Nic Poulos
San Francisco, California, United States
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https://www.euclid.vc
About
Partner to Vertical AI founders from inception.
Articles by Nic
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The 2017 Startup Sales Stack Report
The 2017 Startup Sales Stack Report
I’m pleased to announce the release of our 3rd annual Startup Sales Stack Report! This report is a comprehensive ~250…
611
46 Comments -
The 3 Keys To SaaS Cross-SellingJun 13, 2016
The 3 Keys To SaaS Cross-Selling
Cross-selling, sometimes also known as add-on sales, is a unique process that should be structured quite differently…
38
2 Comments -
The 3 Virtues of SaaS Pipeline MetricsOct 23, 2015
The 3 Virtues of SaaS Pipeline Metrics
Especially for early-stage SaaS startups, sales pipeline metrics can often be better indicators of health and…
51
6 Comments -
The 2015 Ultimate Guide To Startup Sales ToolsJul 21, 2015
The 2015 Ultimate Guide To Startup Sales Tools
An introduction to our newly released 115-page report, including reviews of 100+ software solutions for sales teams…
102
11 Comments -
9 SaaS Discounting Strategies For SalesJul 6, 2015
9 SaaS Discounting Strategies For Sales
Pro tips for SaaS salespeople on how best to use discounting strategies to maximize value for both your company and…
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3 Comments -
Building An Ideal Customer ProfileJun 9, 2015
Building An Ideal Customer Profile
We'll walk through what an Ideal Customer Profile is, why it's critical for early-stage sales alignment & effective…
18
1 Comment -
A Framework For Sales OpsJun 1, 2015
A Framework For Sales Ops
Reflecting on our podcast last week with Emmanuelle Skala, head of sales at Influitive, I'll walk through a framework…
17
1 Comment -
5 Key Elements Of An Effective Sales SLAMay 27, 2015
5 Key Elements Of An Effective Sales SLA
Reflecting on our recent podcast with Sean Kester of SalesLoft, I'll walk through how to setup an effective SLA and use…
12
3 Comments -
5 Opportunities In Sales EnablementMay 21, 2015
5 Opportunities In Sales Enablement
We've seen a big upswing in innovative sales enablement startups over the last 6 months; here are 5 suggestions for…
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2 Comments -
Vertical vs. Horizontal SaaSMay 8, 2015
Vertical vs. Horizontal SaaS
We're now seeing a new class of vertical SaaS business models emerge. I take a look at the numbers to suss out what…
88
3 Comments
Activity
10K followers
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Nic Poulos shared thisThis week on Verticals, we brought on David Haber, GP at Andreessen Horowitz, to discuss why VC is getting AI TAMs totally wrong. Market size #1 VC filters — bigger number, better pitch. Right? David makes the case that in this era of AI (and especially in Vertical AI), market STRUCTURE is a far better predictor of long-term success. > How concentrated are incumbents? > Do customers care more about leads or conversion? > How, where, and when do they do their work today? In fragmented SoR markets — think specialty healthcare with its long tail of EHRs — AI-native companies can start by doing the work around the system of record and back into becoming it. That's exactly what David's portfolio company Camber did in behavioral health RCM... as Luke Sophinos and I discussed w co-founder Christophe Rimann on the pod a few weeks back. Do the work without fighting the system. In concentrated markets — eg Autodesk's Revit at 95% share in AEC — you're forced into a complement position. And the more valuable you get, the more the incumbent wants to tax you, replicate you, or just strangle you altogether. The point is this: Different market shapes demand entirely different playbooks. A $2B niche with 15 equal incumbents and heavy labor spend may be a far better place to build than a $600B market where one player dominates and margins are nonexistant. Our full episode with David covers this and a lot more: + his "messy inbox" thesis on Vertical AI wedges + why AI services can hit software margins + why conventional SoRs may go to zero And of course... what a16z looks for in a Series A. Links to watch below.
