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Articles by Alexander
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GenAI is just beginning to revolutionize sales and marketing functions at every level, starting with smaller companies and point solutions.
GenAI is just beginning to revolutionize sales and marketing functions at every level, starting with smaller companies and point solutions.
Without resorting to the usual hyperbolic descriptions of Generative AI capabilities, we can all agree that GenAI is…
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Announcing Ridge Ventures’ Fourth FundJul 24, 2018
Announcing Ridge Ventures’ Fourth Fund
I’m thrilled to announce the close of our latest fund, Ridge Ventures IV. This fund is our first fully independent one…
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Tom Chavez and Krux: a Great Outcome, an Even Richer JourneyOct 4, 2016
Tom Chavez and Krux: a Great Outcome, an Even Richer Journey
Congratulations to Tom Chavez, Vivek Vaidya and the entire Krux team on the big win — the sale of Krux to…
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Alexander Rosen shared thisAmazing progress from Pim de Witte and his team!Alexander Rosen shared thisGeneral Intuition has raised another $220M at a $6.2B valuation as we start making our models available. These are an entirely new class of foundation models, trained on billions of action-labeled videos, capable of acting in realtime in never seen before environments. Leading foundation models in robotics and world models are trained on less than 1% of the action data we are capable of training on thanks to Medal, which is currently the fastest growing company in games. Medal is on track to 3 billion uploaded videos per year. On any given day, we see more accidents in sim than across the entire USA. And at any moment, more people play with steering wheels than Waymo has cars on the road. This allows us to create General Agents and World Models, capable of handling hard to predict events. We've raised the round from Valor Equity Partners, Atreides, 776, Point72, Khosla Ventures, and General Catalyst. We'll be scaling up the team in New York and Europe. If you're a top researcher or engineer, reach out. Why does this work? Humans play video games and (tele)-operate robots using the same inputs and outputs, namely vision & controller actions. We train directly on the largest dataset of these, similarly to how Tesla trained FSD, but across more actions and environments. What emerges is a model capable of handling autonomy across a broad range of environments and possible actions, including in the real world. We've opened up the waitlist for partners who want to receive early access to the models (first comment has link).
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Alexander Rosen shared thisExciting day for our portfolio and for B2B marketing overall.Alexander Rosen shared thisIt’s been 20 years between these two shirts. The Marketo one was new in 2006… and so was the idea of marketing automation. Since then, B2B moved on. Marketing automation hasn't. We built marketing automation for a different playbook than the one required today. Content to forms, nurturing and scoring, MQLs. But B2B buying is more complex than that, with buying committees and anonymous research. Marketing needs to look beyond pipeline creation to deal acceleration, expansion, and customer success. And AI, of course, is changing how buyers buy and what's possible in our marketing technology. Over the last two years I've talked to more than 200 marketers who are frustrated with the slow pace of innovation in their existing marketing automation platform, and want real AI-native capabilities, not add-ons. But there wasn't anything like that in the market. So I built it! And today, Phave comes out of stealth! 🚀 Phave is AI-native marketing automation. We didn't just bolt AI onto a legacy architecture. We reimagined every aspect of what marketing automation can be when you have access to intelligent AI. This is the evolution from rules to reasoning (thus, the Back to the Future reference in my Phave T-shirt). Phave uses all the context you have available about a person — and their account and buying group — to make intelligent decisions about personalized journeys, segmentation, scoring, buying group mapping, and more. You describe what you want, and Phave builds it for you while always following the rules and guidance set by marketing operations. Phave has been GA for a while and has more than 10 enterprises using it including SambaNova, SPS Commerce, mabl, Servion and Hypha. We built it in stealth because an enterprise marketing automation platform takes years to build properly, and I didn't want to launch an add-on feature. I wanted a product that a company with sophisticated needs can actually switch to, without compromise and nothing lost. I couldn't have built it without an amazing team, including Phave’s CTO/CPO Nick Bonfiglio. He was EVP of Global Product at Marketo from 2009 to 2016 then founded Aptrinsic and Syncari. He and the rest of the Phave team helped to build the last generation and know exactly what large enterprises need to successfully switch off a legacy tool, including complex scalability, security, and integration requirements. I've been working towards this day for a long time and could not be more proud and excited to finally show Phave to the world! 💜 Please check it out and tell me what you think. If you know anybody dissatisfied or looking to switch MAPs, send them our way. If you want to support the launch, please save or reshare this post — or post something yourself! ♻️ What was your first marketing automation platform, and do you still have the swag? Show me in the comments!
