Paul McDonald

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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I was CPO at Blend before their $4 billion IPO. We rejected the “forward deployed engineer” Palantir model that everyone is copying now. Here’s why: Blend could have followed the forward-deployed engineer approach. Both our CEO and CTO were early Palantir. They'd seen it work. Instead, we built a SaaS product with core components. Move fast, iterate, use data to learn. Not custom code for every client. That Palantir model is having a resurgence. Now Sierra, Decagon, and others are hiring forward-deployed engineers. But it’s not the right approach for every company. It’s fundamentally the wrong answer for Assembled’s customers. Here's what we understand about support teams and why we’re not going the forward deployed route: Customer Support teams are used to getting no resources and no support. On the surface, you'd think they'd be excited about engineers building custom solutions. "Finally, someone's offering us developers!" The reality is support teams are terrified of getting help they can't afford to keep. Support teams are wary because they know the math. They don't have budget for expensive, scarce resources. At some point the shoe drops. The engineers disappear when priorities shift. And custom solutions break. Support is left holding the bag with something they can't maintain. They're resource-starved by design. The idea of a dependency on resources they can't control is terrifying. So instead, the goal should be to give support teams the ability to manage, change, and solve their own problems. Not dependencies on engineers they can't keep. Smart, competent people who teach them to do it themselves. Palantir wasn't even a product company for years. They were a custom development shop with reusable components. Show up, build something bespoke, maybe extract learnings. That's not scalable for any but the largest support teams who need sustainability. Forward-deployed engineers are the new shiny object. I wasn’t a fan of it at Blend. And I’m not a fan today either. What I see in the customer support space is that teams need empowerment, not dependency. I want the teams we work with to be able to say: "I love that you’re partnering with me. And I love that I can do this myself too." That's the difference between building a product and building a consulting practice.

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🎙️ From Square to Mercury: How Rohini Pandhi became one of fintech’s top product leaders—and how velocity of learning at high slope companies shaped everything. Rohini dropped so many gems in our first episode of First Builders: 💎 Generalist → Specialist: In her early career, she was a generalist and tried everything. This led her to critical discoveries. She specialized in product and fintech when the “time was right” 💎 Follow the Engineers and Designers: Square was a high slope environment for Rohini, packed with talent density and learnings. She finds high slope environments by studying where smart engineers and designers are going. 💎 Fake News about PMs: PMs don’t just “move fast and break things.” Her team runs 30-50 customer interviews per quarter, providing rigor behind every decision. 💎 It’s Usually Too Early to Hire a PM: A top question she gets from founders is whether they should hire a PM. She usually says: “it’s too early.” 💎 Hiring a World Class Team is Like Tennis: When finding a tennis partner, you want someone a little better than you who can challenge you to improve your game. Give us a listen and leave a review on your favorite podcast location (see comments for links)! #FirstBuilders #StartupPodcast #ProductLeadership #TechCareers #Leadership

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In every product leadership role I’ve taken, my key to success was spending as much time on recruiting as I did on product. Great teams build great products. I believe building teams deserves the same craft and intentionality as building the product itself. And with AI giving teams unprecedented leverage, teams that hire top talent faster will emerge as the winners. Given how much I value recruiting, it’s fitting that Paraform was the first investment I led at Felicis. Paraform is an AI-enabled, recruiter-first marketplace that helps companies hire 3x faster and helps recruiters earn 3x - 5x more than traditional recruiting roles. Their marketplace and tools give elite recruiters more speed, leverage, and time dedicated to helping companies hire their hardest-to-fill roles. The traction to date speaks for itself. Companies like Palantir, Ramp, Rippling, Cursor, Windsurf, and Decagon all rely on Paraform to help them hire the best people. Some recruiters have already earned $1M on their platform, and Paraform’s pace is accelerating. I’m so excited to partner with John Kim, Jeffrey Li, and the entire Paraform team on building the future of recruiting. Read more about why we invested here: https://lnkd.in/g6zb8ZrY

