David Skok

Big milestone for Firmable. We’ve raised $14m Series A led by Airtree. Sales has moved through a few big waves: intuition-led, CRM-led, data-led. We’re now entering the next one – intelligence-led sales. The opportunity isn’t just better data. It’s turning that data into clear direction and action, without adding more work for sales teams. That’s what we’re building at Firmable: a foundation of trusted external data, layered with intelligence that helps sellers know who to focus on and when. Led by Airtree, this round supports our expansion across Asia and into the US – and accelerates the build-out of AI agents that take the admin work off sales teams so they can focus on what they do best. Proud of the team, grateful to our customers and investors. We’re just getting started. Read the exclusive in the AFR. https://lnkd.in/gr66uknb Leigh Jasper | Tara Salmon | Karthik Venkatasubramanian| Chester Thompson| Chath Widanapathirana

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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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Thrilled for OVNI Capital to be backing RIFT in a pre-seed round led by AlleyCorp (Luc Ryan-Schreiber). Rift is building the first real-time aerial intelligence network, a new layer of infrastructure for persistent, on-demand visibility where it matters most. This funding will scale production of their autonomous stations, accelerate deployment in high-stakes environments, and expand the team leading the next phase. 🗞️ Explore the full story: - StartupMafia: https://lnkd.in/dARrg25N - BFM Business (FR): https://lnkd.in/eADSUsn7 - Les Échos (FR): https://lnkd.in/eFG3zAy5 👨‍🚀 Join the mission → https://lnkd.in/erm_SrTG

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The path to AGI is shifting from pure intelligence to owning the entire stack. My conversation wiht Eiso Kant, Co-CEO of poolside (new on the pod 🎙️): Some highlights (all links to full episode below): * Intelligence is a Commodity, Infrastructure is Destiny: Eiso argues that AGI intelligence will become less distinguishable over time. The real, sustainable competitive advantage lies in controlling the three layers: Energy, Compute, and Intelligence. "If you're a foundation model company and you're not building physical infrastructure, you're cosplaying your business". * Project Horizon: The Gigawatt Scale: Poolside is moving to secure its future with Project Horizon, a multi-gigawatt AI factory data center complex. This addresses the core bottleneck—lead time—by owning the physical build. They are pioneering an "incrementally delivered hybrid modular" construction approach for unmatched speed and cost-efficiency. * The Next Frontier in Training: "Reinforcement Learning to Learn" (RL2L): Moving beyond current RL methods, Poolside is developing a generalized objective they call RL2L (internally "Bondi"). This technique seeks to "reverse engineer the web... into the thoughts and actions that created it"—a massive new scaling axis designed to push reasoning capabilities much earlier in training. LINKS: YouTube: https://lnkd.in/ehGd-3qx Spotify: https://lnkd.in/eQHhvSde Apple Podcasts: https://lnkd.in/eeMr866G

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Voice is a super interesting modality right now - maybe the first modality we're seeing move to open source models across a number of scale ups / enterprises. Reliability concerns, high costs, and open source model performance are pushing engineers to do their own fine tuning vs. relying on third-party vendors of proprietary models. Many of these orgs have already been collecting their own first-party data and now with third-party vendors like Extrian, David AI, etc they can train really high quality models. RL has been insanely hyped, but it's been unclear how long it will take scale ups and enterprises to actually lean in. Voice AI might be hitting that inflection point faster than expected.

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Sanity released its AI Content Operating System - a true game-changer for any enterprise, brand or e-commerce player! Way to go Magnus Kongsli Hillestad and team!

