Gayle McDowell

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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NYC is losing young technical founders to SF — and it’s not about vibes. It’s about cost of entry. SF has far more entry infrastructure than NYC: i.e. the housing, community, and early support (financial and otherwise) that lets founders start building before they raise capital. This is widening the gap between the two cities' tech sectors. We are specifically calling for ideas (and hopefully action) to address NYC’s dearth of hacker houses: physical spaces that combine housing, workspace, community, and early peer support. I’m convinced that seeding hacker houses, if done right, is a low-cost, high-impact way to attract more young technical founders to NYC — and critically, something this community can actually do. And we should. The data is stark. Venture investment in NYC is ~19% of SF’s total — the lowest since 2017 — and the biggest, most innovative companies (especially in AI) are overwhelmingly being built in San Francisco. People who want to be entrepreneurs tend to work where those companies are. Unsurprisingly, many students and young builders assume they must go west. But do they actually want to — or is it just easier to get started there? I spent significant time in the Bay last year, and one difference stood out immediately: SF is dense with hacker houses and founder residences that help people get from 0→1. These resources are especially critical for recent grads and first-time founders who might lack capital or built-in networks. NYC may have long-term pull, but at the earliest stage, higher upfront costs and friction (recent Economist data suggests ~50% higher rents than SF) push many founders away. Hacker houses may sound anachronistic, but they’re real centers of gravity. We estimate SF has at least ~10x more active hacker houses than NYC, and that these houses have helped foster hundreds of billions of dollars in market value. I’m an NYC tech evangelist. I’ve worked on tech ecosystem development at New York City Economic Development Corporation, helped design national innovation programs for the Obama White House, and now invest in early-stage companies as a GP at Max Ventures. From a dollars-to-impact perspective, seeding hacker houses in NYC is one of the most efficient levers the city can pull. We wrote an overview doc (linked below) that explores this concept — and we'd welcome feedback from the NYC tech community. We are also hosting a small series of conversations — starting with a dinner in February co-hosted with Tech:NYC, Keel (Brent J. Smith), Company Ventures and Inspired Capital — to bring together leaders across tech, real estate, and policy. 👉 If you’re a founder, operator, VC, student, or have built / lived in a hacker house: • Does this resonate? • What would make this work in NYC? • Who’s already doing something adjacent we should talk to? Would love to connect if you’re interested in contributing or joining the conversation.

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a friend just texted me: "Hearing there are 'senior devs' who are very skeptical of AI generated code (aka not using any AI tools) at a startup I’m interviewing with… interesting that they are still employed in a startup against this backdrop [see image]" my response: seems like there are just different games being played those who embrace AI as a medium for expressing ideas are playing the right game — they're not just treating it as a "better tool" if you've been in industry awhile, you may need an intellectual reboot to see it last night i went to a VentureBeat event; mostly enterprise folks… several people (from IBM, Walgreens, etc) asked about “change mgmt” and how to convince their teammates to adopt AI tools… someone raised the question of brown-field vs green-field opportunity w/r/t AI… is it worth it to upskill existing workers or better to just start fresh…? Great question! for people entrenched in a job fiefdom, e.g. where the power players and how to execute reliably, the perceived risks of novelty are infinite. they're not wrong, they're just not interested in changing the game they've mastered. aaand also, developers are the self-disclosed laziest people in the world. but they're incredibly productive because they'll happily spend a week automating some repetitive task so they never have think about it again. this is why generative AI and coding go so well together: the existing developer culture despises inefficiency, and is always looking for ways to cut corners in favor of a more elegant, efficient route. this is because developers are incredibly sensitive to the tiniest impediment to achieving persistent flow states. it's like noticing a typo in that green-on-black matrix screensaver and instead of ignoring it as a minor irritant, they'd prefer to rewrite the screensaver from scratch to fix it. but for non-developery roles, this gameplay is totally foreign. maintaining those impediments ensures job security. since they know how to navigate around known obstacles, their tacit knowledge is an insurance policy against obsolescence — for now. so it makes that sense that some senior devs, whose sunk costs could represent hundreds of thousands or millions of $$$ into existing, _stable_ systems might resist the quasi-non-deterministic output of AI coding tools. except (as they say) there is more than one way to skin a cat. here's a new game to invite resistants to play: have the skeptical senior devs review several blind PRs, all submitted by “new,” unfamiliar contributors (a mix of humans and AIs), and see if they prefer the human or AI-generated code. if the AI code really is as good as many of us believe, the experience might snap them out of their sunk-cost fallacies. because until you surprise yourself with what these tools can actually accomplish—in a blind taste test—you’ll never really see what winning this new game looks like, even if it's happening right in front of your eyes.

