
Growth VC @lightspeedvp / Duke alum 😈 / Seattle native
San Francisco, CA
Joined June 2012
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- Following260
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Shaurya Aggarwal retweeted
💥 Introducing Warp Sciences, a new company applying AI and robotics to help the world develop new medicines faster.
We’ve raised a $215M Series A led by @lightspeedvp to bring frontier AI out of the browser and into the lab, and build scientific capacity here in America.
I co-founded Warp with my long-time friend @dpatil, FAIR co-founder @manohar_paluri, and drug hunter Sol Reisberg. The early team is full of incredible people like @bkspears9, who helped achieve Q>1 fusion at LLNL.
Shaurya Aggarwal retweeted
Insurance was built to help millions of businesses take risks, but sometimes it can hold growth ambitions back.
Today, @rushilgoel launches @mandlhq to rebuild the full engine of commercial insurance with AI.
Mandl brings together three AI-native companies: insurance pricing (Nirvana), claims administration (Vega), and operations (Cooper).
We led @NirvanaTechInc’s seed round six years ago to help modernize a trillion-dollar industry mired in legacy processes. Mandl is the next step in that mission to price risk for what it really is, keep the promise fast, and get businesses back to their ambitions.
Congratulations to Rushil and the entire Mandl team on this exciting next chapter!
CC @ravirajjain, @AggShaurya
👋 Hello world! We're Mandl, a new AI company rebuilding the engine of commercial insurance from the inside out.
Insurance has always been the quiet force that lets ambition outrun risk: global trade, industrialization, and our modern economy were built on a foundation of innovative risk analysis and promises kept (the modern fire station got its start because of advancements in urban insurance!).
But as any hard-working business owner can attest, the system has now become just another risk that ambition has to outrun: overpriced and generic policies, delayed or denied claims. Even everyday things like pulling a COI still take forever.
At Mandl, we are taking on this entire system, part by part:
→ Underwriting: To advance our understanding of risk, we built @NirvanaTechInc. Now just a few years in, we're leveraging more than 50 billion miles of driving data, all matched to real claims outcomes and the most intelligent models in the industry. The business has grown to more than $300 million in run-rate premium with top-tier loss ratio performance.
🤝 Honesty, it turns out, is the best policy.
→ Claims: A claim is the moment you learn whether your insurance company keeps its promise to get you back to business. In less than two years, Vega has already made claims resolution 4x faster than the industry standard. We're proud of that, but we are not done advancing commercial resilience.
⚡️ Our mission is to resolve a claim in minutes.
→ Operations: To unleash insurance professionals so they can do their best work, we built Cooper, an AI operations platform for the commercial insurance office.
💰 It's early days, but we've already helped customers process more than $1 billion in premiums.
While we're only just getting started, we have ambitions to go deeper where we already operate and expand into more of the industry, investing wherever AI can make commercial insurance work the way it should.
Thanks to our team, customers, and partners for building with us, and to our world-class investors @generalcatalyst, @lightspeedvp, and Valor Equity Partners for backing us along the way.
You can read more from our CEO & cofounder @rushilgoel: mandl.ai/news/introducing-ma…
Shaurya Aggarwal retweeted
open weights: @reflection_ai’s Beam, @MistralAI’s Large 4
open skies: DoorDash drones
open question: if your agent orders your food, what is DoorDash’s moat?
fun having @AggShaurya in the co-host chair for this one
Two new open-weight models in two days.
Lightspeed portfolio companies Reflection and Mistral unveiled Beam and Large 4, all striving to bring greater control, customization, and efficiency to enterprise AI.
More on Lightwork from @Machiz and guest co-host, Partner @AggShaurya:
→ DoorDash builds its own delivery drone and AI ordering agent.
→ Instinct brings agents into group chats as Meta and Sierra develop new rules for agent access.
→ Oura postpones its IPO despite reported strong demand.
00:00 Open Models, Drone Delivery, and AI Agents
01:08 Mistral's Le Chonk Meme and Naming Story
02:33 What Open-Weight Models Mean for the AI Race
03:56 When to Consider Open Models Over Frontier AI
05:30 Why Open Models Compete on Cost Efficiency
06:35 Mixture of Experts Explained
07:32 Is Reflection the DeepSeek of the West?
07:58 Why DoorDash Is Building Its Own Delivery Drones
09:38 DoorDash's Competitive Edge
12:28 DoorDash's Moat Against AI Agents
14:34 Personal AI Agents in Group Chats
16:08 How AI Agents Could Change Consumer Spending
17:26 Meta, Sierra, and the Future of Personal AI Agents
19:56 Oura Postpones Its IPO
22:13 Lightspeed Universe
Shaurya Aggarwal retweeted
Replying to @Neko
@Neko Health is now open in NYC.
CEO and co-founder @HNilsonne joined Lightwork this week to discuss Neko’s origin, business strategy, and growth model.
Hjalmar had avoided healthcare his entire life until he got a cold DM from Spotify co-founder @eldsjal. He was inspired when Daniel asked him, “What if we redid healthcare from scratch?”
