A model is only as good as its data, and we’ve long since exhausted the internet. From here on out, model progress is gated by data production. @mercor_ai’s @BrendanFoody joined us at our Sovereign AI event to talk about how RL environments get built, and why your data might be your real moat: 00:00 Introduction 00:47 A short history of the data market: crowdsourcing to agentic data 02:29 What an RL environment is: worlds, apps, tasks 03:57 Why only humans can measure the frontier 05:35 Building verifiers is the hard part 06:44 Walkthrough: a real legal RL environment 08:18 Leaderboards — and what open weights change 09:45 Post-training results on Apex Agents 11:17 Three ways companies buy data 12:49 Q&A: How do you price data? 14:17 Q&A: What "data quality" actually means 16:42 Q&A: The misunderstanding about synthetic data 18:17 Q&A: Why RL environments now — and what comes after 21:20 Q&A: Can you scale rubric generation with models? 23:00 Q&A: RL environments for cyber defense 25:33 Q&A: Build data in-house or partner?

When should you start post-training your own models? @FireworksAI_HQ CEO @lqiao’s answer: after product-market fit. Not because it's hard... but because only after PMF is the data coming off your product surface worth training on. Lin joined us for our @sequoia "Own Your Intelligence" event to host a workshop on all things post-training; what works, what breaks, and how not to let the model outsmart you. Must listen!! 00:00 Introduction 00:37 What Fireworks sees across thousands of AI applications 02:47 Off-the-shelf APIs and the problem of keeping your taste 03:58 What "owning your intelligence" actually means 05:43 The progression: prompting → RAG → SFT → preferences → RL 07:20 Why this mirrors how humans learn 09:03 Matching the technique to the problem you actually have 10:46 Where teams get stuck: data quality and vibe evals 12:28 Reward hacking: the model that wrote zero lines of code 13:59 Training-to-serving alignment (and why quality drops) 15:55 Post-training in healthcare and security 17:31 From coding to every co-work domain 19:35 Incumbents, cost burden, and not scaling into bankruptcy 21:26 How much control do you want? 23:24 Q&A: What makes a good reward signal 25:00 Q&A: When to start thinking about post-training

AI video is going to be one of the biggest token tsunamis of the next 12 months. @previewio is THE platform for professional AI video production. It's already been used by F100 brands and Oscar-winning filmmakers. We @sequoia are so pumped to be leading this round and working with @fejes713 and team!

Preview has raised $12M in funding to date, led by @sequoia with participation from @thegp and @farooqib. AI video is now part of how real productions get made. What professional work requires hasn't changed: scripts, shot lists, versions, reviews, deliveries, and a clear record of how every frame was made. Preview is the professional platform where AI video production runs. Producers, directors, and artists planning, generating, reviewing, and delivering together, from first idea to editor handover. Studios already run real work on Preview: commercial productions for Fortune 100 brands, and some of the first hybrid films with Oscar-winning talent, arriving on streaming platforms later this year. 3,000 studios are on the waitlist. We're letting the next ones in now.

***Lights, inference, action…*** I’m so happy to share that @sequoia has led the Seed in @previewio. Developers are flying in magical AI-native editors. But creative tooling is still stuck in the pre-AI stone age. @fejes713 is building the production platform for AI video. It is robust, it is full featured, it is everything a professional videomaker could ask for (as our star-studded alpha client list will attest), and most importantly it is built with care and the utmost attention to craftsmanship <3 Grateful to join many greats of the video and creative tools space on the cap table including @emery_wells of @frameio, @farooqib @KyleHParrish ex @figma, @burkaygur @isidentical of @fal, @soleio, and many other friends.

“There should be 10 companies like SpaceX in America.” Sequoia Partner Shaun Maguire says vertically integrated national champions like SpaceX — and potentially Neros — are key to re-industrializing the US and rebuilding critical supply chains

Great to chat with @AndrewYNg at the Agentic AI Conference at Berkeley. Our full talk is now available on YouTube. We discuss if we should expect an AI jobpocalypse, the importance of open models, and whether AGI is still 50 years out or happened 30 years ago.

