Marc Friend

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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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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On-Prem is the New Cloud: Why Enterprises are Reversing Stance in the Age of AI Remember when cloud was the battleground for innovation, agility, and scale? Everyone was tracking the % of on-prem compute moving to the cloud and it was growing every year? Well now there’s a reversal. The reality today? On-prem is the new cloud. Here’s why. AI’s Dark Side: Legal Risk and Data Governance Headaches Enterprises are excited about AI, but also uneasy as vendors like OpenAI face intensifying legal pressure. In a high-stakes lawsuit, a judge ordered OpenAI to preserve all ChatGPT chats (even deleted ones), overriding its 30-day deletion policy. Imagine that: you delete sensitive conversations, but they’re still sucked into legal limbo. The precedent is clear: AI prompts and outputs are discoverable records, demanding integration into enterprise ESI (Electronic Stored Information) policies. For large organizations handling sensitive data, the risk is simple: you can’t afford your AI tools to turn into legal liabilities. Especially when global regulations (GDPR, etc.) and internal governance collide with unpredictably broad discovery orders. Sovereignty, Control, Trust Many enterprises still ban ChatGPT or impose strict rules on usage. Even when allowed, token limits are throttled. Enterprises are cautiously letting AI in (as they should). Enterprises need to have control and trust. SAP is leaning into this with its Sovereign Cloud On-Site, deployable on customer premises with SAP-managed infrastructure. It is built to deliver data, operational, technical, and legal sovereignty in one package. Doubt it will be the last. Scaling Pains Talking to founders, three reasons stand out why cloud isn’t scaling with AI: 1/ The rate at which AWS/GCP lets you provision compute and the compute it wants you to provision both falter as AI and AI driven developers deploy a lot more code more frequently than before. So sometimes it’s not about data governance, but actually about performance and functionality. 2/ Shadow AI is rampant—employees paste sensitive info into models despite restrictions—creating demand for better permissioning tools. 3/ Data leakage remains a live risk, like the August 2025 Grok incident where hundreds of thousands of private chats became publicly accessible and indexed. One thing is for sure - founders selling into the enterprise are scaling faster if they offer the ability to run on-prem. The Future is Hybrid Don’t get me wrong - cloud is not going anywhere. But neither is on prem. A few years agoI thought we were headed for a full cloud shift, but now I think the future is truly hybrid.

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Honored and excited to partner with Drew Baglino and the incredible team at Heron Power as they re-imagine the electric grid for the modern world. Power in America is not zero sum. We deserve access to cheap and abundant electricity and we should win the AI race. And we must build the capacity to manufacture critical technology here at home. When faced with existential challenges, we deploy technology against them. And we win.

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If you care about out-innovating China in the AI war, you need more efficient chips, not just brute force buildout for hot, inefficient chips and architecture. That’s why DCVC backed Mythic — up to 1000x the SWapC of NVIDIA’s 2028 roadmap for real time embedded and datacenter compute. Oh, and by the way, *made on American fabs with zero Taiwan supply chain dependency*… We can’t pour more concrete than China, and our grid interconnect workers don’t slave away under threat of being murdered for their organs. We need smaller, cooler, faster, smarter, more power efficient chips. Mythic is proving that *today* with live silicon.

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🚀 🚀 🚀 Excited to announce the launch of CognitivTrust Inc. – application security reimagined for the AI coding era. Developers are shipping code faster than ever. Copilot, Claude, ChatGPT – AI is writing 40-60% of code in modern teams. We're in the vibe coding era, and it's transforming productivity. But here's the problem: our security tools haven't caught up. Current code scanners weren't built for AI-generated code: AI is generating more code in a day than teams used to write in a week. That's more surface area, more potential vulnerabilities, and more risk – all moving at unprecedented velocity. Legacy platforms are retrofitting AI as a feature, not rebuilding from the ground up. The result? Surface-level improvements that can't deliver the fundamental speed, accuracy, and transformational impact that modern security requires. We built CognitivTrust for how developers actually work today: Real-time prompt analysis that allows ‘security-ready’ AI-generated code, eliminates vulnerabilities from the IDE, contextual threat mapping while the AI is still suggesting code, and intelligent triage that works with your AI coding assistant, not against it. Security at the speed of code, not at the speed of periodical scans. What we heard at Global Fintech Festival 2025: Banking and fintech leaders are caught in an impossible trade-off: innovate at startup speed to maintain competitive edge or maintain bank-grade security while ensuring safe AI adoption by their developers. With AI coding assistants now standard, they need security that can contextualize threats in an actionable manner while keeping pace with AI-generated code. Every false positive/ security debt costs delayed product launches. Every vulnerability destroys trust. They can't afford either in the fast moving fintech or rapidly transforming banking verticals.  Ashna G.Animesh MukherjeeVishal ShuklaLars BuurNikhil SewakSahil MitalSandeep RaghuwanshiGazal GargSandeep VarmaGaurav Chaudhri Ready to secure AI-generated code without slowing down: If you're riding the AI wave and need security tools that match your velocity, let's talk. Drop a comment or DM me to see how CognitivTrust secures modern development workflows. #AppSec #AIcoding #DevSecOps #VibeCoding #CyberSecurity #DeveloperTools Russell RagarKenneth Thomas MorasRuchi KhuranaRuss D.Anshu GuptaBhaumik ShahCristian BrottoTammy SchuringMala RamakrishnanRam SenthamaraiAysha Khan

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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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