Dan Shipper - Every | LinkedIn
Dan Shipper
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BREAKING: Anthropic just dropped Claude Fable 5—this is Mythos, made safe for public release. It is the best coding model in the world. We've been…
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Today we hosted our first ever MCP hackathon at the Ramp HQ with the Every Inc. team! We ran a full end-to-end live workflow optimizing Every's AI…
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My primary contribution to the Every Inc. Slack lately has been very long Proof docs no one in their right mind would read. I finally realized that…
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Experience & Education
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Every
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2010 - 2014
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Visited the Every Inc.team this week. For all the tech talk, Dan Shipper wanted to talk about writing. I'm here for it. The voices I'm most drawn to…
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NEW: Spiral 4.0—a writing partner for you and your agent by Every Inc. -> Stylometry: we built a new Style Engine based on the principles of…
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When Evan and I started our second company together, we spent no time on the usual founding rituals. We didn't write a manifesto. We didn't spend…
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AI agents aren't coming — they're already here. And according to this conversation on Lenny's Podcast, the future of work agents will largely live in…
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My favorite day in the company’s history.
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Going to be a little earnest on main and say it: this week has been one of the proudest of my career. I got to test-drive the latest frontier…
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We just shipped Claude Opus 4.8. It's the most capable model we've put out and the best you can build on right now, outside the Mythos-class systems…
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Tough L for Nvidia
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Ali Rohde Outset Capital • 21K followers This Abram Brown piece captures something I'm seeing everywhere: Tools like Claude Code are giving founders way more agency. For many, it's a reminder of the old days when they were ICs or running tiny companies. To be clear, this is not every founder. But those that were already hands on, in the weeds, in the codebase have just been handed Thor's Hammer. 35 5 Comments
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Max Altschuler GTMfund • 73K followers Episode 8 of the GTMnow VC Series is live! I sat down with Auren Hoffman 📚 (GP at Flex Capital, CEO of NQB8). He’s someone who has built, invested, and thought deeply about data, AI, and venture for decades. We get into how AI is reshaping venture workflows, why software is getting more competitive, and how he’s scaling a fund with hundreds of “AI analysts.” Before the conversation, Paul Irving and I also talk through what we’re seeing in the market right now, from the rise of distribution as a moat to why this is a key deployment window in the AI cycle. A few takeaways from the conversation that are particularly worth sitting with: 1. AI analysts are already part of the investment team. Auren shared they effectively run 500–600 AI analysts across their workflows. Each one handles a narrow task, from sourcing to research to evaluation. This creates leverage at a level that was not possible even a year ago. 2. Sourcing is becoming an agent-first game. Flex uses agents to monitor signals like job changes, stealth startups, and market activity. These agents surface opportunities and even initiate outreach. The human layer focuses on high-context interactions. This is turning venture sourcing into something that looks a lot like a scaled outbound motion. 3. Agent-to-agent conversations are coming fast. Auren believes first meetings between founders and VCs will soon be agent to agent. These interactions will filter for fit before any human call happens. Founders will save time, investors will see more deals, and both sides will enter conversations with more context already established. 4. The best founders are still the entire bet. At Seed, there is almost no data. Decisions are made on people. Auren breaks it down: great founders, invest at any price. Good founders, pass. The hard category is “very good,” where judgment and pricing matter most. This is still an imperfect science. 5. Missing great companies is the real mistake. There are two errors in venture: investing in losers and missing winners. The second one hurts more. Flex tracks every Series A and B company and asks if they saw it and why they passed. They optimize for seeing more deals first, then improving decisions second. 6. Software competition is only getting more intense. Every layer of the stack is becoming more competitive over time. AI is accelerating this by lowering the cost to build. That means differentiation is shifting away from just product into distribution, brand, and execution. 7. Building software vs buying it is a real shift. Auren still prefers buying over building when possible, but AI is changing the equation. More companies will build internal tools when it is faster and cheaper. That creates pressure on traditional SaaS vendors to deliver clear, ongoing value. — Catch the full episode on YouTube or wherever you get your podcasts. Huge thanks to our partner AngelList for supporting the series. 51 6 Comments
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Firas Sozan 10K followers You don’t need a product to raise money - you need belief. In this clip, Rick Caccia (CEO at WitnessAI) breaks down what early-stage founders often forget: investors aren’t betting on code, they’re betting on conviction. The best pitches don’t sell a product - they sell a why. We dive into this and more in the full episode of Inside the Silicon Mind - exploring how to build, scale, and adapt in the age of AI. Ep 12: The Hard Truth About Building Startups in the AI Age Link in comments. #startups #leadership #entrepreneurship #funding #ai 2 1 Comment