BREAKING: Neros Raises $250M Series C at $2.5 Billion Valuation FULL INTERVIEW "America should have the capability to have factories that can make millions of drones per year because that's what modern conflict demands." Co-Founder & CEO Soren Monroe-Anderson (@soren_ma) takes us inside the new 250K sq ft Torrance factory. Currently shipping ~1,250 drones a week. Sized for 1 million a year by 2028. Fresh off a $500M Army IDIQ for 100,000s of drones over 5 years This round was led by @shaunmmaguire of @sequoia & American Strategic Technology Fund w/ participation from Interlagos, Valor Equity Partners, Allen & Company, Thiel Capital, Spark Capital, & Mantis With this capital @neros_tech is now going multi-product with: › Archer AI — Autonomous strike drone › Bandit — Counter-drone interceptor › Marksman — Ruggedized operator handset › Useful Autonomy — Framework for evaluating autonomy claims ICYMI: China builds an estimated tens of millions of drones a year & arms both sides in Ukraine. America is way behind, representing just 1% of the world's small drones. Neros was the first FPV maker to win Blue UAS certification, meaning a fully NDAA-compliant supply chain with no reliance on China. "You can look at our boards, you can look at all of the chips that go onto our boards. Those will not have ever touched China." 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Soren Monroe-Anderson, Co-Founder & CEO at Neros Technologies (01:02) $250M Series C at a $2.5B valuation (03:34) Working with Shawn Maguire (04:25) Inside the Army's $500M drone contract (05:38) How the Ukraine war rewrote modern combat (08:05) The Interceptor drones taking over the battlefield (10:31) How many drones China really makes in a year (12:55) Is it even possible to build a drone without China? (16:09) China is arming both sides of the war (17:08) What actually separates Neros from every other drone company (19:55) The biggest BS claims in defense tech right now (20:51) Why most defense startups never actually deploy? (22:58) Neros’s growth to over 250 employees (24:00) Why defense startups need to screen for spies (25:29) The Talent bottleneck slowing down American manufacturing (26:51) How AI is quietly reshaping engineers (29:43) The biggest myth about re-industrializing America (30:50) Neros's plan to build drones with U.S. allies (32:53) The brutal tradeoff between speed and scale (34:51) Why Neros keeps a permanent office in Ukraine? (35:52) What actually happens on a modern battlefield? (37:41) How Starlink is changing drone warfare (39:14) What happens when a drone loses its signal (40:41) Soren’s learnings from Kelly Johnson

From @sequoia's own your intelligence event. We're starting to see a new category of company emerge, the Full-Stack AI company, where innovation happens at both the product and intelligence layer. That's why @sonyatweetybird led her opening keynote with: "The hottest new labs in my opinion are actually the applied research companies like @harvey @FactoryAI @glean @OpenEvidence @semgrep @tryramp" Proud of what the @harvey research and product teams have have accomplished across benchmarking, post-training, inference-time routing, agent harness optimization, scaling inference, open source, and more -- lots more to come here from this group

Want world class research capabilities, but don’t have the resources of a big lab? At our recent Sovereign AI event, @gabepereyra shared @harvey ’s “moneyball” approach. Here’s the playbook: 00:00 Introduction 00:37 Building a research lab on a budget 02:28 Legal Agent Bench, contracting, and the diligence dataset 03:57 Domain experts guiding synthetic data generation 05:23 Why Harvey open sourced its datasets 06:55 Working with the neo labs – and why more than one 08:20 Post-training in-house: building "Associate 1" 09:44 The model serving matrix: 60 countries, fallbacks, SLAs 11:05 Deciding what stays in production 12:29 Simple open source switches and model routing 13:55 Moneyball: "If we win on this budget, we change the game" 14:53 Q&A: Training with sensitive data 17:16 Q&A: Competing for research talent 18:46 Q&A: Designing rubrics that actually challenge frontier models 20:19 Q&A: Where the pipeline breaks — data, research, or infra 22:59 Q&A: The tension in open sourcing a benchmark 25:02 Q&A: Biggest remaining open problems 27:10 Q&A: Competing with horizontal products

