<aside> 📢 Structure Function was set up as a personal library that curates news from all over the web. A newsletter of newsletters, an aggregator, if you will. This has morphed into a full dataset: wealth of knowledge that filters through my eyes into a database that is me! I use NotionAI and a custom agent to summarize and synthesize these readings.
In a world where you can deep research your way into any topic, why does Structure Function matter? It matters because readings here are a personal opinion on topics I am moved by or interested in. Why let this data gets lost in obscure cookie sharing databases when I can keep my own trail of the internet, and quickly and meaningfully reflect on my readings over time.
These readings funnel in from newsletters, academic journals, news outlets, and company pages and I gate and filter based on the relevance and importance of a source. All this serves the function of making it easy to query, track, and synthesize the ginormous amount of reading out there. It also helps to have ONE place to house everything.
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This is not AI slop. Parikshit picks and chooses topics he cares about and reads/wants to read > AI compiles > Parikshit curates and reflects. This is accelerated brainstorm and content generation, not theory slop for the sake of it! Hope you will see that too as you peruse through the material.
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<aside> 📃 VC Landscape & News
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<aside> 💻 Computation Research
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<aside> 🌏 Planetary Health
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<aside> 💊 Human Health
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<aside> 🔍 Dig Deeper
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<aside> ❤️ Structure Function, as I mentioned above, helps me gate and filter readings from various sources. But it’s part of a bigger effort of mine to create an architecture, to understand the world and drive home key learnings. With the glut of information out there and with my propensity to suffer from analysis paralysis, I need a way to structure my thoughts, to have the confidence to decode patterns around me, both at a micro and at a macro scale.
<aside> 🙋🏽♂️ I am an investor and company builder at SOSV’s IndieBio. I was an early hire (no. 6, I think) to the team back in 2017 — when they had recently built a lab space in a basement in SoMa in San Francisco. It’s been a thrill to see IndieBio take a life of its own and become the birthplace of epic deep tech startups working on making people and the planet healthy.
I am very privileged to have grown up all over the world. I am a desi 🇮🇳 currently living in San Francisco 🇺🇸 🌉. I came to the States to go to Bowdoin College on the East Coast, where I loved studying math and economics as a lens to model our big crazy world. I have loved exploring this big crazy world and stay a student and lover of how wondrous life is.
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<aside> 🤔 Most of the areas of knowledge shared here are work-related readings. These cover interest areas that (1) I have had since my undergrad days, a-la computation and economics, (2) areas that are essential to my job: having a pulse on the venture capital landscape, deals and stakeholders, hot papers and technologies in human health and planetary health (3) areas I want to grow into : people management, understanding culture and humanity on the one hand, and more tactical opportunities such as industrial engineering, circular economics
<aside> 📃 VC Landscape and Reading
This one is simple. I work in venture capital. Choose this adventure if you want to cover deals and players in the ecosystem, general news that catches my eyes and sector funding summaries all live here. The structure of deals, cadence of news cycles, patterns and many excellent analyses will catch many an eye.
<aside> 💻 Computational Research
I am a quant at heart 🤖 I spent my undergrad career diving deep into computational methods to measure and model socio-economic and planetary phenomena and fell in love with the math behind the ways computers speak and how to think in their language. My interest in this field created a bridge to my job where I was exhilarated to learn that machine learning and biology was a match made in heavy. Biological and deep tech systems are data dense and complex and these quantitative approaches work very well in tandem to design-build-test-learn-repeat.
But it was very quickly in my job that I was humbled by how a conceptual understanding of computational methods doesn’t translate to scaleable data pipelines. I saw how efficient architecture design directly impacts both the top and bottom line. Thus, this area covers devotes a significant chunk of reading on these topics of architecture design and new models that capture complexity of datasets with the lowest compute spend. In addition, you’ll find math theory and econometric analyses, my first love, here.
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<aside> 💊 Human Health Reading
I am new to the field of healthcare and biotech, didn’t go to school for it. I love it though, always fascinated by how dynamic biology is. My readings aim to delve into discovery of novel drug targets in the body, the methods scientists use to answer hypothesis in the body, the mechanism of action of new types of drugs. This area is a living embodiment of the Structure Function paradigm, in its own funky ever so changing manner.
Biotech and fundamental science aside, a less elegant but as complicated reading material that lurks here is healthcare. How complex the incentive structures are, the number of stakeholders, sensitivity to economic factors and new technological approaches all present here.
<aside> 🌏 Planetary Health Reading
The planet is getting hotter, resources dwindling, an existential angst rising. This one is a no brainer, yo! 🔥 🗺️
My job gives me the privilege to invest in the cutting edge of deep techs that, if successful, can contribute towards re-materializing and decarbonizing the planet. Is this an optimists striving? Nah, climate change is already here. there’s no way to avoid it, only a few shots to adapt to it. This is more of an absurdist, a Sisyphean odyssey. The technologies are fascinating, and the human costs to climate change gut wrenching. All we can do is to try.
<aside> 👺 Human Capital Resources
Who would’ve thunk that the biggest part of an early-stage investor’s job is to spot and nurture talent, to support their crazy ideas when most laugh them out of the room. I didn’t. Early on in my career, I thought investors were these wicked smart folks who spend their days reading tech briefs and modeling market dynamics. I couldn’t have been more mistaken. Haha.
As I worked with startups, I also saw how quickly some startups scaled, growing from 2 co-founders to 22 people in just 2 years! A big word that was thrown around when it came to headcount growth was culture fit. Culture fit to me has appeared as a double-edged sword. Where it works, it can be an incredible springboard to unleash talent, a right balance of challenge and safety, and trust to take risks. Where it doesn’t is the massive problem that lies with prejudice, lack of diversity, and problems with inclusion the world struggles with.
This area is one where I want to grow and hope you’ll dive in and see what catches my eye.
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