Nissim Lehyani

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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Seattle VC panels are not exactly known for being spicy, which is why my AMA with Brooke and Rolanda at AI House last night was so refreshing. We got into the questions founders are too embarrassed to ask out loud. How do VCs actually decide behind closed doors? What counts as traction when you have zero revenue? Is enterprise SaaS dead? Do Seattle founders actually have a shot against the Bay Area? A few takeaways that stood out: ✅ Associates can't get to yes without a partner sponsor — but partners often can't reach deals without associate diligence. Arm them with the facts to fight for you internally. ✅ Expect 50-70 investor conversations before a term sheet. Send your deck a day early but assume nobody read it 😎 ✅ Enterprise SaaS isn't dead, but it's shifting from seat-based to usage and outcome-based pricing. Large orgs still need governed software. ✅ Traction isn't just revenue. How excited are your customers? Would they advocate for you? Paying pilots with automatic, KPI-specific conversion > free trials, every time. ✅ Learning slope > starting background. The strongest founders they've backed adapted fast — not started perfect. Brooke and Rolanda WILL take a second meeting even if you whiff the first one! ✅ Seattle has fantastic enterprise buyers and founders with sales relationships into them. But the equity-vs-cash culture gap is real, and hiring against big tech is still hard. What made it different was how candid Brooke and Rolanda were about what actually happens after your pitch ends, aka the internal deal dynamics most founders never see. Shoutout to the room full of 100+ early-stage founders who went against the Seattle grain and asked hard questions live to the VCs. That energy is what makes AI House work. Huge thanks to Brooke B. (FUSE) and Rolanda Fu (Madrona) for keeping it real! More AMAs like this coming. Tag a founder who we should invite for the next one! #AIHouse (photo credit to Jonathan G. Blanco 🛠)

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In the San Francisco Bay Area, founders are relentless about growing and scaling their companies. They spot opportunity, scale through uncertainty, and create companies that redefine industries. Recently, we gathered a group of founders and tech leaders to discuss the intersection of company growth and long-term personal success. Eric S. Yuan, Founder and CEO of Zoom Communications, shared the inspiring story behind his company’s growth and eventual IPO. Eric reiterated that the founders who create the most impact don’t just plan for an exit – they prepare for what comes after. Too often, personal wealth planning lags company growth, and that gap can become costly at the most pivotal moments. The rest of the evening was focused on closing that gap. Our colleagues in Goldman Sachs Investment Banking, Pawan Tewari and Nick Giovanni, shared a candid outlook for M&A and IPOs in this environment, along with important steps founders should take to prepare for a strategic exit. We ended with a conversation around the key considerations for maximizing founders’ personal outcomes. Pre-transaction planning isn’t defensive - it’s opportunistic. Your company’s exit strategy and your family’s long-term planning work should not be done in isolation. When investment banking and private wealth work in sync, founders can cohesively optimize outcomes on all sides of the equation. To learn more about the practical “do’s and don'ts” for founders, we are happy to share our ‘Goldman guide’ for navigating wealth planning, business exists, and IPOs: https://lnkd.in/ewhEGjV3. Thank you, Eric, and to all the founders who joined us at The Battery in SF. On behalf of Goldman Sachs Private Wealth Management and Investment Banking, we look forward to the next conversation.

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🎙️ From Square to Mercury: How Rohini Pandhi became one of fintech’s top product leaders—and how velocity of learning at high slope companies shaped everything. Rohini dropped so many gems in our first episode of First Builders: 💎 Generalist → Specialist: In her early career, she was a generalist and tried everything. This led her to critical discoveries. She specialized in product and fintech when the “time was right” 💎 Follow the Engineers and Designers: Square was a high slope environment for Rohini, packed with talent density and learnings. She finds high slope environments by studying where smart engineers and designers are going. 💎 Fake News about PMs: PMs don’t just “move fast and break things.” Her team runs 30-50 customer interviews per quarter, providing rigor behind every decision. 💎 It’s Usually Too Early to Hire a PM: A top question she gets from founders is whether they should hire a PM. She usually says: “it’s too early.” 💎 Hiring a World Class Team is Like Tennis: When finding a tennis partner, you want someone a little better than you who can challenge you to improve your game. Give us a listen and leave a review on your favorite podcast location (see comments for links)! #FirstBuilders #StartupPodcast #ProductLeadership #TechCareers #Leadership

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Talking to users and thoughtfully integrating their needs and wants was woven through everything we did at IDEO—threaded through every prototype and product decision. Reading my colleagues Joanne and Leo's latest work brought that to mind. They describe a future where research runs continuously, with AI agents recruiting participants and instantly surfacing insights. Synthetic personas, carefully trained on consumer signals, mirror human behavior closely enough to extend live studies and compress timelines from weeks to hours. Today user research mostly runs in fits and starts, prohibitively expensive and locked away in dedicated project cycles when it could be a constant companion, seamlessly integrated into the product development process. Joanne and Leo lay out the full vision here: https://lnkd.in/gEUBSG_J

