Aditya Agarwal

For 20 years, quantum was hype without hardware. PsiQuantum just changed that. Thrilled to see Pete Shadbolt and the PsiQuantum team raise $1B Series E at a $7B valuation, making it the most well-funded quantum company in the world. The round was led by BlackRock, Temasek, and Baillie Gifford, with participation from Macquarie, QIA, Morgan Stanley’s Counterpoint, SentinelOne’s S Ventures, Type One Ventures — and NVentures (Nvidia), who will also partner with PsiQuantum on GPU–QPU integration, algorithms, and silicon photonics. It was great to welcome Pete at RAISE Summit in July at the Louvre. His vision stood out: • Betting on photonic qubits instead of exotic physics. • Building a cryogenic semiconductor fab moat that makes replication nearly impossible. • Tackling the million-qubit fault-tolerance challenge head-on, by industrializing quantum chips at scale. Why this matters: • Compute is the new oil. AI demand is already straining global GPU supply. • Quantum is the natural next step in compute — solving problems beyond the reach of even AI-accelerated superclusters. • Applications in climate, new materials, drug discovery, finance could unlock trillions in value creation. Grateful to Tony Kim for the introductions and insights into the quantum space. At RAISE Summit 2026 in Paris, we’ll dedicate a major track to Quantum × AI — where frontier compute converges. The next decade of breakthroughs will be written at this intersection

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GPUs have gotten faster, and models have gotten larger. But storage is still stuck in the past. Modern AI workloads need instant access to huge datasets. Instead, teams waste time on slow retrieval, idle GPUs, and costly pipelines. Archil is changing this. It delivers object-storage scalability at block-storage speeds. The platform allows for seamless, high-performance access to massive datasets. By eliminating cold starts and accelerating throughput 30X, Archil enables teams to fully utilize their infrastructure and move faster on training, analytics, and AI deployment. Archil’s founder Hunter Leath brings rare technical depth and operating experience from building Amazon EFS and optimizing Netflix’s cloud performance. This is a team building the foundational layer that AI workloads desperately need. I’m thrilled to lead Archil’s seed at Felicis with Nancy Wang and support their mission to reinvent cloud storage for the AI era. https://lnkd.in/gWueXhzz

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Thrilled to back Dima Shvets and Alexey Moiseenkov on the launch of Mirai On Device AI. If you’re building AI-native products, this one’s for you. Mirai is an on-device AI SDK for iOS and macOS that lets you run high-performance models directly in your app with zero latency, full data privacy, and no inference costs. So instead of sending tokens to the cloud (and paying for it), you keep things local. Even better, you don’t need a dedicated ML team or weeks/days of infra setup. One developer can integrate inference, routing, and optimization in minutes. Key features: — Supports Llama 3.x, Qwen 3, Gemma 3, DeepSeek-R1 — Bring-your-own-model support — Tools for fine-tuning, evals, structured outputs & fallback — Integration in a health tech product with 100M+ MAUs Built by the team behind Reface & Prisma. Backed by scout funds of Sequoia Capital & Index Ventures, and angels from OpenAI, Hugging Face, ElevenLabs, Unity and us, at NP-Hard Ventures 🤩 On-device AI will be a major unlock over the next few years, and help us to shift the way people interact with technology. And Mirai is early, ambitious, and razor-focused on enabling this shift. Go try it out! Link to SDK and GitHub documentations in comments. NP-Hard Ventures // Paul Veugen // Micha Hernandez van Leuffen

