The key to any lasting data company is controlling a stream of information that is either crucial in real time and/or becomes more valuable over time. Things like environmental sensors, that deliver real time measurements while also serving as longitudinal inputs for earth models, being a prime example.

invest in organoids

please don't send NDAs as word documents "for review"

funny that blockchain actually solves many of the problems with current data markets. Provenance, traceability, fractional ownership, federation, smart contracts… right out of the box.

Finally all those .bio domains will pay off

This is a note between the pathologist and the oncologist that reviewed my case back in 2016: “In truth, the appearances do not fit well with any clearly defined ‘entity’ and I can do no better than label this is an unclassified low grade sarcoma. It is my anecdotal experience that similar lesions have been associated with an indolent clinical course, although I have limited data in this regard.” Today, inspired by @sytses and others who have taken cancer care into their own hands, I’d like to share my journey as I revisit my tumor with the tools available today. I’ll be documenting the process, starting with recovering my original tissue for maximal characterization. Advice/ideas welcome.

Who’s building sensor networks for weather/planetary models ?

Data tokenization is about to make a big comeback as validating provenance becomes central to data licensing agreements

We need more data commons, less data brokers.

We’ve spent the last few years deploying hardware to generate proprietary data across a network of cancer centers, in a region completely out-of-distribution for existing models. Our next step is connecting frontier labs, pharma and cancer centers into a single market network.

The key to winning as a data marketplace is to simultaneously be the highest paying buyer and the lowest cost seller. This explains why data markets ultimately become duopolies.

The only bio companies with durable data businesses are those that: At least: control a live stream of longitudinal data, Ideally: have access to biospecimens to generate incremental modalities, At best: own the last mile deployment of 1st/3d party models

Data licensing will not be a durable revenue stream for every bio company. For generic datasets, pricing will stop making sense as demand plateaus and brokers flood the market. For specialized datasets, licensing will stop making sense as frontier labs start to encroach on their domains. This will likely lead to a slew of data-centric acquisitions.

In my experience, the recent frenzy for bio data brokerage overestimates the supply of existing datasets, while underestimating costs/challenges of generating new/better data. This will become evident as demand shifts towards deeper, outlier datasets.