Partner @LightspeedIndia. Early to categories before they have names or winners. Currently AI Services, Healthcare AI, AI for the next billion. SF ↔ India.

San Francisco, CA
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What I'm looking at in 2026: • AI-led services • Healthcare AI • AI for the next billion I like categories before they have names or winners. If that's what you're building, DM me.
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“My Vampire System,” a vampire-meets-science-fiction series, has made over $90 million as of September 2026. Pocket FM cofounder and CEO @RohanNayak2: “If someone in Hollywood looked at it, they wouldn’t have greenlit that. They would never have greenlit that. But users should vote for what they like. Users should tell us, or anyone, what a hit is.” From Lightwork
Streaming services democratized entertainment consumption, the creator economy democratized distribution, and AI could do the same for high-quality production. @RohanNayak2, Co-Founder and CEO of @PocketFM_app, gave us the scoop on the company’s thesis behind their AI-driven audio drama platform. Pocket FM doesn’t rely on general-purpose LLMs alone. Rohan explains that Sherpa, its multi-agent fiction-writing system built on custom post-trained models, focuses on long-horizon narrative memory, emotionally convincing prose, and long-form planning. Rohan also gets into the harder problem that comes with cheaper creation. More content means Pocket has to get better at identifying the stories people actually want to keep listening to. Pocket’s fiction models are trained on billions of minutes of engagement data, while listener behavior helps determine which stories reach larger audiences. The team is now driving their newest product, Pocket Saga, an app that converts the best audio dramas into AI-animated vertical video series. Rohan sees AI as a tool that creators can use to execute on their ideas. He believes: “For the first time, literally anyone in the world can create studio-quality entertainment at their home.” Watch the full episode with Rohan on Lightwork at the link in the comments.
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Dev Khare retweeted
Streaming services democratized entertainment consumption, the creator economy democratized distribution, and AI could do the same for high-quality production. @RohanNayak2, Co-Founder and CEO of @PocketFM_app, gave us the scoop on the company’s thesis behind their AI-driven audio drama platform. Pocket FM doesn’t rely on general-purpose LLMs alone. Rohan explains that Sherpa, its multi-agent fiction-writing system built on custom post-trained models, focuses on long-horizon narrative memory, emotionally convincing prose, and long-form planning. Rohan also gets into the harder problem that comes with cheaper creation. More content means Pocket has to get better at identifying the stories people actually want to keep listening to. Pocket’s fiction models are trained on billions of minutes of engagement data, while listener behavior helps determine which stories reach larger audiences. The team is now driving their newest product, Pocket Saga, an app that converts the best audio dramas into AI-animated vertical video series. Rohan sees AI as a tool that creators can use to execute on their ideas. He believes: “For the first time, literally anyone in the world can create studio-quality entertainment at their home.” Watch the full episode with Rohan on Lightwork at the link in the comments.
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So many different entry wedges into healthcare AI within administrative and clinical workflows. All heading for similar goals. What'll slow this down? Lots of explicit and hidden incentives in the industry, the massive sprawl of US healthcare and a few large incumbents. In the meantime, several large pockets of opportunity.
Replying to @HelloPatient
@HelloPatient (of @anothercohen fame) announced its acquisition of Converse Health this week, the latest move in the widening copilot wars.
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Every @PocketFM_App launch widens the ecosystem around the same mission, helping creators tell their stories to the world. Listen on Pocket FM, watch on Pocket Saga, and from today, write with Sherpa, a free AI story-writing partner that also localizes your story into multiple languages. Congratulations to the team on the launch!
