David Pann

Most of the AI tooling conversation right now centers on code generation. Cognition, Cursor, Claude Code....these tools are genuinely useful, but engineers are drowning in production issues. At our latest Greylock Partners Change Agents event, Mayank Agarwal and Rushin Shah from Resolve AI walked through a reality most engineering teams live through: with AI coding tools "you can crank out code much faster, but teams aren't really shipping to production that much faster." Some takeaways from our conversation: 1/ The real problem isn't code generation. Mayank saw this at Splunk where engineers spent only 10-20% of their time writing new features. The rest was fighting incidents, navigating tribal knowledge across hundreds of microservices, and operating tools that don't talk to each other. 2/ Production systems are different from code. Your codebase is static. Production is emergent behavior from infrastructure, deployments, traffic patterns, to tribal knowledge buried in Slack. As Mayank stated, the issue is "a coding agent cannot predict these things without the deep understanding of what's actually happening in production." 3/ The scale required is massive. Resolve is now consuming a trillion tokens per quarter—1% of all Azure usage. That's running Stack Overflow from scratch twice per week. "Vibe debugging" is the missing piece, Rushin's term for getting clarity on production systems the way vibe coding accelerates writing code. Check out the full episode in the comments

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New short! For enterprise AI budgets, it's not about speed or scale of implementation but small steps, finding the right partners, and focusing on human role and imapct amidst all the changes. More sharings from WIZ.AI's Robin Li from working with hundreds of enterprise partners across the globe on implementing their conversational AI solutions for their businesses. Follow Insignia Ventures Partners for the latest episode of On Call with Insignia. #AI #ArtificialIntelligence #AIArchitecture #Leadership #Innovation #ashortaday

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Project Vend reads less like a capability story and more like a governability story. Phase two improved because institutional scaffolding (tools, procedures, role separation) reduced predictable judgment failures—persuasion, compliance blind spots, boundary confusion, values drift. The board-level question isn’t “can an agent run a business?” but “what governance primitives must exist before we delegate authority.” Until incentives, escalation rights, and refusal boundaries are structurally enforced—not merely prompted—agents remain high-performing managers, not autonomous executives.

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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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We chose a good subject for our march Autotech Council meeting, and after this year's CES, it seems even more obvious. Our meeting on the AI Defined Vehicle will show how it's a natural next step after the Software Defined Vehicle phase that we've been in Tesla launched the Model S in 2012. Now, most carmakers have SDVs. It's time to layer on the AI in user-facing roles, and in invisible functions managing the vehicle.

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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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Gradial continues to show what focused execution can unlock. Since Madrona led their seed and Series A rounds, the team has demonstrated how AI agents can deliver real, measurable value inside large enterprises. Today’s announcement of their $35M Series B (led by VMG Partners with participation from Pruven Capital) reflects both their momentum and the strength of the category they’re defining. What has impressed us from the start is their discipline as builders, and how they approach "winning the wedge:" tackling the complexity of marketing execution and proving ROI in one of the most operationally demanding parts of the enterprise. That clarity of focus has unlocked strong market demand and the ability to improve adjacent workflows and deepen Gradial’s role as core infrastructure for businesses of the future. We’re also seeing early signals of the next stage of applied AI. Gradial’s agents don’t just generate outputs; they reason through multi-step processes, coordinate across systems, and adapt in real time. It’s a meaningful step toward intelligent applications that collaborate with people and improve with every cycle. We’re proud to continue our partnership with Doug Tallmadge, Deip Kumar, Anup Chamrajnagar, Anish Chadalavada, and the entire Gradial team as they build toward that future. https://lnkd.in/g8ummrCN

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The rise of autonomous agents presents one of the most exciting and complex challenges in AI today. One of the main and hard question is how we scale AI agentic systems safely while ensuring accountability, trust, and long-term value? I recently joined Cognizant's The Agent Effect podcast to dig into that very question alongside Lisa Bechtold, Ph.D., LL.M. , Zurich Insurance’s Global Head of AI Governance. We explored what enterprise leaders need to consider when deploying AI agents at scale, and why thoughtful governance isn't just a safety net. It's a business enabler. Tune in to the episode. Spotify - https://lnkd.in/gDTh7wTq Apple: https://lnkd.in/gjhefN3V #TheAgentEffect #AIAgents #AgenticSafety #ResponsibleAI

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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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