Javier Perez

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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A fintech giant wanted us to build custom features for their deal. My engineering brain said "absolutely not." That custom work became the path to our first $1M+ contracts. A lesson every B2B founder needs to hear: Early at Assembled, we were two engineers and one salesperson, Brian Sze. Brian came in with a $100k deal, With one of the fastest growing fintechs of the 2020s. Now public. "They want us to do all this custom stuff," he said. My immediate reaction: "That doesn't fit our model. We shouldn't do work that's not really what we want to do." Classic engineering mindset. Build the perfect product. Make customers adapt to it. Don't pollute your clean architecture with one-off requests. Brian looked at us like we were insane. We had a heated debate. He wrote up a whole decision document on why this was an exception. Thank god we listened. This giant became our customer. In 2020, Bitcoin and meme stocks exploded. This customer went from hundreds to thousands of support agents overnight. We woke up every morning checking dashboards. Revenue going up and to the right. Usage going up and to the right. Everything growing because we were part of this fintech’s growth story. But here's the deeper lesson: That "custom" work wasn't actually custom. It was vanguard work for the next tier of enterprise customers. Every time we had an order of magnitude contract increase ($100K, $1M, $10M) it started with custom work. But that custom work became the template for the next several deals at that level. The million-dollar custom features became the standard for all million-dollar customers. Your best customers pull you into the future. But only if you're willing to schlep. The engineering mindset says "build the perfect product, then scale it." The winning mindset says "build what your best customer needs, then generalize it." Don't confuse purity with strategy. Sometimes the messiest path leads to the biggest outcomes. We almost said no to custom work that defined our entire business model. The difference between a consulting shop and a $100M company isn't avoiding custom work. It's choosing the right custom work from the right customers.

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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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Engineers, not sales executives, often lead client conversations. He was making a bold statement: great technology should sell itself. What began as a staunchly anti-sales stance has evolved into a hybrid model that blends engineering excellence with strategic outreach Check out the full story here https://lnkd.in/dhaJT3iQ

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The big labs are expansive in their product ambition, especially since foundation models have largely improved in lockstep - in order to compete with them you have to do things they won’t which are: - building a very rich software ecosystem around a primitive - orchestration across multiple models - going insanely deep on product and growth for a narrow vertical domain

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