Madison White

I've been in tech long enough to watch Stripe and Notion go from startup toys to enterprise infrastructure. Win the frontier first. The enterprise follows. "It’s just for startups" is a feature, not a bug. Cristina Cordova from Linear resurfaced this critique last week. Her response: It's "the starting line, not the finish line." She's totally right. Here’s what I’d add: A category isn't just a product - it's a new way of working that makes the old way obsolete. Instead of adding features to email, Slack created a category where work happens in channels vs. inboxes. Figma created a category where design happens in browsers, not desktop apps. Here's why "just for startups" is an incredibly strong signal that a new category will win: 1. Startups have zero switching costs and tons of urgency An enterprise takes 6-18 months to switch tools. A startup switches in 18 minutes. When you're burning $50K/month and have 6 months of runway, every tool either multiplies your velocity or dies. This selection pressure doesn't exist anywhere else. The frontier doesn't tolerate mediocrity. 2. Categories spread through people, not procurement That junior PM using your tool at a seed startup moves into leadership at a growth-stage company within two years. They don't evaluate vendors. They install what worked before. One operator infects an entire org. Discord went from "just for gamers" to the default community platform for countless startups. Airtable was "just a spreadsheet" before VCs started running entire funds on it. Large organizations have immune systems. They reject foreign objects. New formats are foreign objects by definition. They violate existing workflows, break departmental boundaries, and make current expertise obsolete. Startups don't have antibodies because they don't have institutions to protect. Of course, you can build enterprise-first and succeed - especially if you're solving problems enterprises already know they have. But if you're trying to create new categories that change how work happens, the frontier is often the clearer path. You're optimizing for the organism, not the immune system. That’s why "just for startups" isn't a critique. It's confirmation you're building something new.

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The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

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Most founding CEOs own about 15% of their VC-backed company by the time it goes public. But that number can swing dramatically. Take Mailchimp: the founders bootstrapped, never raised VC, and walked away with 100% at exit when Intuit acquired them for $12 billion (!). Compare that to DocuSign’s founder, who owned just 1.5% at IPO. Why does this matter? Founder equity is one of the most powerful tools for long-term alignment. It helps: - Keep founders motivated to go the distance - Ensure a life-changing outcome if the company succeeds By Series A, founders should typically still hold at least 35%. If you give up too much too early, future investors may hesitate. A messy cap table can stall your next round. If you plan to raise, it’s critical to understand the mechanics of each fundraising type: 1. Priced rounds More here → https://lnkd.in/gxi9MDKh 2. SAFEs More here → https://lnkd.in/g_YD6pjR Knowing the difference helps you preserve ownership and options. — Source: SaaStr ♻️ Repost to help a founder make smarter decisions

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I was going through a report from Menlo Ventures released on Thursday. Anthropic just overtook OpenAI in enterprise usage: 32% vs 25% market share. That's a complete reversal from 2023 when OpenAI dominated with 50%. Anthropic jumped from $1B to $4B in revenue in just 6 months, while targeting a smaller, higher-value enterprise segment. Both companies' flagship models are essentially at parity in 2025. It's about Claude's focus on logic, structure, and regulatory compliance, priorities that enterprise buyers actually pay premium for. AI spending is exploding 44% year-over-year, hitting $371B in 2025. And, nearly half of all AI spending is shifting from training to inference by 2032. While everyone's obsessing over model benchmarks, you should push your team to focus on: ➤ Code generation (already a $1.9B market) ➤ Agent-first architectures ➤ Production inference optimization ➤ Enterprise tool integration Model costs dropped 280x from 2022 to 2024, but enterprise spending keeps growing. Why? Because enterprises pay for outcomes, not tokens. They want AI that integrates with their systems, understands their data, and solves their specific problems. What I can say from all of this: stop chasing the latest model release. Start building AI infrastructure that enterprises actually want to buy. The companies winning aren't the ones with the best models. They're the ones solving real business problems at scale. #AI #Consulting #Strategy #Startups

