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.

656

91 Comments

🎙️ 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

25

4 Comments

Thrilled to announce that Together Fund is investing in Sentra, alongside a16z speedrun! You track results in Jira. Decisions in Notion. Conversations in Slack. But the reasoning, the debates, trade-offs, and context behind why you chose A over B, disappears into what we call "Dark Matter." A decision made in March looks insane by July because no one remembers the constraints that made it smart. I lived this firsthand at Twitter scaling from 800 to 8,000 employees, and at Google while launching AI Overviews to billions at planet scale. The problem isn't process. Process is compensation for something deeper: organizational amnesia. An organization’s "Systems of Record" doesn’t solve this, they encode it. They store what happened, never why. That's why we are investing in Sentra. Sentra is the always-on collective memory that eliminates organizational amnesia by maintaining accurate context for all members and agents, functioning as an operational nervous system. It connects to every channel where work happens, meetings, Slack, email, code commits, docs, calendars, and treats them not as artifacts to search, but as living signals to synthesize. The fleeting and the permanent, unified into a memory that understands. The founding team is built for this: - Jae Gwan Park (CEO): Product-first founder, memory systems research at UofT and MIT - Ashwin Gopinath (CSO): Former MIT professor, created "Reflexion" (NeurIPS 2023), agents that learn from mistakes, 2x founder - Andrey Starenky (CTO): Early Vapi engineer, ex-IBM, built to process enterprise-scale data firehose Together is an operator-led fund. We invest in problems we've lived. This is one of them. Many congrats Jae, Ashwin and Andrey, we are so excited to partner with you! Read the full thesis: https://lnkd.in/gixj9cE4 Book a demo: https://www.sentra.app/ #OrganizationalMemory #AI #Sentra #TogetherFund #a16z #ContextGraphs

71

3 Comments

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.

80

6 Comments

Seattle VC panels are not exactly known for being spicy, which is why my AMA with Brooke and Rolanda at AI House last night was so refreshing. We got into the questions founders are too embarrassed to ask out loud. How do VCs actually decide behind closed doors? What counts as traction when you have zero revenue? Is enterprise SaaS dead? Do Seattle founders actually have a shot against the Bay Area? A few takeaways that stood out: ✅ Associates can't get to yes without a partner sponsor — but partners often can't reach deals without associate diligence. Arm them with the facts to fight for you internally. ✅ Expect 50-70 investor conversations before a term sheet. Send your deck a day early but assume nobody read it 😎 ✅ Enterprise SaaS isn't dead, but it's shifting from seat-based to usage and outcome-based pricing. Large orgs still need governed software. ✅ Traction isn't just revenue. How excited are your customers? Would they advocate for you? Paying pilots with automatic, KPI-specific conversion > free trials, every time. ✅ Learning slope > starting background. The strongest founders they've backed adapted fast — not started perfect. Brooke and Rolanda WILL take a second meeting even if you whiff the first one! ✅ Seattle has fantastic enterprise buyers and founders with sales relationships into them. But the equity-vs-cash culture gap is real, and hiring against big tech is still hard. What made it different was how candid Brooke and Rolanda were about what actually happens after your pitch ends, aka the internal deal dynamics most founders never see. Shoutout to the room full of 100+ early-stage founders who went against the Seattle grain and asked hard questions live to the VCs. That energy is what makes AI House work. Huge thanks to Brooke B. (FUSE) and Rolanda Fu (Madrona) for keeping it real! More AMAs like this coming. Tag a founder who we should invite for the next one! #AIHouse (photo credit to Jonathan G. Blanco 🛠)

184

10 Comments

There are plenty of AI note takers doing simple recaps, but if your commercial team is processing high call volumes weekly, transcripts sitting in folders don't help much. Claap just released a significant product update that lets you mine thousands of conversations in three clicks. They let you interrogate all your conversations through an intuitive interface, extracting patterns, insights, and actionable intelligence from what was previously an unwieldy archive. You can see how power users are already finding creative applications through the template library below. While most B2B apps are trying to build data walls around their applications, I'm excited to see Claap take the opposite approach. They organize your sales conversations and data to fuel intelligent workflows across your different applications and jobs, wherever commercial insights add value. Next week, they're launching their MCP integration to make these datasets actionable by any AI agent outside Claap's platform... More to come! Robin Bonduelle Pierre Touzeau Thomas Hernandez https://lnkd.in/dVrUwPUW

19

1 Comment

Venture funding isn't success of course, but it's nearly impossible to win big markets without it. Unlike the AI hype, Ando is very, very real. And unlike others, Ando is here to make lives genuinely better for everyone. (*) #proudinvestor #micdrop #oversubscribed Big thx to Bradford Oberwager and James Kairos, Ben Metcalfe, Andy Mutz, Joseph Chittenden-Veal, Scott Sanders, Massy Ghausi, John Lyman, Thomas Wesley, John Holder… and of course Phoenix Toews and Paul Wellons (to name a few) — (*) Yeah yeah, some pencil-pusher will use AI to over-optimize profits over people, but as American Airlines is learning the hard way (WSJ: https://lnkd.in/ePz_QNzt ), net of competition, "people businesses" don't work that way. You HAVE to care for people or you lose. Ando plays a beautiful collaborative role in that dialogue, helping profit-focused management more clearly see the effects of a motivated labor force. That's why I'm truly #proudinvestor --- Posts co-authored with my LinkedIn secret weapon Dennis Buckley

23

4 Comments

Even the most skeptical voices are starting to acknowledge the value of LLM generated code. Not as a replacement for deep expertise, but as a practical tool for acceleration, exploration, and iteration. When someone as famously disciplined about code quality as Linus Torvalds publicly acknowledges that AI generated code can be useful, it signals a shift worth paying attention to. The takeaway is not that AI writes better software than humans, but that good engineers are learning where and how these tools create leverage. https://lnkd.in/eFbNFqYN

6

Build AI like people matter, or you will build precarity at scale. Chávez’s reminder cuts through metrics and margins: the point of labor rights is people. If algorithms set the pace, assign routes, and rate work, justice depends on how we train, audit, and contest them. People-first means workers define success before code ships, and safety anchors optimization. Bias and burden audits track overtime spikes, heat exposure, and missed breaks, and automatically throttle when thresholds are tripped. Guarantee explanation and appeal that are fast, low-friction, and safe from retaliation, then share gains through bonuses and schedule flexibility. At Global Orbital Imaging AI (GOiAI), field MLOps treated development as labor: forward nodes ran INT8 shadow models on ARM NPUs to A/B score proposed routing and shift policies; workers submitted tap and voice critiques that auto-tagged errors; the edge packaged 160 KB model-card diffs, threshold proposals, and error tiles over a 0.8 Mbps SATCOM link to ATAK/CoT, updating the battalion COP in about 25 seconds so stewards and S2/S3 could approve or roll back with signed rationale. Labor Day should feel like reliability: predictable hours, safe shifts, and decision systems that respect human judgment. Reliability is engineered by contracts, councils, audits, and the courage to pause a rollout when harm shows up. Build like you believe it. Two-step challenge: name one distribution gap you will address this week, then post your first step in the comments for accountability. Optionally tag a teammate and add a 7-day check-in. #GhostSignal #GOAIST8 #Innovation #AI #EdgeAI #Leadership #ProductManagement

2