Introducing Sherpa: the most advanced fiction writing AI We accelerated from $250M in ARR to $500M because Sherpa helped increase content production by 1200% in 1 year Sherpa was trained on 5.5B hours of playtime with minute by minute dynamic retention data. 550K+ creators have produced 2.6M hours of content annualised using it Pocket FM is like Netflix for audio-only dramas, with our own pool of one-person studios. 10% of eligible writers on Pocket FM make >$200K One blockbuster produced >$100M in revenue 3 writers have become millionaires in <2 yrs We built Sherpa to enable anyone to make >$1M by writing world-class fiction stories: 1. The Idea: Drop a 1-2 sentence concept. Sherpa interrogates it like a veteran editor on tension, stakes, and psychology 2. World & Characters: It builds out the complete lore, tone, and character psychologies 3. Sub-Plot planning: Breaks the premise into arcs, arcs into episodes, and episodes into scenes 4. Scene-by-Scene Generation: Outlines and drafts entire episodes, with you able to steer, rewrite, or override anytime 5. Editorial Review: Stress-tests every draft for pacing, engagement drop-offs, prose, and coherence before it locks 6. One-Tap Production: Pick a voice, convert to audio drama, and publish directly to Pocket FM’s millions of listeners 7. Global Scale & Monetization: Revenue-share on performance, with automatic localization so you earn across international markets Test Sherpa for free here: pocketfm.com/sherpa _____________________________________________ Generic LLMs fail at serialized fiction because they lack a long-horizon narrative reward function. Sherpa solves this through three core technical leaps: 1. Narrative World Model (State Tracking & Retrieval): Context windows degrade over long runs. Sherpa constructs an evolving semantic knowledge graph tracking character states, secrets, and plot dependencies. High-speed retrieval surfaces exact context on demand, maintaining zero continuity decay across hundreds of episodes 2. Hierarchical Story Planner: When writing a 500-episode story like Naruto, you need to plan 100s of sub plots. Rather than generating linearly, Sherpa decomposes narrative across discrete levels: season -> arc -> sequence -> episode -> scene. Rather than generating everything upfront, like a generic LLM, Sherpa uses progressive planning and dynamic replanning. As the story evolves, it identifies what changed, traces the downstream impact, and replans only the affected parts. 3. Prose Engine (Trained on series' retention data): LLMs write robotically, but serial fiction needs emotion, tension, pacing, and dialogue that sounds like real people. Sherpa's Prose Engine was designed specifically for storytelling. It was built on 1B+ tokens of Pocket's own stories, trained by learning from what listeners engage with, where they drop off, and what keeps them hooked. Feedback is taken from specialized evaluator models that measure every scene against a 40-item checklist. (Evaluator models were benchmarked against human reviewers and matched them 80–90% of the time.) _______________________________________________ Owning distribution and creation puts us in a very unique spot. More shows -> More data -> Sherpa becomes better -> more creator success -> more creators -> more shows Pocket FM has already seen one $100M IP. I believe Sherpa will soon lead to dozens of single-person studios creating billion-dollar shows. Most people are scared of AI but I think it'll unlock more human creativity, help creators earn more, and bring the next great IPs to life. This will create millions of jobs and new income streams.
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Dev Khare retweeted
@RohanNayak2, Prateek Dixit and @nishanthks91 have shown what it takes to build an AI entertainment platform. Congratulations on the $500M ARR milestone! Pocket FM has cracked the two hardest problems - First, content production at scale, with output up from about 25,000 to over 2.5M hours a year since moving fully onto AI, and adding 550,000+ UGC writers on the platform. And second, global distribution to 20+ countries including US, Germany and France through localisation rather than mere translation. The best is yet to come! @LightspeedIndia @lightspeedvp
