Christopher Keene

As AI workloads eat up global computing supply, DRAM prices are surging like never before—creating major challenges for teams trying to plan out their data center spend. One of my portfolio companies, Mext, is addressing the largest cost component in the datacenter: server memory (DRAM). From being on the board of Redis for the last twelve years, I’ve learned a lot about the various aspects of this problem and approaches to solving them. Mext found a breakthrough, using new AI techniques, for dramatically reducing the amount of server-DRAM required to run applications, all while maintaining performance. It intelligently manages the server’s memory, keeping hot pages (i.e., those in use by applications) in fast DRAM and offloading cold pages (i.e., those less used) to a much less expensive memory tier (e.g., NVMe Flash). Key to the approach is ensuring cold memory pages that are about to be accessed by an application are back in DRAM before the application needs them or notices that they were gone. This is done without modifications to the application or the OS – so it can run in the cloud or on-premise. Mext thus allows applications to either run using less DRAM or keep their DRAM footprint but do more with it. They're hosting a webinar along with Fred Weber (former CTO of AMD) on Jan 22nd at 10am pacific time. You can register here: https://lnkd.in/gXe9GCeE

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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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In the latest episode of the OpenAI podcast I got talk to OpenAI CFO Sarah Friar and legend himself Vinod Khosla about the AI industry. Sarah explained the relation between compute and revenue (OpenAI's revenue keeps 3x'ing each year) and Vinod provided an insightful way to tell if we're in a bubble: API calls. Are they going up or down? (Spoiler: Up, up, up!) https://lnkd.in/g2tTQBdp

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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 hardest companies to build are often the ones the world needs most. Resilience Tech—spanning hardware, software, deep tech, and infrastructure—demands more than just great ideas. The real test isn’t proving the technology works—it’s building it into a company that can scale and last. At B Capital, Karly Wentz and I distilled what we’ve learned into a 10-factor framework for growth investing in this space. It’s how we separate promising pilots from companies that can scale into lasting platforms. Our conviction: the companies that matter most over the next decade will be those that fuse breakthrough science with business fundamentals—and deliver measurable resilience at scale. Read the full article here: https://lnkd.in/gv8jaMPK

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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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Quantum Art is on a mission to build a scalable Quantum Computers, and reach 1 million Qubits within the next few years. Great talk by QArt's co-founder and CEO, Tal David, explaining their unique approach, architecture and goals!

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Jennifer Gill Roberts and I have been exploring the future of the AI compute stack at Grit Ventures, and one concept we believe will play a defining role in the coming years is Sky Computing, the idea of unifying compute across cloud providers into a single, interoperable system. While the original vision was proposed as early as 2009, the Sky Computing Lab at University of California, Berkeley has since brought it into sharp focus through a remarkable body of work: 🟡 2021: From Cloud Computing to Sky Computing reframed the vision and introduced the role of intercloud brokers.(https://lnkd.in/eGRrBB3M) 🟡 2022: The Sky Above the Clouds outlined a broader roadmap for multi-cloud interoperability. (https://lnkd.in/exBM2PmU) 🟡 2023: SkyPilot delivered a working intercloud broker for scheduling and cost optimization. (https://lnkd.in/e2-sXTty) 🟡 2023: Skyplane addressed fast, efficient data transfer across clouds. (https://lnkd.in/eUFAmkaY) 🟡 2024: Skydentity tackled identity and access control across providers. (https://lnkd.in/eDs9xw2M) 🟡 2025: SkyStore reimagined object storage across regions and clouds. (https://lnkd.in/eWryZ8NB) It’s amazing to see one research group turn vision into open-source reality, pushing Sky Computing from concept to ecosystem. If you're building toward a multi-cloud future, we’d love to hear from you. #SkyComputing #CloudComputing #MultiCloud #Infrastructure #AI

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