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Frontline comp teams face the same complex problems as tech and life science organizations with one key difference: The problems are much bigger. The hourly worker talent market is super-massive, highly fungible (low switching costs), competitive down to the city block, and constantly (and non-uniformly) changing with new competition, legislation, and seasonality. If you're like me, you grew up in comp in the tech industry and didn't pay close attention to the frontline - but I've spent the last year learning about these challenges with a group of visionary comp teams in the retail industry, and together we've built a completely new approach. I am proud to announce our newest product, Compa Frontline, real-time market data for hourly workers, our solution to what I believe is the hardest problem in comp. Compa Frontline is map-based down to the zip code and powered by give-to-get validated data. Comp teams can now see the market shifts across tech fulfillment centers, coffee shops, grocery stores, big retailers, and more, and adapt in near-real-time to stay competitive. Congratulations to our amazing launch partner customers and our product team for achieving this milestone together! Read more about it in my newsletter post linked below. #compensation #totalrewards

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A bit late to the party, but Brett Queener's post on the death of the schema from earlier this month is one of the best pieces of writing out there on the future of SaaS x AI. Paralleling, in a way, Andrew Karpathy's most recent YC Startup School talk on the future of programming, Brett talks about the nerdiest, yet most vital leg of the stool that traditional enterprise software stands on - the database schema. A schema is fundamentally a way to model a business process (e.g., CRM, HCM, ERP) into a data model (e.g., a database). Schemas are why we use terms like "Accounts" and "Opportunities" so fluidly. But they're also why SaaS can sometimes feel rigid. But with AI, schemas don't have to be fixed anymore. And because schemas aren't fixed, UIs don't need to be either. Brett hints at the big prize at the end of the post - Reinforcement Learning for enterprise domains. While RL has been optimized (through Human Feedback) for our general use of chatbots like ChatGPT and has been further tuned for technical fields like math and coding, it still has a ways to go in being optimized for vertical and domain-specific use cases. Dynamic schemas + contextualized UIs + domain-specific RL = software that will be more powerful than any of us can imagine. It's not coming overnight, but it will be here soon. Link to the post is in the comments. Definitely worth a read. Thanks to Brett for writing it!

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Series A Needs to Change I've been in Silicon Valley for almost 20 years and during that time the Series A round has barely changed. It takes months of work and meetings, the process is almost entirely opaque, you need to find warm introductions, and you're usually expected to give up at least 20% of your company. Plus, after all that, you have to pay the VC's legal fees! The only things that have changed are the round sizes, how much is done over video vs in-person and now there are a few standardized term sheets, e.g. Y-Combinator and the NVCA. For a community that claims to invent the future, we are sorely lacking innovation here. Thankfully, this week we saw someone finally offering fresh ideas. Having been long term partners at Y Combinator, Paul Bucheit and Dalton Caldwell are bringing that ethos to the Series A with Standard Capital. The list of changes is significant: - Apply online - You don't need an introduction - 10% dilution - You don't have give up a huge chunk of your company - Pay their own legal - You don't have to pay the VCs legal fees once the round is closed. - Peer groups instead of Board Meetings - You don't have to listen to someone trying to sound knowledgeable about your business I expect the very best founders will prefer this approach and hopefully that will force change across the market. Series A is about to change and it's long overdue.

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What if every business function had its own software engineer? Finance could get custom analytics showing where money is being spent. Marketing could run real-time attribution models. Sales could test different territory splits, compensation plans, and discounting strategies. Operations could automate workflows and re-optimize logistics on the fly. We don’t do this today because embedding engineers into every department is prohibitively expensive (and hard to hire for). But with AI software engineers, this starts to change. Suddenly, every function can have its own engineering capacity. The future I’m most excited about is one where every department has the power of software engineering at its fingertips. That means better decisions, smarter resource allocation, and more efficient growth. Two predictions along these lines: 1. We are still underestimating the opportunity for AI in software engineering, keying it off number of software engineers… instead it is indexed to all work! 2. Vibe coding will be most common for tasks in non-software engineering functions. Everyone outside of R&D should be using these tools today - this is the future! https://lnkd.in/ggWaX47A

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