We grew from ~0 to $500M ARR, adding $250M last year alone while being EBITDA profitable. 1,300-word post on every growth tactic that worked for us: 1. What got us from 0-$400M ARR in the US works in every country. 2. Retention and Monetisation Hacks. 3. Localization > Translation. 4. Experiment with $1M internal seed checks. 5. Pivoting to only AI content production unlocked 100% ARR growth. Pocket FM is like Netflix for audio-only dramas, with our own pool of one-person studios. 1. Acquisition Playbook for 0-$10M ARR in any country in 6 months We run a 90 sec video trailer of an audio drama as an Ad and ask users to download the app if they're interested in the rest of the story. Our core insight after spending >$100M on acquisition is: If the clickthrough rate (CTR) for an ad goes from 2% to 2.25%, our customer acquisition cost (CAC) decreases by ~ 30%. We remodelled our system around this insight and built an AI-first 2.25% CTR ad manufacturing machine that works in every country. For every new country, we take our hit shows -> use LLMs to extract and most intriguing moments -> Write a 5-min script combining all the best parts. The first 60 seconds has to have a hook every 5 seconds and needs to end with a crazy cliffhanger to force a download mid-scroll. If a marketing video is not hitting our benchmarks (2.5% CTR and 55% 3-second through-play), a creative director gets involved to change the hook or cliffhanger to get the numbers there. AI lets us make 1,000 Ads per show, and in total we do ~17.5k Ads per month. When a business creating scales from 1k to 10k Ads, the normal thing is for CAC to skyrocket. But with what I just shared, we 7-8x'd our User Acquisition budget without increasing our CAC materially. It took us 8 months to figure this out, but then the timeline from 0-$10M in every country got shorter and shorter: US revenue grew to $25M in 19 months. (US is now 78% of total) Germany to 21M in 11 months. France to $10M in 3 months. 2. Retention and Monetisation Hacks. We knew we wanted to create an audio entertainment platform, but there was no standard format. For the first 2-3 years, we tried 10 different formats before landing on the winner. After we got it right with audio drama (8-12min chapters, written for mobile fiction, serialized, episodes have strong hooks and end on cliffhangers), avg daily streaming time went from ~25mins to 150+. Audio drama made us realize that Pocket FM was creating a whole new medium. There was no playbook for anything that we were doing. Everything had to be thought & built from scratch. This is what we did for each major bullet: - Monetization: Users have some free daily minutes to listen; then it's pay per episode. We also added the option for users to unlock episodes by watching ads, which is doing extremely well. Ads scaled from zero to a ~$90M run rate in 12 months. - Engagement & production: You don't become obsessed with an app you open once a week. To have users engage daily, we make the next episode free every day. Also helps them build the habit. - Discovery. Huge problem because people consuming Pocket FM enter the app, tap the show, and lock the screen. We fixed it by doing "playlists" of episodes. Once users's free minutes on a show are done, we ask users if they want to pay. If they say no, we play a new show, one where they haven't used their free mins. And we stitch episodes of different shows together that way, creating natural discovery. 3. Localization > translation. 78% of our revenue is still concentrated in the US. Localisation is fixing this: You might write a show for a Spanish audience where language, jokes, folklore, have a certain flavor; if you merely translate the show for, say, a Norwegian audience, that color is lost and hurts the show in the Norway. Listeners would relate more if the show was written by a Norwegian. That's why, instead of translating, we localize shows to different regions. We use AI to take the spine of stories and adapt their whole cultural layer to the other country. The results: a US show that was localized for a German audience had 50% higher retention than the translated version. Localization + our user acquisition playbook led to: - $10M+ ARR in France within 3 months - $21M+ ARR in Germany within 11 months. And this expands writers' addressable market. We get messages of writers thrilled to have revenue coming from the US, India, EU, LatAm, without them doing much incremental work. 4. Experiment. We give $1M checks to new internal initiatives, and the team has 12-18 months to prove their thesis. If they prove it, we double down. This is how Pocket Saga came about, our AI video microdrama app. It's an AI video equivalent of Pocket FM. Same shows and structure, but as a vertical 2-min mobile video series. We launched it two months ago and it's at ~$15M ARR. Pocket's broader thesis is to help creators tell their stories to as many people as possible. Start with audio drama -> multiple languages or localize to diff countries -> microdrama -> more formats like movies, TV shows, and games. Best of all is that writers get revenue streams not only from countries they wouldn't have tapped into, but also from formats they wouldn't have thought possible. 