Then, Hjalmar and the Neko Health team spent 4+ years in research and development. Neko Health’s hardware, software, and AI systems were built in-house to keep pricing accessible. Currently, the scans are priced at $499, but Hjalmar hopes that as technology improves, costs can be lowered to widen accessibility.
Their newest location in NYC will be the ninth, and the first in the US, following clinic openings in the UK and Sweden.
Congratulations to the entire Neko Health team on this huge milestone.
Full episode link in the comments.
0:00 "The Most Important Hour You Can Spend On Your Health"
0:46 Growing Up In A Family Of Doctors And Daniel Ek's Direct Message
6:02 Five Years Building And The Vertically Integrated Approach
9:35 The Neko Scan Experience
11:24 Why Build Everything In-House And Neko As A Real Estate Company
17:47 Myth Busting Worried Well And Over-diagnosis
21:43 Do You Still Need A PCP And Why Pay Out Of Pocket
24:11 What Happens To Your Data And Why New York First
26:24 Adapting To The US Healthcare System And Landing On 499 Dollars
28:07 What Is Hardest To Scale And The Big Vision
30:58 The R&D Team And Why Longitudinal Data Matters
33:02 Thank You And Closing
@clairejyz @Machiz
Shaurya Aggarwal retweeted
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.
Shaurya Aggarwal retweeted
Today, we are excited to announce our participation in @assort_health’s $120M Series C.
Healthcare currently carries $1.1 trillion administrative burden, and it starts the moment a patient tries to book an appointment. Assort Health is working to change this by transforming the patient experience.
What began as the first voice AI agent to schedule a specialty appointment has grown into a full-stack platform, spanning scheduling, intake, referrals, document processing, medication refills, real-time eligibility, lab requests, and payments. Every layer of the patient journey, designed to be more efficient and individualized.
We first backed the team for their Series B last year. The platform has expanded significantly since, and we can't wait to see where they take it next.
Congratulations to @_JonWang, @_jeff_liu, and the entire Assort Health team.
CC: @GalymImanbayev, @BrentonFargnoli, @AggShaurya
Today we raised a $120M Series C at a $1.2B valuation, led by @MenloVentures.
We now have over $220M to become the standard for how healthcare providers transform the patient journey with AI.
As a medical student at UCSF, I watched patients fall through the cracks for reasons that had nothing to do with medicine.
Healthcare has spent decades improving clinical outcomes, yet patients were still repeating their story, navigating disconnected systems, and too often giving up before they got the care they needed.
I couldn't accept that patients spent more time on hold than with their doctors. It felt like one of the biggest unsolved problems in healthcare.
So I left to build @assort_health.
Our first chapter was learning how complex each practice's care protocols really are. It took us months to build a single agent.
That's why we built Synapse, our agentic model. It gets sharper with every edge case it sees, and with 190M+ patient interactions behind it, no one's model has seen more. That's what lets non-technical people build agents in weeks, even for the most complex workflows, with industry-leading automation rates.
This is how we grew revenue 20x in 15 months, as leading providers chose the one platform proven to handle the complexity of healthcare workflows at scale.
But this was always meant to be just the first chapter.
The first interaction isn't only where care begins. It's where patient context is created. Get that right, and every step after becomes easier.
Today we're a platform for the whole journey. Our AI agents answer calls, schedule appointments, manage referrals, complete intake, refill medications, reach patients proactively, and collect payments, all personalized to each patient.
Grateful to @jpsanday, Matt Murphy and the team at Menlo Ventures for leading, and to @lightspeedvp, @felicis, First Round Capital, Chemistry, @QuietCapital, and Joe Montana for backing us again. To the Assort team: none of this happens without your relentless focus and care. Fired up to build Healthcare that Remembers alongside you. Still Day One.
I'm thrilled to announce that Lightspeed is leading Aledade, Inc.'s $260M Series F financing round. Aledade is revolutionizing healthcare by empowering independent primary-care practices with deep data integrations, best practice w…lnkd.in/gMwpmjnK lnkd.in/gnHRjWAt
I recently had the pleasure of moderating a discussion with @CKevers and members of the Lightspeed Finance Community. Charly shared valuable insights on how CFOs can maintain strong relationships with their boards and ensure effective communication. Check…lnkd.in/gHV99eUT
Shaurya Aggarwal retweeted
Excited to announce our investment in @EnableSoftware, which powers incentives across the B2B supply chain.
@anoushkavaswani, @AggShaurya and I wrote more here.
medium.com/lightspeed-ventur…
Shaurya Aggarwal retweeted
Penned a few thoughts on the vibrant SoCal startup community and what has made @LightspeedVP so bullish on the ecosystem for the past 15+ years! link.medium.com/eTvo5mLB6sb
I am happy to share I recently joined the @lightspeedvp family! I am truly excited to support founders build generational companies in this new chapter of my career. #VentureCapital #startups
tinyurl.com/yc2ubrf5
Shaurya Aggarwal retweeted
Every *single* thing in your product that creates friction limits adoption. Even the difference between requiring a user to click a button vs. see information on mouse over can be an order of magnitude more discoverability of a feature.













