The biology is increasingly solved. The vectors work. The regulatory path is clearer. So why does every gene therapy company struggle to survive commercialization? The answer is almost entirely economic — and the field hasn't reckoned with it honestly.

79% of US biotech companies have at least one contract with a Chinese CDMO. The BIOSECURE Act is forcing them to diversify. This is the once-in-a-generation opening India has been waiting for — but capturing it requires building capabilities we don't fully have yet.

Phase-separated condensates — membraneless organelles that concentrate proteins and nucleic acids — are emerging as a new class of drug targets. The biology is real. Whether it translates into druggable chemistry is the open question the field is racing to answer.

A deep survey of where in vivo gene editing actually stands — delivery, payload, safety, and the distance between what's possible in the lab and what works in a patient. More honest than most coverage in the space.

Synthetic biology is moving from proof-of-concept to product. The question is no longer whether we can program cells — it's which applications are commercially viable first, and what it takes to build a real business on programmable biology.

Platform biotech companies promise multiple shots on goal from a single technical capability. But the BD strategy, the pipeline prioritization, the partnership model — most founders get these wrong early. Here's how to think about it.

Every biotech now claims AI at the core. Most of it is curve-fitting on small datasets with no mechanistic grounding. This piece cuts through the noise — what actually constitutes meaningful AI in drug discovery, and what's just a pitch deck keyword.

The two most important intellectual projects of our time are colliding. AI gives biology a new language for reading and writing the code of life. Biology gives AI the hardest, most consequential problems it has ever faced. The convergence is not metaphor — it's infrastructure.

Deeptech has outperformed traditional VC by 60–70% over two decades. The software era delivered incredible returns — but those platform opportunities are mature. The next wave is physical: energy, biotech, space, defense, advanced materials. Two frameworks for timing your entry: AIP and RTPS.

Platform companies have more optionality than any other biotech archetype — and more ways to destroy value through bad strategic choices. The BD decisions made in years two and three almost always determine whether the platform compounds or fragments.

When AI agents can do most of what analysts do in days, what does venture capital actually look like? The edge shifts from information gathering to judgment — and judgment requires a point of view that can't be automated. What survives, what doesn't.

The standard VC evaluation frameworks — TAM, growth rate, team-market fit — were built for SaaS. Applying them to a company with a ten-year development timeline and government as a first customer leads to bad decisions. A different set of questions to ask.

A practical guide for deeptech founders raising in India — how to think about narrative construction, investor selection, timing, and the specific challenges of pitching hard science at pre-seed. Written from the investor side of the table.

Every engineering problem we think is hard, evolution solved three billion years ago under harsher constraints. The humility this requires of us — and the ambition it unlocks — is the most underrated idea in science and technology.

I failed 8th grade math and won a state olympiad the same year. I failed IIT-JEE and got into IISER. I failed a startup and found my way to venture capital. The pattern is not accidental. Failure is not the opposite of learning — it's the mechanism.

I left a job without one in hand. Ten months, no income, no plan. I learned yoga, boxing, AI, writing, and investing — not because I was disciplined, but because I had no choice but to absorb everything. Some thoughts on what that period actually produced.