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Chris Kelly Stackpoint Ventures • 11K followers 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 12 1 Comment
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Stevie Case Vanta • 34K followers Coming out of Vanta Delivers, one thing feels really clear: Customers aren’t asking for more compliance work. They’re asking for a better way to do it. The reality today is a lot of teams are stuck in cycles that repeat over and over again. And as Alex Stamos pointed out in the discussion, all that activity doesn’t always translate to better security outcomes. That’s the frustration we hear most often. And honestly, adding more process on top isn’t the answer. The goal is to take work off the system. If teams can spend less time chasing evidence and answers and more time understanding what actually matters, everything else moves faster. That’s what customers are really asking for. Get all of our latest updates in Vanta Delivers here: https://lnkd.in/g8zfQvxC 109 1 Comment
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Jordana Stein Enrich • 16K followers The co-founder of Replit just dropped the most contrarian take on product strategy I've heard all year. She deliberately built their coding platform for people who can't code. At our Enrich event last week, Haya Odeh said something that completely flipped how 40 CTOs and VPs think about building products: “Figma is for engineers. Figma is never for designers. I say Replit is for designers, not developers.” A coding platform that's not for coders? Here's what she meant: The people actually using Replit aren't engineers with strong opinions about their dev tools. They're PMs and designers who want to build something but don't have the technical background to set up environments on their own. These users have nowhere else to go. They'd be completely stuck without a tool like Replit. Meanwhile, engineers already have solutions they love. They're opinionated about their tools and know exactly what they want. Fighting for their attention means competing with deeply entrenched preferences. This is the strategic insight most founders miss: Sometimes your best users aren't who you'd expect. They're the people who have no other option. Haya wasn't being provocative - she was demonstrating radical clarity about product strategy. Being willing to say "our coding platform isn't for coders" lets Replit move 10x faster than competitors trying to please everyone. This clarity changes what features you prioritize, how you talk about your product, who you hire, how you structure your team. Every decision becomes obvious when you know exactly who you're serving. Replit's success proves something important: Sometimes the best strategy isn't expanding your market. It's having the discipline to ignore the obvious users and obsess over the underserved. These are the kinds of insights we explore at Enrich - a selective network where VPs, Directors, and CXOs connect with peers, gain strategic insights, and accelerate their leadership growth through intimate dinners and candid conversations. Thanks to Haya for taking the time. If you want an invite to the next talk, DM me! 33 5 Comments
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Taylor Black Microsoft • 8K followers Reading Metronome’s Monetization Operating Model, I kept coming back to one idea: pricing has become product. Software now delivers outcomes, not access. Yet most companies still charge as if they’re selling seats or licenses. That disconnect creates friction: for customers, unpredictability; for companies, stalled growth. The paper’s argument is simple but sharp—monetization isn’t a late-stage decision. It’s strategic infrastructure. Pricing needs the same ownership and iteration as any feature. Treat it like a surface that customers touch, not a spreadsheet buried in finance. If value is continuous and dynamic, pricing must be as well. That means product, GTM, finance, and engineering working from one system of truth. How many of us still treat pricing as an afterthought—when it should be a growth engine? https://lnkd.in/gnH7WzYf #Monetization #ProductStrategy #AI 8
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Ritvik Pandey Pulse • 15K followers Pulse just won a head-to-head against 27 providers including foundation models, legacy OCR systems, and newer AI startups This is a multi-billion dollar enterprise with a six-month evaluation process.They tested everything on the market. The evaluation criteria: accuracy, speed, reliability, and scalability. Not just on the initial document set, but on real enterprise workloads at production scale. Results: Legacy systems couldn't handle complexity. Foundation models performed well on clean test documents but degraded significantly under actual enterprise volume. Most AI startups failed at scale. The deciding factor was consistent performance on their most complex documents at their highest processing volumes. Enterprise evaluations separate the systems that work from the ones that just demo well. 56 8 Comments
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Jim Anderson Beacon • 8K followers Y Combinator put out its request for startups for its Spring cohort, and given what Beacon is doing, two of them jump out to me: AI-Native Agencies, and AI for Government. The challenges as described are broad, and startups that "win" in these spaces will have overcome many challenges. A few that come to mind: 1. Agencies (aka, Consultants) do tend to share similar economic models: bill by the hour, and hire more people to grow revenues. But the actual work of say, an engineer, a lawyer, or an advertising creative are radically different. So the idea of augmenting human labor with software will be radically different across disciplines, and even within a discipline (e.g., engineering for a bridge's structure, vs. engineering for the traffic that drives over it). Specialization and domain knowledge matter. 2. In conversations with people outside of government, I've noticed the tendency to view what happens from a consumer perspective: "I need to go to the DMV. Or get a building permit. Or pay my taxes." But what about all of the government work behind the scenes? Transportation departments employ thousands of engineers, doing engineering work. Justice departments employ thousands of lawyers, doing legal work. And for both, the work is about so much more than simply filling out, or processing, forms. It's exciting to see the increased focus on both of these areas. We need more attention, more talent, and more innovation around problems that matter. And it means that those of us already working in the space better move fast, because competition is coming... #transportation #ai #artificialintelligence #govtech #consulting 16 1 Comment