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Alexander Rosen shared thisMayank M. is an amazing founder, thrilled to back him in this adventureAlexander Rosen shared thisToday we're launching Gather's Customer Simulations. It moves GTM teams away from guessing what customers want and instantly simulates what they will say and do. We’ve grown 10X in eight months and are lucky to be learning from dozens of incredible customers who are moving faster than ever before. Before you spend another dollar scaling an assumption, bring us the question behind it. Simulate what’s next. Built on what’s real. 🚀🚀🚀 www.gatherhq.com.
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Alexander Rosen shared thisp(doom) above 10% is getting lots of clicks this week. Few of us have a clue what will happen tomorrow, let alone in a decade. In the meantime, this discussion highlights the trichotomy of the asymmetric dispersion of AI today. There are basically three groups of AI users: 1. Pioneers. Researchers at frontier labs, Nvidia chip designers, neocloud operators. The ones spending $7000/day in tokens, optimizing model architectures, scaling parameters counts, generally pushing boundaries of AI capabilities at breakneck pace. 2. AI Normies. Anyone using AI for code generation, legal document reviews, automating customer support, transcribing meetings. We alternate between loving automatic meeting summarization and managing low-grade anxiety of not learning fast enough 3. Everyone else. My guess is 80%. These are the companies just now hiring heads of AI, establishing AI councils, and debating guardrail tradeoffs. Reasonable behavior just too slow. As a result the gap is widening. Every quarter spent analyzing is a quarter not spent cleaning data, training agents, seeing the limitations firsthand, and getting better. AI executives tweeting about humanity's final days will only increase this asymmetry and existential risk will become the justification for further procurement delays.Exclusive | Anthropic Researcher Quits Over ‘Out-of-Control’ AI FearsExclusive | Anthropic Researcher Quits Over ‘Out-of-Control’ AI Fears
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Alexander Rosen shared thisAI/Software M&A market today has 4 segments: 1. AI-natives: Hugging Face, Open Router, Cursor. Priced on strategic and scarcity value, not revenue multiples. Amazing if you founded or invested in one of these. 2. High-growth enterprise SaaS: Companies which likely bridged the gap to AI in both reality and importantly narrative. Also cyber leaders. Wiz, Fin, MaintainX. Based on public numbers multiples range from 20-40X revenue 3. Solid SaaS businesses: either growing 30% without losing much money, or systems of record like Workday that are super sticky and profitable. Likely 5-6X revenue which is basically where SaaS traded for years This is where most *good* acquisitions are happening today. 4. Modest SaaS growth. Think Airtable. Growing 10%-20% with future looking increasingly murky. That's trading at 2.5X-3X revenue, which is a reality most companies are not acknowledging My prediction: there will be a lot more of all 4 kinds of deals in the next few months.Why Nvidia’s Hugging Face Acquisition Signals AI’s Full Ecosystem PlayWhy Nvidia’s Hugging Face Acquisition Signals AI’s Full Ecosystem Play
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Alexander Rosen posted thisWe interviewed two dozen engineering leaders in private tech companies about how they're using AI in software development today. Almost everyone had some stat like "95% of our code is now AI-generated." That’s a given these days and just counts lines of code which came out of the model. The more interesting story is in the details. Five conclusions stood out: 1. Wide dispersion of competence. Some teams are still in the early innings, with one-shot prompting and individuals building their own tools. Others have structured, spec-driven workflows, harnesses managing multiple agents used by the entire team, and they are reporting the biggest improvements. Most importantly you can see them shipping production features in weeks not months. 2. Code generation is just the start. Writing new code has only been 20–40% of the job. Designing, debugging, reviewing, maintaining is “real” software development, and AI is just beginning to change that. Which is why we heard simultaneously "all of our code is AI-generated" and "we're only modestly faster". 3. Current limitation is verification/QA. Most developers now have open 4 windows, and the best ones are spinning up 20 agents in parallel. Testing that output, making sure it is secure, scalable, compliant, i.e. enterprise-grade, and maintainable it is still hard. Generation is cheap, verification/QA/evals are limited by humans. At most companies have automated only 25% of QA through AI. 4. New code is easier to build. Code generation looks amazing when you're starting from scratch with a small, well-specified code. It gets much harder with monolithic legacy code bases especially in languages not well suited for code gen. That's one reason why AI-native companies are moving so fast: they have smaller, clean codebases, and why enterprises lag: legacy systems plus complex reviews. It's also part of the reason why some senior engineers resist using AI. The other limiting factor is frankly the love of the craft: some developers simply enjoy writing code by hand, it is the reason they went into the profession. 5. Building AI into your product is a much harder problem. Writing code tolerates making mistakes and can be verified as part of the PDLC. Shipping AI to customers means risking non-determinism, evals, latency, cost, and liability. It requires convincing customers that AI is safe and good for them. Which presents a different set of challenges entirely which is why that is proceeding slower. Clearly we're still at the beginning of this wave of. Verification, rewriting legacy systems, and launching AI-native products are almost entirely unbuilt. One caveat: our sample skews to product companies, not internal IT, and didn’t include any frontier labs. We think this is a reasonably representative sample of today startups and therefore the future. Thank you Peter Zatloukal and Eliyahou Amsellem for the partnership and collaboration.
