagining a confident stranger helps.
Complexity gets rewarded more than simplicity — not by design, but because it's easier to describe. Meanwhile, talking to executives is less about showcasing work and more about helping them decide: lead with conclusions, say "I don't know," and follow their lead.
This issue explores the dynamics of engineering management vs. technical tracks, effective collaboration strategies, and innovative hiring practices for engineers. Gain insights on navigating workplace challenges and team motivations.
This issue explores key strategic choices in leadership, the impact of executive behavior, and the intricacies of compensation planning. It also delves into the balance of software system design and the importance of engagement in management.
In this issue, we explore essential life lessons from Nabeel Qureshi, the need for hands-on leadership in the AI era, handling mistakes at work, and the challenges of hiring from big tech. Plus, discover how metaphors can bridge gaps in communication.
This issue explores the nuances of leadership, decision-making, and productivity in tech. Discover insights on managing managers, overcoming decision fatigue, and harnessing AI effectively to drive real organizational value.
This issue explores essential insights for career advancement and effective leadership. Learn how to build trust for promotions, enhance responsiveness as a manager, and prioritize engineering maturity for AI success.
In this issue, we delve into effective technical due diligence, explore the pitfalls of misguided incentives, and emphasize the importance of political capital in driving change. Plus, learn to prioritize what truly matters in your projects.