I have been thinking lately about a framing that would help clarify where to invest one’s energy while exploring a problem space. I realized that my previous writing about layering might come in handy.
This framing might not work for problem spaces that aren’t easily viewed in terms of interactions between layers. However, if the problem space can be viewed in such a way, we can then view our investment of energy as an attempt to create a new layer on top of an existing one.
Typically, new layers tend to emerge to fill in the insufficient capabilities of the previous layers. Just like the jQuery library emerged to compensate for consistency in querying and manipulating the document object model (DOM) across various browsers, new layers tend to crop up where there’s a distinct need for them.
This happens because of the fairly common dynamic playing out at the lower layer: no matter how much we try, we can’t get the desired results out of the current capabilities of that layer. Because of this growing asymmetry of effort-to-outcome in the dynamic, I call it “the asymptote” – we keep trying harder, but get results that are about the same.
Asymptotes can be soft and firm.
Firm asymptotes typically have something to do with the laws of physics. They’re mostly impossible to get around. Moore’s law appears to have run into this asymptote as the size of a transistor could no longer get any smaller.
Soft asymptotes tend to be temporary and give after enough pressure is applied to them. They are felt as temporary barriers, limitations that are eventually overcome through research and development.
One way to look at the same Moore’s law is that while the size of the transistor has a firm asymptote, all the advances in hardware and software keep pushing the soft asymptote of the overall computational capacity forward.
When we think about where to focus, asymptotes become a useful tool. Any asymmetry in effort-to-outcome is usually a place where a new layer of opinion will emerge. When there’s a need, there’s potential value to be realized by serving that need. There’s a potential value niche around every asymptote. The presence of an asymptote represents opportunities: needs that our potential customers would love us to address.
Depending on whether the asymptotes are soft or firm, the opportunities will look differently.
When the asymptote is firm, the layer that emerges on top becomes more or less permanent. These are great to build a solid product on, but are also usually subject to strong five-force dynamics. Many others will want to try to play there, so the threat of “race to the bottom” will be ever-present. However, if we’re prepared for the long slog and have the agility to make lateral moves, this could be a useful niche to play in.
The jQuery library is a great example here. It wasn’t the first or last contender to make life easier for Web developers. Among Web platform engineers, there was a running quip about a new Web framework or library being born every week. Yet, jQuery found its place and is still alive and kicking.
When the asymptote is soft, the layer we build will need to be more mercurial, forced to adapt and change as the asymptote is pushed forward with new capabilities from the lower layer. These new capabilities of the layer below could make our layer obsolete, irrelevant – and sometimes the opposite.
A good illustration of the latter is how the various attempts to compile C++ into Javascript were at best a nerdy oddity – until WebAssembly suddenly showed up as a Web platform primitive. Nerdy oddities quickly turned into essential tools of the emergent WASM stack.
Putting in sweat and tears around a soft asymptote usually brings more sweat and tears. But this investment might still be worth it if we have an intuition that we’ll hit the jackpot when the underlying layer changes again.
Having a keen intuition of how the asymptote will shift becomes important with soft asymptotes. When building around a soft asymptote, the trick is to look ahead to where it will shift, rather than grounding in its current state. We still might lose our investment if we guess the “where” wrong, but we’ll definitely lose it if we assume the asymptote won’t shift.
To bring this all together, here’s a recipe for mapping opportunities in a given problem space:
- Orient yourself. Does the problem space look like layers? Try sketching out the layer that’s below you (“What are the tools and services that you’re planning to consume? Who are the vendors in your value chain?”), the layer where you want to build something, and the layer above where your future customers are.
- Make a few guesses about the possible asymptotes. Talk to peers who are working in or around your chosen layer. Identify areas that appear to exhibit the diminishing returns dynamic. What part of the lower layer is in demand, but keeps bringing unsatisfying results? Map out those guesses into the landscape of asymptotes.
- Evaluate firmness/softness of each asymptote. For firm asymptotes, estimate the amount of patience, grit, and commitment that will be needed for the long-term optimization of the niche. For soft asymptotes, see if you have any intuitions on when and how the next breakthrough will occur. Decide if this intuition is strong enough to warrant investment. Aim for the next position of the asymptote, not the current one.
At the very least, the output of this recipe can serve as fodder for a productive conversation about the potential problems we could collectively throw ourselves against.
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