When not admin-ing, fundraising, hiring, team-building and vision-ing, I’m on a constant quest of getting good enough in the multitude of domains and disciplines that leading dognosis R&D entails.
As of October 2024, this involves gaining fluency in computational neuroscience, full-stack development, data science, mechanical engineering, embedded and sensor systems, machine vision, clinical research operations, diagnostic regulations, oncology, health economics, biostatistics and volatile organic chemistry. To be comfortable in the language of each of the these domains feels essential in building a roadmap towards our vision and effectively leading our team.
This is hard.
Clearly, the goal cannot be to “get good” at each and every one of the fields. To truly do this in a single life-time feels impossible, unless perhaps you’re von Neumann, and while I may be slightly above the median in IQ, I am no genius.
A big challenge is that the fields in question often do not have much in the way of commonality. At 11am I am thinking about the optimal calculation of sample power size for a set PPV/NPV, at 12 on how we can convert 3D poly-models to b-reps, at 1 on the nuances of ISO 15189 and CLIA, at 2 on the use of graph neural networks to map relationships between EEG channels, at 3 on the modelling of molecules, odor receptor and percept interactions, and at 4 on designing novel stacks of companion diagnostics into standard-of-care. Don’t get me wrong - I absolutely love the variety - to bridge between silos and boundaries is what I intellectually relish the most. However, the lack of an underlying set of common principles to anchor and double-down on means there are often no easy compounding returns to accelerate learning across questions.
This access to generalizable first-principles to accelerate learning is vital because to be a founder of a startup means to be in constant pursuit of unfair leverage to move at speeds others cannot. If the success of dognosis cracking scents is constrained on my ability to learn the language of the needed disciplines, and this is bottlenecked by time x focus, leverage is in limited supply.
Except dognosis is not a one-dog show.
I need to constantly remind myself that my job is to get up to speed, to be just good enough in a discipline, that I can find as well as convince someone who is _very good_ to join. Then, I need to remain good enough to understand the best ways to work with them to orchestrate people and projects and processes that will get us towards our shared goals.
The best leverage ultimately lies in the emergent, difficult to predict, easy to underestimate, often challenging, and sometimes magical ability of interdisciplinary (and interspecies) teams to hold capabilities that are far greater, often exponentially so, than the sum of their individual abilities.