Patrick Burns

A great day for the team here at Cleanlab with yet another partnership and integration with our friends at Weaviate. This comes on the heels of our recent integrations with NVIDIA, Langtrace, Arize AI, MLflow, Langfuse (YC W23), LlamaIndex, and more. We've found a sweet spot -- we are the most accurate way to measure "is this LLM response correct" for any LLM/Agent response for any prompt/query and context. Works with any LLM, Agent, or RAG AI system. Now available within a dozen of the major platforms or directly through our APIs. 3 lines of code -- see how easy it is to accurately evaluate any Agent, LLM, or RAG response with Cleanlab in the Colab screenshot below for a standard compliance use case. Typical customers span customer support, IT services, financial services, insurance, and tech-forward DIY RAG/Agent platforms. We're excited to join forces with Weaviate to ensure Weaviate's users can build the most accurate Agents in the world by checking every response coming out of their AI systems. For Weaviate users looking to get started today: https://lnkd.in/gCgAathi Grateful to our employees and our friends at Weaviate who helped make this happen!

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This week, I had the chance to speak at a Harvard Business School (HBS) Association of Northern California event. One of the key discussion topics was how to adapt your scaling strategy to the “blitzscale” pressure of your category. If you’re operating in a high-blitzscale-pressure environment—with many well-funded competitors and a mild tolerance for bugs (e.g., AI customer support agents)—you should: * Target a lower percentage of customers achieving their LIR * Set a lower minimum threshold for Go-To-Market Fit * Accept a higher burn ratio In these markets, you often have to build the plane while flying it. That said, it’s still critical to measure and monitor your levels of Product-Market Fit (PMF) and Go-To-Market Fit (GTMF). If you’re in a low-blitzscale-pressure category—with fewer well-funded competitors and low tolerance for bugs (e.g., airport software)—you should: * Aim for a higher percentage of customers achieving their LIR * Set a higher minimum threshold for Go-To-Market Fit * Maintain a lower burn ratio For more guidance on "The Science of Scaling", you can order the book here. 100% of the proceeds are donated to mental health. https://buff.ly/aBa9mb3

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We just did a big analysis of Clay's 10,000 monthly support tickets. If you're running a support team, here's what you need to know: A few weeks ago, George Dilthey (Head of Support), went to Karan Parekh with a problem, "Hey, the org is growing really fast. We're hiring all these support people, but they still feel underwater. Help me figure out what's going on." The obvious answer? Hire more people. But candidly, we hadn't really studied the data yet. The thing we needed to know first was, how are we getting through ticket volumes? When are they coming in? What time of the day? From what geography? Then once we had that data, we had to go back to first principles: What is the actual point of support? Of course, I want to give our customers an excellent experience. I want it to be world-class. I want it to solve their problem quickly and expediently. But how would you define expediently? Is it zero wait-times? 10 minutes? 60 minutes? In this video, Karan and I break down: - The bimodal curve we discovered -- tickets aren't flat, there's a pattern you can plan for - Why Thursday-Friday have half the volume of Monday-Wednesday (and what we do with that capacity) - How to staff for sub-1 hour response times without having people idle most of the day - Our weekend coverage strategy (that doesn't burn people out and eliminates the Monday morning queue) - What our support team actually does during low-volume periods If your support team feels underwater, watch below 👇 PS I don't know why the team chose a thumbnail where my eyes are closed 🙈

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We are announcing Potpie AI's $2.2M pre-seed fundraise to advance Spec-Driven Development for large enterprise codebases. The round is led by Emergent Ventures, with participation from All In Capital, DeVC, and PointOne Capital, along with the support of some amazing angel investors from companies including Atlassian, OpenAI, Meta, Razorpay and Flobiz. As AI accelerates code generation, the constraint inside large enterprises has shifted from coding to maintenance and assurance. The limiting factor is no longer writing code, but understanding complex systems, aligning teams around intent, and safely evolving large, interdependent codebases. In most organizations, specifications exist as static documents, while production systems evolve independently. Context is fragmented across repositories, tickets, logs, reviews, and floating documents making reliable AI adoption difficult. We are building the foundational layer that makes Spec-Driven Development executable at scale. By unifying engineering context and operationalizing the spec as a structured source of truth, we enable AI systems to reason with architectural awareness rather than surface-level code completion. We are already working with large enterprise customers, including Fortune 500 organizations. This milestone allows us to deepen those partnerships and support more teams transitioning from experimental AI usage to structured, production-grade AI-first engineering workflows. If you are leading engineering at scale and evaluating how AI should integrate into mission-critical systems, we would love to chat with you!

