Data+ Latent Platforms + Computation = Opportunity

Christian Hernandez
4 min readDec 3, 2018


I have been lucky to have been involved with the early rise of four transformational platforms: Relational databases and “big data” in the late 90s, mobile and the app economy leading up to the launch of iPhone and Android in ‘07/’08, and the early days and explosion of social and the social graph starting back when MySpace still led Facebook, and most recently the acceleration of machine-powered computational algorithms as an enabling technology.

This has led me to continuously think about “what comes next” in terms of three core components coming together to create and disrupt industries: Data sets combined with latent platforms to which we add computational layers to extract value. I therefore wanted to lay out why I think this framework is currently unleashing new opportunities for founders and funders:

On the data set side, Cisco recently stated thatcross-border bandwidth used grew 90-fold between 2005 and 2016, and is expected to grow an additional 13-fold by 2023” with an expectation that by 2020, 1.7MB of data will be created every second for every human. As we become surrounded by billions of connected sensors and devices two new opportunities will emerge: The first is that incremental computation will happen at the edge given the “cost” to transport this scale of data fully onto the cloud, the second being a broader swath of “data-as-a-service” companies combining or creating proprietary data-sets and selling micro-transactional access to it for others to extract value.

On Platforms, the formal definition of a Platform is:
“A platform is a group of technologies that are used as a base upon which other applications, processes or technologies are developed.”

but I expand that definition to think about standardised assets which are, or could be, digitalised leveraging its latent data. As an example, I think of the electrical grid as a latent platform. It’s pervasive, integrating every home in every developed nation, with underlying standards (usually established by regulators and between utilities themselves)and financial transaction models, which is now being digitised and decentralised, as we as consumers also become producers via solar panels and batteries, and as the role of a “utility” goes from dumb (and price-gouging) pipe of electricity, to what will have to become a trusted (and likely algorithmically-powered) financial advisor telling us when to buy and when to sell and even enabling P2P and P2B models.

The same concept of a “latent” platform could be applied to the factory floor which is evolving into “Industry 4.0” via smarter use of data, new ways of manufacturing and robotics; the urban stack, enabling “smart cities” and new usage models for our urban environments; the good-old fashioned farm in which smart use of data and algorithms makes farmers from Iowa to Kenya more efficient and productive benefiting consumers with higher quality food and the planet with less wasteful supply chains.

Back in the late 90s when we were building “advanced” analytical models for business intelligence, we felt mighty proud to be applying statistical techniques to data sets to help clients learn that diapers cross-sold well with beer, or that a financial customer was likely to churn 24 hours after a large withdrawal. The world has advanced a tad-bit since then, with the acceleration of machine learning now enabling the algorithms to teach themselves. And this is just starting… as someone put it recently “NIPS is the new South-by…”

From computer vision assisting doctors for computational-assisted healthcare, to companies like and DeepMind now applying AI to the biological realm to help better understand, or discover, molecules. And if we think about biology as a “data set”, we are now beginning to learn how to read and write to that massively distributed platform, unlocking opportunity around computational biology, but also allowing us to synthetically modify biology (and Chemistry) to create new materials, from spider silk to “fake” meat.

So as I refine a lens through which to look at where the world is going, I believe this framework of data + platforms + algorithms provides a handy way to scout for the companies that are transforming or creating the industries of the future and I look forward to meeting the founders and funders whose own view might align with mine.

Would welcome your comments and questions below, and if you liked it enough, a clap or two, and of course, feel free to share via your preferred medium for others to provide their own feedback. Thanks.



Christian Hernandez

Partner at @2150-vc backing technologies that make our world more resilient and sustainable. Salvadoran-born Londoner. YGL of the @wef Father ^3