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I’ve been working on the theory of why small, shippable factories are the perfect inversion to the centralized prefab factory model. It all collapsed into one line of math (credit to Gilles Retsin): Factory Efficiency = Usage × (Throughput × Product Value ) / ( CapEx + Deployment OpEx) The idea is that the closer you can drive Usage to 100 % while keeping the denominator tiny, the faster the flywheel spins.  A shippable microfactory tackles this by primarily focusing on a high Usage factor. Because the microfactory can be packed up afterwards and reused, its CapEx gets amortized over multiple projects, effectively raising its overall Usage across the year. If one project alone doesn’t fully occupy the factory, it can simply roll over to another job to maintain high Usage. Its CapEx is often an order of magnitude lower than a centralized factory (< $1M vs. $10-50M min), so the breakeven throughput is more achievable on a small pipeline of work. The real breakthrough here is portability. There is near-zero stranded capital and almost no idle time. To flesh this theory out, I wrote 3000 words on the topic for Brad Hargreaves and Thesis Driven. The article breaks down: - why “fixed factories, shipped goods” is being inverted to shipped factories, fixed goods - what a sub‑$1 M robotic cell does to CapEx per home versus a £45 M off‑site plant - early production/cost data on a shippable microfactory from Mollie Claypool, Gilles Retsin, Sam Baker and the Automated Architecture (AUAR) team - the hybrid reality of robots tackling the heavy structural components and human crews assembling faster and cheaper - technical constraints to pull this model off and future considerations on the optimal business model (own vs. rent) Major shoutout to Gilles Retsin for helping me pull this together. https://lnkd.in/eRTnbZPa

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At our recent LP Advisory Council meeting, LPs asked about headlines claiming AI is undermining SaaS. Our view is that the two are mutually reinforcing, not mutually exclusive. We are bullish on AI, and we believe certain SaaS leaders (with systems of record/action, first-party data and data rights, network effects, ambitious AI roadmaps, etc.) are well-positioned to capitalize on the agentic AI opportunity. There are multiple reasons for this. To start, consider what’s happening today with SaaS. The median company in the SEG SaaS Index grew 13% in Q2 while doubling its EBITDA margin year over year, with a top quartile growth rate above 20%. SaaS accounts for only 25% of overall software revenue in 2025, according to Gartner, so secular growth from on-premise to SaaS migrations should continue for years. Notably, this growth largely comes before many SaaS firms monetize a new wave of agents. Meanwhile, small AI startups with little revenue are burning cash and trying to sell. At the other end of the spectrum, Klarna’s much-hyped move to replace Salesforce with AI proved overstated; the CEO later said he was “tremendously embarrassed” by the fallout. SaaS pessimists might ask themselves: Has any large enterprise successfully replaced SaaS systems of record with an LLM? Rather than the AI-SaaS story being one of complete disruption, I believe it’s moving in waves, not unlike how businesses have historically incorporated new technologies:  ● Wave 1 (2023): “Get me some AI so we’re not left out” (a reprise of 1996’s “build a web page and we’ll figure it out later”). ● Wave 2 (2024-mid 2025): “Show me the budget and ROI. Also, why do we need an expensive AI model over a cheap one?” ● Wave 3 (late 2025): "I need enterprise-grade AI that integrates seamlessly, delivers measurable results and won’t disrupt core ops with experimental tools." We can’t say for sure what will happen next, but we do know how software brings value to the enterprise. SaaS is more than software code; it’s digital business intelligence using code to express that intelligence. That takes massive data sets and business experience brought to bear on enterprise functions, at scale and over time. It’s about executing tasks efficiently today, then innovating new tasks over time. For these reasons, we see a supercharged future of AI-enabled enterprise software that enhances productivity and innovation in dramatic ways. That future is less about ever-larger foundation models or narrow AI startups that complicate the stack. Instead, we see integrated software systems that orchestrate digital services to accelerate complex tasks, with agents as the 2026 step-function. AI is a massive force multiplier in that equation, but not a radical disruptor. At Thoma Bravo, we welcome the AI-SaaS conversation. We’re healthily paranoid as part of our mindset. But evidence, history, and business logic suggest that the AI revolution is set to accelerate SaaS value and growth, not undermine it.

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