Shanea Leven

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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Quality beats brute-force slop. Edwin Chen built data-labeling company Surge AI into a $1B+ business without raising a dollar. It outpaces its closest competitor, Scale AI, in revenue, and he did it by obsessing over quality, not scale. In a rare interview, he shared his experience trying to build a sentiment analysis model while at Twitter. The first labelers they hired couldn't grok the slang, the cultural subtext of the tweets. The output sucked. That’s when it clicked: better data beats more data. As AI gets cheaper and easier to ship, the delta between mediocre and great will widen fast. And quality will be the only thing left worth betting on.

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Excited to back Lightpanda, the first browser designed specifically for machines and AI. While most of the web is still built for humans, the next wave of innovation will be powered by intelligent agents. Lightpanda removes the graphical layer and gives full control over the automation stack—unlocking speed, efficiency, and native orchestration for AI workflows. It’s a strong alignment with DataDome's mission to help secure the evolving internet—by anticipating how it’s being used, and by whom. https://lnkd.in/ebTvugiM

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Reading Metronome’s Monetization Operating Model, I kept coming back to one idea: pricing has become product. Software now delivers outcomes, not access. Yet most companies still charge as if they’re selling seats or licenses. That disconnect creates friction: for customers, unpredictability; for companies, stalled growth. The paper’s argument is simple but sharp—monetization isn’t a late-stage decision. It’s strategic infrastructure. Pricing needs the same ownership and iteration as any feature. Treat it like a surface that customers touch, not a spreadsheet buried in finance. If value is continuous and dynamic, pricing must be as well. That means product, GTM, finance, and engineering working from one system of truth. How many of us still treat pricing as an afterthought—when it should be a growth engine? https://lnkd.in/gnH7WzYf #Monetization #ProductStrategy #AI

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A useful discussion of the differences between co-pilots, agents and AI workflows. From Jack Mu at Paragon. Clarity leads to better design, and to better pricing. https://lnkd.in/g_Xkf5AT? #AI #agents #copilots #workflows

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My two cents on the Claude Squeeze Application layer AI companies being built right now will not survive if they incorporate human in the loop as a core part of how work gets done. If your agent requires a human to approve the output, you’re building a better UI for Claude. If your agent helps an accountant do more accounting or a lawyer do more lawyer-ing, you might have a business, but companies won’t pay a lot for it. It’s not that Claude is going to steal your customers. It’s that the delta between what you’re offering and what someone gets from just using Claude directly is getting smaller every month. The complexity you’re solving for, better UX, smarter prompts, cleaner folder structures, is real, but it’s not worth a big check. And buyers are figuring that out. I’m already seeing it. A real estate brokerage looking at AI underwriting tools realized they can dump a rent roll straight into Claude and get 80% of the output for free. They’ll pay a little more for a better interface, but not a lot more. If you’re not doing the job end-to-end, your value is the productivity layer. And Claude is coming for the productivity layer. Hot take: I don’t think Harvey would get funded in 2026. They were funded when they were because the models weren’t advanced enough to reveal how thoroughly they’d eat the application layer. Harvey may survive, they’ve built real brand and enterprise relationships. But with that exact pitch today I think you’d struggle. Here’s what I think survives: Agents that own the outcome. They have to clear every one of these bars: 1. Fully autonomous. No human in the loop. The agent does the job, not assists with it. 2. Accountable for outcomes. Not activity. Results, and the agent is measured on that basis. 3. Mission-critical metrics. The outcomes it owns show up in the Monday morning operating review. The numbers the CEO reports. Going back to Harvey, if they said we’ll just do your legal work for you, end-to-end, accountable to outcomes, that’d be a defensible business in 2026. But notice what it is: outsourced labor with AI as the delivery mechanism. Not a productivity tool. A law firm that doesn’t charge law firm rates. Most AI companies won’t make that leap because it means owning outcomes. It’s hard. That’s what we’re building at Uniti. If you can autonomously run the entire leasing funnel for a property manager and own their rentals-per-month and conversion rate, you’re not a software vendor. Then do the same for all their workflows. You’re outsourced labor. Completely different moat. The mistake is selling the tool when they should be doing the job. If there’s a human in the loop, the customer is paying for the human’s time and your software. As models get better, the human becomes the cost center and your product becomes the overhead. The comparison isn’t your tool vs. Claude. It’s your agent vs. a full-time hire. That’s a different conversation, and a new category.

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Incredibly excited to announce our acquisition of Runebook (acq. by Keycard), and to share more about the work we’re already doing with customers to deploy production-ready MCP agents and tools into production. When we met with Peter Cho and Matte Noble earlier this year at MCP Night, it was immediately clear they were thinking about the future of computing in a special way. Like us, they saw MCP as the fulcrum for bringing the advances of LLMs into the systems that power modern life, but also understood that it was too difficult to adopt and challenging to secure. So when the opportunity came to bring them into the fold, it was an easy decision. They’ve been working to make MCP easier to adopt, and with their experience building beloved developer ecosystems at Heroku and Sentry, they’ll expand the work we’re already doing to let developers build trusted agentic applications in minutes — not days or weeks. You can’t become agent-native without enabling your developers to build, your employees to adopt, and your security teams to govern with full observability and policy enforcement out of the box. We’re helping customers do exactly that today. More in the blog post, linked in the comments below 👇

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