Jake Flomenberg

GPUs have gotten faster, and models have gotten larger. But storage is still stuck in the past. Modern AI workloads need instant access to huge datasets. Instead, teams waste time on slow retrieval, idle GPUs, and costly pipelines. Archil is changing this. It delivers object-storage scalability at block-storage speeds. The platform allows for seamless, high-performance access to massive datasets. By eliminating cold starts and accelerating throughput 30X, Archil enables teams to fully utilize their infrastructure and move faster on training, analytics, and AI deployment. Archil’s founder Hunter Leath brings rare technical depth and operating experience from building Amazon EFS and optimizing Netflix’s cloud performance. This is a team building the foundational layer that AI workloads desperately need. I’m thrilled to lead Archil’s seed at Felicis with Nancy Wang and support their mission to reinvent cloud storage for the AI era. https://lnkd.in/gWueXhzz

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The old SaaS pricing playbook doesn’t work for AI. Madhavan Ramanujam and I partnered with Emergence Capital and Jake Saper to write a new one—built for where AI is going, not just where it is today. We lay out what state-of-the-art AI pricing looks like now—and what it will look like: ✅ Hybrid pricing (seats + usage) is the current best practice 🎯 Outcome-based models are the future—pricing tied directly to impact 💰 The best AI companies already capture 25–50% of the value they create As autonomy and attribution improve, outcome pricing will go from rare to expected. Founders who move early will win. Full read here 👉 https://lnkd.in/gwp6tDtp

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2 years ago today, A16Z announced they put $100M into Pinecone, as everyone and their mother tripped over each other to invest in vector databases. Given how little standalone vector DBs are valued now, the hype cycle is wild to look back on. Quick timeline below: Late 2022: • ChatGPT launches. • Early RAG architectures emerge. • First buzz around Pinecone, Weaviate, Milvus, Chroma. Q1 2023: • Massive demand surge for vector search. • Vector DBs seen as critical infra. • Heavy VC excitement (“new database category!”). Q2 2023: • Peak hype. • Pinecone raises $100M, Weaviate raises $50M. • Every AI infra diagram has a vector DB layer. Q3 2023: • Saturation hits. • Postgres, Redis, MongoDB add native vector search. • Clouds bundle it. Q4 2023: • Hype fades. • Vector search becomes a commodity. Early 2024: • Market tightens. • Attention shifts to agents, fine-tuning, orchestration.

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A few weeks back, I watched Maggie Hott, GTM leader at OpenAI, confidently navigate her first board meeting at Unify. Having worked with her through Emergence Capital's Operator in Residence (OIR) program, seeing her immediately contribute valuable insights made me think about how most board members receive virtually no training for this critical role. At Emergence, we've built our firm around developing board excellence. We grow all our partners from within and have established a culture of mentorship focused on board service. Junior investors aren't thrown into the deep end—we pair them with senior GPs to observe effective board dynamics firsthand. My initial experience was at DroneDeploy alongside my partner Kevin Spain, where I got great mentorship before taking on independent board responsibilities. We extend this methodology to our OIR program, where operators learn how to be effective board members. Based on my experience mentoring directors, here are the fundamental principles I share with first-timers for how board members can best support founders: 1. Reframe the purpose: Problem-solving, not reporting If your board meeting is primarily reporting, you're wasting your management team's time. Information sharing should happen asynchronously, with board members engaging with materials before the meeting. This enables the live session to leverage collective intelligence on critical challenges. This rarely happens because many directors overextend themselves across too many boards—another reason we maintain a disciplined investment pace. 2. Master the Socratic approach The most valuable contribution often comes through thoughtful questions rather than declarative statements. Your objective is to enhance the decision-making capability of management. I enter each meeting with 1-3 specific areas where I know I can add value, focusing questions on these topics. 3. Follow-through separates professionals from amateurs Diligently document your commitments, establish clear action items, and execute them. It's crazy how just doing this proactively makes a board member stand out. 4. Understand your unique contribution to the board ecosystem A high-functioning board resembles a great basketball team—you need complementary skills, not redundant ones. In every meeting, I stay conscious of my distinct value relative to others in the room, whether that's SaaS expertise, AI knowledge, or a particular relationship dynamic with the CEO. I calibrate my role based on needs—sometimes assertively addressing areas where others have less experience, other times asking probing questions where fellow members have deeper expertise. -- To my knowledge, Emergence is the only VC firm with a formalized program dedicated to board excellence. It's an investment that yields returns where they matter most—in bending the odds of success for our founders. Founders, I'm curious: What board member behaviors have you found most valuable?

