Ankit Gupta

We closed Brex during the W26 batch! Brex's startup sales team uses Autumn to find venture-backed founders before they’ve been publicly announced. Last week, our agent surfaced 150+ unannounced founders by piecing together GitHub commits, Luma event pages, LinkedIn bio changes, and Delaware incorporation filings. Brex moves fast. First call to contract + implementation in days. Super grateful to Jimmy Stephens for giving us the opportunity to build with Brex. The best products get built with the best customers.

We just released SciDesignBench: a benchmark for inverse design with LLMs.

My take on the whole "AI cures cancer in dog in Australia". It's a very interesting story, but perhaps not for the reasons that are being noted. In 2007, Freeman Dyson published an essay in The New York Review of Books called “Our Biotech Future.” It contains one of the most memorable predictions about the future of biology I’ve ever read. “I predict that the domestication of biotechnology will dominate our lives during the next fifty years at least as much as the domestication of computers has dominated our lives during the previous fifty years.” Dyson believed biology would eventually follow the trajectory of computing. At first, powerful tools live inside large institutions - universities, government labs, major companies. Over time those tools get cheaper, easier to use, and more widely distributed. Eventually individuals start doing things that once required entire organizations. “Biotechnology will become small and domesticated rather than big and centralized.” He even imagined genome design becoming something almost artistic: “Designing genomes will be a personal thing, a new art form as creative as painting or sculpture.” Dyson's words rang in my mind as I read the "AI cures dog cancer" story. Much of the coverage framed this as an example of AI discovering new science. But that’s not really the interesting part of the story. The scientific pipeline involved here is actually well known. It closely mirrors the workflow used in personalized neoantigen vaccine research that has been under active development for years. The steps are fairly standard: sequence the tumor, identify somatic mutations, predict which mutated peptides might be recognized by the immune system, encode those sequences in an mRNA construct, and deliver them to stimulate an immune response. The biological targets themselves were almost certainly not new discoveries (I have been unable to find out what they are, but mutations in targets like KIT which are common might be involved). Partly therein lies the rub, since the hardest part of drug discovery, whether in humans or dogs, is target validation, the lack of which leads to lack of efficacy - the #1 reason for drug failure. In neoantigen vaccines, the proteins involved are usually ordinary cellular proteins that happen to contain tumor-specific mutations. AlphaFold which was used to map the mutations on to specific protein structures is now a standard part of drug discovery pipelines. The challenge is identifying which mutated peptides might plausibly trigger immunity. What is interesting though is how the pipeline was assembled. Normally, this type of workflow spans multiple domains - genomics, bioinformatics, immunology, and translational medicine - and in institutional settings those pieces are distributed across specialized teams, document sources and legal and technical barriers. Navigating the literature, selecting computational tools, interpreting sequencing results, and designing a candidate mRNA construct is typically a collaborative process. In this case, AI appears to have helped compress that process, pulling together data and tools from different sources. Instead of requiring multiple experts, a motivated individual was able to assemble the workflow with AI acting as a kind of guide through the technical landscape. I’ve seen something similar in my own work while building lead-optimization pipelines in drug discovery. The underlying science hasn’t changed, but the friction involved in assembling the workflow can drop dramatically. Tasks that once required stitching together multiple tools, papers, and areas of expertise can now often be executed much faster with AI helping navigate the terrain; and by faster I mean roughly 100x. That kind of workflow compression is powerful, to say the least. When the cost of navigating technical knowledge drops, more people can realistically assemble sophisticated research pipelines. This story is a great example of what naively seems like a boring quantitative acceleration of the research process. In that sense, therefore, the real novelty here is not the biology but the combination of three things: a non-specialist orchestrating a complex biomedical pipeline, AI acting as a navigational layer across multiple technical domains, and the resulting decentralization of capabilities that were once confined to institutional research environments. But I think the story also points to something deeper, which is a challenge to modern regulatory environments. Modern biomedical innovation does not operate solely according to what is scientifically possible. It is structured by regulatory frameworks - clinical trials, safety oversight, institutional review boards, and regulatory agencies. Those systems exist for important reasons, but they also assume that the development of therapies occurs primarily within large, regulated organizations. When individuals begin assembling pieces of these pipelines outside those institutions, the relationship between technological capability and regulatory oversight starts to shift. The dog in this story sits outside the human regulatory framework. That fact alone made the experiment possible. In other words, the story is not just about technological capability; it is also about how certain forms of experimentation can occur when they bypass the regulatory pathways that normally govern biomedical innovation. One is reminded of another Australian, Barry Marshall, who received a Nobel for demonstrating through self-experimentation that ulcers are caused by bacteria. This raises an interesting question: what happens when the tools for assembling sophisticated biological workflows become widely accessible while the regulatory structures governing them remain institution-centric? That tension may ultimately be the most important implication of this moment. Regulatory frameworks will need to adapt to this kind of citizen science. Seen in this light, the story about the AI-assisted vaccine is less about a breakthrough in cancer therapy and more about a glimpse of the early stages of something Dyson anticipated nearly two decades ago: the domestication of biotechnology. If AI continues to reduce the cognitive overhead required to navigate biological knowledge and assemble complex pipelines, the boundary between professional research and motivated individuals may begin to blur. That shift will require careful thinking about safety, governance, and responsibility. But it also carries an exciting possibility. Dyson imagined a world in which biological design might eventually become something like a creative craft practiced not only by institutions but also by curious individuals experimenting at smaller scales. For a long time that vision felt distant. Now, it feels like we may be seeing the first hints of it.

Also excited to have @aadit_palicha, co-founder of @ZeptoNow, joining the speaker lineup at Startup School India! events.ycombinator.com/yc-sus-india

“sir they’ve paid $100 for the Apple Developer account” “good. wait 2 days to confirm their account then reject the app 10 times over the next 20 days. if they make it through take a 30% cut of the revenue and pay them out 2 months later. ban them if they try using Stripe”

Today, we're excited to release 10,000 fully AI-designed enhancer sequences for the research community. Axis was prompted to design sequences with targeted activity in one of three widely used cell-lines. AI allows us to explore a vast design space, going beyond the natural genome.

Guys, relax, the only risk in transiting the Strait of Hormuz is that Iran will shoot a missile at your ship. Otherwise it's open for transit. An all-time quote from our Secretary of War.

Our first clinical-stage pharma partnership. CellType models working on a real drug, in real patients. This is what we built for.

excited to bring Startup School to India with @ycombinator and meet the next generation of founders!

good reminder of the willingness of CA ballot measure voters to approve obviously bad ideas

Students who've attended YC events get $25K in free credits/tools Now includes a year of Cursor Pro+

Instant GPU access. Right from your laptop.

Announcing AgentMail's $6M Seed led by @generalcatalyst No pressure, right?

@n0w00j @hyrumjb3 😂

@agupta @hyrumjb3 this is the most condescending "interesting" ever 😂😂😂😂😂😂😂

im sorry but i just don't get how this YC company is a viable product when this API exists. maybe i'll launch Powerpoint for Agents

@hyrumjb3 @n0w00j interesting!

@agupta @n0w00j Gmail's moat lies in their connections across all Google products, right?

@n0w00j same place gmail's moat lies

@agupta genuine question, where do you think the moat lies? its not obvious that there is one, especially for a primitive like emailing

@haakamaujla @generalcatalyst so proud of you guys

No pressure

My 2 best friends and I just raised $6M to turn our dorm room idea into one of the biggest companies of all time. No pressure, right?