Ed Donner

New York City-based Betaworks has closed its third fund at $66M to invest in early-stage AI startups. Betaworks, a staple of the NYC tech ecosystem since 2008, will focus its new fund on investing in pre-seed and seed-stage AI companies. The firm has been investing in AI since 2016 and has backed companies like Hugging Face. Fund III is larger than the firm's previous funds ($46M Fund II in 2020 and $48M Fund I in 2016) and saw many of its previous limited partners return to invest, a strong signal in a challenging fundraising environment for VCs. The fund will seek to make at least 25 pre-seed to seed investments, as well as 50 investments into startups as part of the Betaworks Camps program. Congratulations to the Betaworks team. TechCrunch: https://lnkd.in/d--hfMpa #VentureCapital #AI #EarlyStage #SeedFunding #NYCTech #Fundraising

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Chase is essentially saying "we need to invest in companies that can predict the future while we ourselves have no idea what's coming." Meanwhile, AI is speed-running through entire startup lifecycles in 12 months, making due diligence feel like reading yesterday's newspaper. https://lnkd.in/gXhpJGAP

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The next frontier of AI isn't just about choosing the right model. It's about building moats through implementation. Across the Emergence Capital portfolio, companies like Bland and Federato are scaling faster by embedding Forward-Deployed Engineers. These engineers bridge the gap between AI and reality, ensuring real results in production. Read more from my colleagues Gordon and Kyle on why FDEs are the key to unlocking AI's full value. https://lnkd.in/gcsRRGX2

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Trying something new this week - a studio industry roundup with my take on what matters. The studio model is no longer an experiment being run in a handful of places. It is infrastructure being built across geographies, sectors, and institutional types. 5️⃣ stories from the past seven days:  1️⃣ OSS Ventures hit a EUR 40M first close on a EUR 75M follow-on fund -- capital raised specifically to back the 30 companies their studio created since 2019. The "build then fund" lifecycle is maturing. B'More Venture Studio with a similar thesis: studio operations first, then dedicated capital to scale what's working.  2️⃣ Delta40 raised $20M from 54 investors across 13 countries to launch Africa's first integrated venture studio and fund. The LP base is almost entirely DFIs and foundations. Impact capital is discovering the studio model as a deployment vehicle, something I seeing across geographies (conversations with Michael Bob Starr and Damias McDonald this week alone) 3️⃣ Start Holdings launched Start.vc out of NYC and Barcelona after a $200M EV infrastructure exit. Another operator-turned-studio-founder. This is the pattern we see quite often in our work at 9point8 -- experienced operators like Jason Goldsmith who've built and exited deciding the studio model is how they want to build next.  4️⃣ UBC HATCH admitted 10 new deep-tech spinouts for Winter 2026 -- mostly climate tech. University venture builders are quietly becoming one of the fastest-growing segments. Follow Evan Allen and Jaimie Testai to keep track of its rapid evolution.  5️⃣ The INVEST Act is advancing through the Senate. If passed, it expands qualifying VC fund size from $10M to $50M and loosens general solicitation rules for accelerator and university events. For studio-funds -- and for folks in the The Venture Studio Forum community tracking policy -- this could be the most significant capital formation legislation since the JOBS Act. If this roundup format is useful, I'll keep it going. What other noteworthy news hit your desk this week, and what stood out to you?

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A Founders Guide to The Long Build™ - Physical AI Thesis Part III This one is for the builders. In this final post, my partner Will Porteous and I along with insights from builders in the trenches and investors who play the long game share our guide for founders building intelligence in the real world. A few core takeaways: • Sim before steel: Runway is precious, model the system behavior • Capital intensity is not the enemy — misaligned capital is • Build complete solutions before you talk platform • Hardware opens the door, software wins the room • Trust is earned through resilience, not hype

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Imo fundraising is actually quite binary You’re rather building in a hot category (vertical AI SaaS) with an in-meta team (Stanford / MIT / OAI / PhD) and VCs are tripping over themselves to invest Or you’re not an it’s a three month slog to get a few believers. The core of this dichotomy is the myth that venture is contrarian. It’s often not. There are contrarian outcomes and bets for sure but the vast majority of early stage bets are consensus and heuristic driven

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I like the sentiment but honestly shouldn't this be exactly what Advanced Research + Invention Agency (ARIA) should be doing? This feels like duplication. https://lnkd.in/eBBf9kYm I'd rather see that 40m e.g. go out as a commercial tender to two or three winning companies for the first step toward a UK sovereign AI model. We have lots of R&D in the UK via universities and other places; what we DON'T have is the ability to scale those technologies with promise commercially without them flipping to the US. And the UK gov is not offering big customer contracts to startups to help them prosper or help them stay in the UK. This is solving the WRONG problem IMHO.

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I've seen a lot of solo founders getting scooped up by OpenAI, Coinbase, and others. I'm not sure if this is a corp dev strategy or purely coincidental, but it's much easier to acqui-hire solo founders than teams with multiple founders. Smaller amounts of cash can be life-changing for a solo founder, but when you have to divide the pie, it becomes less interesting. I have a feeling that corp dev teams are very aware of this and specifically target solo founders for small scale M&A. As a seed investor, this makes the underwriting of solo founders more complicated. We invest in teams and/or solo founders that want to go the distance. Knowing that a few million dollars is what it takes to get a solo founders attention is a challenging thing to figure out. By the way, I have zero judgment for solo founders who take these phone calls and go down the acqui-hire path. Incentives are powerful, and I understand the game.