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Bradley Jones ThatRound • 9K followers One of the reasons early-stage fundraising feels inefficient is that founders are forced to figure out investor fit through trial and error. Different investors back different stages, sectors, and levels of traction. And when that alignment is off, conversations either stall quickly or never really get off the ground. This is one of the pain points that we’re focused on closing at ThatRound. We help founders see which investors actually make sense for their business before they start applying for funding. Fundraising works best when alignment comes first, and this review shows how we’re simplifying the process for UK founders. 23 2 Comments
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Isaiah Hall Circle • 2K followers Unpopular opinion among VCs and tech founders in our industry: AI is going to make insurance brokers more necessary, not less. The Circle team spent two days at InsurTech NY talking to agency leaders, carriers, and technology builders. The takeaway on AI was clear: the industry is split. There are those who think AI will fully replace the broker. And they’re partially right. Personal lines, standard small business policies, and other relatively simple products that brokers are already losing to direct channels will continue down that path. AI will accelerate what’s been happening there for over a decade already. But then there are those who understand the nuance of commercial insurance, and why that nuance means brokers are here to stay. The most important reason is trust. Insurance is inherently adversarial. Insureds want maximum coverage for minimum cost. Carriers want to stay profitable, which means pricing for risk as aggressively as the market allows. Both sides are acting rationally, but their incentives don’t align. Brokers exist in the middle of that tension, advocating for the client while navigating a system that wasn’t designed with the client’s interests first. AI will only make the issue of trust more pronounced. Carriers will use it to identify risk factors faster, draft more targeted exclusions, and price more aggressively. Automated underwriting will get better at limiting exposure in ways that are harder for an average insured to catch. The more sophisticated the tools become on the carrier side, the more critical it is to have a knowledgeable advocate on the other. Even if you tried to build that advocate as an AI-first brokerage, you’d still need humans. Specialty placement runs on relationships with specific underwriters. High-value claims advocacy requires someone who can pick up the phone and escalate. Nearly 90% of commercial premium flows through brokers because the work demands it, not because the industry is behind. Beyond trust, there’s the practical reality. Full automation of commercial insurance isn’t close. E&S, professional liability, large commercial property, specialty casualty: these lines don’t follow a playbook. Every placement carries its own combination of exposures, locations, contracts, and coverage requirements. Together, these judgment-dependent lines represent the majority of all U.S. commercial premium. The infrastructure behind that volume still runs on legacy systems, submissions still move by email and spreadsheet, and there is no plausible path for that to change soon unless distribution applies pressure on carriers first. The broker’s role in the complex, non-standard parts of this industry is here to stay, and that’s exactly why we’re building Circle. 37 3 Comments
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Max Ruderman Harmonic • 12K followers Ride the fractious horse 🐎 Had a blast talking with Harrison Chase from LangChain the other day to a great group of founders, builders and investors about building agentic systems. One learning I shared: our team builds effective agentic systems by spending way more time doing than speculating. 🏇 In other words, we "ride the fractious horse": New models drop daily. New frameworks emerge constantly. Best practices get declared and discarded overnight. By the time you survey the landscape and theorized about how today's reasoning models can serve your system, both the landscape and capabilities have shifted. So we write playbooks instead of adopting them. The only way to develop those playbooks? Build and experiment at the fastest possible rate. ✈️ Wilbur Wright laid a great analogy back in 1901: He'd just realized that existing "standard" measurements of lift and drag were wrong, and that solving flight meant spending serious time actually trying to fly (previous "pilots" had logged minutes, at most, of cumulative airtime across all experiments). From his Chicago speech (this guy was such a clear writer btw): "There are two ways of learning how to ride a fractious horse: one is to get on him and learn by actual practice how each motion and trick may be best met; the other is to sit on a fence and watch the beast awhile, and then retire to the house and at leisure figure out the best way of overcoming his jumps and kicks." "The latter system is the safest; but the former, on the whole, turns out the larger proportion of good riders." As for planes: "It is very much the same in learning to ride a flying machine; if you are looking for perfect safety you will do well to sit on a fence and watch the birds; but if you really wish to learn you must mount a machine and become acquainted with its tricks by actual trial." We're not inventing flight, but constantly evolving LLMs and probabilistic workflows won't behave as you hope on first contact. If you want to figure out how to build effective products around those fractious beasts, get on the horse (keyboard) and start riding. 73 5 Comments
