Mark Lee

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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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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We attended CEO Jensen Huang's keynote at GTC 2026 in San Jose. We expect the stock to hold its initial gains (up 2%) following an upbeat event that we believe satisfied two key investor expectations. First, given its disclosure of $1 trillion of datacenter revenue orders through 2027, Nvidia provided visibility into a strong growth outlook for 2027, consistent with our estimates and well above the Street - which we think can help resolve investor concerns around “peak CapEx” in 2026. Second, Nvidia’s launch of Groq's LPX rack helps cement the company’s commitment to the inference market - which we think is a critical, yet increasingly competitive growth market within AI infrastructure. We maintain our Buy rating as we continue to see a positive catalyst path ahead for the stock in the coming months, including greater visibility into hyperscaler CapEx plans (and Nvidia’s growth) in 2027, plus new LLMs trained on Blackwell that should help the company re-assert its performance leadership.

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At CRV, we’ve been fortunate to partner with some of the most visionary founders reshaping the future of cybersecurity over the past five decades. From cloud-native data protection to developer-first security and AI-driven detection, our portfolio reflects the breadth and depth of innovation happening in the industry today. Companies like Cyera, Cribl, Astrix Security, Tailscale, and Vega (among many others) are tackling the hardest problems facing CISOs and developers — from securing non-human identities, to modernizing log pipelines, to making zero-trust networking effortless. What’s exciting is not just the individual companies, but the ecosystem effect: each is building in complementary areas, and together they’re advancing how enterprises secure data, infrastructure, and applications in an AI-powered world. Cybersecurity has never been more mission-critical, and the next decade will see even greater disruption as AI, automation, and new architectures redefine what’s possible. We’re proud to support founders leading that charge.🚀 If you’re building in cyber, we’d love to connect!

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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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Varun Sivaram has a truly unique ability to pull together stakeholders across AI, energy, and policy. This is exactly why Earthshot Ventures invested in Emerald AI - there is a grand bargain to be struck in getting data centers online fast if they're willing to bring flexibility to the grid, and Varun is the man to strike that bargain. The latest illustration - bringing together $5T in market cap consisting of the world's leading chip company and the 5 leading IPPs in the US to make this happen!

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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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"Build the event you wish existed" That's what we at Monadical did last week at #NYTW. We wanted an AI Engineers discussion for Engineers. There's always a lot of events out there for VCs to network, or for startups to learn about G2M, but very little on lessons learned from actual engineers in the field. So that's the event we hosted. Big thank yous to our awesome panel, Roy Pereira, Ben Cohen and Corey J. Gallon. Here's the key takeaways and the AI tools we're using. 🚀 All three panelists independently called AI Agents the most transformative LLM application they’ve used. They specifically called out Claude 3.5 Sonnet for its accuracy and reliability. 🪨 We dug into how LLMs are “jagged”, not general. They can be shockingly good at some tasks and completely fail at others. Everyone agreed: good evaluations are critical (and hard.) 🧪 Corey noted how public benchmarks and reality are two different things. Most public evals are saturated or gamed. ♊ Ben emphasized that AI projects are actually two projects: building the tool and building the evaluation process. 🧱 We explored how falling dev costs may impact startup defensibility and labor demand. Roy shared that founders are already shifting strategies in response. ⚒️ In a world of daily AI launches, the panel discussed how they decide what’s worth attention, and what’s just noise. They called out tools like Goose, Aider, Claude Code, and Monadical’s own Cubbi, which helps run agentic workflows safely in dev environments. (links in the comments). CTA: What would you want to hear in an AI Applied Engineering talk you attended?

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