Andres Blank

With all the crazy acquisitions last week, we didn’t want to be left out of the party. Excited that Prefab has joined Reforge! AI has created the opportunity to “collapse the stack,” as Scott Belsky has said and enable “full-stack builders” as Tomer Cohen has written about. The result will be not only increased speed from Insight to Launch but, more importantly, better products. Since early this year, Reforge has been on a mission to build this platform: First, with the launch of Reforge Insights, which aggregates, analyzes, and acts on your customer data. Next, with the creation of Reforge Research, which helps you capture new insights with AI-powered user interviews and surveys. Most recently, with the closed beta of Reforge Build , which helps you explore and prototype ideas for your existing product. Prefab will help us fill in the final step: Launch. Launches are the last mile between your team’s best ideas and your customer’s experience. The entire Prefab team has joined us. I’m extra excited to reunite with Jeff Dwyer from our HubSpot days. In the coming months, Prefab’s feature flag and config system will be rebranded and integrated into the Reforge product suite, but you can start using it today.  More info 👇 https://lnkd.in/eqkryPdi

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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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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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Product-market fit genuinely feels different...and it'll show up in the data Demo to close rates are the easiest thing to look at for product-market fit right now. Vertical AI startups in our portfolio are seeing 50%+ demo to close rates, even with brand new account executives selling the product. It's happening in every market from AI takeoffs to AI CSR/Voice software to AI procurement solutions. When you see a demo to close rate over 50% chances are you have a jaw dropping customer experience and you should put your foot on the gas. If you're seeing something dramatically lower in mid-market or SMB ACV categories, it's worth revisiting your product and/or your pitch.

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