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Alexander Rosen shared thisExcited for continued evolution of Lightyear!Alexander Rosen shared thisToday, Lightyear gets one step closer to fulfilling its vision as we become the first agentic platform for enterprise telecom. Our founding vision for Lightyear was to build The Telecom Operating System: not just telecom workflow software, but software that leveraged the strongest business telecom dataset possible to give you practical insights and eventually, do things for you. The advancement of LLMs has made that vision more viable than ever, especially given the dataset we’ve built. I’m excited to share that Lightyear is now Telecom’s System of Action: a system of AI agents, software, and proprietary data that manages your telecom lifecycle. Today we launch Dispatch, the entry point to AI across Lightyear, as well as our first two operational agents: the Quoting Agent and Implementation Agent. This release is the starting point of an exciting new direction for us. We'll be adding material functionality to Dispatch, continuously improving our agents, and launching a series of agents to cover the full telecom lifecycle in short order. For more detail on what was released, please read our release blog post: https://lnkd.in/gnTSGRn3 (also, check out the awesome new look on our website when you get a chance! - https://lightyear.ai)
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Alexander Rosen shared thisLast week Autodesk completed its acquisition of MaintainX for $3.6B in cash, and Ridge Ventures distributed the proceeds to our LPs. It was a magical run from the seed round in early 2019 with Chris Turlica, Nick Haase, Hugo Dozois-Caouette, and Mathieu M-Gosselin, here in their first fundraising deck. Our overall investment in MaintainX returned well over 100% of the entire Ridge IV fund and showed the power of intelligent software building especially in the AI age. Everyone at Ridge is immensely grateful to the MaintainX team for their tireless work, and excited to see how Autodesk will benefit from their talents.
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Alexander Rosen shared thisI continue to be amazed by complete lack of objectivity at The New York Times. Probably should not be at this point. A 2,500 word article devotes 2% to benefits like finding criminals, missing children, missing adults, and reducing car theft to near zero when deployed. Seems like public benefits worth at least discussing. But not at the police-bashing, criminal-supporting NYT. Disclaimer: I'm not an investor in Flock but would have loved to be.Flock Cameras Can Track Every Car in America. Police Love Them. Citizens Don’t.Flock Cameras Can Track Every Car in America. Police Love Them. Citizens Don’t.
