Dylan Fox

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

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With RL techniques like GRPO, it’s become easy to fine-tune small models to outperform foundation models on vertical tasks. We’re open sourcing Osmosis-Apply-1.7B: a small model that merges code (similar to Cursor’s instant apply) better than foundation models. Links in comments! Using foundation models for high specificity, low complexity tasks like code merging can be overkill - instead, it’s better to use a specialized model instead for higher performance and latency. We fine-tuned the model on 100K real-world code commits where the model needs to merge a code edit snippet into an original code snippet. On the test dataset (subset of CommitPackFT), foundation model performance ranged between 0.77-0.93 reward score. In comparison, Osmosis-Apply-1.7B achieved a 0.98 reward score - while also being 3X-5X more affordable than the next cheaper model AND ~10X faster! Why this matters: the best AI agents use multiple models to ensure task specialization and reliability at scale - on top of cost and latency benefits as well. Links to the model and full write-up (with reward function, hyperparameters, etc.) are in the comments - reach out if you’re interested in using reinforcement learning!

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Design-native founders are positioned to win the application layer. Talked about Taste, LLMs, human preference modeling and more with the brilliant Erica Wenger 🏕️. Big fan of the pod and the guests Rex Woodbury Maya Bakhai 🌶 full episode link below 👇

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Check out the latest funding news from across the Techstars portfolio. → Zipline (Techstars 2011), a drone delivery and logistics startup, raises more than $600M in new funding, boosting its valuation to $7.6B, as it expands commercial deployments. → Language learning startup Preply (Techstars 2015) raises $150M and becomes the 23rd known unicorn from Techstars accelerators. → Hydrosat (Techstars 2019) raises $60M in Series B funding → Tive (Techstars 2017) receives a $20M investment, valuing the shipment-tracking systems developer at slightly more than $500M. → Parambil (Techstars 2023) raises an additional $6M in seed funding ($8M to date). → Seasats (Techstars 2021) receives $24M in Department of War APFIT funding to accelerate the fielding of its autonomous surface vessels. → Hawaiian Airlines’ newly announced $600M Kahuʻewai Hawai‘i Investment Plan features a strategic investment in Ampaire (Techstars 2018) to support hybrid-electric aviation in the islands. → Fintalo (Techstars 2025) secures $500K+ in oversubscribed Pre-Seed funding. → PraxisPro (Techstars 2024) closes an oversubscribed $6M seed round. → Samara Aerospace (Techstars 2024) closes $10M seed round to help bring more stability to sats in orbit. → MyARC (Techstars 2022), a platform that enables fitness creators to train their communities at scale, secures £1.5M investment. Massive congratulations to these incredible portfolio companies!   🔗  Read more at https://lnkd.in/dxtCTvrg 

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Solomon Labs, a New York based AI tax automation startup, secures $4.7M additional seed funding from Dunamu & Partners and Bass Ventures, reaching $14.5M total, aiming for $3M ARR by 2025. https://lnkd.in/g5eunsM2

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Proud to share ParaFi Capital's seed investment in ATXP. We issued a term sheet before the company was even formed, backing Louis Amira— a longtime friend and neighbor — and his cofounder David Noël-Romas. Over the course of countless coffees and walks, it’s exciting to see their idea become reality. The conviction was simple: for agentic finance to become real, AI agents need the ability to move money in tiny increments. Today, sub-$1 payments are economically impractable on banking APIs and traditional rails (ACH, credit, debit, etc). Micropayments on the internet are clunky because of high fixed fees, settlement delays, and lack of programmability. Stablecoins, as the first true programmable money, remove that friction, enabling agents to send fractions of a dollar as easily as sending an email. ATXP aims to make agentic finance possible by building a neutral protocol layer powered by stablecoins. We’re thrilled to co-lead this seed round alongside Primary Venture Partners, Stripe, and other great partners.

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We’re thrilled to share that Usul (YC S24) has raised $3.3M in Seed funding led by Scout Ventures with participation from BVVC, Teamworthy Ventures, True Ventures, Y Combinator and more. Usul is the modern platform for defense contract capture and federal growth. Our customers have won hundreds of millions in defense contracts for capabilities across Air, Land, Maritime, AI/ML and more. When Oliver Gomez, Joonghyun Lee and I joined forces during Stanford’s Hacking 4 Defense class, we interviewed 300+ defense companies to intimately understand how difficult it is for national security solutions to get deployed quickly at scale. In normal capital markets this is called dead weight loss. In warfighting, this costs lives. We're building an elite team of hackers and defense enthusiasts in San Francisco. If you’re interested in powering the next 1000 defense primes, email us at founders@usul.com or dm me. Read more about our journey in Erin O'Brien's Tectonic article below in the comments. Lastly, thank you to our amazing cap table for joining the ship. Bradley C. Harrison, Cody H., Nate George, Zach Beecher, Michael Keane, Joe Musselman, Gene Ebersole, Nick Nowlin, Thomas Lehrman, Stephen Schmalhofer, Michael Montano, Gustaf Alströmer 🇺🇦, Jared Friedman, Steve Blank, Jacqueline Tame, Jack Shanahan, Peter Newell, Steve Weinstein, Jeff Decker, Ivan Kirigin, Jeff Heitzman, Keith W. Gibson, CFCM, SCPM, John Tincoff, Michael Malis, Austen Allred, Tom Hsieh, Donald Donckers

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Excited to announce Emergence Capital lead investment in Vesence's $9M seed round!   Legal AI should be a slam dunk. It’s a text-rich domain with tons of training data; well-suited for LLMs. Thus, it's no surprise that AI demand amongst lawyers has been extraordinary.   Unfortunately, in many scenarios, the reality hasn't yet delivered on the promise. Many AI tools fail for the same reason: they prioritize generation over precision.   Hallucinations and inconsistencies turn time-saving innovation into a liability and lawyers have to re-review every clause and citation, which adds hours of work instead of saving them.   Before AI, firms solved this with armies of reviewers scrolling through Word docs line by line.   Vesence is tailor made to bring precision to law at scale.   Instead of generating new text that might be wrong, Vesence uses AI to ensure what’s already written is right.   It enforces firm-wide standards, validates references, flags inconsistencies, and polishes language to perfection — all inside Word and Outlook.   Even more impressive than the product are the founders. Henrik and Ludvig embody the precision and persistence we look for at Emergence.   Henrik built the first version solo, including full Microsoft integrations. And Ludvig, a former pro athlete, brings the discipline of an elite competitor. Indeed, they literally slept at a top Swedish law firm to study the workflows firsthand as they built out the product. Intensity driving precision.   We're thrilled to welcome Henrik and Ludvig to the Emergence family.

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