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Nic Poulos shared thisVertical AI is coming for commerce, and its a multi-$T dollar opportunity. We call them Dark Marketplaces, and there's one key to the kingdom. When AI enables transactions to occur autonomously, the need for UI or even for human intervention, disappears... the Marketplaces goes Dark. In our new essay this week, Omar El-Ayat and I discuss why the key to this holy grail is *judgment abstraction*... using new wedge products + modalities of AI to learn how humans make trusted purchase decisions. It's not a function of model quality — it's a function of per-vertical, per-company, per-user data. What informs purchase satisfaction, consciously or not? How to do you get the data (integrations, voice, vision, direct input, etc.)? It's a fundamentally hard problem, and LLM agents aren't enough. In this essay, we introduce 2 frameworks to understand Dark Marketplaces: 1) The Four Stages of Judgment Abstraction. Mapping the journey from decision support to Dark Marketplace. Each a deeper level of abstraction & trust. No company operates at Stage 4 yet. 2) The Engagement × Proximity Matrix. → how much behavioral signal a product captures (engagement) → how close it sits to the actual transaction (proximity) We also cover: → Why Dark Marketplaces emerge in B2B first → The ~40 companies closest to the threshold today → Five principles for founders building toward Dark Marketplaces More in this week's essay at The Verticalist: https://lnkd.in/dX4nKbMa
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Nic Poulos shared thisHonored that Euclid was included in this year's Confluence 50 — a curated list of the highest-signal emerging VCs, nominated by investors. Big thanks to Clay Norris and the ConfluenceVC team for the rigor they put into this. Also exciting to watch Clay put these learnings into practice at Outlaw. One thing that jumped out from the data was the grow in EMs doing more in Vertical AI over the last few years. When we started Euclid, the narrative was "what's Vertical AI?" or "why this category?" — no longer. And that's no surprise to us. $18T is spent annually on US B2B services, ex SaaS. If Vertical AI captures even 10%, it's multiples of the current ~$500B SaaS market. We're in the 1st inning of the largest addressable theme in AI. As more funds cluster around the label, something worth saying out loud: expertise in vertical AI is not the same as expertise in a specific sector. And many investments in vertical AI is not the same as building a fund — content, community, conviction — purpose-built for the category. That distinction matters. For founders, it means understanding whether you're partnering to get customers / domain access... or support iterating to vertical PMF and learning from the larger diaspora of vertical founders. For LPs, it means understanding what drives value to founders, right to win, portfolio diversification, and scalability as a specialist across VC cycles. Is a fund thesis dynamic and durable enough to support a franchise brand? VC differentiation has never been more important. And for founders and LPs, determining the why and how has ever more nuanced. The one point that matters for Euclid — we stand for Vertical AI from inception. 👏 Kudos to the other 49 managers here blazing their own trail 👇 Link to full breakdown on the Confluence 50 from Clay and team below
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Nic Poulos shared thisSoftware forward PE multiples just fell below the S&P 500 for the first time in the modern era. Not in 2008. Not in the dot-com crash. Now. A clear case of AI-induced malaise, with Cowork's launch as the final straw. The easy read is that this is an overreaction. Net retention hasn't collapsed. JPM is still projecting double-digit growth in both sales (+16.50%) and earnings (+16.80%) for software in 2026. But the market is pricing in something real: a structural question about which software companies have a defensible right to compound for another decade — and which ones are sitting on features that can be rebuilt in a weekend. On this week's Verticals, I dug into it with Morgan Livermore, and Founding Partner of Supercruise (previously at Accel and Quiet Capital). His framing of the software rout stuck with me: "When mobile happened...there was a period of like 6 or 12 months where nobody wanted to price SaaS companies over like 5 or 6 times revenue because they said, well, mobile's coming. The reality is mobile sort of became an extension of software more than it became a true replacement." Certainly in Vertical AI, new solutions are tapping into new budgets over and above SaaS or IT spend — which should represent market extension, as much as it does displacement. But, Morgan agrees, AI does mean that feature-level differentiation degrades faster than ever. What endures in the era of AI sits deeper — in domain expertise or in true technical differentiation. Morgan shared a wild example of the latter: One of his portfolio companues has tens of thousands of AI agents as "customers" — none acquired through a sales call. The agents found the product through its documentation. Documentation as GTM. Domain expertise as a moat comes at defensibility from the opposite side: as the AI stack commoditizes, the founders who understand why (e.g.) a specific X integration unlocks Y invaluable data — not just how to build it — are the ones able to grow commodity wedges into durable platforms. Veeva doesn't charge what it does because its interface is irreplaceable. It's pricing power is an output of its deep understanding of and integration with life sciences workflows and regulatory data in top enterprises. At 20-22x forward earnings, software is priced like it has no edge right now. Some of it doesn't. But even in the legacy SaaS sohort, the compounders — ones with real technical or domain depth — are getting thrown out with the bathwater. Watch the full episode to get Morgan's view on what's changing in software and AI — and why that led him to launch his new firm: Supercruise.
