Berk Ozer

Is your AI startup's first $10M ARR really just a tech echo chamber? Our latest analysis reveals that for Bay Area founders, 60-65% of early revenue comes from fellow tech companies—not the broader U.S. market in states like Ohio or Florida—based on data from CB Insights, PitchBook, and McKinsey. We've unpacked global variations, counterexamples like Veracyte and Kabbage, and actionable strategies to break free—check out the full breakdown in my new post! What’s your take on diversifying beyond the Valley bubble? Share your experiences or tips in the comments! https://lnkd.in/gCKTVyT4 #AIStartups #TechRevenue #Entrepreneurship #VentureCapital

37

We are pleased to announce a new investment in a startup developing quantum computer hardware. The neutral atom approach has rapidly emerged over the past few years as one of the most promising candidates for realizing quantum computing. At the same time, research and development toward building a truly useful quantum machine is still in the midst of global competition—an area where we believe Japan’s science can stand out on the world stage. From before the company was even founded, we have worked closely with the founding team, sharing the goal of “building a quantum industry in Japan that leads the world.” We are truly excited to embark on this great challenge together with a dream team of top domestic leaders now united under one vision. Beyond Next Ventures will spare no effort in supporting the company until the day a truly practical quantum computer is realized and changes the world. https://yaqumo.com/en/

66

1 Comment

How AI is changing venture building + numbers & best practices of PSL startup studio ($50M AUM studio funds + $180M AUM venture funds): 1) 3 dedicated studio funds: $12M (2015) → $16M (2018) → $20M (2023) for a 25-person build team (salary, sprints, infrastructure). Venture arm: $80M (2018) + $100M (2021) — invests in external startups and PSL studio spin-outs (but never prices later rounds). Geographically focused on the Pacific Northwest. 2) 5-stage company creation funnel: – Ideation (problem hunt, AI research),~4,000 ideas – Validation (customer calls, landing page), 470 validated ideas – Creation (MVP, sales), 50 created products – Spin-out (legal, PSL seed round, CEO equity), 41 spin-outs – Scale-up (external VC funding, recruiting). 37+ scale-ups with external funding. 3) Throughput: 4-6 new spin-outs per year target. Typical time of a startup in the studio: 3–4 months. Fastest build ever: 6 weeks. At any moment: ~2 projects in Creation, 3-5 in Validation. 4) How AI is shrinking Ideation & Validation phases with 2 PSL internally created tools: – Spark – researches the market, auto-drafts decks & landing pages, creates an initial ICP list, then A/B-emails prospects until it gets 10 yes/no signals. Founders start with data, not vibes. – Mesh – maps 70,000+ operator/investor contacts of the PSL team, scores fit, then sends emails and tracks reply rates; the warmest path shows up as a Slack thread the same day. Together they compress “customer proof + talent hunt” to < 24 h instead of 2-3 weeks earlier. 5) Seed checks. Non-AI spin-out: $300-500K from PSL Ventures. AI spin-out: $1.5M co-led ($750K PSL Ventures + $750K AI Studio Fund with Mayfield). Follow-on rounds are syndicated — outside VCs set price; PSL follows its pro-rata. 6) Typical equity split is 50/50 between studio & founding CEO. But can vary a bit based on circumstance (founder track record, founder-idea fit narrative, existing work done by the founder prior to the Studio engagement...) 7) The 4 founder archetypes PSL is partnering with: – Successful repeat founder (typically, 40-50 y.o.) They value the speed & the focus, which is possible due to the studio team + higher conviction in an idea you will bet 5+ years of your life on. – Product Manager to CEO (~25-40 y.o.) They value studio expertise and support as a first-time CEO. They can also find some strong ideas inside the studio if they don't have one. – Young Tech Builder (YTB) < ~30 y. — typically miss and undervalue a new company's business development/sales roles. Studio helps with it. – Corporate exec/domain expert. They know how to get the maximum from the corporate partner but need support as a first-time CEO. Watch the full interview with T. A. McCann – Managing Director @ Pioneer Square Labs to learn insights about venture building with AI and managing a startup studio: youtu.be/YP-i5mhXNiY Bonus from PSL: Would you like to see a demo video of how Spark works? I can DM the link if interested

105

10 Comments

"Build the event you wish existed" That's what we at Monadical did last week at #NYTW. We wanted an AI Engineers discussion for Engineers. There's always a lot of events out there for VCs to network, or for startups to learn about G2M, but very little on lessons learned from actual engineers in the field. So that's the event we hosted. Big thank yous to our awesome panel, Roy Pereira, Ben Cohen and Corey J. Gallon. Here's the key takeaways and the AI tools we're using. 🚀 All three panelists independently called AI Agents the most transformative LLM application they’ve used. They specifically called out Claude 3.5 Sonnet for its accuracy and reliability. 🪨 We dug into how LLMs are “jagged”, not general. They can be shockingly good at some tasks and completely fail at others. Everyone agreed: good evaluations are critical (and hard.) 🧪 Corey noted how public benchmarks and reality are two different things. Most public evals are saturated or gamed. ♊ Ben emphasized that AI projects are actually two projects: building the tool and building the evaluation process. 🧱 We explored how falling dev costs may impact startup defensibility and labor demand. Roy shared that founders are already shifting strategies in response. ⚒️ In a world of daily AI launches, the panel discussed how they decide what’s worth attention, and what’s just noise. They called out tools like Goose, Aider, Claude Code, and Monadical’s own Cubbi, which helps run agentic workflows safely in dev environments. (links in the comments). CTA: What would you want to hear in an AI Applied Engineering talk you attended?

