Aakash Gupta

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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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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Zapier’s AI fluency framework is gaining traction. Lenny's Newsletter (1M+ subscribers) just spotlighted this framework as a leading strategy for scaling AI impact. Zapier's AI adoption practices are featured alongside tips from Ramp, Shopify, Intercom, and WHOOP. (Shoutout to Peter Yang for writing this!) Here’s an excerpt: “Zapier tracks the impact of AI adoption by function. In sales, for example, when targeted leads engage with marketing content, AI auto-packages that information for the account rep—leading to 10 hours saved per week per rep.” The full post is for Lenny's subscribers and contains 25 ways to accelerate AI adoption at your company. A truly great read.

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We got a few good questions on our operating principles, so I'm answering them here and sharing more detailed thoughts. "Why no standing 1-on-1s?" ↳ Manager 1-on-1s can be helpful when managing more junior teammates, but I've found just being ultra available via Slack/calls/desk visits works well for my direct reports. ↳ Issue with scheduled one-on-ones → people save topics instead of solving problems immediately. ↳ Peer 1-on-1s often feel obligatory vs. actually valuable. Schedule them as you need! Caveat: Clay is largely in-office so a lot of peer bonding happens organically. "Why not send calendar links?" ↳ This is for when you're asking for a favor (sales, intros, etc). ↳ Maybe it's technically faster, but it can impact the feeling. The person asking for the favor should adhere to others' schedules and make it easy for the other person. ↳ You can use Vimcal to automate sharing your specific availability. "Why communicate in open Slack channels?" ↳ Great ideas can come from anywhere. More people having the information they need = better outcomes for everyone. ↳ We have few private channels. Keep everything public and transparent, while encouraging people to mute channels they don't need as often. Hope this is helpful or interesting! Take what feels authentic for your team and leave the rest. See all of Clay's operating principles here: https://lnkd.in/eXZyCp-G

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YC startups have some of the best prompt engineers in the world. Here's a single prompt that instantly upgrades your AI results to YC startup-level quality. YC startups know how to prompt. Their prompts force structure, clarity, and strategic depth. Their prompts: - Assign a clear role to the AI - Forces a tight scope, e.g., what to include and what to leave out - Breaks tasks into actionable steps - Uses structured formatting (headers, bullets, tags) - Self-improves by recommending prompt upgrades Here is a prompt that can turn any goal you have into a YC-style prompt. P.S.: It recommends ways to improve the initial prompt it gives you, so you can continue to improve the output. PROMPT ----------- You are a senior AI prompt engineer at a YC-backed startup. Your role is to craft an advanced prompt that will guide an LLM to achieve the following goal: [Insert goal or desired output here, e.g. "Generate a GTM strategy for a PLG SaaS company expanding into healthcare."] Your prompt must include: 1. A clearly defined **persona** for the LLM (e.g. expert marketer, CFO, customer support agent) 2. A precise description of the **task** and its **desired outcome** 3. Any **constraints** (tone, word count, must-use frameworks, etc.) 4. A step-by-step **plan or structure** for the AI to follow 5. A clear specification of the **output format** (e.g., markdown headers, bullet points, tables) Then, output: - ✅ The final optimized prompt - 🔁 1–2 smart suggestions for improving or sharpening the prompt Your goal is to produce a prompt that delivers clear, reliable, high-quality results every time.

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Founders love addition. More hires. More dashboards. More products. But the real test of leadership at scale is subtraction. Every layer you add feels like progress. Yet over time, the weight of complexity exceeds the growth it’s meant to drive. What once looked like momentum turns into drag. I’ve seen companies stall not because markets dried up, but because leaders couldn’t stop doing what no longer served compounding. The heroics that got you to $10M are the very habits that break you at $50M. Three questions I use when pressure builds: 1. What have we added that no longer compounds? Vanity features, pet projects, or feel-good metrics. 2. What are we holding on to that no longer creates edge? Legacy routines, outdated partnerships, roles that drain more than they deliver. 3. Where are we duplicating effort? Teams solving the same problem differently, or layers of reporting that repeat the same story. Capital doesn’t forgive clutter — it amplifies it. The discipline to subtract is what separates scale from stall. Clarity compounds faster than capital. Question for you: What’s the hardest thing you’ve had to stop doing as your company scaled? ♻️ Share this story with your network - let's spread inspiration far and wide! 👉 Follow Ben Botes for more insights on Leadership, Scale-Ups & Impact Investment.