OWN YOUR INTELLIGENCE Last year, building on open-weight models was primarily a cost rationalization exercise. Slightly worse performance for a much cheaper price. Now, it is increasingly an existential and strategic topic for our portfolio. Intelligence is the product. Companies want to shape it and own it and let it compound within their own walls. Not your weights, not your product. Now, with frontier open-weight models and fantastic tooling/infrastructure, owning your intelligence at the frontier is finally becoming possible. The result: every application company we work with is embarking on the journey of doing their own research on post-training, evals, harnesses, etc. The hottest neolabs may just be @Harvey, @FactoryAI, @Ramp, etc. The list goes on. We held a summit @sequoia to convene our portfolio on this topic, together with @gabepereyra (@Harvey) on building Harvey Labs, @lqiao (@FireworksAI_HQ) on post-training, @hwchase17 (@LangChain) on harnesses + evals, @BrendanFoody (@mercor_ai) on RL environments and synthetic data, @QuantumArjun (@trajectorylabs) on online continual learning. Opening talk below; rest to come this week! 00:00 What is sovereign AI (and what it isn't) 01:24 Centralized vs. decentralized intelligence 02:54 Four reasons companies own their models: cost, speed, performance, destiny 04:22 "Not your weights, not your product" 05:32 The application companies are the newest neo labs 07:05 Step 1: Deciding what to own vs. rent 09:51 Step 2: Build the team (and don't shoehorn your platform team) 11:17 Step 3: Legibility – why your research has to be visible 12:33 Step 4: The technical roadmap 13:56 The stack: production vs. development 15:16 Opening Pandora's box – base models, harnesses, context

Today, we announce our Series C Funding of $250M at $2.5B post money valuation to scale our Autonomous and Interceptor drone programs. This round of funding is co-led by Sequoia Capital and American Strategic Technology Fund (ASTF) with participation by Interlagos, Valor Equity Partners, Allen & Company, Thiel Capital, Spark Capital, and Dylan Field. These funds will accelerate the development and production ramps for new drone programs including Archer AI, an FPV platform augmented with autonomy features including Terminal Guidance and GPS-denied Position Hold, and Bandit, a c-UAS interceptor drone intended to counter Class 2 and 3 drone threats including Shahed-style systems. These platforms are being developed with the hardware and compute required for multi-asset control (often called “swarming”) while retaining the cost efficiency Neros is known for – and that’s essential to achieving true attritable mass. Both Archer AI and Bandit will be deployed for combat by the end of 2026.

We led @CormaAI's seed, following @vkhosla who saw its potential before us (and led the pre-seed) @AlonPluda is a generational cyber talent -- I'm not saying this lightly -- and we're already seeing this play out with customers Also, as a total aside, it has been fun to team w/ my partner @sonyatweetybird! Two years ago we disagreed on almost everything and now we have a mind meld 🤣

Today we are announcing Corma, and our $60M seed round led by @sequoia. @CormaAI is building foundation models toward superintelligence for defensive cybersecurity. We do that because general AI is becoming exponentially more capable in offensive security, while it hasn't been able to improve at the same rate in cyber defense - creating an inherent imbalance between attackers and defenders that otherwise cannot be addressed. Recent events have shown that building frontier AI models for defensive cybersecurity is one of the crucial unsolved problems of the AI age - and solving it is the generational mission Corma was founded on. Our foundation model has already far surpassed every general-purpose frontier model on defensive cybersecurity tasks, and we are committed to remaining the frontier of defensive cybersecurity intelligence. Corma is already protecting Fortune 100 companies and some of the world's most important organizations, across healthcare, finance, critical infrastructure, retail, and more. This is just the beginning, as we see it as our calling to protect every organization in the world that needs frontier defense. We are grateful to our partners @shaunmmaguire and @sonyatweetybird at @sequoia , @vkhosla at @khoslaventures , @DanRose999 at @coatuemgmt , and @Weiner_Lia at Netz Capital, who believed in our ability to lead the intelligence race in cybersecurity and make sure the defenders always win.

Great insights from my partner @DavidCahn6 on the latest @BigTechPod with @Kantrowitz. Worth a listen if you care about the state of AI and how these big bets will pay off. A few good ones: On Jensen: "He was one of the first people to make a bet on AI. The way that everybody else is fighting over share and this and that, I think Jensen just wants the pie to be really big. His fundamental world model is you win, I win." On resource allocation: The winners will be decided by what resources each player has (cash, talent, chips, distribution) and how coherently they allocate them. Founder-led companies play coherent games; committees don't. On market reactions: The lab leaders have told us what they believe and they're all playing for AGI. Back-test their decisions against that world model and the moves make sense. Markets get confused because they underprice both AGI and a correction while overpricing the status quo.

Thanks to @Kantrowitz for having me on the @BigTechPod last Monday. We discussed the strategies of each of the big tech companies, and why I think AI is turning into the greatest strategy game in history, a la StarCraft or Azad.