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⚠️ Founders - consider the below reasons to not use Forward Deployed Engineers (FDEs) to acquire your *first* customers. This signals (1) lack of PMF; (2) miscalculation b/w development and sales timeframes; and (3) mischaracterization of Palantir Technologies timing when they deployed their FDE model. 💡 At the early-stage, it's crucial to demonstrate ability to repeatedly acquire customers with limited resources - capital, team, & time - that supports the narrative of 'more funding will amplify my traction.' Bespoke solutions for one company demonstrates zero repeatability. These custom solutions will not carry over to other customers within the same cohort. 💡 AI accelerates engineering but sales, system integration, and stakeholder buy-ins are perpetually slow. This imbalance results in concentrating all your engineering resources into one company, ignoring other opportunities, and still not going live on an accelerated timeline. 💡 Palantir didn't start using FDEs until year 3 when they got their first *gov't* customer, not their first customer. Gotham was already built and had strong PMF. Gotham allowed Palantir to take GTM risk by using FDEs b/c if it didn't work, Gotham was humming in the background. TLDR: At the early-stage, repeatable customer acquisition is the holy grail. Don't let AI deceive you that it can accelerate human behavior like it has with development cycles. It C.A.N.N.O.T.

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▶️ We started Sandgarden after a year of brainstorming—with Britt Crawford, now our CTO, stubbornly saying “WPEngine for LLMs” on repeat. 🎵 Turns out that broken record wasn’t broken at all. It was a beautifully pressed 180-gram vinyl. And when we finally really heard what he'd been saying all along, it was music to our ears. Sandgarden was born. Like WPEngine simplified and scaled WordPress hosting for developers and businesses, we knew AI workflows needed the same treatment. The problem was clear: - PMs couldn’t spec - engineers got stuck doing grunt work, and - nobody trusted deployments. 🏋️ AI shouldn’t bottleneck teams—it should empower them to move faster, collaborate better, and deploy with confidence. That's what we're building at Sandgarden. 📩 Read the story of how it all began: https://lnkd.in/ghsuW3ws

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Qlay co-hosted Jeff Seibert, CEO of Digits (prev. sold Crashlytics to Twitter for ~$100M) and Jean-Denis Greze, ex-CTO of Plaid and co-founder of Town, which has raised $18M. Here's what I learned: 1. There’s no one-size-fits-all GTM strategy, especially in AI Should you license your AI model? Sell to players who already have customer bases? Or use the AI yourself and act as a service provider? This all depends on how the industry is structured and which business model opens doors to the most paying customers. 2. Brand is key, especially if you don't have huge traction Jeff asked Jean-Denis about the domain Town.com, which opened up a key insight: in a crowded market, your brand matters early. A simple, real-word domain can make your company look polished and credible, even before you’ve built significant traction. 3. Leadership is tested when things are hard Jean-Denis shared how a major acquisition deal falling through tested him as a leader. Getting the team re-energized and focused again after the setback was not an easy task. Thanks to everyone who joined us at DG717 Digital Garage for a high-signal conversation. DG717 Digital Garage · Tokumasa Yamashita · Digital Garage US, Inc. · Tom Nakata · Open Network Lab · Ethan Adam #Plaid #Digits #Qlay #Dropbox #Town #JeanDenisGreze #fintech #startups #techleaders #founders #entrepreneurship #angelinvesting #venturecapital #scalingstartups #ctoinsights #productleadership #seedfunding #earlystagefounders #startupjourney #startupgrowth #betweentwofounders #startupcommunity #DG717 #DGUS

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Excited to share episode 5 of the Slow Hunch Podcast, with Zoe Weinberg, founder of ex/ante . Zoe makes a compelling case for why technology isn't neutral - it's purpose-built, and architectural choices matter enormously. We explored how the next wave of founders have the potential to build tech that enhances human agency rather than diminishing it. A key insight for me was that the tradeoff between user convenience and user control is not inevitable. We have the potential to build technology that's both magical to use AND preserves individual autonomy. Full episode here: https://lnkd.in/eaGc2sah

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While we haven't officially launched, can't help but be excited to see this great artcle in the @WSJ featuring LGND AI, Inc.(http://lgnd.io) and the opportunity for AI applied to earth observation data for all sorts of important uses. This is a huge and important data source that is vastly under represented in the current AI platform shift - and we aim to change that. Big stuff ahead! https://lnkd.in/ghd76mBB

