Nicholas DeGiacomo

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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Actively using AI to repurpose content and automate reporting. What I learned: content generation is low-hanging fruit — but decision automation and workflow orchestration are the actual multiplier. If you stop at drafts and posts, you’re missing the point.

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My friend runs a DTC supplement brand. Clean greens powder. Was doing about $47K a month and wanted to scale faster. I said listen, you need AI automation. Full stack. They went all in. Claude API. n8n workflows. Built entire content engine scraping Reddit health threads and auto-generating blog posts. Complete revolution. Result ... Still doing $47K a month. But now their tools cost $1890/month and they spend 15 hours a week debugging webhooks. It's data-driven stagnation though. AI-powered loss with full tracking so they know exactly which automated system isn't working. Their CMO suggested they just... talk to their customers and improve the product taste. They said he doesn't understand AI led growth hacking.

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Endgame just dropped their 2026 AI Benchmark Report and it's one of the best GTM data pieces I've read in a while. 31,000+ real AI interactions. Not survey data. Not "how do you feel about AI." Actual behavioral data from hundreds of GTM teams. The whole report is worth your time, but here's the stat that jumped out at me: 💡 22.8% of all AI activity is Account Intelligence Most people will read that and think "sales research tool." I think it's the unlock that makes everything else possible. Here's what I mean...AI adoption in GTM doesn't happen all at once. It moves in waves. And Account Intelligence is the forcing function that breaks the first wave open. Nobody wakes up and says "let's go all-in on AI across the revenue org." They have a pain point. Usually it's pre-meeting research or account context. They solve that one thing. The skepticism drops. The hallucination anxiety fades. And then something interesting happens. The jobs-to-be-done start migrating downstream. We're living this at Handle.com right now. We started with account intelligence. Once that was working, the prompting behavior drifted toward deal advancement. Then pipeline monitoring. Now it's pushing into post-sale and renewal workflows. The wave is real. At least for us. Two other things from the report that deserve more attention: #RevOps is the most leveraged persona in AI. 14% of users driving 53% of Pipeline Inspection activity. That's 4x their expected share. If you're a RevOps leader and you're not looking at this data, you should be. Teams spend 2x more AI time understanding accounts than producing deliverables. That's not a productivity story. That's a context story. The highest-value use of AI isn't "write me an email." It's "help me understand what I'm walking into." 👇 Full report linked below. Worth 15 minutes of your day. https://lnkd.in/g-BzFifN

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#ChatGPT prioritizes retailer listings first for its product recommendations. Then, manufacturer sites (DTC) Community forums like Reddit last. This is based on me asking ChatGPT “What sources do you prioritize most for recommending products?” (For context, I was asking ChatGPT to help me buy a slushie maker) So … if you have a healthy retail #digitalshelf, you should be in good shape for winning Chat GPT. Another reminder to not let #AI distract you from fundamentals. Review syndication might be the best investment you can make these days! Try the exercise of asking ChatGPT how it chooses pruducts for yourself. Curious of you get a different response,

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Tuning Spark, Ray, or Dask for ML isn’t one-size-fits-all. In a new Domino Blueprint, Principal Solution Architect Sameer Wadkar shows how API-driven scaling complements autoscaling to right-size clusters on demand—cutting idle spend and speeding bursts for GPU/I/O-heavy work. Practical steps, a lightweight Python client, and Helm install included. If you care about FinOps and MLOps, this is for you. Read the blueprint: https://gag.gl/WwWso7

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TL:DR - Where do you think shopping is headed in an AI driven ecosystem? Want to hear your thoughts. --- One of the things I've done with the University of Washington and specifically Shailendra Pratap Jain's class is once a quarter I come and lead a lecture/conversation with his class. The general gist of the lecture is how marketing + retail is evolving digitally. 7, 8, 9 years ago, or whenever the heck I did it first - the conversation was obviously very different than it is now. Now, everyone wants to talk about AI. And I posed a question that I posed here some time ago. On one side of the other room - there is what I call "The Jaded Capitalists". They believe that the ChatGPT, Perplexity of the worlds aren't going to fundamentally alter the shopping experience... we'll just have another huge player in the advertising space - similarly to Google Meta and Apple before them. On the other side of the room - there is what I call "The Agentic Futurists". Shopping will be forever altered. I will have an agent, you will have an agent, and all the brands will have an agents - and we will shop without even being involved as our agentic shoppers curry favors amongst brands and marketplaces to find the best items at the best prices. And I asked these Technical MBA students to pick a side. And a CRAZY thing happened: It was right. down. the middle. About 50% on one side, 50% on the other. I'm going to tag some of the students in this post and would love for them to chime in with their POV - but would love to hear from all of you as well. How is shopping going to change in an AI dominated universe, and why? Rajesh Turlapati Brenton Mizell David Crow Shirin Hallaji Kovid G. Arlo M. Jagadish Kulkarni Thi A. Van Jiaqi Liu Lucy (Melbinger) Grubbe

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Meta's new magic number is 49%. First it wrote a $14B check for 49% of Scale AI, poaching CEO Alex Wang. Now the same playbook hits venture land: Meta is offering to buy up to 49% of Nat Friedman & Daniel Gross's $1.1B NFDG fund - and hiring the duo to run its shiny new AI lab - letting early LPs cash out at a 4x markup while still calling the fund "independent". Antitrust alarms? Snoozing. All thanks to that cute little missing 1%.

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