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Feels like we just hit “product-moment fit.” Call it a subset of product-market fit: when the tectonic plates of society shift and suddenly align with your mission. ✅ At Extern, I think we’re there. After years of highs and lows, pivots and near-death experiences, the forces reshaping how young people enter careers have intensified in ways that make our work essential. ⚠️ There’s one problem we're after: How do we help students prove themselves when traditional doors are closing?🚪 Over the last year or two, I've watched these forces accelerate: 📉 The collapse of the traditional internship pipeline - HR leaders are cutting intern budgets and eliminating structured programs. When I started Extern, this was a hunch. Now it's in the data. 🤖 AI is making "almost ready" no longer enough - Each week I talk with students who applied to 200+ jobs without a response. Companies don't want potential; they want proof. 📜 The credential crisis - I remember when I needed an internship during college that got canceled over the holidays, derailing my winter plans. Today's students face this at scale – paying more than ever for degrees while watching opportunities vanish. ↔️ The widening gap between education and employability - The stats showing recent grads with higher unemployment than the general population isn't just a number to me – it's the anxiety I hear in every student conversation. We didn't create these forces, but we've spent years building for them, sometimes struggling to explain the urgency of our mission. Now the world has caught up. 🏃‍♀️ The question isn't whether this moment is real. It's whether we can build and scale fast enough to meet it. #collegegradcrisis #employabilitycrisis #credentialcrisis #externships #internships

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What separates good CRO's from great ones? I unpacked some of my learning from interviewing close to 20 of the best in the world on the Topline Podcast with Asad and AJ this weekend. We got into: 👉 the qualities of exceptional CRO's 👉 what it means to be a stage appropriate leader 👉 strategic planning and probabilistic thinking 👉 the impacts of reasoning models like o1/o3 on startups 👉 the role of rev ops in best in class companies 👉 what we've changed our minds on in 2025 💊 👉 and much more Hot takes were flying 😅 Eager to hear your thoughts!

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We sat down with Andrew Yan to understand how AI SEO actually works: ChatGPT dominates consumer intent; Claude skews dev/technical; Grok shapes crypto/X communities. Andrew’s rule: “Go where your customers start their buying journey, not where you hang out.” Half your ranking power now comes from off-page signals (citations, mentions, PR) instead of pure site optimization. 50% of Google searches now trigger AI overviews. Meaning your “real” homepage might soon be ChatGPT’s answer box. Clean sitemap = crawlable brand. AI models still start there; a messy sitemap makes your site invisible to crawlers like OpenAI and Anthropic. FAQ pages are gold. Structured Q&A content gives models clear, direct answer snippets to quote. Persona-based content actually matters now. Train AI to “see” you as relevant for each buyer type - parents vs. teens, health nerds vs. casual shoppers. Mentions are the new backlinks. It’s not just who links to you, but who names you in trusted domains. PR strategy = AI SEO strategy. The sites that influence AI answers are often review blogs, Reddit threads, and niche publishers, not Forbes or WSJ. Affiliate content is undervalued. Those listicles like “10 best gut-health gummies” directly shape what models recommend. Agentic commerce is coming. One-click buying inside ChatGPT will collapse the funnel from “search → site → cart” to one prompt. AthenaHQ's gameplan: AI-powered PR assistants. Tools that turn one PR person into a team of ten by automating affiliate + mention research. We spent a decade learning how to rank for humans. Now it’s time to rank for machines. Thanks for the chat Andrew!

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One of the things I enjoy most about hosting Subscript’s Diving Deep podcast is getting to hear how smart operators think. In our most recent episode, Amy Kalnoki (Co-Founder and COO of Bitwave) shared a perspective that stuck with me: “I don’t think an AI is gonna go to an ATM and take a stack of cash out and go pay for something. I think that AI agents will be interacting with digital assets because it is programmable money.” It’s a clear reminder that as AI shifts from surfacing insights to taking action, finance systems will need to keep up. If you’re building or enabling AI workflows, on the product side or the finance side, it’s worth asking: how will your systems support that autonomy? Give the full episode a listen here: https://lnkd.in/gEpk6ZvR

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Scaling a product team without losing agility is hard. But Lucid has done it - growing from a scrappy startup in 2010 to a visual collaboration powerhouse with 70M+ users, 90 patents, and a top 12 app ranking in 2024. How did they pull it off? What frameworks, decisions, and strategies kept them innovative while scaling at speed? Let’s find out. I’m sitting down with Lucid Software's CPO, Dan Lawyer to unpack the exact playbook they used to scale, align teams, and drive innovation: → The key pivots that made Lucid an industry leader → How they use thematic roadmaps to align teams & innovate → Strategies for scaling *without* losing agility → Why autonomy is a superpower for product teams - and how to enable it Dan has been in the trenches - leading product at 𝗔𝗱𝗼𝗯𝗲, 𝗔𝗻𝗰𝗲𝘀𝘁𝗿𝘆, 𝗮𝗻𝗱 𝗩𝗶𝘃𝗶𝗻𝘁 𝗦𝗺𝗮𝗿𝘁 𝗛𝗼𝗺𝗲 before joining Lucid. His insights? 🗓️ Date: Thursday, March 27th ⏰ Time: 3pm GMT | 4pm CET | 11am EST 📍 Sign up now → Link in comments 👇 Join us as we dive into what really makes product orgs thrive. See you there!

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Setting new clickbait records with my latest Substack post: "Everyone’s been running community rounds wrong for a decade". Usually, strategic realizations drive the product roadmap. But occasionally, product innovations unlock a strategic revelation. Founders running a community round should (usually) gauge investor demand before executing on their raise. It took Wefunder rolling out our new landing page tool to help me figure that out. https://lnkd.in/ggRGfr3q

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