Swapnil S.

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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YC has done a good job supporting ambitious deeptech projects. Seeds sowed over many years are starting to sprout in meaningful ways. Biggest support to founders (esp those building in the physical world) isn’t money - it’s the right advice, network, and peer support.

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personalization at scale = laziness at scale? this is what Anshul Gupta from Actively AI dropped on our podcast and it hit HARD what's happening in your inbox right now? - "congrats on your funding! btw want some debt financing?" - "saw you visited our site, here's our demo link!" this is NOT personalization! it's thin-logic automation pretending to be human the problem? those signal platforms are just triggering sequences based on basic events: funding announcement = trigger sequence A website visit = trigger sequence B job posting = trigger sequence C Anshul got it right: this approach is DEAD what's working instead? the best humans go DEEP: 1- actual research on the business and challenges 2- conversations with team members 3- hypothesis based on real signals (not just events) the truly innovative teams aren't doing "personalization at scale" they're doing RELEVANCE at scale - and they can command premium pricing for that that's the difference between "hey {first_name}" and actually understanding what keeps you up at night are you still doing personalization? or have you evolved to relevance (and outcomes)? check out the full convo with Anshul in our podcast Agent Talk wherever you listen to podcasts

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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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As an investor, I often observe product teams leaving large companies to found startups. Many continue operating as if they were still within the corporate environment, following the same motions. They start with technical validation using a small user group, collect data similar to what their previous company would gather, and proceed through various launch phases in the same manner. However, startups often lack the runway to support this lengthy process. A potential failure might go unrecognized because the team is focused on adhering to a familiar process. In a large company, someone else usually decides to terminate a failing product. In a startup, without external intervention, teams can spend months working on a product that may not be viable simply because they are following established procedures.

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I recently spoke with an early-stage AI app founder who was desperate to hire sales reps because he dreaded founder-led sales. This is one of the most common failure modes I see with technical founders—and it significantly impedes the path to product-market fit. Here's how to think about the right order of operations in early sales motions: Phase 1: Prototype & Validation In the earliest stage, the feedback loop between customer conversations and product roadmap must be extraordinarily tight—making founder-led sales absolutely non-negotiable. This phase is critical because you're identifying your true ideal customer profile (ICP) and learning how to effectively communicate your product story and address common objections. As you accumulate hundreds of demo repetitions (while refining your product based on feedback), you gradually assemble a winning process. Phase 2: Founder-led Sales Scale-Up Your mission here is to create the sales playbook that will guide future reps. You need sufficient pattern recognition to understand which messages resonate with which personas. I recall meeting Desmond Lim, CEO of Workstream, several years ago (not an Emergence portfolio company, but I deeply admire what they've built). He showed me the remarkable 60-page playbook he crafted documenting their entire sales process—before hiring a single AE. Every nuance. Every objection. Everything a new rep would need to succeed. While perhaps extreme, this perfectly illustrates the principle: scaling go-to-market requires mastering your ideal sales motion before delegating it. Phase 3: Hiring Initial Sales Reps Most founders default to sequential hiring—start with one rep, evaluate results, then proceed. However, we recommend hiring 2-3 sales reps with diverse backgrounds simultaneously, enabling you to effectively A/B test different profiles. Regardless of approach, ensure these early hires are "renaissance reps" with rapid iteration capabilities rather than purely "coin-operated" sellers. Mark Leslie has a great foundational article on the Sales Learning Curve provides excellent guidance. I'll link it below. So embrace the early sales work, even when it feels uncomfortable. It's fundamental to building a foundation for lasting success.

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Is VC turning into PE? Now, Hemant Taneja is trying to pull of the boldest move in Venture Capital: turning General Catalyst the first AI native Private Equity firm Venture capital is a broken industry right now. AI valuations are crazy and most investors will lose their money in the bubble 🧨 General Catalyst just called it and is playing a game they can't lose: Acquiring service companies and transforming them with AI. Hemant Taneja believes there are trillions locked up in traditional service businesses. That is why General Catalyst is acquiring call centers, hospitals, law firms and accounting firms and rebuilding them with AI. He calls it the AI roll up strategy. Instead of selling software to incumbents, General Catalyst owns the incumbents and drives transformation from within. Examples include: • Crescendo, which acquired a 3,000 person BPO and is deploying AI agents to handle customer interactions • Hippocratic AI, which has trained an AI nurse already making hundreds of thousands of patient calls each month • New ventures in legal, finance and property management where low margin service firms are being rebuilt with AI As Taneja puts it: “AI is the zeitgeist all over the world: every CEO in every industry, in every country, is thinking about applying AI at the same time.” 👉 I broke down General Catalyst’s entire AI roll up playbook in my newsletter. Comment “roll up” and I’ll send you the link 🔥

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