5. Pivoting to only AI content production unlocked 100% ARR growth. In mid-2024, we pivoted to only AI content production. Our growth took a very direct hit. Pocket was already at $200M ARR, growing 50% YoY, and we flatlined for a whole semester. Six months later, the business exploded. After the switch, we went from ~25k hours of content produced per year to over 2.5M hours, which are also higher quality. Because AI orchestrates the writing and more data is fed into it, more blockbusters come out. Over 90 titles have $1M+ in lifetime earnings and 13 crossed $10M. Quality control is done with LLM as a judge, and LLMs are quite tough. Results are very encouraging: - 12-month revenue retention went from 44% to 76%. - In the past year, we added $250M in net new ARR. __________________________________ Pocket Entertainment (Pocket FM + Pocket Saga) has become the largest AI entertainment platform. We have the largest storytelling catalog with 770,000 titles, 550k creators, and 5.5B hours of playtime with minute by minute retention & engagement data. We are using all of this data to improve every aspect of our business. Our Bet: In the next 3 years we will be able to produce Naruto-like series for $1000. Netflix has to spend $17B to find 100 Blockbusters per year. What happens when you can produce 1M high quality shows for $1B? The streaming wars caused a $300B reallocation of market cap. The AI entertainment wars may be a $1T+ reshuffling of market cap. We are at a unique spot. Unlike other AI creator tools like Runway or Midjourney, we own both the supply and demand side of AI content. We've attracted over 550,000 writers who are producing an annualized 2.5M hours of content every year. This pairs with 137B+ minutes streamed. Because of this, growth is accelerating as we scale further. We have multiple S-curves inflecting at the same time: - AI produces better and more ads -> scale faster in new countries - ⁠Writers + AI produce more shows -> more blockbusters - ⁠Localisation -> more reach per blockbuster - ⁠Audio to video format expansion -> larger TAM -> more creators Every aspect of our business is designed to improve another, and everything is aligned so that the number of blockbusters continues to rise. If you're interested in building at the intersection of ai, tech and entertainment, DM me.
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$500M ARR, $250M of it added in the past twelve months, EBITDA profitable throughout. A global consumer AI company with 75%+ of its revenue coming from a mainstream audience in the US. 🚀 We backed @RohanNayak2, @nishanthks91 and Prateek at Pocket's seed round. Watching them grow as founders, leaders and humans over these years has been super energizing.💓 Congratulations to the whole Pocket team! @LightspeedIndia @lightspeedvp
We grew from ~0 to $500M ARR, adding $250M last year alone while being EBITDA profitable. 1,300-word post on every growth tactic that worked for us: 1. What got us from 0-$400M ARR in the US works in every country. 2. Retention and Monetisation Hacks. 3. Localization > Translation. 4. Experiment with $1M internal seed checks. 5. Pivoting to only AI content production unlocked 100% ARR growth. Pocket FM is like Netflix for audio-only dramas, with our own pool of one-person studios. 1. Acquisition Playbook for 0-$10M ARR in any country in 6 months We run a 90 sec video trailer of an audio drama as an Ad and ask users to download the app if they're interested in the rest of the story. Our core insight after spending >$100M on acquisition is: If the clickthrough rate (CTR) for an ad goes from 2% to 2.25%, our customer acquisition cost (CAC) decreases by ~ 30%. We remodelled our system around this insight and built an AI-first 2.25% CTR ad manufacturing machine that works in every country. For every new country, we take our hit shows -> use LLMs to extract and most intriguing moments -> Write a 5-min script combining all the best parts. The first 60 seconds has to have a hook every 5 seconds and needs to end with a crazy cliffhanger to force a download mid-scroll. If a marketing video is not hitting our benchmarks (2.5% CTR and 55% 3-second through-play), a creative director gets involved to change the hook or cliffhanger to get the numbers there. AI lets us make 1,000 Ads per show, and in total we do ~17.5k Ads per month. When a business creating scales from 1k to 10k Ads, the normal thing is for CAC to skyrocket. But with what I just shared, we 7-8x'd our User Acquisition budget without increasing our CAC materially. It took us 8 months to figure this out, but then the timeline from 0-$10M in every country got shorter and shorter: US revenue grew to $25M in 19 months. (US is now 78% of total) Germany to 21M in 11 months. France to $10M in 3 months. 