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Yesterday Clay hit $100M ARR and I shared a post on the GTM bets that got us here. Today, I'm sitting down with our Head of Finance to dive deep on the business - metrics, our view on the AI market, and the path to $1B. Karan often gets this question from candidates: "Look, I have to dedicate my livelihood to Clay. All the upside of my equity is baked into one asset. How should I frame the real risk/reward?" It's a great question! So I wanted to use this video to go beyond the tactics and share how we think about the business holistically. The fundamentals, market dynamics and where we're really going. Some of what we cover: - The AI market and why GTM is actually the least crowded - Cohort data that shocked even us (enterprises never churn!) - How we've built an entire economy around Clay - The three things that create our customer flywheel - Why we're not burning cash while growing at an insane rate - The vectors to $1B: new geos, personas, use cases, channels, and verticals At the end, we get candid about the real risks and why World of Warcraft helps explain them. Watch here 👇

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I often see marketing leaders respond with insufficient urgency to creative performance degradation. I view any meaningful drop in creative performance as an all-hands-on-deck emergency that needs to be addressed aggressively. A few years ago, in one of the most popular posts on Mobile Dev Memo, I proposed the idea of a "creative performance half-life": the point after which creative performance degrades precipitously because of saturation. That half-life is essentially a replacement timeline. If the creative production process requires two weeks from concept to deliverable into testing, then any significant decline in creative performance means that the creative production process either malfunctioned or became misaligned with market conditions *two weeks ago*. That's a catastrophe. The team should respond accordingly by dramatically increasing production output. This is why I advocate for working with a large number of agencies and freelancers, since their output can be flexibly scaled at a moment's notice. But the broader point is that a considerable drop in creative performance reveals a process problem in the past that will have accumulated over the half-life of the advertiser's creatives. Fixing it now requires a deliberate expansion of creative output and diversity.

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AI is changing the structure of most companies we see. Lots more coming from us on how startups can build faster, leaner teams -- and where we see all of this going. Great piece from Pete Flint to kick it off: https://lnkd.in/dcUebcJj

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Checked out Forerunner/Kirsten Green's "The Architects of Experience" piece (linked in comments). Sharing some thoughts below. Every 10-year platform shift resets consumer expectations (Web → Mobile → AI). Kirsten says the AI era belongs to products that feel effortless: proactive, predictive, always learning. It’s not about adding an LLM garnish; it’s about architecting the entire business around a compounding data loop. So, why does it matter? Moat ≠ code → The defensible layer is a proprietary data flywheel that learns faster than users can churn. Talent remix → Winning teams look less like pure engineers, more like “orchestrators” who weave off the shelf AI, data strategy, and distribution. Trust is the gate → Human in loop design and transparent governance are table-stakes for adoption, and eventual regulation. Questions to consider for consumer AI founders... 1. Moat depth: What makes your data flywheel unique, and how quickly does it compound vs. the big LLM platforms? 2. Distribution edge: When AI tooling is commoditized, what non-technical channel (community, brand, regulated access) gets you in front of users first? 3. Human-machine balance: Where does your product deliberately stop automating to preserve agency and delight? 4. Regulatory resilience: How will privacy portability rules affect CAC, margins, or data lock in? My counterview: Total automation can flatten delight. Some categories thrive on intentional friction (e.g. luxury, craft, serendipity). Winners will know when to keep a human in the loop.

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I’ve come to believe that early experiences carry disproportionate weight - far more than we often acknowledge. For designers and builders, “minimum viable” may suffice for validating core functionality, but it rarely earns trust or sparks genuine delight. While velocity matters, I’ve always struggled with the notion that early versions should be merely viable, especially in a world where attention is scarce and emotional resonance is everything. This extends beyond product. Do you show up as the minimum viable version of yourself in your work, your relationships, your craft? I would encourage you to challenge the conventional MVP framework. Replace it with the Minimum Awesome Product. This doesn’t mean it’s fully-featured or pixel perfect. But it should be worthy of attention. Memorable, even evocative in the way it triggered emotion. The kind of product that prompts someone to say, “I didn’t know I needed this, but now I can’t live without it.” You can refine mechanics over time, but the emotional experience of a first interaction is singular and unrecoverable. Don’t settle for viability. Build something worth remembering… and make it awesome! ❤️‍🔥

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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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