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Josh Carter Defense Innovation Unit (DIU) • 7K followers Great example of a founder that didn’t fall in love with the product, but rather they fell in love with solving a real problem for their customers. Bravo! 9
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Dave Lambert Right Side Capital Management • 5K followers Founders often scramble to prep materials after a VC shows interest. That’s backward. You should be ready for diligence before your first meeting with a VC. Smart founders: 🗂 Have their data room ready 📊 Can share a clear KPI dashboard if asked 💸 Keep clean, up-to-date financials 📣 Track and communicate metrics Flailing around getting your files in order can erode investor trust. Put in the work ahead of time and it will build confidence in you and your company. #FundraisingAdvice #StartupTips #RSCMFounderFriday 23 4 Comments
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Mike Rosengarten Builders VC • 6K followers Very happy to share that we are leading Pursuit's Series A. Mike Vichich and Brandon Max are exceptional leaders. I met Mike in December 2024 and was immediately struck by his focus on building in government, one of the most important and under-innovated sectors. Big vision, strong execution, and optimism. At the time, I was between roles and doing some angel investing. That conversation made it clear I wanted to work more closely with founders like him. A year later, I get to do exactly that. Leading this round is a full-circle moment. If you're building in GovTech please consider taking a look at their fantastic product. And reach out to us at Builders VC as we love the market! 129 12 Comments
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Varuni Sarwal TriFetch • 33K followers When we first started building, the standard SF SaaS playbook told us to pick one tiny problem, go super deep, and sell that widget. But when we actually sat down with clinic operators, we realized how broken that model is in practice. What we kept hearing from physicians was not "your product does not work." It was: "I already have five different vendors poking around my EHR. I do not trust any of them. Why would I add a sixth?" The skepticism was not about AI. Clinics don't want "just another point solution" that they have to figure out how to integrate. So we changed how we showed up. We stopped pitching a point solution and started telling practices: “We are your trusted AI partner. We will hold your hand and not leave until we bring you the revenue savings. And we will build everything end to end, around your workflows, not ours.” In healthcare, the biggest unlock isn't having the flashiest model. It’s the trust that comes from a team willing to get in the trenches with them and own the outcome. 94 9 Comments
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Reid Christian CRV • 18K followers 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 95 5 Comments
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Arteen Arabshahi Fika Ventures • 9K followers 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. 15 1 Comment
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David Zhang Stealth Startup • 2K followers The best investors in Silicon Valley are all pushing AI-native services startups. Here's the truth behind building one to $612,000 ARR then failing: I co-founded Ply Health (YC S24), a credentialing service for behavioral health providers. In theory, we'd sell the high ticket outcome then collect the spread by using software to do the work. But the reality is trickier. 1/ A lot of work isn't automatable by AI (yet) AI couldn't navigate payer portals or fill PDF forms with enough accuracy, so we still needed a human in the loop. Even with AI supercharging every human, this still capped our output on headcount like a traditional services business. 2/ More of the value chain, more of the responsibility When you sell software, many failure points are absorbed by the company using your tool. When you sell the outcome, those now become your problem. Instead of a product making money while we slept, we spent many sleepless nights figuring out how to appease an uncooperative payer. 3/ Selling services is not selling software Like most startup founders, we were two young bright techies. I'd like to think you'd reasonably buy software from us. Would you buy credentialing from us? What if your alternatives are RCM companies with 100+ years of experience, processing millions of cases? Still, I'm bullish on AI native services. We just can't assume every service is a pile of cash waiting to be automated. You need two things: a critical look at what AI can do for that specific workflow, and founders who can actually get in the door. --- If you or someone you know works in an industry with lots of repetitive outsourced work, please reach out. I'd love to learn more. 75 13 Comments
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Lakshmi Shankar Together • 3K followers 8 months to $100M ARR. That’s not a growth story. That’s a platform shift. What Mukund Jha, Madhav Jha and the Emergent team have proven is simple: software creation just crossed a structural inflection point. The distance between idea and deployed product is collapsing — to natural language, across platforms, for anyone! Becoming one of the fastest-growing startups ever couldn’t have come at a better time. With India AI Impact Summit 2026 putting the country firmly on the global AI map, this moment feels symbolic — a world-class AI company, built from India, scaling at global speed. We at Together are proud to have backed Emergent from day one. This is what the early innings of a generational company look like. 🚀 #AgenticAI #NextBillionBuilders #IndiaAI 19 1 Comment
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