Instead of formally studying different systems, a more effective path to T-shaped expertise is to deep-dive into adjacent systems only when they block your work. This "just-in-time" learning is highly motivated, practical, and builds cross-stack knowledge and credibility over time.
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Greg Jackson, founder of Octopus Energy, seeks "T-shaped" employees. This model values individuals who possess deep expertise in one specific area (the T's vertical bar) while also having the broad, adjacent knowledge to collaborate across functions (the horizontal bar).
To combat the overwhelm of a long to-do list, commit to only one topic per learning category for an entire quarter. This constraint prevents surface-level browsing across many subjects and gives you permission to go deep, integrate knowledge, and achieve meaningful progress.
Average performers avoid learning new technologies by claiming their customers don't use them. High achievers operate with the discipline of proactive learning, assuming that mastering new tools is essential for future success, regardless of immediate application. Their mindset is, "I don't know this and I need to, therefore I'm going to learn it."
To thrive in the AI era, go beyond a "T-shaped" profile. Develop deep expertise in one core skill and strong proficiency in two or more adjacent ones (an "E" or "F" shape). This combination makes you non-fungible and irreplaceable, as economist Larry Summers advised.
To transition into management, engineers should prioritize gaining broad technical knowledge across disciplines. This breadth allows them to understand team-wide pain points, facilitate collaboration, and implement effective systems, rather than being the deepest expert in a single area.
Instead of learning skills based solely on personal interest, a more strategic approach is to identify the biggest, most expensive pain points in your target industry. Then, deliberately acquire the specific skills needed to solve those problems, making yourself an invaluable asset before you even apply.
To avoid becoming an "ivory tower" manager, engineering leaders should use side projects as a playground for new technologies. This practice ensures they understand the limitations of new tools like AI and can provide credible, concrete, hands-on guidance to their teams.
Most people learn things "just in case" they might need them, like in university. The most effective approach is "just-in-time" learning—acquiring knowledge from books, courses, or mentors to solve a specific, immediate challenge you are facing right now.
To stay current in a fast-moving field like AI, passive learning through articles and videos is insufficient. The key is active engagement: experimenting with new platforms, trying new features as they launch, and even building small applications to truly understand their capabilities and limitations.
In a rapidly changing world, the most valuable skill is not expertise in one domain, but the ability to learn itself. This generalist approach allows for innovative, first-principles thinking across different fields, whereas specialists can be constrained by existing frameworks.