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Nic Poulos shared thisKiller piece visualizing the future software stack and vertical's place in it by Luke Sophinos today. "In a lot of verticals, software alone is not enough. A model alone will not be enough either." Whether proprietary data or full-on AI Services — figuring out why your Vertical AI concept NEEDS to exist in this new world should be one of first question founders ask themselves Otherwise you risk going downhill faster than GoT in Season 7The Seven AI Kingdoms: A Chronicle for the Coming AgeThe Seven AI Kingdoms: A Chronicle for the Coming AgeLuke Sophinos
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Nic Poulos shared thisEvery AI founder is asking the same question right now: Will someone eventually vibe-code my product? Alexander Jekowsky, co-founder & CEO of Cents, just raised a $140M Series C led by Sumeru Equity Partners, joined by Camber Creek. The largest VC deal in the history of his industry — laundry. We brought him onto the pod this week to understand what's working. Alex dropped out of college to build a payments company, sold it at 23, and stumbled into laundromats looking for a cash-flowing side business. Instead he found an industry where 70–80% of machines were still coin-operated — and decided the technology gap was too obvious to ignore. Following a Seed from Bessemer Venture Partners and Series A from Tiger Global, Cents found a unique approach to defensibility in their space: Owning thousands of hardware devices, physically bolted to machines across 4,500+ locations, processing >$1B in payments annually. Alex saw that when horizontal AI voice companies tried selling into laundromats, they hit a wall. They didn't know which machines were available, what inventory looked like, or how customers paid. That context lives in Cents' hardware layer — and it's what powers their agentic product suite now. Getting there wasn't easy, requiring a mid-stream acquisition that not all investors understood. But the numbers are now speaking for themselves: 99%+ customer retention. More CS reps than salespeople. CAC-to-LTV ratios "in the thousands of X." Every time Alex tried to copy the Toast playbook — give away hardware, hire a massive sales army — he missed plan. When he built for how laundry operators actually buy, through trusted equipment distributors who wouldn't even take his call before the hardware acquisition, he hit it. In a world where intelligence is commoditizing, the scarcest asset in some verticals may be the *physical* authoring layer that feeds them. Data captured at the machine level, payments processed on-site, customer embedding so deep that ripping out Cents means rewiring every unit, in every store. How many other verticals have a hardware wedge hiding in plain sight? Full conversation with Alex Jekowsky of Cents on this week's Verticals: https://lnkd.in/ggtwGHir
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Nic Poulos shared thisVertical AI founders: your legacy incumbent can't outcompete you — but you've still got to survive the "kill window." Here's how. The key is having a plan for the inevitable collision. Alexander Niehenke at Scale Venture Partners has watched these fights play out across construction, insurance, legal, and logistics — he's one of the sharpest vertical investors working today. This week on Verticals, he shared this w Luke Sophinos and me: → The incumbent's weapon isn't a better product, it's platform control. API cutoffs, litigation with unlimited budget, supply-chain leverage. The companies most likely to deploy them are public / PE-owned, probably over-levered, and run by hired-gun CEOs whose comp is designed to protect the base business... not cannibalize it. They can block you. They almost certainly can't build what you're building. So the playbook isn't just "outrun them on product" — founders already know how to do that, and it doesn't guarantee survival. It's finding your path to surviving the incumbent kill window. We usually see founders face this problem between $5M and $15M ARR (easy to smother) and again in the $50-100M ARR range (becoming a real threat). And it's more existential the more market share the #1 player has, and the fewer 10%+ share-incumbents there are. In today episode, we walk through the solves that have worked: ... build standalone paths around the ERP ... partner with the #2 and #3 systems of record ... give up margin to secure distribution if you have to And above all — build what Alex calls a "system of love:" Your only real leverage against a hostile incumbent is shared customers who will raise hell on your behalf. In Vertical AI, NPS isn't a vanity metric — it's a survival strategy. If you're building in Vertical AI with a dominant legacy SaaS incumbent, you need to watch this one (link in comments below).
