# Being a High-Leverage Generalist Jan 12, 2025 The "T-shaped person" discourse is played out. The real move is being an emergent complexity monster. I've spent the last 6 years watching my career accelerate dramatically through what I call "high-leverage generalism." Not the wishy-washy "jack of all trades" kind, but a deliberate approach to building a unique combination of skills that compounds in value over time. The traditional narrative around career development is broken. We're told to pick a lane early, specialise hard, and climb the ladder in our chosen field. This advice made sense in a world of stable, well-defined industries. But that world is dead. Today's most interesting opportunities exist at the intersections - where different domains collide and create new possibilities. The people who thrive aren't the ones who went deep in a single direction, but those who built unique combinations of capabilities that let them navigate complexity and create value in unexpected ways. This isn't about being a dilettante or refusing to commit. It's about recognising that in a world of increasing complexity and rapid change, the ability to synthesise insights across domains becomes more valuable than narrow expertise. It's about building a career that compounds in interesting ways rather than just accumulating years of similar experience. ## The Core Insight Most career advice optimises for the wrong thing. They tell you to specialise early, to become the best in one narrow field. But this advice comes from an outdated model of how modern work actually creates value. Think of it this way: A specialist is like a highly optimised function that performs exceptionally well on one specific type of input. A high-leverage generalist, on the other hand, is like a powerful meta-algorithm that can adapt to entirely new classes of problems. The traditional career path pushes you to pick your lane early - become a frontend developer, a data scientist, or a product manager. This made sense in an industrial economy where roles were clearly defined and stable. But modern work is messier, more fluid, and increasingly interdisciplinary. Consider how technology companies actually create value today: The most impactful projects rarely succeed through deep expertise in a single domain. Instead, they require synthesising insights across disciplines - understanding technical constraints, user psychology, business models, and organisational dynamics all at once. This is where high-leverage generalists thrive. While specialists dig deeper into increasingly narrow trenches, generalists develop a unique ability to: - Spot patterns across seemingly unrelated domains. - Identify novel solutions by combining existing ideas. - Navigate ambiguity and adapt to changing conditions. - Bridge communication gaps between different teams. The key insight isn't that specialisation is bad - it's that the returns to specialisation have diminishing returns in an increasingly connected world. The most valuable skills often emerge from the intersections between disciplines, not from their cores. ## The Career Acceleration Effect The real power comes from what I call "skill stack arbitrage." When you can competently operate across multiple domains, you start seeing opportunities that specialists can't even perceive. Here are two powerful combinations I've observed: **Infrastructure + Analytics** Most companies have infrastructure teams that don't understand data flows, and analytics teams that don't grasp infrastructure constraints. But when you understand both, you can architect systems that handle real-world scale while actually delivering business value. You see opportunities to optimise infrastructure based on usage patterns that others miss entirely. **Machine Learning + SysAdmin** While ML specialists often struggle with deployment and sysadmins grapple with ML requirements, combining these domains unlocks new possibilities. You can build self-healing systems that predict failures before they happen and automate complex operational decisions. This isn't just about running models - it's about building intelligent infrastructure that actually works in production. These combinations aren't just theoretical - they represent real arbitrage opportunities in today's market. While specialists focus on optimising their narrow domains, generalists can bridge these gaps to create disproportionate value. **A Real-World Example** During a massive cloud migration project, I leveraged multiple skill domains to deliver disproportionate value. While specialists might have treated this as a simple lift-and-shift operation, understanding the intersection of infrastructure, systems administration, data analytics, and security allowed me to: 1. Architect a migration strategy for 160+ VMs across diverse workloads and customers. 2. Use data analytics to evaluate and compare workload patterns between customers. 3. Implement security controls that worked harmoniously with the infrastructure. 4. Design systems that would scale effectively in their new cloud environment. This kind of project perfectly illustrates how high-leverage generalism works in practice: 1. A pure infrastructure specialist might focus solely on VM sizing and network topology, missing critical security implications around data sovereignty and access controls. 2. A security specialist might implement stringent controls that cripple system performance or make maintenance impossible. 3. A data analyst might suggest optimisations based on usage patterns without understanding the infrastructure costs or security boundaries. 