Houman Haghighi

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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Skilled labor is the bottleneck in industrial ops. Elevāt's platform basically tries to “productize the expert” using machine signals + docs + prior fixes. Now they have $12M to scale. Congrats to them. In industrial AI, the tech is usually the easy part....adoption is the battle. Curious what you think is the real gating factor:  -- trust (techs believe it’s right and won’t get them hurt),  -- workflow integration (it fits into CMMS/ticketing/parts + works offline/on-device), OR  -- incentives (the field team is rewarded for using it vs “just fixing it their way”). Which one kills deployments most often in your experience?

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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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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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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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DeepSeek-V3.2 is also available on Atlas Cloud now. We worked with SGLang to fix tool-use in non-streaming mode. The model now supports tool_call, tool_choice, response_format and reasoning. Price: $0.28/M Input & $0.4/M Output. Try it on Atlas Cloud: https://lnkd.in/gUb9i_B8

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Most Operators Still Treat ARR Like a Monolith But not all ARR is created equal. Some ARR is sticky, compounding, and margin-rich. Other ARR is flashy until it vanishes. Modern operators ask: → Where does each dollar live on the margin-retention map? → What’s expanding vs. eroding? → Is our revenue mix actually compounding? The best teams manage ARR like an asset portfolio. Do you?

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Will Zhao and Feng Qian at Shiplight AI (PearX W26) help companies ship faster. AI accelerates coding and Shiplight provides autonomous and reliable testing.

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