The Life Sciences Industry is Ripe for Platform Disruption
By: Stefani Robnett
Tags: AI, Platform, Biotechnology, Social Network, Disruptive Innovation

Facebook AI generated road map layer, image credit: https://techcrunch.com/2019/07/23/facebook-and-openstreetmaps-empower-the-mapping-community-with-ai-enhanced-tools/

I’m the co-founder of Sci Find, the Life Sciences collaboration network. I had been deeply interested in self-organized systems and network effects since studying sustainable development in university. I have a lot of respect and admiration for self-organized systems and love to study visualizations of network interactions and dynamics. My professional experience began with a sustainable development fellowship out of Rajasthan, India, where my interest evolved into understanding open sourced mapping. It was the perfect intersection of data visualizations, self-organized structures, and community based resource management systems.

After university, I never saw myself in tech, but rather working in urban planning or public policy, but I got lucky when a close friend recommended a job at Facebook to me. He could not tell me what the project was, but he said I was uniquely positioned for the team and that I should come in for an interview.

The project ended up being Facebook’s answer to GoogleMaps — a massive open-sourced AI project where our team needed to figure out how to process open-sourced road data and mimic those contributions using an AI. Google was investing a huge amount of money on a fleet of contractors to manually process satellite images, GPS data, and street view photos to create useable map layers.

The satellite images were expensive, but they were available. The labor was the real inefficiency. Because the data wasn’t being processed by users on the ground, it was prone to more errors and false interpretations. The worst part was that it was a continuous investment, meaning the project was never over. Cities evolve, land use evolves, one day a building might be there, the next day it may be torn down and replaced by a high-rise. Therefore the data must be continuously updated, and the maps adjusted.

Red Cross disaster relief in Mexico

Facebook was smart in using OpenStreetMaps for their base layers. They realized that the best way to get accurate data in an ever-evolving ecosystem is by processing open sourced data. In essence, by providing value for the people on the ground and encouraging their involvement, they would receive a better data pipeline and on-the-ground data validation. Non-profit organizations use these maps during disaster relief programs. If a hurricane blows through a region, any maps used to assess the damage and distribute resources are likely made on OpenStreetMaps. It’s a stunning example of how AI can be made to empower people.

I met my cofounder, Guy Rohkin, while he was working at a genomics start up and was performing niche sequencing outsourcing services. I had still been working at Facebook, but was interested in exploring new areas where networks and connections were still underdeveloped.

Over pizza and beer, he told me he had been struggling to develop a marketing pipeline for his company’s niche sequencing services, and told me he spent up to a third of his weekly hours trying to target buyers. He dove into a lecture on the complex and rapidly evolving Life Sciences ecosystem, and questioned why it was so hard to market niche services and find collaboration partners… I was instantly intrigued.

We went back to his apartment and began running through his sales process on a loop. Each time it became clear that 2 main problems plagued his marketing system:

There was a lack of centralization in the sector that kept him from aggregating data on organizations’ R&D operations. There was no standardized categorization schema to dissect that data.

Guy’s particular sequencing niche was only one out of presumptuously a large complex ecosystem… How do you develop a system for an evolving ecosystem? We ran the problem by a friend who worked at Roche and he confirmed that all the big Pharma companies would love a solid categorization schema to give them market insights, but to his knowledge that kind of analysis wasn’t available for Life Sciences.

Photo by National Cancer Institute on Unsplash

This is the problem that we decided to embark upon together, and the one that we are continuously trying to solve. Guy wrote a great piece about his personal experience trying to build his Life Sciences network as a young scientist, and why it is so difficult. In his article he references a little exercise you can do at home where you try to query a Life Sciences term and compare results on popular search engines:

“If you want to try it out, type, “tumor microenvironment” into Google. You’ll see tons of academic articles and information which is great, but it’s not pointing you towards people and companies. If you try the same thing on LinkedIn you’ll get a list of people who are currently doing some work in the field. The exposition they give can range from minimal to substantial and it’s a mixed bag–it’s contingent upon the user disclosing the information. If you try CrunchBase you’ll get a list of companies that explicitly say they work on the subject. These are all avenues that solve different problems, but none of them are speaking with each other and are prone to error. CrunchBase is limited in its quantity, Google is scholarly and LinkedIn is inundated and loosely categorized. Companies do not verbalize all the things they do–you have to look at the people who work there to know. People on the other hand, have tons of expertise, so without a look at the company you don’t know what they are currently working on. Take TB microbiologist from before as an example. They’ve got lots of technical expertise working with mycobacteria (bacteria as a whole) so chances are that’s what they will be working on regardless of which company they are in. However, to know the topicality of it, you would have to know the company’s inner workings. If they are working on infectious diseases but TB is not in their portfolio, then it’s probably something else. Ultimately. there is an intersectionality between these entities that is paramount in science.”

The results to a non-niche specialist seem legitimate, if not pertinent… But ask a TB microbiologist specialist, and you will find that the results leave them without a good understanding of the players in that space or pertinent service offerings. This is because none of these search engines are can handle the complexity of Life Sciences research. It’s a natural language processing issue that requires AI to unravel its text.