4. A sysadmin might configure systems for maximum uptime without considering data flow patterns or cost optimisation opportunities. But by operating competently across these domains, you can orchestrate complex transformations that create massive value - implementing right-sized security controls that don't impact performance, designing infrastructure that scales efficiently while maintaining compliance, and optimising costs based on actual usage patterns while preserving system reliability. This approach directly accelerated my career progression from technical analyst to senior technical analyst to technical consultant. Each role transition wasn't just a title change - it represented an expansion of my capability stack. While my peers were deepening their expertise in single domains, I was building unique combinations of skills that let me tackle increasingly complex challenges. The cloud migration project became a perfect example of this compound advantage - instead of being "just another infrastructure project," it became a showcase of high-leverage generalism in action, leading to more strategic roles and opportunities. The key wasn't that I became the best at any single aspect - it was that I could orchestrate all these moving parts in ways that specialists couldn't. This ability to see and execute on the bigger picture consistently opened doors to more impactful projects and accelerated career advancement. What started as infrastructure work evolved into strategic technical leadership, proving that high-leverage generalism isn't just a theory - it's a practical path to disproportionate career growth. ## The Hidden Advantage The most counter intuitive aspect is that being a generalist actually makes you better at specialising when needed. While specialists often get trapped in domain-specific thinking patterns, generalists develop powerful learning capabilities that accelerate learning in any new field. Think about how knowledge compounds: When you learn distributed systems, you start seeing distributed patterns everywhere - in team dynamics, in market behaviours, in biological systems. When you understand game theory from economics, you suddenly have new mental models for understanding everything from API design to organisational incentives. This cross-pollination of ideas creates what I call "learning leverage." Each new domain you explore doesn't just add to your knowledge linearly - it multiplies your ability to: 1. Recognise fundamental patterns that show up across different systems. 2. Transfer problem-solving strategies from one domain to another. 3. Learn new technologies faster by mapping them to familiar concepts. 4. See second-order effects that specialists often miss. The real superpower isn't just knowing multiple domains - it's developing an intuition for how systems work at a fundamental level. This isn't about being mediocre at many things. It's about building a unique constellation of capabilities that lets you: 1. Rapidly prototype solutions in new domains. 2. Identify high-leverage intervention points in complex systems. 3. Bridge communication gaps between different technical cultures. 4. Adapt quickly as technology and markets evolve. In a world of increasing complexity and rapid change, this ability to learn and adapt becomes more valuable than any single area of expertise. Your unique combination of knowledge and "learning to learn" capabilities makes you irreplaceable in ways that narrow specialisation never could. ## The Implementation The key is to approach generalism strategically - this isn't about random skill collection, but deliberate capability building. Here's how to execute this in practice: **Map Your Industry's Intersection Points** Start by identifying where different domains collide in interesting ways. In tech, this might be where ML meets systems engineering, or where product design intersects with data analytics. Look for areas where specialists struggle to communicate or where problems fall between traditional role boundaries. **Build Foundational Mental Models** Instead of memorising domain-specific details, focus on understanding core principles that transfer across fields: 1. Systems thinking from engineering. 2. Feedback loops from control theory. 3. Incentive structures from economics. 4. Information theory from communications. These mental models become your toolkit for understanding new domains quickly. **Create Learning Feedback Loops** Each new domain should enhance your understanding of previous ones. When learning distributed systems, think about how it applies to organisational design. When studying product management, consider how it relates to API design. These connections compound over time. **Follow Your Curiosity Vector** Build your skill stack around genuine interests - authenticity compounds. The market may want more blockchain developers today, but sustainable advantage comes from pursuing combinations that naturally interest you. Your genuine enthusiasm becomes a competitive moat. The beauty of this approach is that it gets easier over time. Each new domain you explore adds to your "learning to learn" capabilities, making the next one easier to grasp. You're not just learning new skills - you're building a unique lens through which to view and solve problems. The goal isn't to know everything about everything. It's about building a unique combination of capabilities that compounds in value over time. Your career becomes exponentially more interesting when you stop trying to fit into existing boxes and start creating your own category instead.