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10 Years In VC: What I Got Wrong at the Start "VCs are like LLMs - they feel compelled to give you an answer if you ask a question, even if they have no knowledge or experience of the topic." A founder recently said this to me, and after laughing out loud for real, it made me reflect on my own experience as a VC. After a decade in venture capital, I've learned a counterintuitive truth of this business: The best thing you can do for a founder is often nothing at all. When I started, I thought my job was to help. To have answers. Hustle like hell to find them if I didn’t have them. To share “wisdom” from other companies I’d seen. It came from a good place—wanting to add value. But it was wrong. Because most advice is context-poor. What worked in one company, in one market, in one decade…is often irrelevant, or even harmful, in another. Founders need real, relevant perspective—from people who are in the arena building right now, or customers adopting today, so they can decide for themselves. So now I spend my time helping by keeping my network more refreshed than theirs so I can get them to these people faster. Or I help by just listening when they need it. Another uncomfortable truth we don't always want to admit as VCs: exceptional founders don't need us. They will stop at nothing to achieve their mission. We might help them get a key hire or a customer faster, but they would have gotten there anyway. The reality is liberating once you accept it. Instead of trying to be indispensable, you can focus on being genuinely useful in the moments that matter. This shift has required growth on my side too. In a session with a coach, I once realized my own sense of self worth came disproportionately from feeling like I helped someone. I was optimizing for feeling useful, not founder success. The two aren't the same thing. I now think about it as finding exceptional people and enabling them to truly thrive. How you enable someone to thrive is so different and specific to that person. But largely, if it’s something I can uniquely help with, I’ll push like you wouldn’t believe - just ask my founders. If not, I leave teams alone to build and minimize annoyance, which they truly appreciate. The best investors are probably like great jazz musicians - they know when to play and when to stay quiet. My approach today: - Connect, don't correct. Prescribe almost never. Always connect founders to someone with recent, relevant experience. - Listen, don't lecture. The founder journey is isolating & hard. Sometimes the most valuable thing to offer is listening without judgement. - Enable, don't dictate. Create the conditions for amazing founders to thrive. And then… just get the fuck out of the way. Great venture capital isn’t about having all the answers. It’s about helping founders unlock their own. Founders — what’s the most useful thing a VC has ever done for you? VCs — what’s the most counterintuitive thing you've learned about venture?

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Martin Eberhard played a key role early at Tesla and even brought in Elon Musk. Then he was pushed out of the company for pennies. Founders get most of the attention in Silicon Valley, but the ecosystem is built on the people who take a chance and join early stage ventures. Unfortunately, those same people are often the easiest to cut out. Founders can dilute early employees, change vesting terms, and restructure equity with little effort and zero consequences (you can literally do it in Carta in 5 minutes). Daniel Kottke was a close friend of Steve Jobs and helped build the Apple I and II computers. He got no equity (Steve Wozniak eventually gave him a few of his own shares). Brian Acton co-founded WhatsApp and stayed through the Facebook acquisition. When he left Facebook over privacy concerns, he had to walk away from $850 million in unvested stock. There’s more awareness now around how vesting works, what happens during a liquidity event, and how to think about re-ups and dilution. That’s good. But the truth is, if someone really wants to screw you, they can. It still comes down to one thing: trust the people you work with. Or don’t work with them at all.