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Alexander Rosen liked thisAlexander Rosen liked thisCMU, welcome to Miami! This will be transformative for Miami and for higher education more broadly. Happy to have played a small part in making this a reality. https://lnkd.in/eMhcWQjgKen Griffin Is Making the Largest Donation to Higher Education in U.S. HistoryKen Griffin Is Making the Largest Donation to Higher Education in U.S. History
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Alexander Rosen liked thisAlexander Rosen liked thisEliseAI just raised $350M at a $4B valuation led by a16z and Bessemer. I’ve been on the Board since 2024 and the thesis I believed in then is playing out at scale now. Housing and healthcare are America's two largest household expenses. They're also the two industries technology has avoided the longest: thin margins, heavy regulation and staff buried in admin work instead of serving the people in front of them. That's not a reason to stay away. And it's exactly why the opportunity is so large. EliseAI didn't pick the easy entry point and stop there. It started with leasing and resident communications, then expanded into maintenance, renewals, and now, with Apollo, task execution inside the systems property teams already use. That's the pattern that matters: once you automate the work itself, you stop selling only against software budgets and start addressing labor and operating spend. The market gets dramatically bigger and the product gets much harder to rip out. The numbers back it up: - $200M+ ARR - 5 straight years of 100% YoY growth - 1 in 6 U.S. apartments - 30M+ Americans have interacted with the platform Healthcare is the same playbook. Go deep on one workflow, earn the right to the next one, repeat. That is exactly the vertical AI thesis in practice. Congrats to Minna, Stoyan (Tony), Andrew, Ian, Francesca and the rest of the amazing EliseAI team! Excited for this next chapter together. Sapphire Ventures, Andreessen Horowitz, Bessemer Venture Partners https://lnkd.in/dXXGakHcExclusive: AI housing unicorn EliseAI hits $4 billion valuation in new funding round led by a16z and Bessemer | FortuneExclusive: AI housing unicorn EliseAI hits $4 billion valuation in new funding round led by a16z and Bessemer | Fortune
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Alexander Rosen liked thisAlexander Rosen liked thisGeneral Intuition has raised another $220M at a $6.2B valuation as we start making our models available. These are an entirely new class of foundation models, trained on billions of action-labeled videos, capable of acting in realtime in never seen before environments. Leading foundation models in robotics and world models are trained on less than 1% of the action data we are capable of training on thanks to Medal, which is currently the fastest growing company in games. Medal is on track to 3 billion uploaded videos per year. On any given day, we see more accidents in sim than across the entire USA. And at any moment, more people play with steering wheels than Waymo has cars on the road. This allows us to create General Agents and World Models, capable of handling hard to predict events. We've raised the round from Valor Equity Partners, Atreides, 776, Point72, Khosla Ventures, and General Catalyst. We'll be scaling up the team in New York and Europe. If you're a top researcher or engineer, reach out. Why does this work? Humans play video games and (tele)-operate robots using the same inputs and outputs, namely vision & controller actions. We train directly on the largest dataset of these, similarly to how Tesla trained FSD, but across more actions and environments. What emerges is a model capable of handling autonomy across a broad range of environments and possible actions, including in the real world. We've opened up the waitlist for partners who want to receive early access to the models (first comment has link).
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Alexander Rosen liked thisAlexander Rosen liked this👋 SF, I'm back! Just recorded my first Origins Pod from the new LGT Capital Partners office in downtown SF. Been a wild couple of weeks launching my son to college and a big move back to the Bay, but so happy to call this place home (again!) 🌁
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Alexander Rosen liked thisBessemer Venture Partners closed $5.75B in new capital, including $4B dedicated to growth. Grateful to the LPs who back us, and to the founders with whom we get to partner. None of this happens without trust. AI-native companies are scaling faster than any category we've ever backed. Our expanded growth practice is built for that velocity: dedicated capital and partners leading concentrated, high-conviction rounds in the companies defining this era, whether we've been with them since seed or we're meeting them for the first time. Now, back to work.Alexander Rosen liked this$5.75 billion more for founders 🚀 $1.75B for seed and early-stage. $4B for growth. For generations, we've had a front-row seat to the biggest shifts in technology, backing founders early and staying with the strongest as they grow. AI is transforming how quickly companies emerge, scale, and create enduring value. We're built for that, with the capital to lead at any stage, from first check to a company's defining growth moment. To the founders building what's next: we're ready. Learn more → links in the comments
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Alexander Rosen liked thisIf you're running any physical AI models on the edge, feel free to use!Alexander Rosen liked thisIntroducing InstinctFlash, the unified open-source physical AI model runtime for fast, efficient inference. End-to-end robot policies don't fit on the edge. VLAs are already extremely slow to run on-device, but WAMs are even slower, taking 20-50 steps to even de-noise a single clip for an action. Flash allows you to run generalist models from 8 model families on a single commercial GPU, in real time. This is GI0, our internal foundation model, running locally on a single Jetson Thor through Flash. GI0's diffusion transformer allows it to generate actions conditioned on predicted future videos for more accurate trajectories. Flash allows it to run smoothly in real time. For Lingbot-VA, we see speedups about 1.2x to 7.9x from runtime optimizations alone and up to 33.78x when we combine those runtime optimizations with a distilled few-step diffusion scheduler, going from the original 25 visual / 50 action steps to 2 / 4 steps. We also tried running the same task on GPT-6 Astra. Astra took a very detailed (~300 words) prompt and the tele-operated ground-truth trajectory + video but wasn't able to one-shot the task. The attempt was 5.75 times slower than GI0 optimized on Flash. We believe an essential step to deploying real physical AI is to be able to run intelligent models on the edge in real time. InstinctFlash is our first attempt at solving that. Read more about it and access in the blog: https://lnkd.in/gtS_FnXz
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Harvrinder Athwal
XSS Capital Ltd. • 28K followers
Can fundraising teams detect a stalled process before silence becomes the strategy? The forwardable lesson is that long sales cycles need an early-warning layer, not simply more activity at the top of the funnel. The system is designed to recommend action, not merely record that progress slowed. The Long Cycle RAISE combines mandate scoring, allocation signals, trigger detection and pipeline intelligence to surface issues earlier. The Warning Layer The workflow can track investor stage, relationship activity, soft commitments, hard commitments and risks around the target process. The Waiting Problem The company is addressing a process that managers already understand and repeatedly need to solve. RAISE is raising capital and looking for investors. See company website https://raiseplatform.eu and then DM me for more info. Is faster feedback more valuable than faster outreach in institutional fundraising?
