Manu Kumar

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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Thrilled to announce that Together Fund is investing in Sentra, alongside a16z speedrun! You track results in Jira. Decisions in Notion. Conversations in Slack. But the reasoning, the debates, trade-offs, and context behind why you chose A over B, disappears into what we call "Dark Matter." A decision made in March looks insane by July because no one remembers the constraints that made it smart. I lived this firsthand at Twitter scaling from 800 to 8,000 employees, and at Google while launching AI Overviews to billions at planet scale. The problem isn't process. Process is compensation for something deeper: organizational amnesia. An organization’s "Systems of Record" doesn’t solve this, they encode it. They store what happened, never why. That's why we are investing in Sentra. Sentra is the always-on collective memory that eliminates organizational amnesia by maintaining accurate context for all members and agents, functioning as an operational nervous system. It connects to every channel where work happens, meetings, Slack, email, code commits, docs, calendars, and treats them not as artifacts to search, but as living signals to synthesize. The fleeting and the permanent, unified into a memory that understands. The founding team is built for this: - Jae Gwan Park (CEO): Product-first founder, memory systems research at UofT and MIT - Ashwin Gopinath (CSO): Former MIT professor, created "Reflexion" (NeurIPS 2023), agents that learn from mistakes, 2x founder - Andrey Starenky (CTO): Early Vapi engineer, ex-IBM, built to process enterprise-scale data firehose Together is an operator-led fund. We invest in problems we've lived. This is one of them. Many congrats Jae, Ashwin and Andrey, we are so excited to partner with you! Read the full thesis: https://lnkd.in/gixj9cE4 Book a demo: https://www.sentra.app/ #OrganizationalMemory #AI #Sentra #TogetherFund #a16z #ContextGraphs

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Don't Build a Better Wheat Farm" - Why Defensibility Stakes Are Higher in Deep Tech Just published a new piece on my "Ignore the Confusion" blog, building on thoughtful insights from Eric Ver Ploeg at Tunitas Ventures about startup defensibility. Eric's core thesis: Too many startups pitch like wheat farmers - "huge TAM, slow incumbents, growing market, domain expertise" - but fail to think through long-term defensibility until it's too late. From a deep tech perspective, the stakes are even higher: ** Unlike software, deep tech founders must commit to defensibility strategies from day one - their funding depends on it ** Patent vs. trade secret decisions are often difficult to reverse and shape your entire competitive strategy ** Even "picks and shovels" providers (the tools that make industries more efficient) become commodities without proper moats The key insight that resonates: Defensibility can't be retrofitted. Whether you're building software or deep tech, your moat must be architected into the business model from the start. Thanks to Eric Ver Ploeg for sharing these insights on startup strategy and letting me build on his framework from a deep tech lens. Read the full post: https://lnkd.in/dEj_iF-Q #DeepTech #StartupStrategy #Defensibility #VentureCapital #Innovation

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Vertical AI feels like a horserace 🏇 but after stepping back and thinking about the current state of market, I think what's equally unique is you have three different models competing at once. I think about it through this lens: 1️⃣ AI-enabled SaaS: the familiar path Take a proven vertical category and supercharge it with AI. Clear ROI, legible GTM, easy for VCs to understand. But real questions around differentiation and long term moats. 2️⃣ AI Roll-Ups: PE gets a facelift Acquire businesses, deploy AI to improve unit economics, and own the data + operations. Powerful if executed well, but operationally and financially complex. Big open questions around end state valuation. 3️⃣ AI-native industry disruptors: Become the thing Don’t sell software. Don’t buy incumbents. Build a fundamentally new version of the industry from scratch. Highest upside, but probably the highest casualty rate. You have to choose the model where the physics match the market. Each model has different capital needs, metrics, risk profiles, and founder skill requirements. I did a deep dive on each model 🤿 : ▪️where each model wins (and fails) ▪️how data dynamics actually differ across them ▪️why valuation expectations may diverge more than people expect ▪️and where I think the biggest outcomes will come from... As always, if you are building, investing, or just trying to make sense of vertical AI landscape, I always love to jam. Full post here: https://lnkd.in/g9a_9rtj

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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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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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This week, Gloo also updated the general Flourishing AI (FAI) Benchmark. Originally launched in July, it showed leading frontier LLMs struggled to support holistic human wellbeing across 7 key dimensions (Character, Finances, Faith, etc.). The new data is promising! It shows that models made steady gains in core reasoning, safety alignment, and pragmatic guidance. But also confirms earlier patterns -- strong in fact-based dimensions, weak in personal viewpoints and values. Essentially, LLMs handle pragmatic guidance well, but struggle with nuanced, value-aligned reasoning. Still work to be done in supporting holistic human flourishing and wellbeing. www.gloo.com/fai 

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Big milestone for Firmable. We’ve raised $14m Series A led by Airtree. Sales has moved through a few big waves: intuition-led, CRM-led, data-led. We’re now entering the next one – intelligence-led sales. The opportunity isn’t just better data. It’s turning that data into clear direction and action, without adding more work for sales teams. That’s what we’re building at Firmable: a foundation of trusted external data, layered with intelligence that helps sellers know who to focus on and when. Led by Airtree, this round supports our expansion across Asia and into the US – and accelerates the build-out of AI agents that take the admin work off sales teams so they can focus on what they do best. Proud of the team, grateful to our customers and investors. We’re just getting started. Read the exclusive in the AFR. https://lnkd.in/gr66uknb Leigh Jasper | Tara Salmon | Karthik Venkatasubramanian| Chester Thompson| Chath Widanapathirana

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