2. Retention and Monetisation Hacks. We knew we wanted to create an audio entertainment platform, but there was no standard format. For the first 2-3 years, we tried 10 different formats before landing on the winner. After we got it right with audio drama (8-12min chapters, written for mobile fiction, serialized, episodes have strong hooks and end on cliffhangers), avg daily streaming time went from ~25mins to 150+. Audio drama made us realize that Pocket FM was creating a whole new medium. There was no playbook for anything that we were doing. Everything had to be thought & built from scratch. This is what we did for each major bullet: - Monetization: Users have some free daily minutes to listen; then it's pay per episode. We also added the option for users to unlock episodes by watching ads, which is doing extremely well. Ads scaled from zero to a ~$90M run rate in 12 months. - Engagement & production: You don't become obsessed with an app you open once a week. To have users engage daily, we make the next episode free every day. Also helps them build the habit. - Discovery. Huge problem because people consuming Pocket FM enter the app, tap the show, and lock the screen. We fixed it by doing "playlists" of episodes. Once users's free minutes on a show are done, we ask users if they want to pay. If they say no, we play a new show, one where they haven't used their free mins. And we stitch episodes of different shows together that way, creating natural discovery. 3. Localization > translation. 78% of our revenue is still concentrated in the US. Localisation is fixing this: You might write a show for a Spanish audience where language, jokes, folklore, have a certain flavor; if you merely translate the show for, say, a Norwegian audience, that color is lost and hurts the show in the Norway. Listeners would relate more if the show was written by a Norwegian. That's why, instead of translating, we localize shows to different regions. We use AI to take the spine of stories and adapt their whole cultural layer to the other country. The results: a US show that was localized for a German audience had 50% higher retention than the translated version. Localization + our user acquisition playbook led to: - $10M+ ARR in France within 3 months - $21M+ ARR in Germany within 11 months. And this expands writers' addressable market. We get messages of writers thrilled to have revenue coming from the US, India, EU, LatAm, without them doing much incremental work. 4. Experiment. We give $1M checks to new internal initiatives, and the team has 12-18 months to prove their thesis. If they prove it, we double down. This is how Pocket Saga came about, our AI video microdrama app. It's an AI video equivalent of Pocket FM. Same shows and structure, but as a vertical 2-min mobile video series. We launched it two months ago and it's at ~$15M ARR. Pocket's broader thesis is to help creators tell their stories to as many people as possible. Start with audio drama -> multiple languages or localize to diff countries -> microdrama -> more formats like movies, TV shows, and games. Best of all is that writers get revenue streams not only from countries they wouldn't have tapped into, but also from formats they wouldn't have thought possible. 5. Pivoting to only AI content production unlocked 100% ARR growth. In mid-2024, we pivoted to only AI content production. Our growth took a very direct hit. Pocket was already at $200M ARR, growing 50% YoY, and we flatlined for a whole semester. Six months later, the business exploded. After the switch, we went from ~25k hours of content produced per year to over 2.5M hours, which are also higher quality. Because AI orchestrates the writing and more data is fed into it, more blockbusters come out. Over 90 titles have $1M+ in lifetime earnings and 13 crossed $10M. Quality control is done with LLM as a judge, and LLMs are quite tough. Results are very encouraging: - 12-month revenue retention went from 44% to 76%. - In the past year, we added $250M in net new ARR. __________________________________ Pocket Entertainment (Pocket FM + Pocket Saga) has become the largest AI entertainment platform. We have the largest storytelling catalog with 770,000 titles, 550k creators, and 5.5B hours of playtime with minute by minute retention & engagement data. We are using all of this data to improve every aspect of our business. Our Bet: In the next 3 years we will be able to produce Naruto-like series for $1000. Netflix has to spend $17B to find 100 Blockbusters per year. What happens when you can produce 1M high quality shows for $1B? The streaming wars caused a $300B reallocation of market cap. The AI entertainment wars may be a $1T+ reshuffling of market cap. We are at a unique spot. Unlike other AI creator tools like Runway or Midjourney, we own both the supply and demand side of AI content. We've attracted over 550,000 writers who are producing an annualized 2.5M hours of content every year. This pairs with 137B+ minutes streamed. Because of this, growth is accelerating as we scale further. We have multiple S-curves inflecting at the same time: - AI produces better and more ads -> scale faster in new countries - ⁠Writers + AI produce more shows -> more blockbusters - ⁠Localisation -> more reach per blockbuster - ⁠Audio to video format expansion -> larger TAM -> more creators Every aspect of our business is designed to improve another, and everything is aligned so that the number of blockbusters continues to rise. If you're interested in building at the intersection of ai, tech and entertainment, DM me.