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CRV Security: Request for Startups I never know if this actually works for our friends over at YC but figured we'd try. Here's what we want to fund in 2026! 1. Golden Artifacts: Think Chainguard but more broad. Artifact attestation exists for open source. Almost nothing exists for internal software — especially the vibe-coded tooling now running in production. We want the company building cryptographic proof of secure software delivered from secure artifacts: who built it, how, and whether it was reviewed. If more things are being yeeted into the world via Claude Code (myself included), this feels like an issue. 2. MCP & Agentic Security: Agents are getting real credentials and taking real actions. The security posture of most orgs around this is basically zero. That changes fast. You'd never give an employee hardcoded API keys or write access to your email without supervision/trust. Why give it to agents? 3. AI Governance: Boards are asking CISOs to account for AI risk. CISOs have no good answer other than "Palo has a module" 4. Next-Gen Endpoint: CrowdStrike was built for a world of static binaries and human operators. AI workloads, cloud-native infra, and AI-assisted attackers need a new architecture. The category is ready to be reinvented. 5. Networking in the AI Era: Zero trust was designed for humans. What does network security look like when the entity requesting access is an agent? Nobody's really solved this. 6. Email Security + Next-Gen Phishing: LLMs have made spear phishing infinitely scalable. I've never truly understood why Abnormal and KnowBe4 aren't one company. Maybe this time it's different. 7. Frontier Security Lab: We'd back a credible, well-staffed lab focused entirely on red-teaming models and setting the evidentiary standard the industry needs as LLM built apps become the norm. 8. Dependency Security: That Actually Remediates Malicious and vulnerable dependencies are a top attack vector. The tooling is mostly noise — scanners that don't close the loop. The winner here ships fixes, not just alerts. 9. Critical Infrastructure Cyber: Data centers, satellites, power grids, undersea cables. The physical backbone of the internet is increasingly exposed and wildly under-defended. We have data centers in space, for God's sake. Surely we need better cyber for critical infrastructure? 10. PAM for the Modern Era Legacy: PAM was built for static roles, human users, on-prem directories. Cyberark was founded in 1999.....Agents, ephemeral workloads, and cloud-native infra have broken all of those assumptions. Is anyone rebuilding this from scratch? If you're building in any of these areas — or something we haven't thought of — reach out. james@crv.com

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The most salient narrative in tech today is the end of software. Public software companies have been pummeled, down 20%+ in the last 3 months. But I think the bear case rests on weak logic. There are 3 main categories of concern driving this narrative: 1. Vibe coding means businesses build their own software 2. AI automates away jobs, and seat licenses plummet 3. AI-native companies take share from legacy incumbents The first one doesn't hold up. The second is too important to dismiss casually, but lacks evidence thus far. The third strikes me as most threatening, but it, too, rests on a flawed logical foundation.   On rolling your own software, customers who choose to build almost always regret it. An internal IT team can build a CRM, but they can't maintain and improve it like a dedicated software company. Anthropic spending 8-figures on Workday for HR software shows that incumbent providers have more to offer than software skeptics realize.   Sure, there's a risk of AI automating away seats (and therefore seat licenses). But this creates opportunity for software incumbents to deliver AI-infused software that allows customers to do just that. Software vendors can easily shift to usage-based or other value-based pricing models. Relatedly, there's a larger fear that AI will trigger massive unemployment among knowledge workers. This deserves to be taken seriously. However, unemployment is currently quite low by historical standards, and current AI is beyond what most considered AGI a few years ago. I think the worriers here are moving the goalposts. The third concern--AI-native companies decimating software incumbents--is the most formidable. 20-25 years ago, Google hoovered up the attention economy by building the dominant consumer Internet product. Could Anthropic or OpenAI do something like this in b2b? It's not my base case, but it's the scenario that goes the farthest with me.   That said, I think there are 2 misunderstandings embedded here.   The first is that only AI startups can use AI tooling to accelerate product velocity. They aren't. The engineers at Shopify and Workday have the same access to Cursor, Claude Code, or open-source AI models.   The second, bigger misunderstanding is that these markets are zero-sum. By definition, technology expands the existing pie. So I disagree with the idea that AI growth will come at the cost of existing software companies. AI will massively expand software spend.   Which brings me to what's hardest to reconcile. If you believe AI-native startups will create major value, you're implicitly underwriting a world with fragmentation and switching costs among knowledge work tools. You can't hold that public software companies are all in the blast radius of Anthropic AND back the Harveys or Open Evidences of the world.   It doesn't add up, as the bull case of one is implicitly the bull case of the other. And while the market is voting like these are opposites, I expect the weighing machine to disagree.