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I've had the pleasure of connecting and working with fantastic marketplace founders in recent years, mostly via the Everything Marketplaces community. The ways that marketplaces are built and marketed are changing rapidly, but the best teams are seeing these changes as opportunities and not setbacks. Here's what a few are doing exceptionally well: Jawa.gg – Amanda Stefan and team have built a textbook example of community-led growth for P2P marketplaces. Their commitment to nurturing the gaming community through channels like Discord and influencer partnerships drives meaningful traffic, feedback, and genuine excitement to their PC gaming builds and parts marketplace. Rebound Jerseys – Aidan Scurato and team are laser-focused on hockey jerseys as Rebound's beachhead into the wider world of sports collectibles. This tight focus sharpens prioritization and allows them to build things their target community loves. For example, their collection logging feature creates a pathway for people to engage with their site even when they're not actively buying or selling. Stock: The Logistics of Inventory Donation – Abby Nawrocki built a platform connecting brands and 3PLs with nonprofits to simplify donating excess inventory. I've never encountered a marketplace quite like this, and it's clear to me how much Abby and team are driven by their mission and vision. I cannot wait to see the positive impact this company is going to make. Athletes Untapped – AU is a marketplace to connect vetted sports coaches with athletes, and talking to Gene Williams recently, it was clear to me how much his team cares about building an amazing tool for this community. Their foundational SEO work has also laid the groundwork for organic growth in the years ahead. Conquer Pickleball – I have immense respect for the way that Louis Long and team followed the data and user feedback to focus exclusively on pickleball, helping players of that sport find local games and courts. That disciplined focus has created an awesome app for the community and is a great example of how narrowing your scope can lead to outstanding product outcomes. ThrowbackBuys – While many used goods marketplaces focus on a niche category or feature set, LeMarc J. built his marketplace around an emotion: nostalgia. Talking to LeMarc, you hear his obsession with the psychology behind why people buy collectibles, which is a super intriguing way to approach marketplace. These are just a few examples of marketplaces I'm excited about. If you're building in the marketplace space, let's chat!

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I am excited to announce my first official investment since joining Plural full-time: we are leading a $30m round into Validio, the category-leading platform that helps enterprises make their data AI-ready. As enterprises rush to adopt AI, they are hitting a hard truth: the models are only as good as the data you feed it. Validio’s technology is solving this "garbage in, garbage out" problem by helping companies monitor, fix, and prevent data quality issues. Founder Patrik Liu Tran brings a rare combination of ambition, expertise and operational chops which is translating into rapid traction: 8x revenue growth in the past year, zero churn, 1m$+ contracts with blue chip customers from across industries. This new round, joined by previous investors like Lakestar and J12, gives Validio the firepower it needs to cement its position as the best-in-class platform for AI data quality in a booming enterprise market. And I look forward to backing Patrik and the team on this journey :) You can read more about why I invested here: https://lnkd.in/dsMgxYyS

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My guests on Uncapped this week are Kevin Hartz and Bennett Siegel, co-founders of the early stage VC firm A*. They've backed companies like Notion, Mercor, Ramp, Decagon, Similie, and many more. They also announced a new $450m fund today. We discussed the state of venture capital firms in the current AI cycle, what it means for seed specialists, trends with great founders, and what they're seeing in AI. https://lnkd.in/g7v8fdb4

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"When you're building in isolation, it's easy to think you're moving quickly when you're actually moving too slowly compared to the competition." So many founders I know do not grasp this simple idea. When you're pitching your startup, you are being compared to the 20 other companies that investors saw that week. If they are flying and you are running, your message gets lost. But more than that, if you don't understand "the discourse" around your sector - no matter how dumb, btw - you will come across as clueless. You will either be saying things the VC thinks obvious or things they do not understand. Either way, the pitch falls flat. Know your customer is still the original commandment of business, and this applies whether you're selling a product or selling equity.