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Today, we’re pulling back the curtains. After collecting thousands of original, human-written coding problems, AfterQuery created internal, contamination-free evals to test LLM code generation. No leaderboard tricks. No test-set leakage. Just raw task execution. We’re publicly releasing the results of our Python Competitive Programming & Algorithmic Reasoning benchmark—500 novel human-written coding problems. Pass@1 (single-shot prompt) accuracy results: - OpenAI's o3: 46% - Anthropic's Opus 4 (Extended Thinking): 37.6% - DeepMind's Gemini 2.5 Pro: 23.4% - xAI's Grok 3: 8.4% - Amazon's Nova Pro: 6.8% - Meta's Llama 4 Maverick: 6.4% Why this matters: 1. The frontier isn’t crowded—it’s a two-horse race. With a 30-40% gap, OpenAI and Anthropic are miles ahead of the other labs. 2. There’s a significant gap between current model performance and human-level algorithmic reasoning. Half the models scored under 10% on foundational reasoning tasks. 3. This is our "easiest" coding benchmark. If models struggle with these toy problems, what happens when they're thrown into enterprise codebases or ambiguous specs? If you're working on training, evaluating, or fine-tuning LLMs, feel free to reach out—always open to comparing notes. This is the tip of the iceberg. Comment below, and I’d be happy to share more internal AfterQuery evals.

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the last few weeks feels like a mini breakthrough for AI apps hiding in plain sight - partial autonomy, high agency models, and context eng vs prompt eng. are all incredibly important concepts a lot of these principles were apparent but were just recently articulated (mostly by Karpathy - legend!!). one is the concept of partial autonomy apps - autonomy sliders, app specific UI, and “keeping the AI on a leash” two is the idea of small, high agency models that know how to solve problems vs knowing answers via tool-use / reasoning / multimodality (“maximally sacrifices encyclopedic knowledge for capability”) three is context engineering vs prompt engineering - putting the right stuff into working memory, building around the “jagged intelligence” of models etc these should probably be the starting point for founders thinking through AI native apps Refs: Partial autonomy apps: [Karpathy @YC startup school] High agency mini models: https://lnkd.in/gthCXtMe Context Eng: https://lnkd.in/gha2i7BX

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It’s an exciting day today! After four years of working in relative stealth-ish, we’re excited to publicly announce the launch of Camber (formerly Juniper), and the $50M in funding we’ve raised from Andreessen Horowitz, Craft Ventures, Y Combinator, Manresa Ventures, and many others who are committed to improving the accessibility of care. I joined Camber for the same reason I stay at Camber: the tangible impact we deliver every day. To date, we’ve helped turn three clinics around from bankruptcy. We’ve helped clinics collect thousands and thousands dollars more — we’ve seen them hire, open new clinic locations, and provide more care in our space as a result. We now power billing activities for ~90,000 patients across 42 states, getting our providers paid with no denials 93% of the time, and helping them collect 98% of the cash they’re owed over time. Honestly, that’s pretty awesome. The past four years have shaped so much of how I think about engineering. Engineering is cool, and building things is cool, insofar as it drives real impact in the world. I haven’t been more exhausted but more fulfilled than in the past four years. — Christophe Rimann, Celina Qi, Nathan Lee: Thanks for showing me this problem, and for burgers all those years ago. I guess the true partnership and back-breaking support is nice as well. Jade Shenker, Zack Zhu, Revan Sopher, Laura Skillington, Chris Maddox, Zachary Strenfel, Cayla Wanderman-Milne, Melanie Rogers, Chloe Oppenheim, Sy Yeu Joe C., Justin Sian, Mark Secada, Amy Zhang, Peggy Li: Thank you guys for taking a bet on us. I can’t imagine doing this without each of you. — We’re just starting! If making healthcare work sounds cool, reach out. I’d love to have you on our journey. https://lnkd.in/g8SjUq-j

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