Scarcity transforms throughout history. From food, to labor, to knowledge, to attention – the leverages that society has been able to accumulate and compound have shifted, and always landed where the next hard constraint was. The place where it has been accumulating is now where most people are not looking.
AI makes execution abundant. Writing, coding, design, analysis – all of these things either get significantly cheaper or disappear as options compared to five years ago. This leaves an uncomfortable question for anyone that has invested significant time practicing any of these skills in the past decade or so – if execution becomes abundant, where is the next constraint?
The default answer to what makes one valuable in the last decade or so has been fairly simple: narrow focus. Anybody that has managed to make a name for themselves in a particular domain would point to the importance of specialization. The ability to solve a particular class of problems becomes a leverage by virtue of being the only person that can solve these problems.
The dynamic is intuitive: in a world where capabilities compound, the easiest way to get to the next level is to get very good at one specific area. But specialization has one other effect: it leads you to believe that the domain is the only place where value is generated. This is a useful delusion as long as the constraint is capability, but stops being a viable strategy once the constraint moves elsewhere.
What creates leverage in the new world?
The failure to become broadly capable across domains and contextually fluid has costs. The tendency to think in patterns that worked well for one domain starts to dominate one’s heuristics, even in other areas where these patterns do not necessarily apply. The compounding leverage comes from synthesis: becoming broadly capable enough in a number of domains that one develops a certain fluency in the ways of thinking specific to these areas and how they apply to one another.
The synthesis itself is rarely rewarded directly (compared to being able to deliver value inside a specific domain), but allows one to recombine existing solutions in novel ways and apply domain-specific shortcuts in new areas, identifying blind spots, opportunities and constraints.
Context switchers have always existed. But until recently they have served mostly as enablers for specialists: people that saw across domains but did not possess the deep knowledge needed to execute anywhere, and thus were limited to advising others. This created a value capture problem of a sort: most of the value that context switchers created went to other people (specialists) that were able to take their ideas and execute on them. The emergence of AI dramatically reduced the transaction costs associated with context switching, and allowed these ideas to be implemented directly.
The context switcher’s mindsets create value by allowing them to see patterns and apply solutions across different fields.
Over the last year, I have been context switching extensively between product development, writing, design, and AI. The opportunities for creating value tended to emerge in hybrid spaces that combined at least two of these areas, but in most cases were not obvious at the time.
My ability to execute in different areas was limited (and in most cases, I was merely a competent generalist, barely more capable than anyone else), but these skills allowed me to see what was possible. The most valuable opportunities were often right in front of me, hiding behind assumptions.
The awareness came from seeing these opportunities through the lens of multiple domains, often combining my own ideas with what I have seen others do, and noticing patterns that I could then go and look for in other areas. These patterns would rarely manifest themselves in one area, but would become apparent as opportunities once viewed through the prism of another field.
A context switcher can see the connections between areas that most cannot. This ability can become an enormous advantage when designing products: most obviously by allowing one to identify blind spots, but also by enabling the discovery of opportunities for synthesis, and applying solutions from one field to problems in another.
Someone that can see across domains can identify an opportunity, define the interface to it, and create a simple enough prototype for it to be further developed or even launched successfully using AI. In many ways, this is the person that represents the new constraint: most products of the last decade or so would have required such a person to exist and be moderately successful to begin with. They may not have had to be an expert in any one area, but were expected to know enough to avoid most roadblocks and identify the right direction.
The world has changed, and the people that built the last paradigm are being rapidly replaced by a new group: the context switchers, who are leveraging their ability to see across domains, to synthesize ideas, and identify opportunities for new products to be built.