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AI is changing the structure of most companies we see. Lots more coming from us on how startups can build faster, leaner teams -- and where we see all of this going. Great piece from Pete Flint to kick it off: https://lnkd.in/dcUebcJj

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🚀 Figma’s Journey: From Acquisition to IPO Figma's shift from being acquired by Adobe to going public is a powerful story of resilience and adaptability. Originally valued at $20B, regulatory hurdles forced Figma to pivot to an IPO. Now, it’s on track to raise up to $1.5B in one of the largest tech IPOs of 2025, with a current valuation of $12.5B. This journey underscores the importance of pivoting when faced with challenges. For startups and founders, Figma’s story is a reminder that resilience in the face of setbacks and adaptability to changing circumstances can lead to even greater success. Excited to see where Figma goes next and how its journey will shape the future of design tools. #TechIPO #Figma #VentureCapital #StartupGrowth #Resilience #Adaptability #DesignTools #Innovation #InspireGlobalVentures

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The debate about whether founders need to move to San Francisco misses the real point. It's not about capital. It's about density of ambition and understanding competitive pace and landscape. The biggest advantage of SF isn't access to money or smarter people - smart, talented people are everywhere. It's being surrounded by other ambitious founders so you can see how fast everyone else is moving. This is like the Olympic effect. Records get broken when athletes can see what's possible. It took forever to break the 4-minute mile, but once someone did it, many others followed because they knew it was achievable. The same applies to startups. You need to understand what "fast" actually looks like. When you're building in isolation, it's easy to think you're moving quickly when you're actually moving too slowly compared to the competition. Outside SF, it's hard to grasp just how competitive things are. When you pitch to VCs later, they're comparing you to 20 other companies they've seen in SF who are moving much faster. It's not that VCs won't fund companies outside the Bay Area - it's that the comparison makes the choice obvious. My recommendation: Even if you plan to build elsewhere, spend at least a couple months in SF. Not to stay forever, but to calibrate your understanding of the bar you need to hit. Because if you don't get that sense of pace and competition, you might end up moving too slowly without even realizing it. The density gives you the benchmarks you need to compete globally. What's your take - does location still matter for startups in 2026?

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Essential services are under more strain and complexity than ever—but most still operate with limited visibility, reacting to failure instead of anticipating it. At BrightAI, we’re changing that. With $51M in Series A funding co-led by Khosla Ventures and Inspired Capital, we’re scaling Physical AI to bring real-time observability to the physical world—so essential services can act before things go wrong, not after. We’ve spent the past few years proving what’s possible—now it’s time to scale. Thanks to Dina Bass for the thoughtful writeup in @Bloomberg: https://lnkd.in/gisEct4G Grateful to Upfront Ventures—and especially our board member Nick Kim—for being such thoughtful, steady partners in this work, and to BoxGroup, Marlinspike, and Rsquared VC for their participation in this round. Because when the systems that support daily life run with preventative monitoring, everything else grows stronger.

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You can have customers saying nice things and still not have product market fit. Andy Greenfield, partner at TIA Ventures, spent decades doing qualitative research for major CPG brands. He pioneered a methodology to separate polite responses from visceral pain. Getting different audiences together in panels to understand if products were solving real problems. When they'd do these panels, they'd try to get to one specific question: "If this product were taken away tomorrow, would your life be okay or couldn't you live without it?" That's a very different question than "do you like this product?" Jesse Caesar from First Round Capital explains the difference: "If you want to know what your target is doing or how much, go for quantitative research. But if you want to know why they're doing it, or why they believe what they believe, qualitative research gets you that depth of perspective." Most founders confuse customers being polite with customers who actually care. It’s much easier to do that vs facing hard truths sometimes. You ask someone about your product and they'll say "Yeah, this is super useful." But when you dig deeper with the right questions, you realize they don't actually care that much. I can’t overstate the value of qualitative user research whether investing or building a product myself. Talking to 10 customers with very specific questions to gauge how important the problem really is.  It’s also very helpful to cross reference this with quantitative metrics with tools like PostHog or Mixpanel. “Trust but verify”. The real magic is when you do both of these things in concert. For example, a client you interview may say “I really love using your feature that lets me do XYZ” and they may be very specific about the value it delivers…so it may seem like a totally credible piece of feedback, but then when you look in PostHog or any user analytics platform, you see that they’ve only used the feature once. So this begs the question “if you find feature XYZ so valuable, why have you only used it once in the past month?”  This gap is often the difference between a company that scales and one that plateaus.

