Jeremy Burton

Excited to see SAP and Uptycs partner to bring 👉 verifiable 👈 AI-powered security to the front lines of enterprise defense! Exactly the kind of innovation needed to tame today’s AI-driven cyber threats. #cybersecurity #SAP #Uptycs

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The more autonomous AI becomes, the more binaries it will create. The more binaries created, the more security is required at the gate, and the more governance is needed before deployment to production. 🐸 https://bit.ly/4seEMPq #JFrog #LiquidSw

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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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"What has become increasingly clear over the last 24 months is that ... AI has become capable of performing what looks more like the work that humans do — versus the software tools they use. So if AI is moving from tool to worker and starting to do jobs itself, this is beyond the current "application layer." These are systems that can plan, act, think, reason, and actually get real work done. And when companies sell these types of systems, they are selling into labor budgets of customers — not just software budgets. This is what we think is the new frontier for the next few years." Thank you Kristian Marquez, CFA for capturing our approach so well!

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Excited to see our portfolio company Allstacks launch their Intelligence Engine today - providing instant efficiency insights for engineering teams. Allstacks has been pioneering machine learning and AI-driven intelligence to track engineering efficiency since 2017. The launch of the Intelligence Engine takes that vision to a new level. From M12, Microsoft's Venture Fund co-investing with us at the Series A, to seeing Hersh Tapadia, Adam Dahlgren and the team continuing to win major enterprise customers across the US and Europe, it’s been clear they’re building something category-defining at Allstacks. Engineering teams waste tens of millions each year trying to identify problems and engineering fixes. By connecting data across GitHub, Jira, change management and quality systems, the Intelligence Engine helps leaders move from static dashboards to AI-driven insights into what’s happening, and what to do about it. This is the new foundation for turning engineering data into action - identifying bottlenecks, flagging delivery risks before they compound, and measuring the real impact of generative AI tools across teams. Quadri Ventures is proud to be to backing Allstacks as they continue to redefine excellence in engineering. #AI #SaaS #EngineeringIntelligence #DeveloperProductivity #VentureCapital

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🚨CFO at Redsand - Sandeep Dama informs us of the downstream risk of unplanned costs in Ai compute. Corporations will realise that unbundled decentralised contracting, and spend management is the only way to budget accurately #decentralised #private #sovereign #computeAI

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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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The best AI apps today are being built like infrastructure companies. Great breakdown from the awesome 💡 Yazan "Yaz" El-Baba on why technical depth, rapid iteration, and margin-aware architectures are becoming table stakes for AI startups. Definitely worth a read: https://lnkd.in/gMVFFXgH 

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