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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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Excited to share the news on Definite $10M fundraise from Costanoa Ventures and Asad and Acrew Capital We're nearly a decade into the era of the modern data stack, but the reality is that for the vast majority of companies, setting up Snowflake + Fivetran + dbt is too expensive, too time consuming, and ultimately still requires a team of humans on the other side to get anything useful. In the new era of AI, it doesn't have to be so hard. Definite is the AI-native data stack in a box--leveraging the maturity of OSS alternatives to bring a fully powered data stack to customers with built in AI agents that bring the insights and actions you've historically had to hire humans to get. Michael Ritchie has lived this story for over a decade as a data leader and multi-time entrepreneur in data. We're thrilled to be partnering with him in the journey ahead!

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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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fresh off the a16z press (link in comments)... We are at an interesting crossroads in what it takes to build Enterprise-focused software companies in the AI era. On one hand, companies are ramping faster - no longer is a 0 to 1M enough to raise your Series A a few years from launch. Companies are building with sub-5 member teams, and crossing previously unseen revenue benchmarks with extreme revenue efficiency and often profitability in a matter of months. On the other hand, fund sizes are becoming larger, Seed rounds are growing to 10s of millions and "early-stage" VCs (think non-Seed, multi-stage funds) are becoming conservative, wanting to see more traction before they fund. YC and other platforms are emerging as natural winners. How will the venture world look like 2 years from today? 🤔 (1) Will we see companies raising small rounds of a few million dollars, launch and then grow from their own profits, maybe raising very large rounds just to scale distribution, breach 100s of millions in revenue, and go for an IPO? (2) Or will we continue to see the typical Pre-Seed -> Seed -> A -> B -> C -> D -> E -> IPO journeys as the dust settles on AI prototyping and enterprises start measuring ROI on their AI spend? I'm all ears 👂

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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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The best founders don’t just build products; they reframe the problem. Ryan Alshak has done exactly that with Laurel - turning time from a background task into a source of insight, alignment, and leverage. IVP is leading Laurel's Series C to help build the system of record for how work actually happens. Read more from Allie Garfinkle: https://lnkd.in/gweNiwsc

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most interesting deals right now combine the defensibility of physicality (four walls, vertical integration, holding / moving / selling real things) with the velocity of tech, usually, but not always, ai. while its likely they wont trade at historical pure-play tech rev multiples, they can get to massive scale within tech timelines (<15 years) with minimal equity dilution (debt only past the seed or A)... so the math works out if you get in on the pre-seed. most importantly, they have actual defensibility-- a team of randos in india cannot vibe code physicality. 80% of deals I've done in the last couple years follow this pattern. I anticipate close to 100% in the future will. 

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Fullstack startups are the latest craze: Marc Andreessen talked about this phenomenon on Jack Altman's latest podcast Today Browserbase launched Director.ai for consumers to automate the web This follows Vercel launching @v0 for consumers to build sites and apps Both of these companies are infrastructure (picks and shovels) providers that have now moved "up the stack" to launch consumer applications on top of their infra This is the latest in vogue playbook following: Compound startups (multi-product - i.e. Rippling) Layer cake vertical startups (SaaS+Payments+Lending - i.e. ServiceTitan) Links in comments: Jack altman Marc podcast Director.ai launch video Paul Klein IV, Guillermo Rauch, CRV

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