Tejas Manohar

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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We’re seeing a clear and growing pattern: BigCo SaaS now scrambling to AI-fy their Product Catalog. What used to be simple flat-rate subscriptions or tiered plans is now being reimagined- because AI isn’t a “user” in the traditional sense. It’s dynamic, automated, unpredictable, and increasingly autonomous. Over the past few months, we’ve seen: - AI-native startups looking to monetize from day zero - Enterprises re-architecting their engines to support usage-based models - A rise in credit systems, scoped access, and outcome-based billing And most interesting: Agent-to-SaaS monetization Where agents - not humans - require and even negotiate access to SaaS APIs, features, or datasets via licensed, metered contracts. We’re entering a world where your catalog needs to speak machine. Where your monetization logic needs to support autonomous usage - at scale. The question is no longer “What’s your pricing page?” It’s becoming “How does your platform entitle and meter access by agents?” This shift isn’t theoretical anymore. It’s happening. And the infrastructure isn’t ready. While native AI-startups can pick and choose greenfield solutions to solve it for them, BigCos are in a deeper problem - you can't carve out a 12-years old billing engine/architecture - especially not under the timelines your business needs to survive, or say, win.

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We’ve been jamming with Y Combinator’s Summer 25 batch, and have been blown away by the quality and pace of the teams. What jumps out: -Infra > interface: some excellent teams are building in the execution fabric, trust rails, or evaluation loops. They’re tackling latency budgets, anti-bot arms-races and SOC-2 checklists: the plumbing that lets agents graduate from demos to enterprise workloads. -Data moats by design: the strongest vertical plays lead with proprietary claims corpora or HIPAA-grade call graphs. -Hard-tech is back: Flywheel’s tele-ops excavators and Verne's one-hour-training vision stack prove YC is funding robots again, but with a software-first margin profile. -Founders who’ve lived the pain: Ex-Cleartax PMs fixing GST reconciliation. Ex-Goldman post-trade leads building zero-trust for agents. Wet-lab scientists automating plate handling. Why we’re excited: -These teams aren’t asking whether agents will work; they’re solving the reliability, audit, and compliance gaps so agents can be boringly dependable. -Several are already in production with paying customers pre-Demo Day. -Defensibility comes from data moats, regulatory grounding and real infrastrucutre. You can see the agentic web moving from prototype to platform. Looking forward to seeing some of you tomorrow at Demo Day.

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Controversial take (at the risk of getting kicked out of future YC demo days): What if getting into Y Combinator actually made your outcome worse? I've invested in 6 YC companies since 2014. 5 out of the 6 were pre-YC or early in the program, one was post-demo day. This includes winners such as Deel and May Mobility. Here's what nobody tells you: Most of what accelerators provide, you can get for free: - Mentorship? You are going to find the best mentors through your network - Network? Warm intros through investors who actually take board seats - Demo day? Most deals come from direct outreach, not demo days - Validation? Having revenue is better validation than any batch The worst part? YC invests in 400+ companies per year. They don't have time to actually help you beyond the program. You're a lottery ticket in a portfolio. The 2-3 breakout companies subsidize the other 197. You're probably one of the 197. And worse. Because they only make money from the breakout companies they would rather optimize their outcomes by pushing everybody to $20m-$40m post money SAFEs to optimize for the winners... Even though it's the wrong valuation for you and can kill your company. Yes, getting over your skis on valuation can kill your company. And no, YC doesn't care because while your company is dead they make more money on the winners. Here's when accelerators make sense: - You're 22, first-time founder, need basics - You have zero network in Silicon Valley - You need the brand to unlock initial meetings Here's when they're a terrible deal: - You're a second-time founder (you don't need the basics) - You already have investor relationships - You're giving away 7% for what amounts to a 3-month crash course Here's my controversial recommendation: Skip the accelerator. Take that 7% and: - Hire an exceptional early employee - Keep more equity for the people actually building - Raise much more money for the same equity to fuel your business If you absolutely must do an accelerator, negotiate. In most incubators/accelerators the 7% isn't set in stone, especially if you are further along. Some accelerators take 5%, some do notes that convert later. Is 3 months of mentorship worth 7% of everything I'll build for the next decade? For most founders with any serious experience, the answer is no. But everyone's too afraid to say it. #YCombinator #Accelerators #StartupAdvice

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Datadog has a 96% renewal rate. And we still target their customers. The game plan is simple (but hard): 1/ get to data parity super fast (< 1 hour) - all the data from Datadog --> into groundcover. logs, metrics, distributed traces. all of it. 2/ migrate dashboards and monitors automatically - give us your Datadog API key and we'll do the rest. 3/ fight renewal timeline with aggressive first year offers and delayed payments. ---- More details on how we do it: * We integrate with all the Datadog protocols and the SDK itself so customers can send data immediately through dual shipping from the agent or directly from the code. * We're about to release our self-served Datadog migration experience for monitors and dashboards so you can try it out yourselves. stay tuned. * We utilize our BYOC model to the extreme - we operate at a high margin so we can allow ourselves to be aggressive on first year prices and payment schedules. Many customers move even 6-9 months before the Datadog renewal.

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AI is revolutionizing how software companies build & scale. Noah Lin & I have been codifying successful strategies in the market, some of which we shared on Crunchbase last week. Given the enthusiasm and questions we received, I'm excited to share a more in depth follow-up with tactical strategies for building early GTM in an AI first world. We unpack the strategies that AI-first companies are using to redefine value and growth. AI-first products are shifting from traditional subscription models to dynamic, usage-based, or hybrid pricing. At defy.vc, we’ve identified four key ways top companies are capitalizing on this trend in their GTM strategies: 💡 Pricing & ROI: Crafting compelling ROI narratives to align with budgets and deliver clear value. 🔍 Discovery & Qualification: Sharpening sales fundamentals to identify high-potential leads early. 🤝 Consultative Selling: Guiding buyers to reimagine workflows and build long-term trust. 📈 High-Leverage POCs: Proving transformative value with tightly scoped, metrics-driven proof-of-concepts. We share specific questions to ask and actionable tactics to navigate unfamiliar spend structures and turn POCs into lasting partnerships. Link to the full article below. At Defy, we’re passionate about partnering with founders who are pushing the boundaries of GTM, pricing, or post-sales strategies in the AI-first world. If you’re innovating in this space, drop a comment or DM us—we’d love to connect! #AI #SaaS #GoToMarket #Innovation #Tech

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