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Steve Vassallo
Foundation Capital • 19K followers
Boards are supposed to help founders. Over the past 19 years, I've watched many trap them instead. As companies scale, many boards turn from insight to oversight and stop doing what founders actually need: Help making big, hard decisions. Early boards tend to be small, the board members are close to the business and highly invested in it. They argue from first principles. They help founders think. Later-stage boards often look more impressive on paper - they have more independent directors, committees and process. Somewhere along the way, collective problem solving gets replaced with oversight. The board shifts from helping the CEO make better decisions to monitoring decisions that have already been made. Strategy discussions get safer, real debate gets rarer and meetings become more about risk management than judgment. This usually coincides with the introduction of more professional board members. For better or worse, they often optimize for governance, optics, and liability management. That’s their job. But it’s not always what the company needs in moments of real uncertainty. Then, CEOs stop using them as thought partners. That’s a problem. So what should founders do? A few principles that help: • Keep the board as small as you can for as long as you can • Add directors for new insight they bring, not what boxes they check • Treat board seats like senior hires, not trophies • Design meetings for debate rather than reporting • Be explicit about when you want input vs approval Good boards should improve decision-making. If your board isn’t making you think harder, it’s probably not doing its job.
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Matt Ocko
DCVC • 14K followers
Yesterday, two DCVC-backed companies raised about a half billion dollars. In today's funding environment, that's not that exceptional... Together, the two companies just happen to enable gigawatt-scale AI compute, except in the unprecedentedly small space of only about 40 shipping containers, no exotic cooling required. You have to wonder what the US military might do with a gigawatt-equivalent of compute they could fit in a few C5 flights... or a 100MW-compute platform that fits in a single rocket launch... Even that, in DCVC's world, isn't that exceptional. We like to say we help make the impossible and essential, profitable and available. But what is exceptional, and this is a theme with us folks, is that they are each only two of many of our companies delivering robustly on outcomes considered impossible on VC dollars or in reasonable time, at the point when we backed each company from very early on: - a safe, cost-effective, meltdown-proof, truck-portable 1 megawatt nuclear reactor (Radiant, see https://lnkd.in/gF3JAHwS) for American energy dominance - American-made AI semiconductor chips that match or outperform everything else at 100x the energy efficiency, on off-the-shelf models (Mythic, see https://lnkd.in/gWFdvZFT) We were able to make the calls on these, and how they could work together, along with similar calls on all of their brother and sister impossible companies, because of a very deep bench of unified scientific, engineering, operational, and business building experience considered passé by a lot of other folks. It's how we make the exceptional routine.
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Aly Madhavji
Blockchain Founders Fund • 31K followers
Institutions don’t fail all at once. They fail when design can’t keep up with change. In Davos this week, I’ll be joining the inaugural gathering of the Institutional Research Network, a new initiative convened by The Digital Economist. The focus is not another set of conversations but the patient work of connecting research, markets, and governance so ideas can take institutional form and hold under real-world pressure. Some initiatives are about visibility. This one is about durability. #Davos2026 #InstitutionalDesign #IRN #TheDigitalEconomist
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Daniel Dart
Rock Yard Ventures • 10K followers
🚨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: https://lnkd.in/ehQUQ2EM Spotify: https://lnkd.in/eU4FExpg
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