Fluids. I think life is fluid.
Our thoughts are fluid.
Our urge to do something is fluid — and the urge to do nothing at all is just as valid.
The biggest blocker to this fluidity is the environment we live in.
Sometimes you are successful. You have everything more than half the world can only dream of. Yet we exist in surroundings that constantly gaslight us into believing we are not enough.
If I could erase one word from my environment,
it would be “should.”
We are part of a system that prevents confidence by scrutinizing every decision with unmeasured, inhumane consequences — constantly.
If you don’t do this, you won’t get that.
If you’re not thinking about it now, you’ll never have access to it later.
It’s a deeply manipulative conditioning environment.
How dare you exist so fluidly?
We have one life.
The real challenge is living many versions of it.
So what if you change your mind after decades of effort to reach somewhere?
Since joining a bioscience company, I’ve felt the strongest urge — in the longest time — to explore science.
Science is magical. Vast areas of it remain unexplored.
Now, with AI, we have more chances to live out our probabilities and permutations. If our brains churn a thousand unique thoughts a day, AI lets us try them faster — to fail fast — without exhausting ourselves by executing every idea.
It’s a deeply privileged time to be alive.
We are just getting started.
Centuries from now, human evolution will be astonishing to witness.
To give this context, imagine if we solved most disease-prone genes or DNA — and synthesized them in ways that increase the human lifespan — just as our ancestors once died from diseases that are now completely curable.
Human life expectancy has already stretched from around 40 years to nearly 100.
Imagine solving food-related aging precursors.
Imagine not glycating at accelerated rates.
Imagine surviving well beyond 100 years — for generations.
Imagine being forced to redefine time itself — calculating a year as 730 days instead of 365.
If many galaxies exist, if signs of organisms exist on other planets, then it’s okay to believe we are here to forge our own path — to be discovered by our own species.
In the larger scheme of things, if our memories or consciousness were erased solely to devote us to finding immortality, then we are here on purpose.
We exist collectively to find answers.
We are a reinforcement learning project.
Billions of variations — permutations and probabilities — searching for the version that discovers living answers and evolves into something more than human.
AI may simply be the next medium of our evolution.
Throughout history, wisdom has survived through mediums. Centuries-old temple designs still inspire modern machines. Our ancestors used stone because it could outlive them.
Now we have the internet — capable of holding the accumulated groundwork of human knowledge.
AI becomes the medium that holds the secrets of humankind, passed from generation to generation — until immortality is achievable — while reshaping how we define time itself, much like the difference between dog years and human years.
Another question lingers:
How do you measure memory in AI?
If billions of us interact with AI, it begins to know us personally. But how long does that memory last?
Will AI forget me — my ideas, my personality, my experiences, my context — after 20 years?
Will that depend on data centers, government policies, or relevance at that time?
Or will it simply be replaced?
And if, someday, a wild child has a breakthrough idea similar to mine — will they find references?
Proof that someone once brainstormed the same idea with AI decades earlier?
Some ideas are simply ahead of their time. Just like this one.
So yes — science is my only hope.
Nothing else excites me anymore.
With that in mind, I want to start exploring gaps in science.
When I collaborate with scientists — especially geneticists — I often hear discussions about gene-level discoveries, open targets, and pathways.
One of my colleagues, Aakriti, once mentioned something that stuck with me:
We still don’t have robust solutions for mapping a locus to a gene, or an SNP to a gene.
So I thought — why not use the holidays to explore this problem?
Even if I don’t solve it, I can try to understand it.
I don’t have a formal background in bioinformatics or genomics.
But I do have free will — and the skills I’ve accumulated in AI feel like a solid starting point.
For now, this is just learning.
So — what the hell is a gene?
And what is its relationship to a locus?
A gene is a functional instruction.
A locus is a coordinate.
Most disease-associated loci don’t sit neatly inside genes — which is why mapping locus → gene is one of the hardest unsolved problems in genetics.
1. What is DNA?
DNA is the entire library.
Every book together = DNA.
It contains all instructions for your body.
Most pages are:
- notes
- spacing
- reminders
- switches
Not actual stories.
DNA = everything written, useful or not.
2. What is a gene?
A gene is one useful story in the library.
It tells the body how to: build something, fix something and run something
Examples: eye color, how cells grow, how your heart works etc.
Gene = a meaningful instruction inside DNA.
All genes are DNA. Not all DNA is genes.
3. What is a locus?
A locus is an address.
Like: shelf 3, book 12, page 45
It tells you where something is — not what it does.
Locus = location.
4. What is an SNP?
An SNP is a tiny spelling difference at one address.
One book says color. Another says colour. Same story. Slight change.
That’s an SNP: one letter difference at a specific locus
SNP = the change.
Locus = where it happened.
5. How are DNA, gene, locus, and SNP related?
Let’s stack it cleanly:
DNA → the whole library
Gene → a useful story
Locus → the address
SNP → a typo at that address
Sometimes an SNP sits inside a gene. Sometimes it’s far away — yet still changes how the gene behaves.
This is where biology gets tricky.
DNA
├── genes (instructions)
├── switches (on/off controls)
├── spacers (unknown)
└── SNPs (tiny changes)
SNPs happen at loci. SNPs can alter gene behavior.
Genes make proteins >Proteins affect cells > Cells affect organs > Organs affect disease.
Blood gives us DNA. DNA has SNPs. GWAS finds SNP locations linked to traits.
The hard part is figuring out which gene those SNPs actually affect in a real patient.
Why this is still an open problem
Because: GWAS gives where, not what, genes behave differently across: organs, ages, environments
Biology is contextual — not static.