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I've talked to hundreds of founders spending 40+ hours a month on ops work that shouldn't be theirs. Writing briefs, scoping roles, explaining the same task twice. So I put together 10 Claude prompts to remove that layer completely. → One-line task into a contractor brief → Slack message into a clean handoff doc → Vet a candidate portfolio in under 10 minutes → Scope a role without a job description → Audit your week and cut what shouldn't be yours Copy, paste, and move on with no setup needed. Completely free. Comment "PROMPTS" and connect with me. I'll DM it over.

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Anthropic just published a fascinating piece on something they call the Assistant Axis. LLMs have an internal “direction” that makes them behave like the calm, helpful assistant we expect. Over time, they can drift and start sounding like storytellers, philosophers, or overly confident experts. Anthropic shows how identifying and constraining this axis can keep AI behavior more stable and aligned. This research is interesting because it makes persona measurable and controllable. The real question is how much control is too much. When you use different LLMs today, what persona do you most often experience? Helpful assistant, creative partner, confident expert, or something else? https://lnkd.in/gcfs65hD #AI #LLMs #Anthropic #AIAlignment #FoundationModels #MachineLearning #ResponsibleAI

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Reading Metronome’s Monetization Operating Model, I kept coming back to one idea: pricing has become product. Software now delivers outcomes, not access. Yet most companies still charge as if they’re selling seats or licenses. That disconnect creates friction: for customers, unpredictability; for companies, stalled growth. The paper’s argument is simple but sharp—monetization isn’t a late-stage decision. It’s strategic infrastructure. Pricing needs the same ownership and iteration as any feature. Treat it like a surface that customers touch, not a spreadsheet buried in finance. If value is continuous and dynamic, pricing must be as well. That means product, GTM, finance, and engineering working from one system of truth. How many of us still treat pricing as an afterthought—when it should be a growth engine? https://lnkd.in/gnH7WzYf #Monetization #ProductStrategy #AI

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Gemini + Chrome. Perplexity + Comet. Now ChatGPT has Atlas. People have already used Atlas to buy hot dogs for a kid’s birthday party. Was it clunky? Yes. But it worked. That’s the shift: these aren’t search bars. They’re task agents. We’re moving into the era of: - Asking, not searching - Summaries, not scrolls - Actions, not just answers So here's the real question: If your customer never opens your website… Can an agent still find, understand, and trust your data? In this new AI-first web, visibility doesn’t mean blue links. It means being summarized, cited, and acted on without a single human click. It’s early and messy, but it’s coming fast. The brands designing for this shift today will power agent decisions tomorrow.

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NEWS: Yan LeCun lands a new job in a " tell it all interview " The ex Chief AI scientists at Meta does not believe in LLMs (I do) and has decided to bet on world models. His new company, Advanced Machine Intelligence Labs, is run by CEO Alex LeBrun. LeCun took the Executive Chair role instead. He says he's "too disorganized for this, and also too old" for the day-to-day stuff. Instead, his job is to inspire. France is back in AI.

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We’re all sold on OpenClaw, but can it ACTUALLY be useful to the average person who doesn’t just sit around writing code? Turns out it can! Here’s how Matthew Berman used OpenClaw to help solve his stomach issues!

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Wow! Fascinating article Andrew Antos & Nischal Nadhamuni about how the Klarity platform does knowledge capture! A total disruptor indeed for how consultants traditionally did this work! Capturing not just what happens but why it happens has always been the missing link in enterprise AI, and Klarity ’s approach to surfacing human reasoning through its intelligent platform is a real step forward. The #AI Advisor’s ability to traverse vast context graphs, uncover cross-team patterns, and turn knowledge into actionable insights shows how innovation in knowledge capture can truly transform enterprise decision-making.

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Question to those using LLMs every day: Which LLM work best for team collaboration and shared AI workflows? I'm personally split 50/50 between ChatGPT and Claude. But as we look to invest in an Enterprise account for Remento, I'm realizing I definitely have some blind spots... Our primary goal is to easily create reusable AI assistants that any team member can access, trained on our specific knowledge base. For example: 👉 Marketing copy aligned with brand voice 👉 Product feedback based on customer research 👉 Content strategy from audience data For those who've made this transition: what's working?

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I’ve spoken to over 2 dozen MDs at PE firms I can confidently say that the arb of figuring out how to implement Vertical AI at portfolio companies is very real right now It will fundamentally change underwriting for those who can do it predictably and unlock generational returns. Most are aware they need to act. Very few have.

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I’ve cancelled more software subscriptions this past year than ever before. Same story in my group chats. This feels like a canary in a coal mine: 90% of enterprises now say software consolidation is a priority. It makes sense. Why piece together hundreds of software subscriptions when AI can help pull from a unified database and build custom apps and workflows that fits you and your own? This is a massive market to disrupt. Enterprises in the US spend over $8,700 annually per employee, with a staggering 51% of licenses that goes unused. question I keep coming back to: what happens to the massive valuations handed out during SaaS’s heyday?

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