Managing Partner @Inflexor_VC. Backing India's Science, Engineering & Tech (SET) founders: semis, space, materials, health IP.

Bangalore, India
Joined June 2009
What I look for in SET founders. At Inflexor we call our focus SET: Science, Engineering and Tech. Some call it deep tech, but that word means different things to different people. A short thread on what matters to me.
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Many deep tech pitches spend most of their slides on the science and one on the customer. I'd flip that. I'm not backing a breakthrough for its own sake. I'm backing the advantage it gives a buyer: lower cost, better yield, or something that simply wasn't possible before. If you can't say that in one sentence, the science isn't the problem yet. Founders building on hard science, what's the one line you use to explain why a customer should care?
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The best question in a deep tech diligence usually isn't mine. Before I go deep on a company built on hard science, I try to bring in someone who has spent years in that exact field: a materials scientist, a chip designer, a clinician. They often find the real technical risk faster than a generalist investor ever could. Founders, this works in your favour. A sharp expert pushes on the right question early, and the good ones keep being useful long after the money is in. What's the toughest technical question an investor's expert has ever asked you?
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Intel is buying quantum software startup Classiq for $2.3B, the biggest quantum acquisition yet. What I notice: a chipmaker paying up for the software layer. Deep tech exits often go to a strategic that needs your piece of the stack. Who's that buyer for India's quantum startups?
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The cap table decides this long before the exit. Stacked liquidation preferences, more capital than the business needed, and a sale below the last round's price mean investors get paid back first and founders get nothing. Raise what the business needs, not what the market will give.
88% of VC-backed founders who exited their companies got $0. Eduardo Azevedo and Kent Smetters analysed 157,669 startups and 248,454 founders from 1987 to 2025. Let that sink in...
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A markup is not a return. In Indian venture, a higher valuation in the next round feels like progress. But until someone actually buys the shares, it's an opinion about what the company is worth. LPs can't spend opinions. So I try to think about the exit from the first meeting. Who could realistically buy this company one day? What would it need to look like to list? Is there a path to cash, or only to the next round? Founders, has an investor ever asked you who your eventual buyer is? Did it help, or feel too early?
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Bedrock Robotics just raised $600M at a $4.1B valuation. The detail I care about: its autonomous excavators have been doing paid work on live job sites since August. That's the bar I hold physical AI founders in India to: a task a customer pays for, done reliably in the field.
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A patent filing is not a moat. When a deep tech founder tells me their edge is IP, I ask three questions. Does it protect something a customer actually pays for? Would a well-funded competitor have to design around it, or could they simply ignore it? And is the know-how that makes it work written down, or does it live in two people's heads? The IP I value most keeps compounding. Every customer deployment teaches the team something a newcomer can't copy from a filing. File the patents. Then build the part that can't be filed.
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Anthropic's S-1 is public. Revenue went from $386M in 2024 to $16.3B in just the first half of 2026. The number I care about more: customers paying $100K+ a year grew from about 950 to 11,900 in twelve months. That's paid enterprise demand, not TAM slides.
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India cleared about ₹2.12 lakh crore of defence purchases, 82% from Indian industry, with counter-drone systems and long-range drones on the list. These are in-principle approvals, not contracts. For defence tech founders, what matters is how fast they become purchase orders.
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Many hard tech decks in India model the product in detail and leave the cap table for later. Before your first institutional round, map the whole journey: the capital it takes to reach a product you can manufacture, the working capital each order will tie up, and how many rounds sit between you and real revenue. Then ask what you'll still own at the end. If the honest answer is too little to keep you going for ten years, change the plan now, not at Series B. Capital intensity isn't a reason to avoid deep tech. It's a reason to underwrite it with open eyes.
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Ten companies, led by Nvidia and Micron, account for about two-thirds of corporate profit growth. That isn't a broad earnings boom. It's a bet on AI and chips. If India doesn't own a piece of the compute and memory stack, it is just renting the boom.
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Malaysia's PM says SoftBank plans a $50B AI and data centre investment there. It's a proposal, not a contract yet. But the pattern I keep seeing: AI infrastructure goes where power, land and approvals move fastest. India has the demand. We have to win on grid and speed.
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I'm rebuilding how I do early-stage deep tech investing around AI. Not as a tool on the side, but as the way the work itself gets done. Over the coming weeks I'll share what's changing, one angle at a time. Starting with the basic split.
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What this should mean for LPs: Discipline that doesn't depend on a few smart people remembering things. Every thesis, every piece of evidence, every pass and every outcome gets captured and tracked, so the firm keeps learning and partner time goes to judgment.
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What I believe most: the model is not the moat. Everyone will have the same models. What compounds is the evidence you gather and the workflows you build on it, year after year. That's what I'm building. Founders and LPs, what would you want an AI-first VC to do better for you?
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