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Nic Poulos shared thisThe next $1T company may start with services — but it will win thanks to software... and 1000 AI services will die along the way. In our latest essay, "Services Level Disagreement," Omar El-Ayat and I unpack the VC debate raging around AI-native services and why these businesses ultimately can't escape the gravity of the software business model. The current discourse, to quote Sequoia's piece: "every improvement in the model makes your service faster, cheaper, and harder to compete with." But that logic is a bit... arbitrary. Every improvement to the model benefits tools and services equally. If the model gets good enough to replace a code-review tool, it's good enough to replace code-review-as-a-service. And there's a real argument that the latter is easier to displace. So how do AI Services companies build an advantage? At the early stage, we see two distinct models: 1) Wedge — Replaces a vendor. Starts with high automation + attractive early margins. But must race to find durable product extensions before their wedge is commoditized, as the intelligence layer gets cheaper for everyone. 2) Delivery Engine — Embeds into customers so deeply that they earn the right to ingest data & automate. Starts with heavy human-in-loop + thin margins, but has a structural advantage IF it can prove out AI-driven automation. One needs to escape the wedge. The other needs to earn the right to sustain it. (Framework for how we think about these models attached grid) AI Services founders: the risks are as real as the rewards. We've seen the movies in prior cycles — An AI wedge-services company gets commoditized, while celebrating its fast initial growth all too soon... An AI delivery-services company scales too quickly, its margins never materializing, perpetually more human than loop as incumbents slowly catch up. The strongest startup moats have never been the infrastructure they're built on. They're proprietary data loops, platform integration, distribution, network effects built on top. That's truer than ever in the AI era. The biggest risk in AI Services is becoming a traditional services company. The biggest reward is a path to defensible software. ------ 👇 Link to the full essay in the comments below. Founders / investors in AINS — what do you think? Jake Saper Chris Hladczuk Deepak Chhugani Dakotah Rice David Haber Rick Zullo Alexander Niehenke Nick Tippmann Wenz Xing Nikola Lazarov
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Nic Poulos posted thisThe AI services companies that win will look like Salesforce, not Accenture. Every vertical AI startup eating the services layer — implementation, integration, ongoing labor — is running Salesforce's playbook against Siebel. Siebel's CRM cost nearly $10K per employee in year one, most of it going to infrastructure and SI partners, not the software itself. Salesforce didn't win on features. It ate the complement, collapsed the cost structure, and built a sticky core platform accessible to much, much wider market. The complement was the wedge. The software was the moat. That's the fork every AI services company faces right now. Cheaper delivery gets you in the door — but if the product underneath isn't building workflow lock-in or a system of record, you haven't disrupted the services layer. You've become it. Salesforce is now the canonical example of enterprise software. Easy to forget their original tagline was "No Software." They redefined what software meant — infrastructure went from a separate line item to something bundled inside the product. AI services will follow the same arc. The winners will still be software companies. They'll just automate what we call services today. We're releasing an in-depth essay on this Thursday, weighing in on the AI-native services debate. In the meantime, check out our past essay on Commoditizing Your Complements (in the comments below). It's a lesson as relevant for AINS startups today as it was for SaaS when the legendary Joel Spolsky published it in 2002.