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AI is reshaping every industry — and VC is no exception. great catching up with Nathaniel Barling, operating partner at Andreessen Horowitz, who leads a 10-person engineering team building internal AI tools to supercharge their VC workflows. 🚀 at Creandum, we’re right there on the same journey. Over the past year, we’ve rolled out multiple AI-enabled apps (some built with Lovable ofc 😎) that now power everything from sourcing + dealflow analysis to portfolio management. the productivity impact is real — and we’re only just getting started. in VC (like everywhere else): adapt and embrace…or get run over. ⚡️

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Today we’re launching our Physical AI Lab, led by UCLA professor and renowned researcher Bolei Zhou, who’s joining Coco as Chief AI Scientist. Bolei has spent his career advancing physical AI with 60,000+ citations and pioneering work. Now, he’s bringing that expertise to Coco. What makes this lab special is Coco’s fleet itself. Our robots run urban deliveries across the U.S. and Europe, navigating unpredictable environments (construction, pedestrians, weather, etc.). These are the kinds of real world challenges you can’t capture in simulation and that data is becoming the raw material for advancing physical AI. As we scale to 10,000+ robots in 2026, this fusion of research and deployment will define the next generation of physical AI and reimagine how goods move through cities everywhere. TechCrunch has the scoop - link in comments.

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The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

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I took my second Waymo across Los Angeles last night, and the experience was largely excellent. From the smooth pickup to navigating the freeway (which is now supported here in LA!), the technology is undeniably impressive. However, the "last mile" wasn't quite perfect. I was dropped off on the left side of a two lane, one way street, directly in traffic rather than on the shoulder. Also, my destination was on the right. As I've written previously (https://lnkd.in/gHw7drrc), there is great promise in using technologies like AI in countless ways, from autonomous driving to talent measurement. However, my recent Waymo experience highlights two critical lessons: 1. Humans Must Own the Outcome: Whether you are a passenger in an autonomous vehicle or managing talent, you cannot delegate accountability to the machine. Technology is a powerful tool, but humans must own the final result. In hiring, as in transport, we must remain in the driver's seat to ensure the "drop off" (i.e. the final decision) is the right one. 2. The Feedback Loop is Non Negotiable: Without human feedback to these systems, technology will never improve. I made sure to provide feedback on my ride, and we must do the same in our talent systems. If we don’t provide constant, iterative feedback, we lose the opportunity to refine them into something better. Progress happens when human intuition meets technological scale. We provide the feedback; the tech provides the lift.

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I was CPO at Blend before their $4 billion IPO. We rejected the “forward deployed engineer” Palantir model that everyone is copying now. Here’s why: Blend could have followed the forward-deployed engineer approach. Both our CEO and CTO were early Palantir. They'd seen it work. Instead, we built a SaaS product with core components. Move fast, iterate, use data to learn. Not custom code for every client. That Palantir model is having a resurgence. Now Sierra, Decagon, and others are hiring forward-deployed engineers. But it’s not the right approach for every company. It’s fundamentally the wrong answer for Assembled’s customers. Here's what we understand about support teams and why we’re not going the forward deployed route: Customer Support teams are used to getting no resources and no support. On the surface, you'd think they'd be excited about engineers building custom solutions. "Finally, someone's offering us developers!" The reality is support teams are terrified of getting help they can't afford to keep. Support teams are wary because they know the math. They don't have budget for expensive, scarce resources. At some point the shoe drops. The engineers disappear when priorities shift. And custom solutions break. Support is left holding the bag with something they can't maintain. They're resource-starved by design. The idea of a dependency on resources they can't control is terrifying. So instead, the goal should be to give support teams the ability to manage, change, and solve their own problems. Not dependencies on engineers they can't keep. Smart, competent people who teach them to do it themselves. Palantir wasn't even a product company for years. They were a custom development shop with reusable components. Show up, build something bespoke, maybe extract learnings. That's not scalable for any but the largest support teams who need sustainability. Forward-deployed engineers are the new shiny object. I wasn’t a fan of it at Blend. And I’m not a fan today either. What I see in the customer support space is that teams need empowerment, not dependency. I want the teams we work with to be able to say: "I love that you’re partnering with me. And I love that I can do this myself too." That's the difference between building a product and building a consulting practice.

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We at Blitzscaling Ventures are proud to invest in Atrios, an a16z portfolio company where we saw CEO Taylor Offer demo the product at the a16z Speedrun Demo Day. Atrios is an AI-powered network intelligence platform that maps who knows who and makes those relationships actionable for go-to-market teams. How it works: -Tech vendors can post offers and specify the types of roles they want to connect with. -Connectors (often founders, VCs, business development professionals between roles, or highly connected individuals) can peruse the offerings and choose the best fits. -The connectors reach out to their prospects for a mutually agreed-upon introduction. -The tech vendor gets a qualified, warm intro to a lead. The connector gets compensated. The business decision-maker accelerates their decision-making process. -All three parties benefit: the vendor, the connector, and the business decision-maker, which is why the company is cleverly called “A-Trios,” Atrios. And yes, Taylor Offer that's his actual given name, perfect for using AI for modern business development. Link to our post in comments

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