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Dev Khare retweeted
Outcome-based Marketing To sell AI to enterprises, you need to sell Outcomes For 20 years, software companies sold tools to enterprises. Today, the best and fastest growing companies sell outcomes, not tools. @pepper_content is doing this for marketing. Pepper got its start running organic growth programs for more than 250 large enterprises. The team was responsible for monthly results, so it learned the work in detail and documented every step. Pepper used that experience to build Atlas, a system of 365 agents. Atlas tracks the questions buyers ask, publishes pages designed to answer them, earns mentions across sources AI systems trust, and attributes the leads that follow. Agents handle 80% of the work. A senior marketer on the customer’s team owns the number. Pepper sells organic and AI search-sourced pipeline as a single line item. Its commercial model puts the company on the hook for the customer’s result. That accountability matters. AI-native services depend on detailed knowledge of the work: which decisions recur, where judgment is required, and which actions produce results. Pepper accumulated that knowledge by serving customers for years. It then encoded those decisions into software while keeping a person responsible for the KPI. @SinglaAnirudh started Pepper at 18. He has spent every year since learning what CMOs need and building the company around those needs. Building a services company is hard. Automating most of its work while remaining accountable for customer results is harder. Pepper is showing what an outcome-based AI company can look like. I’m excited to support them on this journey.
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Wow. Instinct is a moment where the world changes. Like it did with Google Maps and email.
My boyfriend and I got on the Jumbotron last night at the US Open and he wanted the footage. I told him impossible but I’ll ask Instinct bc why not. And of course, Instinct got us the video in 18 hours. Here's what it did (1/5)
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Announcing our guest speaker for the Google for Startups (@GoogleStartups) x Lightspeed Sprint: Ronnie Screwvala (@RonnieScrewvala), Chairperson & Co-Founder of upGrad (@upGrad_edu). Ronnie needs no introduction. After growing UTV into a media conglomerate and taking it through its sale to Disney, he went on to build upGrad into one of India’s largest higher education and upskilling platforms. We’ll also dive deep into what the next decade of fintech and consumer tech could look like, and where the opportunities for disruption lie. Rahul Taneja (@rahultaneja) and Ishaan Preet Singh (@ishaanpreet), Partners at Lightspeed, will share perspectives shaped by decades as operators and investors, from how AI is reshaping the way people consume to where new opportunities are emerging. If you’re a founder or CTO at a Seed to Series B startup building in consumer tech or fintech, you don’t want to miss this. Applications close September 13. RSVP: goo.gle/gemini-sprints-gurga… @ragingdas @_kkumar @MohapatraHemant @nityabaskar @SeekingN0rth
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Congratulations to Naman Pushp (@TheRealNamzoo) and the entire Airbound (@Airbound_Aero) team on their $37M Series A, led by Greenoaks (@Greenoaks), with participation from Lightspeed, DoorDash (@DoorDash), Lachy Groom (@lachygroom), and Humba Ventures (@humbavc). Airbound is building a new class of aircraft, designed to weigh less than the matter it moves. The internet made distance irrelevant for information. Airbound is doing the same for physical goods. And it's already underway: 1,000+ autonomous flights with Narayana Health (@NarayanaHealth), zero mission failures. Now, a new deployment agreement with the state of Andhra Pradesh will target 10,000 flights a day across three cities, serving healthcare, e-commerce, and retail. We’ve seen Airbound grow from a bold idea into real-world deployment. Hemant Mohapatra (@MohapatraHemant), Partner at Lightspeed India, looks back at where it all started: "When we led Airbound's first institutional round in 2024, it was on the back of a very early prototype and Naman's vision. He saw, in excruciating detail, how the world of transport would change if he solves one of the hardest problems in aerospace – the L/D ratio of aircraft – using new carbon-based materials that are becoming part of modern aircraft design unchanged since the 60s. That ambition isn't theoretical anymore as the team has now built and deployed aircraft that solve real world challenges. Naman is one of the most ambitious founders we have seen coming from India. We are glad to keep building Airbound with him." Watch Naman explain how the technology comes together in a conversation with Hemant Mohapatra.