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Nic Poulos liked thisThis week on Verticals, we brought on David Haber, GP at Andreessen Horowitz, to discuss why VC is getting AI TAMs totally wrong. Market size #1 VC filters — bigger number, better pitch. Right? David makes the case that in this era of AI (and especially in Vertical AI), market STRUCTURE is a far better predictor of long-term success. > How concentrated are incumbents? > Do customers care more about leads or conversion? > How, where, and when do they do their work today? In fragmented SoR markets — think specialty healthcare with its long tail of EHRs — AI-native companies can start by doing the work around the system of record and back into becoming it. That's exactly what David's portfolio company Camber did in behavioral health RCM... as Luke Sophinos and I discussed w co-founder Christophe Rimann on the pod a few weeks back. Do the work without fighting the system. In concentrated markets — eg Autodesk's Revit at 95% share in AEC — you're forced into a complement position. And the more valuable you get, the more the incumbent wants to tax you, replicate you, or just strangle you altogether. The point is this: Different market shapes demand entirely different playbooks. A $2B niche with 15 equal incumbents and heavy labor spend may be a far better place to build than a $600B market where one player dominates and margins are nonexistant. Our full episode with David covers this and a lot more: + his "messy inbox" thesis on Vertical AI wedges + why AI services can hit software margins + why conventional SoRs may go to zero And of course... what a16z looks for in a Series A. Links to watch below.
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Nic Poulos liked thisNic Poulos liked thisTwo weeks ago I took a walk with the CEO of a SaaS business with hundreds of millions in ARR. He told me he's pivoting his company to a services business. Can't share who yet. But I can share the thinking, because it's the most important strategic question facing every SaaS leader right now: if AI lets you deliver the whole outcome, should you still be selling software? The buyer you sell to is being laid off. Foundation models can approximate your features in minutes. And you're already sitting on the product, the data, and the workflows you'd need to deliver outcomes directly. Stop selling the seat. Start selling the result. The linked article lays out when this transition makes sense, what changes when you make it, and why waiting is the bigger risk.
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Nic Poulos liked thisNic Poulos liked thisOn Pi Day (3/14) in 2012, at a Two Sigma strategy offsite, I proposed the launch of Two Sigma Ventures, based on three primary hypotheses: 1) New York was emerging as a startup ecosystem; 2) data + advanced computing were going to be the core drivers of innovation over the next 30+ years, and 3) Two Sigma had tons of smart people that could help. 130+ companies later, I'm incredibly proud of and grateful for what TSV has built over these last 14 years. Today, building on those same convictions, my colleagues Dusan Perovic, Sidney Costabile, and Jonathan Golden and many others launch Deviation Capital as the spin-out of the Two Sigma Ventures' business. At Deviation, the team and I will continue to invest in ambitious visionary founders who are trying to be the exceptions to conventional wisdom and status quo. Founders optimistic that information and AI can unlock new experiences, insights and discoveries. I owe thanks to all my now former colleagues at Two Sigma, especially including its founders who backed Two Sigma Ventures in many ways from day one, and who will continue as investors in Deviation’s next fund. We are excited to also maintain a formal connection with Two Sigma through a network of Two Sigma senior technical experts to continue to help us realize this exciting vision of what data + computing can create. We will now do it with a strong Deviation Capital presence in both NYC and San Francisco. Read more about it: deviation.com/launch The future is built by those who deviate.
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Jason Shuman
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I’ve spoken to over 2 dozen MDs at PE firms I can confidently say that the arb of figuring out how to implement Vertical AI at portfolio companies is very real right now It will fundamentally change underwriting for those who can do it predictably and unlock generational returns. Most are aware they need to act. Very few have.
