Reducing the Time to Asymptote: A Framework for Thinking About the Ingredients of Scale

We’ve all seen this chart before.

It is an illustration of how technology adoption has accelerated over the past 100 years. The standard telephone took over 60 years to reach 80% adoption amongst U.S. households. Cellphones reached the same milestone, but took only 20 years.

Some of these technologies will peak at ~100% adoption among households in developed markets. Others will not quite get there…

While nearly every household has access to electricity, refrigerators and color tv, not all technology adoption is created equal. There are natural limitations to every market. Hair dryers, for example, while used by the majority of the population, will never see adoption by those who are bald or indifferent to good grooming practices. Bicycles will naturally find their market limit bump up against those who are not physically able to ride a bike, those who lack readily available infrastructure to make it a convenient mode of transport, and those who find other transportation substitutes (cars, buses, ubers, etc.) more appropriate for their lifestyle.

Amazon once found its market limitation in shipping fees. This was the collar holding back growth in most of the ecommerce space in the early 2000s. That is, until Amazon launched Prime as detailed in Eugene Wei’s 2018 framework defining article:

People hate paying for shipping. They despise it. It may sound banal, even self-evident, but understanding that was, I’m convinced, so critical to much of how we unlocked growth at Amazon over the years. People don’t just hate paying for shipping, they hate it to literally an irrational degree… It turns out that you can have people pre-pay for shipping through a program like Prime and they’re incredibly happy to make the trade. And yes, on some orders, and for some customers, the financial trade may be a lossy one for the business, but on net, the dramatic shift in the demand curve is stunning and game-changing.

Eugene used the Prime example to illustrate the concept of an Invisible Asymptote, which he defines as a ceiling that a company’s growth curve will eventually bump up against if it continues down its current path. For Amazon, shipping fees were what started to level off their S-curve of adoption. For hairdryers, this is baldness. For bicycles, it is a variety of things. The key is figuring out how to break through.

Today, products and services are finding their natural market limitations faster than ever (ie. reaching the point of mass adoption, and consequently, market saturation).

We can thank rising middle class wealth for some of this increase in speed, but most of this is attributable to three simple factors: we are able to distribute things faster than ever, we are able to generate ‘demand’ from consumers faster than ever, and we are able to create useful Products faster than ever.

  • Speeding up distribution: Globalized and increasingly efficient supply chains were able to distribute product faster than ever before. The internet turned this into hyperdrive, bringing down the cost of physical good distribution even further through economies of scale, and bringing the cost of digital goods and services distribution to near zero.
  • Generating awareness: The rise of consumerism through the Mad Men era played a role in generating broader awareness and demand for new products through the magic of marketing. Today’s always-connected and Tiktok-driven culture has pushed that discovery element to new heights.
  • Creating utility: Products have also become more ‘useful’ than ever. Product Development best practices have spread far and wide. Engineers and designers are constantly hired to identify consumer jobs-to-be-done, design-think their way into a set of potential solutions, then iterate through rapid build-measure-learn cycles. The degree of utility contained in your average product today has arguably never been higher.

When we zoom in on the digital arena, adoption cycles are even shorter:

Open AI recently caused all of these charts to be reprinted with introduction of ChatGPT, which reached 100 million users in a couple of months! This is an incredible feat, but to simplify how it happened, we can boil it down to the same three factors that were discussed above:

  • Distribution: ChatGPT had incredible reach online and through social media
  • Awareness: ChatGPT had incredible virality because of its novelty and demonstrability
  • Utility: ChatGPT had incredible practicality due to its ease of use and impressive outputs

These three factors form the basis for a product’s ‘time to asymptote’.

Defining time to asymptote is simple: it is the amount of time a technology, product, feature, or service takes to reach the inflection point in their adoption S-curve.

To be clear, ChatGPT and most LLMs are still far away from reaching their inflection point—but they will get there faster than any technology every has in the past.

Some products will have incredibly short time-to-asymptote. Cloud-based services, for example, will reach their inflection points quickly because they can push their changes out instantly to all users. Tesla owners will have an incredibly fast time to adoption of their self-driving capabilities, once fully functional and cleared as road-ready by regulators.  

Some products will have a much longer time-to-asymptote. Physical and analog products are on the other end of the spectrum from cloud-based services. Compare the roll out of self-driving capabilities to the adoption of seatbelts and airbags, which took the auto industry decades to complete, one new manufactured car at a time.

Thinking about invisible asymptotes is important because it forces a company to consider what will naturally limit the growth of a product/service. If you can identify it accurately, you have time to come up with creative solutions, much like Amazon did in using Prime as a solution to the ‘shipping cost’ asymptote.

But why is time to asymptote important? For a similar reason: it forces you to consider how much time you have before hitting your growth ceiling. For rapidly scaling companies, this is key to understanding how fast you need to move. For slow moving companies, it is key to understand which of the three levers (distribution, awareness, utility) can be pulled in order to run faster toward adoption.

Reducing time to asymptote is an ambitious goal, but one that is useful in understanding the competitive dynamics in an industry. In zero-to-one markets, much like ecommerce or crypto, non-competitive factors will often be the rate limiter to growth (ie. Amazon is much better off contending with shipping fee issues than fighting for market share against the Shopify’s and Alibaba’s of the world). These markets are often ‘blue oceans’ with ample space for competition. In more crowded ‘red oceans’, in addition to the natural product- and market-related factors, competition may be a more prominent asymptote against which S-curves are inflecting.

In both cases, time-to-asymptote is speeding up across the board. Blue ocean companies are scaling faster than ever before and red ocean companies are fighting competitive battles more fiercely than ever before.

Without a sound approach to each of the three levers, it is easy to get left behind.

Identify those asymptotes, then chase after them as frantically as possible.

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