African countries were among the fastest in the world to adopt mobile payments. In the U.S., we were still swiping credit cards while they were creating faster, borderless, and intuitive payments infrastructure. (View Highlight)
Mobile was several step functions better than what they had – it felt like magic compared to the status quo. Meanwhile, the U.S. had such things as credit cards and digital bank accounts, which were totally adequate, and we had little incentive to close the gap from good to great. (View Highlight)
We believe that the same way mobile payments leapfrogged credit cards in some markets, and mobile phones leapfrogged desktop computers in developing economies, AI too will (at least initially) leapfrog more legacy technologies that don’t have a “good enough” palliative (I mean alternative) in place. (View Highlight)
Founders are eager to apply AI to digitized industries that seem ‘the most ready for it.’ But that’s not where we’re going to make the biggest impact. (View Highlight)
Spaces like agriculture, hospitality, education, legal, construction, and manufacturing are primed for what we call “AI leapfrogging.” If executed correctly, AI has the potential to reach new demographics of users who were bypassed by the previous software revolutions. (View Highlight)
Technological leapfrogging occurs when an industry or market (usually an outmoded industry or emerging market) skips a step along the technology transformation chain. (View Highlight)
Instead of learning to use a personal computer and then a mobile phone, you skip right to mobile. In many emerging markets, mobile is the dominant computing paradigm. It started as early as 2010, just three years after the iPhone’s debut. By the end of 2010, the number of broadband subscriptions for mobile overtook the number of subscriptions for fixed technologies in many emerging markets. (View Highlight)
Between 2013 and 2018, China’s two major mobile payment platforms, AliPay and WeChat, completely bypassed credit cards as the dominant payment scheme. AliPay’s active users increased from 100 million in 2013, to 900 million in 2018. WeChat grew from 350 million to 1.1 billion. (View Highlight)
Kenya and China jumped from A to C with mobile payments. And it’s no surprise that they were also first to really embrace crypto too. Decentralized finance was a perfect fit for countries that had already sidestepped the issues of a traditional, centralized financial system. (View Highlight)
Uber and Lyft wouldn’t have been transformative for cab drivers. That’s incremental. Instead, it made anyone with a car into a potential cab driver. That’s transformative. (View Highlight)
The overarching idea here is that switching costs must be as low as possible to facilitate a major leapfrog event. Think of it as two sides of a balancing scale. On one side you have the burden of change. On the other, the value of the change.
The value of the change must greatly outweigh the burden of the change. You can tweak this balance by:
Creating huge value with a change
Lightening the burden of the change itself
Ideally, you want to do both. (View Highlight)
The first step to creating an AI leapfrog event is to identify our non-transactors. In this case, we’re looking for places where there are historically low levels of SaaS adoption: construction, legal, manufacturing, hospitality, agriculture are good places to start. (View Highlight)
Construction is one of the largest industries in the global economy. But it’s highly fragmented, and is one of the slowest to digitize. (View Highlight)
At this point, any industry that lacks a SaaS solution probably lacks it for a reason.
For years, we have underestimated the burden that changing to SaaS platforms represents for many technologically outdated verticals. For years, SaaS has been too expensive for many of these companies. The onboarding has been complex and unintuitive. (View Highlight)
Remember: the value of the change must outweigh the burden of changing. The value proposition of semi-automation isn’t strong enough to justify the switching costs for industries reliant on entrenched, analog processes. (View Highlight)
Generative AI is an instantaneous push-button solution. It generates a legal brief or construction plan from scratch. Interacting with software is like chatting to a friend. You don’t have to re-learn your whole process – you simply remove tedious tasks from your to-do list. (View Highlight)
We’ve called this approach “AI inside.” Your selling point is not the “cool factor” of AI technology, but the ease of use, and the value that AI brings to your organization. It’s about delivering instant value, seamlessly on day 1. (View Highlight)
“AI inside” is the technique that will allow generative AI to “leapfrog” SaaS in archaic industries. (View Highlight)
Leapfrogging doesn’t happen often. And it’s not easy to execute. Many of the industries we’ve highlighted (construction, agriculture, manufacturing, etc.) are the ones that scare people away because of burdensome processes. (View Highlight)
That’s because once you become a dominant paradigm, the value proposition scale recalibrates: you need another extremely big technological change to lower switching costs enough to justify another shift. (View Highlight)
If history is any indication, these leapfrogging opportunities don’t last long. We saw it with the emergence of mobile, and with digital payments. Within three to five years, the major players had already executed the jump. (View Highlight)
