Who am I and why do I write?
Briefly, I work on and think a lot about some combination of ML, bio, and software. In a prior life, I worked in the tech industry but got so excited about AI and bio that I couldn’t resist making the jump
The two major reasons I write, besides because it’s fun, are:
I have dangerous thinkboi tendencies and so writing feels like a step up from idly theorizing and annoying my friends with random texts. That said, I am always checking to make sure I remain a thinker-doer.
I love meeting interesting people and writing is a bat signal for said people to hopefully find me and reach out.
I generally write for my past self, which means my audience is hard to pinpoint. Some of my writing benefits from AI and/or biology knowledge, but other posts, such as Energetic aliens should be understandable by a broad audience. To deal with this, I’m experimenting with tagging to differentiate posts.
How can I reach you?
In line with goal 2 above, I’d love to hear from you! (Yes, you!)
In addition to commenting on a post, you can reach me at any of the following, although I generally prefer email:
Guide to my writing
The most popular things I’ve written are:
Energetic aliens: In which I lay out the concept of “energetic aliens” aka “people who seem to be able to work on hard intellectual and creative tasks for many more hours than we typically expect to be possible”. This post is definitely my most popular post, having generated substantial discussion (more) at the time and still occasionally coming up. If you’re someone who enjoys reading biographies and marveling about interesting outlier attributes of people, you’ll like this post.
Ideas not mattering is a psyop: Co-written with my friends Leo and Alexey, we argue that the (at the time) somewhat widespread idea that only execution, not ideas, mattered for startups was a psyop. We also make the point that good execution is itself very reliant on having good ideas about what to do tactically. In hindsight, I think this post holds up quite well given recent focus on hard tech/AI, where it feels even more obvious that ideas matter a lot at every level.
That said, my personal niche favorites are:
Energetic aliens (again)
Book review: Elmer Gates and the Art of Mind-Using: I still think Elmer Gates was really onto something and it’s shocking to me how his impenetrable writing means he’s almost entirely unknown, whereas much less interesting grifters from the time such as Napoleon Hill continue to be widely read and discussed.
Decaf vs. regular blinded coffee self-experiment: I often lament that the internet is filled with horribly designed self-experiments. See, for example, every r/nootropics post where a person explains how they feel so sharp on some new compound but fails to blind, randomize, or even track any before/after metrics. That’s why I’m so proud of this post where I actually went to the trouble (with help from my wife) of doing a well-designed experiment for a very basic Q: does caffeine actually affect various well-being and cognitive metrics? My analysis does leave something to be desired, but I think my results hold up relatively well.
What I’ve recently (as of August 2024) been thinking about:
Slow feedback loops rule every (living) thing around me: This post lays out one of my core views on biology, that accelerating progress in biology requires orders of magnitude faster feedback loops across the board (experiments, trials, etc.).
Unclogging the human data bottleneck: This post was written in collaboration with two friends, Eryney Marrogi and David Brown. In it, we argue that one of the key bottlenecks for faster progress in translational biology is the scarcity of human data and propose adding cheap, self-administered assays to a large fraction of trials as one of the most promising ways to unclog this bottleneck. Human data scarcity is a topic I think about often and expect to come back to more in the future.
Low fidelity digital twins may be closer than they appear: In this post, I sketch out the concept of a “low fidelity digital twin” and argue that they may be possible in the next few years. Even though it’s about a specific idea, this post ends up giving a good sense of what sorts of AI developments I think are likely and possible in the next few years.
Fifty AI theses: Fifty relatively pithy takes on where AI is going, along with some on bio. I suspect this post is high information but very high context, so it’s hard for me to tell whether this is actually a good place for a new reader to start, and the answer probably depends a lot on where a given reader is starting from.