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The most critical bottleneck in AI infrastructure right now isn't energy, chips, or software. It's heat, and no amount of better plumbing can reach the place where it forms. Today, Lightspeed is partnering with Akash Ramdas (@Akash__Ramdas) and Advaith Sridhar (@advaith_sridhar), co-founders of Discovered Materials (@discoveredmat), who are solving exactly this. We are leading their $9M seed round, joined by Y Combinator (@ycombinator) and angels including Paul Graham (@paulg), Gokul Rajaram (@gokulr), and Thariq Shihipar (@trq212). The numbers behind the problem: Hopper ran 700W on one die. Blackwell runs ~1.2kW on two. Parts on the roadmap are headed past 4kW. And cooling doesn't scale linearly; double the heat and you need roughly 11x the cooling work. Beyond that, better pumps don't help. The constraint is the material itself. Akash and Advaith have worked this problem from both ends: in Stanford's materials labs, deep in the atomic physics of heat, and building verifiers and harnesses at frontier AI startups. They are building Discovered Materials to accelerate materials discovery and the lab-to-fab timeline. Its engine proposes 2,000+ candidate crystal structures a day, scores each for thermal performance in minutes, runs physics simulations on a shortlist, and synthesizes what survives, compressing quarters of materials R&D into days. They start with thermal interface materials and the destination is inside the fab itself, in a semiconductor market headed well past $1.5T. Hemant Mohapatra (@MohapatraHemant) and Abhiram Tarimala (@AbhiramTarimala) lay out the full thesis, from the reticle limit to the cooling-power law to why the verifier is the moat, in our latest Substack post: lsip.substack.com/p/partneri… Welcome to the Lightspeed family, Advaith and Akash.
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Notes from our session on AI-led Services last week in Bangalore. Packed room, probably one of the highest volume segments of AI-centric startup activity in India, albeit still an emerging category relative to AI-native products/tooling to automate services. 🧵below:
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GTM, BUDGET AND ECONOMICS Take over an existing budget line. Don't ask the customer to create a new budget for you; capture one that already exists. For Rocketlane, their wedge is to land a few seats alongside the incumbent tool, build the connectors, and expand once you reach the data. Go upmarket (or way downmarket!). Sell where speed is worth money: there seems to be a barbell here - enterprises and SMBs pay for it, while mid-market tries to in-house the work. On the SMB side, Avoca, EvenUp, Gush have successfully targeted SMBs. Underwrite ~60-70% gross margins, with a durability plan. Set the benchmark there: pre-AI services ran 20-30%, and if you're not near 60-70%, relook at the business. According to Karthik, every industry is susceptible to compression over time so track margins by customer cohort, show them improving as automation rises, and explain why you keep 60%+ when pricing compresses. 10/n
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AI-led services will be a fragmented market, not a winner-take-all, and that's fine: in a market this large, even a non-winner can be enormous. Venture firms have historically funded technology companies more often than services companies, so build to clear a high bar: margins that reach ~70%, revenue that can reach hundreds of millions of dollars within a decade, and growth that is fast and isn't local and relationship-bound. Clear it, and the market you are playing for is the one we described in AI Will Eat Services. Not the few hundred billion dollars a year the world spends on software, but the trillions it spends on work itself. 11/11
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