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Founders: every rule (about fundraising and everything else) is artificial and meant to be broken. For example, consider the “rule” that the lead investor should invest at least X% of the entire round. It’s BS. For example, the amazing Kirsten Green led Bonobos’ $30m Series C round with $1m (and helped the founder raise the remaining $29m). Ignore the so-called rules. Do the right thing for your company. Be the exception.

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I am amazed at how much time is wasted on boards discussing topics that are not strategic and core to the business. Founders are asked to answer informative questions, which can be addressed during a catch-up call with the founder, rather than at a board meeting. I’d love to learn some best practices to get the most out of board meetings. 💡 From my perspective, preparation is the absolute key. Founders spend hours pulling together materials. The least we can do as board members is read them before walking into the room. It’s an absolute pet peeve of mine to sit in board meetings where half the board hasn’t even reviewed the deck - it’s so disrespectful to founders spending hours on preparing the deck in advance. Some other good practices I’ve seen: 📊 Using a live metrics sheet – that is constantly uploaded, and we (investors) can review at our own time. This helps avoid wasting time reading through financials during the actual board meeting. 🎥 Record a short video – I love what Matthijs Welle from Mews does. He summarises the highlights and lowlights ahead of the session, so everyone arrives aligned and with up-to-date questions. 🧠 Use the time for checking on strategic discussions – the board should be the founders’ sparring partner, not a silent audience. Focus on how the board can help and support the business. Board meetings don’t have to be painful or performative. With the right prep and practice, they can be high-value and high-trust sessions that actually move your business forward. I’d love to hear what other best practices you’ve seen in your board meetings. I am tagging a few founders and investors - I would love to hear what works and what doesn’t for you on boards Chris Tottman, Kamil Mieczakowski, seth m phillips, Dan K., David Sutter, Jeff Handler, Niklas Radner, Maxime Eduardo, Côme Dartiguenave, Tom Le Bras, Harrison Rose, Avinav Nigam, Hugo Fdez.-Mardomingo, Henri Tilloy, Oriol Juncosa #VC #Startup #BoardMeeting #Founder #StartupAdvice #Leadership

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The process of applying to jobs can often feel like a black box. In preparation for our upcoming 𝗦𝘁𝗮𝗿𝘁𝘂𝗽 𝗧𝗮𝗹𝗲𝗻𝘁 𝗦𝘂𝗺𝗺𝗶𝘁, Initialized Capital is hosting a webinar to unpack what gets you hired/ noticed and what gets you overlooked/ disqualified at startups. Join us for 𝘀𝘁𝗮𝗿𝘁𝘂𝗽 𝗿𝗲𝗮𝗱𝘆: 𝗮 𝘄𝗲𝗯𝗶𝗻𝗮𝗿 𝗳𝗼𝗿 𝗷𝗼𝗯 𝘀𝗲𝗲𝗸𝗲𝗿𝘀 𝗪𝗲𝗱𝗻𝗲𝘀𝗱𝗮𝘆, 𝟯/𝟭𝟴  𝟭𝟬𝗮𝗺- 𝟭𝟭𝗮𝗺 𝗣𝗧 Get your spot 👉🏿 https://luma.com/ny9ojxo3 Our panelists have worked at FAANG companies, growth stage companies, agencies, and in house at startups. Our talent specialists & recruiter pro panelists, Laura Johnson 🌟, Suzie Ross-Schneider, & Drew Georges will: 1) de-mystify the often opaque recruiting process 2) offer insights on the current startup job market and hiring in early stage companies 3) share tips for standing out in this wild job search environment. We hope to see you there! #StartupJobs #JobSeekerSupport #RecruiterAMA

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I just posted a Chief of Staff podcast with Elyse Levine and can I say, what an inspiration she is. I appreciate people that don't take no for an answer, that carve their own path forward. Here's a part of her story I loved. She wanted to run USC's new capital campus in DC. She told the president. The answer was no. So she did it anyway. Flew out monthly. Built the structure. Became the person everyone associated with the project. "And ultimately she had no choice but to say, yeah, you should have this job." That's the part of the Chief of Staff role people don't talk about enough. It's not just about supporting your principal. It's about finding the things no one else is raising their hand for and making them yours. Patience and persistence. You need both. Elyse has been a Chief of Staff at the FBI, Disney Parks, and USC. Her story is full of lessons for anyone thinking about how to move from advisor to builder. 🎙️ Link to the full episode in the comments!

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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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