33

5 Comments

#AmTech Weekly Roundup: CuspAI raised $100 million in Series A funding at a $520 million valuation from New Enterprise Associates (NEA), Temasek, NVIDIA, Samsung Ventures, Hyundai Motor Group, Basis Set, FJ Labs, Giant Ventures, and Northzone. Cusp is building foundational models for chemistry. Their projects include developing new materials for carbon sequestration, water filters, hydrogen fuel cells, and other frontier use cases.   Max Welling, Chad Edwards Strive Health raised $350 million in Series D funding at a $1.8 billion valuation from New Enterprise Associates (NEA), CVS Health Ventures, CapitalG, Echo Health Ventures, and Town Hall Ventures. Strive is a value-based kidney care company working with commercial insurance, CMS, and providers to care for 80,000 chronic kidney disease and end-stage renal disease patients.   Chris Riopelle, Bob Badal, Will Stokes Apex - Spacecraft Manufacturing raised $200 million in Series D funding at a $1 billion valuation from Interlagos, Andreessen Horowitz, Point72 Ventures, and 8VC. Apex is a vertically integrated manufacturer of satellite buses (the main structure and housing for other systems on satellites).   Maximilian Benassi, Ian Cinnamon #AmTech #Space #Aerospace #Healthcare #HealthTech #VBC #AdvancedMaterials #AI #Fundraising 

22

3 Comments

BREAKING 🇦🇪: Skipr just raised $2M to build the trust layer for autonomous AI systems. Operating from Hub71, Skipr is building a sovereign AI infrastructure that enables AI systems to communicate, coordinate, and exchange value securely. Co-founded by Andreas Hartl, Adnan Fatayerji and Weynand Kuijpers, Skipr addresses a critical gap: as AI systems become autonomous and operate across organisations, clouds, and borders, there's no secure way for them to work together safely. Skipr's is an autonomous trust fabric for AI-to-AI interoperability under sovereign control. Skipr's technology utilises cryptographic identity, policy-driven routing, and auditable interoperability to ensure that AI systems can safely share data, decisions, and transactions while maintaining compliance with regulatory and national requirements. The platform is purpose-built for sovereign-grade use cases: governments, telecoms, national AI infrastructure, and enterprise deployments. This is foundational infrastructure for the AI era. Not AI models. Not applications. Abu Dhabi is positioning itself as the sovereign AI infrastructure hub. Skipr is building the infrastructure layer. Congrats to the entire Skipr team 👏🏼 👀 Interested in MENA tech & startups? Sign up to my weekly newsletter here: https://lnkd.in/dJMzb8Tt #MENA #startup #VC

46

4 Comments

𝐓𝐡𝐞 𝐒𝐭𝐢𝐜𝐤𝐲 𝐖𝐫𝐞𝐧𝐜𝐡 Despite decades of automation efforts, industrial vision; the eyes of the factory; remains stuck in the past. Current Computer Vision (CV) systems are essentially bespoke, non-transferable tools: ➖ Time-Consuming: Each new inspection task requires a lengthy, months-long, custom engineering engagement. ➖ Data-Starved: They need massive, perfectly labeled datasets to achieve even moderate accuracy. ➖ Infrequent Failures: Subtle changes in product, material, or environment cause brittle systems to fail, leading to significant scrap and downtime. 🚀 Allus AI (YC F25) is powering the factory floor with next-gen vision foundation model. 𝐓𝐡𝐞 Allus 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐋𝐚𝐲𝐞𝐫 Unlike traditional CV that has to "learn from scratch" for every new factory or product, the Allus model is pre-trained on an immense corpus of 1.5 Billion+ industrial, robotics, and manufacturing visual data points: ✅100x Faster Deployment: New inspection or monitoring tasks can be customized and deployed in minutes—not months—using an "implementation agent" and minimal reference examples. ✅Ultra-High Accuracy: The model reliably achieves > 99.95% accuracy across diverse sectors, including high-stakes automotive and electronics manufacturing. ✅Immediate Value: Already being leveraged by five Fortune 500 manufacturers, there is ability to scale intelligence instantly across production lines, improving defect detection accuracy by up to 100x. 𝐓𝐡𝐞 𝐀𝐥𝐥𝐮𝐬 𝐂𝐫𝐞𝐚𝐭𝐨𝐫s 🧠Christopher Kai Cui (CEO): The Domain Expert. Kai's journey began literally on the factory floor, growing up in a manufacturing family and eventually managing iPhone back panel production at the age of 18. This gives him an unparalleled, visceral understanding of manufacturing constraints and the cost of quality failures. 🧠Shijie Wang (CTO): The AI Scientist. Shijie previously served as an LLM Scientist at NVIDIA Labs, a pedigree that demonstrates deep expertise in training and scaling massive foundation models. He brings the world-class AI knowledge necessary to translate cutting-edge research into a deployable industrial model. 🧠Zhisen An (Jason): The Industrial Architect. Jason specializes in developing universal solutions for robotics companies, giving him critical insight into how AI integrates into real-world industrial enterprises and motion control systems. The three began exploring how to empower industry while studying at Georgia Institute of Technology, addressing every facet of the problem: operation, AI development, and industrial deployment. 🤝💡 At Allus AI (YC F25) the rubber has already met the road with solid sales traction within few months of launch - Product-Market-Fit (PMF). Congratulations to the team for their maiden fund raise which was oversubscribed. DECACORN is glad to join the journey at Allus AI (YC F25) (GP: Pete Koomen) as they build the core visual intelligence for the future of tech led manufacturing

44

2 Comments