At pi Ventures, we invest in companies which address global use cases on the back of 10x disruptive technology innovation - we call these the "10x Startups”
We do not invest in science or incremental innovation. Quite often it becomes hard to make that judgement for a given startup while making investment decisions. To make it easier, we use an internally developed framework called Demand & Supply Resonance Map.
Let us try and take a look at it.
Understanding Demand & Supply Resonance Maps
The framework is based on a simple fact - if you really think about it, every startup at the end of the day is solving a demand and supply problem!
According to the market demand at a given point in time, some use cases are more ready for adoption than others and this is represented by the Demand axis. We broadly classify them as apparent and needed (like the Covid vaccine these days) or Latent or Unaware.
This demand can be met by a technology solution (represented by the Supply axis) which can be relatively easy to build (Incremental Innovation) or Harder tech or Hard Tech (leaning towards science)
If we map the characteristics of different zones, Zone B seems to show a good balance between what the market needs and the relative difficulty of realizing that solution via technology.
Interestingly, this mapping isn’t static, but is in a state of constant flux driven by tech and market momentums. Let us look at the momentums in the next section
Momentums driving the bigger picture
Things which are hard to do today become easier tomorrow from a technology standpoint. Hence, technology always moves from Right to Left. However, is that true for Market demand? Not really - the market demand is a bit fickle especially when played out over a period of time. What is in demand today, can go out of demand tomorrow and similarly what is not in demand today can eventually come to the fore. So, the market momentums are difficult to predict and characterise.
To understand how the resonance map and momentums play out, we take Cloud Computing and Physical Qwerty Keyboards as examples (2008 vs 2021)
A mapping of two technologies - cloud computing and qwerty-style keyboards from 2008
In the late 2000’s, computing was restricted to clunky metal boxes containing silicon. You and I had clunky metal PCs at our homes, while companies had entire rooms - data centers - full of these metal boxes containing silicon. Companies with the means to afford these data centers had a monumental advantage over those that didn’t, as in-house computing facilities provided them with facilities to churn out comprehensive solutions faster. So, companies shelled out massive sums of money to invest in in-house computing infrastructure. However, data centers are maintenance heavy. They require electricity, software updates and a dedicated IT team.
Cloud computing promised to erase that world away. With the cloud, companies could rent their metal boxes and silicon and the rental agency - a cloud company - would pay the electricity bill and maintain them. All the enterprise customer needed to do was log in remotely and have access to unlimited computing resources.
In hindsight - the eventual shift towards the cloud seems obvious. However, at the time, the inertia to move away from in-house datacenters was strong, with companies skeptical of hosting their data beyond company firewalls.
Fast-forward to 2021, and a world without cloud computing is unimaginable. During the pandemic, it has emerged as a panacea - enabling remote teams across the globe to work from home. Services from market leaders like AWS, Microsoft Azure and Google Cloud are used world over, by enterprises and startups alike.
Physical Qwerty Keyboards
It’s mobile prehistory at this point, but there was once a time when the ultimate smartphone one could get was a BlackBerry. Emails and messaging at their fingertips meant every professional owned one, or wished to own one. The personal messaging feature extended its appeal to adolescents too, and the device sold in the tens of millions each year up until the 2010’s.
At the time of the iPhone and Android’s arrival, the whole mobile industry was on the precipice of moving to bigger touchscreen displays. That was the destination that technology was evolving toward, and it was a trend that Apple jumped on with perfect timing, and later companies like HTC and Samsung exploited to the fullest.
Accompanying their touchscreen devices was a developer-friendly app ecosystem that introduced consumers to a host of new apps. Nokia, BlackBerry and other qwerty smartphone manufacturers saw their demand peak in 2011, after which they scrambled to compete, eventually making way for Android and the iPhone.
Cloud Computing has seen its demand skyrocket, while qwerty style mobile phones have seen their demand dwindle
Analysing the cloud computing case, market demand has skyrocketed, while the technology has advanced to a point where the differentiator isn’t the ability to provide computing power at scale, but an additional suite of services or a layer of analytics. AWS, Microsoft Azure and Google Cloud offer a host of services such as database management, DevOps tools and speech recognition to name a few.
Coming to physical qwerty keyboards, the shift towards touchscreen displays and their accompanying app ecosystem saw market demand for them erode significantly. Attempts by manufacturers to develop their own touch screen devices and closed app ecosystems failed spectacularly, indicative of how playing a catch-up game to evolving technology hardly ever ends well. At the time of its release, the BlackBerry was revolutionary. Failing to stay ahead of changing consumer demands and a technology to match those demands ensured its slow, but certain demise.
Let us now try and map some of the emerging technologies on the framework and see how they may pan out.
Although this is how we think of some of this tech today, this mapping is always in a state of flux, driven by the push and pull momentums of demand and technology advancement.
Taking the example of AI-powered drug discovery to illustrate these factors:
Demand: The pandemic has refocused the world’s collective attention onto its importance.
Technology: Several unsolved problems exist within the space. For example, it took us, as a society, 5 months from when the coronavirus was sequenced, to identify its structure which is critical to its understanding. And this was with the world’s top minds working on the problem. AI-powered protein structure prediction could be a breakout technology, with DeepMind and other startups making great strides in the space.
Over time, the market demand may go either up or down and the technology is bound to advance, requiring solutions that can disrupt the traditional drug discovery process to have a strong technological moat.
The Art of Possible - the
As DeepTech investors, our area of interest lies at the intersection of hard tech and market readiness. This means companies solving for large, global problems that are apparent or those that will be, with differentiated tech and a core-IP insulating them from technological advancements in the near to mid-term.
While it is obvious to look at zone B for a good balance between market demand and state-of-the-art technology, we need to account for the momentums as well. So, we need to allow for certain tech which is more on the science side today (especially for seed stage investments) and also consider demands that may be latent today but can become apparent with time.
We call identifying the startups that lie within this region - “The Art of Possible”*
* Thanks to Raj Shah for the phrase