If I was a scientist looking to find an outsourcing service, find another specialist in the space, find a collaboration partner, or find a funding opportunity (the list goes on…), you have very few narrow online resources to get your answer. Contacting that specialist is then involves hunting for the email address, sending cold emails or worse — cold calling. From our research, most scientists when looking for an answer will try to remain self reliant and won’t reach out… They give up because communication frictions are too high.

Photo by National Cancer Institute on Unsplash

Like open-sourced mapping, Life Sciences echoed some of the same characteristics of the open-sourced mapping problem. There are already a fantastic array of Life Sciences community clusters which are largely decentralized in nature. It’s also an industry that benefits deeply from a large base of courageous and idealistic individuals who are compelled by the mission of science and dare to run experiments where no one else has attempted to venture. Like mapping, there is also a large pool of rapidly evolving data in the form of academic research articles, patents, and grants. It just lacks a coherent categorical schema and a centralized platform for its community to engage.

As an industry outsider with a tech background, I’ve racked my brain trying to understand why this sort of platform space has yet to exist. Sure, examples of marketplaces and platforms developed for 1 or 2 sides of the industry exist, but there is nothing that captures that pool of innovators upon which the industry is propelled.

ScienceExchange and Scientist.com are the two largest Life Sciences outsourcing marketplaces, each that made around $10 million in revenue in 2019. Life Sciences business process outsourcing (BPO) was estimated to be valued at $127.4 billion in 2014. It is expected to touch $286.3 billion by 2023 by PharmaExec.com (although estimates vary widely). They both entered the space in 2009 and 2007 respectively, each with a message of “democratizing Life Sciences.” They both created similar 2-sided marketplace models that have since been coined “Enterprise-gateway-software,” which essentially means that they build out e-procurement enterprise software tooling for their top buyers who tend to be Pharma, as they have the deep pockets to spend on the platform commission rates.

In solidifying their revenue models to capture value from big buyers, they ended up sacrificing any possibility of capturing collaboration exchanges between the key innovator parties we discussed earlier. The reason? Their commission rates frequently end up being too high, positioning the platforms almost exclusively for big buyers to participate.

No one has been able to capture this demographic. In order to gain the indirect network effects necessary to have a true market network, you need to capture the attention of Life Sciences innovators — the every day scientists working at an academic research core, or a start up. The truth is that this core innovator demographic is extremely difficult to capture. It’s a difficult problem to solve, but one that we believe makes the space ripe for platform disruption.

The implications of a platform disruption in the space are monumental. HBR published an article called, “Why Platform Disruption Is So Much Bigger Than Product Disruption.” They state that disruptive platforms,

“not only create new categories but also enable a whole new population of people to make money (think of Airbnb, ING Direct initial deposit, Quicken for your taxes, etc.). Like new-market product disruptions, when they begin they’re not even on the radar of incumbent firms. But the effects reach further because what the incumbent once considered a toy is now challenging the economics of a product and the entire rationale of how that entire industry is going to work.”

While platform disruption is the most coveted achievement in the new age of business, it is arguably the most difficult to achieve. Deciding whether or not an industry is right for platform disruption is most importantly determined by whether or not there are significant frictions you are solving. Let’s take a look at Alibaba, the largest and most successful B2B exchange platform, as an example:

“Alibaba succeeded by identifying and reducing key frictions that limited markets for Chinese SMEs. It believed SMEs in China confronted five challenges: ‘(1) limited geographic presence which restricts their ability to develop customer and supplier relationships beyond their local markets; (2) fragmentation of suppliers and buyers which makes it difficult to find and communicate with suitable trading partners; (3) limited communication channels and information sources to market and promote their products and services to find new markets or suppliers; (4) relatively small scale of operations which limits their resources for sales and market, and (5) absence of efficient mechanisms for evaluating the trustworthiness of trade partners.’” — Matchmakers, The New Economics of Multi-sided Platforms

We’ve spent the past 6 months learning from beta testers and from Life Sciences incubators, done aggregate market analysis to understand the industry players in order to comprehend the frictions plaguing the industry. We believe that Life Sciences start ups experience almost all of the same frictions as the Asian SME’s that Alibaba alleviates.

For example, one of the most important factors in determining whether or not to pursue a Life Sciences business transaction, “trust”, which outweighs “price” and “quality,” has yet to be addressed in the sector. This is why when we’ve engaged in dialogues with top industry experts, they state that there will,

“never be a platform solution because all large business transactions are based on old relationships and word of mouth references.”

Life Sciences entrepreneurs and researchers all experience a deficit in trust indicators to such an extreme extent that it effectively keeps them from pursuing collaboration opportunities, restricts communication, and perpetrates network fragmentation.

Photo by ThisisEngineering RAEng on Unsplash

Can we imagine a space where a young scientist can collaborate with other researchers in her domain interest or other compatible domains? A space where scientists can form global communities based on their compatible research interests? A space where organizations can connect and build collaboration partnerships? A space where an entrepreneur can push her niche technology and test it on the market? A space where investors can research their next investment opportunity?

It’s true, this pool of innovators and every day scientists have historically been the most difficult demographic to capture. But it is this difficult mission, to create a global collaboration network of Life Sciences innovators, that we at Sci Find are working tirelessly towards. With a combination of AI to help us understand, categorize, and track Life Sciences innovation patterns, we believe that releasing our powerful search engine to the public will allow for millions of scientists to find, prospect, communicate, and ultimately execute on collaboration partnerships. We are here to serve our innovators.