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You’ve likely read several takes on last week’s YC batch. Still, I thought I’d share mine with a few themes I’m tracking. Given our focus on Series A, the value in YC for us is in tracking early rising stars and identifying where some of the smartest founders are starting to build. I won’t talk about the volume of AI agents — that was an interesting highlight from the last batch. What stood out instead was how much more grounded the applications felt — especially when it came to vertical focus, operational depth, and knowledge work. Here are three-ish themes that stood out: 1. Less focus on SMB, more focus on knowledge workers. I was surprised to see so few companies focused on the 90% of companies in the U.S. that employ 200 employees or fewer. The SMB market is sizable, and we, along with many other investors, have been bullish on tools meant to serve this market. Instead, this year’s batch included tools for accountants, pharma reps, legal back office, researchers, and more — a theme consistent with our observation of increased usage of AI as an assistant for teams with human bottlenecks. 1a. "Cursor for X." Cursor has left an indelible mark on the industry, so much so that founders are using the product as a blueprint for workflow-focused AI agents. A few companies that stood out were Vesence (Cursor for Lawyers), Nomi (Cursor for Sales), and Mesmer (Cursor for CTOs). To the point above, some of the most exciting tools are AI-native partners for high-skill professionals and knowledge workers. It’s worth mentioning that not one demonstrated clear evidence of a replacement. 2. Vertical Operating Systems. Our next evolution from vertical agents is vertically focused operations and management systems or platforms that function as full-scale operational support. You can think of these as AI-native versions of Toast, Veeva, or Guidewire. Standout examples include Kaelio (healthcare), Hemut (trucking), and Waffle (SMBs), all of which are building AI-powered infrastructure to replace legacy software stacks completely. 3. Healthcare – AI for clinical ops. A number of founders are building for the messy middle — the process-heavy healthcare system that, despite exorbitant costs, still isn’t functioning optimally. Standouts, in addition to Kaelio above, were Aegis (healthcare claims), Novoflow (medical ops), and SynthioLabs (Voice AI for pharma). There was also at least one personalized healthcare agent, Galen AI, that leveraged patient records to provide personalized healthcare (a step beyond ChatGPT and bots already used in virtual care delivery). My take: The X25 batch shows a market that is clear evidence of maturing in the right direction. I’m most excited about clear, immediate use cases and defensible, specialized systems that solve complex, industry-specific problems. AI is rapidly moving from a novelty to the core engine of our economy. Anything I missed? Feel free to share your thoughts.

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I had a great time chatting with Patrick O'Shaughnessy on Invest Like The Best. I've known Patrick since college, and this is the first time we've talked markets and investing at this much depth. The fundamentals of company building haven’t changed: people, products, and markets matter. But obviously, private markets have evolved substantially over my career: there are now ~6x more private unicorns than public companies with a $1b+ market cap. And at the end of 2010, just 2 public technology companies were among the top 10 in market cap; today it’s 8 of 10. AI (alongside software eating everything more generally) is clearly driving a lot of this. But it’s instructive to look at everything from the steam engine, to the early days of Facebook and Google user monetization, to real-time success stories like Databricks, Anduril, OpenAI and Waymo, to get a clear picture of where the opportunities lie. It was a pleasure to go deep on all this and more!

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In my experience, ~17 people is the tipping point where an org stops operating as a single atomic unit and starts fragmenting. Fragmentation kills speed. AI lets you push scale without pushing headcount. With all the tooling we're building, I don't see why Eniac Ventures would ever need to exceed 17 That's the future of VC

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I keep hearing this from founders at Pre-Seed -- we are dilution sensitive. Why? Not much is built yet. You don't have a ton of leverage. Optimize for BEST PARTNERS + SPEED TO CAPITAL. Then you execute, create a lot of value and THEN you can be more sensitive to dilution.

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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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