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Sean Smith
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I spoke with Christien Louviere of BDE Capital about his journey from a $330mm exit to becoming an independent sponsor. Christien shared excellent insights for folks looking to partner with business owners, rather than buy sellers out completely. Below are a few of the topics we covered: - Why he moved from “zero-to-one” startups to a buy-then-build strategy - How Christien's background shaped a focus on growth vs. cost-cutting - Why 20–40% rolled equity is central to his deal structures—and how it builds trust with sellers - Using scenario analysis with AI tools to evaluate management teams and uncover hidden key-person risks - How to identify when a $3–5M EBITDA company truly has a middle management layer—or is still founder-reliant For anyone investing in or buying small businesses, Christien’s approach provides a fresh lens on growth, alignment, and deal structuring. 🎥 Watch the full interview here → https://lnkd.in/ekfkaiej 🎧 Listen on Spotify: https://lnkd.in/e86Agx6V
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Peter OBrien
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Every hire, product feature, and GTM experiment is a capital allocation decision, most teams just don’t call it that. I’m kicking off a 3-part series on capital allocation for founders, operators, and finance leaders with a simple question: ➡️ Will the next $1 you deploy become worth more than $1? Series overview Part 1: defines the core concept + the math Part 2: where capital goes + how to measure whether it’s working Part 3: AIMS framework for communicating allocation decisions to management teams and your board Part 1 (attached here) lays the practical foundation: 🔹 The $1 invested test and ROIIC vs. hurdle rates (scoreboard vs. decision lens) 🔹 A lightweight “investment brief” to evaluate bets (GROW / BUILD / BUY) 🔹 The small metric toolkit that translates finance into operating decisions (NPV / payback / ROIIC / incremental margin) 🔹 Early-stage proxies when DCF inputs are unknowable (burn multiple + revenue quality) 🔹 Operator “vital signs” to spot drift early (profit engine, leverage, cash conversion, optics) Series Roadmap ✔️ This series (Parts 1–3): breadth-first. shared, lightweight framework to define value creation, choose decision-grade metrics, and communicate tradeoffs clearly ✔️ Next series: depth-first. momentum drivers + operational decisions that need near real-time measurement to spot drift early and course-correct fast Read Part 1 here and Part 2 and 3 on substack. Next Series will be out next week. 💰 What’s one bet you’re funding right now, and how will you prove it’s working?
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Maddi Holman
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💡Emerging GP Fundraising Insight #8: Rolling Closes Keep You Moving Small funds can't always afford to sit still until the target is hit. Rolling closes let you start deploying earlier, build a track record, and show momentum to prospective LPs. One GP told me that for Fund I ($5M target), he took capital as it came, signed, wired, and got to work. It wasn't perfect, but it kept the lights on and the deals moving. Sometimes the "sign and wire as it comes" approach is the only way to get moving. Takeaway: Momentum is a fundraising asset and rolling closes can help you keep it. Has anyone here used rolling closes as a strategic advantage?
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Jeffrey Seah
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Paul Perrett
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Gordon Ritter
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Growth at all costs is out. Strategic expansion is in. One stat from our new report that says it all: By $100M ARR, 67% of growth comes from expansion, not net-new customers. We’re seeing a mindset shift among top-performing founders. They’re prioritizing customer success early, building in product-led growth loops, and measuring how they grow, not just how fast. Full analysis and benchmarks here: https://lnkd.in/gZruurYs #AI #SaaS #CustomerSuccess #BeyondBenchmarks
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Steve Vassallo
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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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Kal Amin
7K followers
I’m thrilled to officially announce our $3M seed investment in Propel People, a company we built inside the 1848 Ventures studio to tackle one of the biggest challenges facing the #construction industry today: the skilled #labor shortage. For small and medium-sized contractors, hiring isn't just a challenge. It's the number one threat to their growth, profitability, and safety. With 94% of contractors struggling to find qualified workers, it’s clear that traditional hiring methods aren't built for the trades. That’s why we built Propel People. It’s a mobile-first, AI-powered hiring platform designed for how construction actually works: in the field. By leveraging smart candidate ranking, instant #SMS-based screening, and a fully #bilingual interface, Propel helps contractors build great crews faster and more efficiently. I’m also thrilled to formally announce that industry veteran Dexter Bachelder is at the helm as CEO. Having worked with Dexter and the team over the last few months, we've already seen the impact of his leadership. His 25 years of experience scaling construction tech companies will be instrumental as Propel People enters this initial stage of growth. This investment reinforces our core thesis at 1848 Ventures: building AI-native companies that solve fundamental pain points for the #SMBs that form the backbone of our economy. A huge congratulations to Dexter and the entire Propel People team on this milestone. We are incredibly proud to partner with you to support the people who build our world. Read the full announcement below. #constructiontech #venturecapital #seedfunding #ai #skilledtrades #smb
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