Jeff Titterton

Just dropped on The Rebooting with Brian Morrissey. We talked about the transition from media’s industrial logic—scarcity, gatekeepers, mass production—to platform logic: abundance, algorithms, iterative optimization. When everyone can publish, the bottleneck shifts upstream to distribution, incentives, and the orchestration layer. One line that stuck: the creator economy can feel like a content casino—millions of dependent suppliers, a few breakout winners, and constant pressure to optimize for what the machine rewards. AI accelerates that dynamic by resetting the floor. “Good enough” gets cheaper and more abundant, fast—which raises the bar for the middle tier and concentrates leverage even more in distribution and incentives. Link in comments.

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Seth Levine nailed it! The concentration problem isn't just a GP pain point... it's choking innovation. When capital pools around the same 20 funds, emerging managers and founders get shut out. Love that he's using his platform to call this out. Foundry has backed 50+ emerging managers. That's the diversification the ecosystem needs. This is exactly why Carta exists — making capital allocation visible and accessible. Capital Evolution hits at exactly the right moment. Thanks for having us Daniel Dart. Team Carta loves the community of FUTURE TITANS you have built 🚀

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I spoke w/ Alex Komoroske for the latest episode of The Slow Hunch. Alex is the co-founder & CEO of Common Tools. Before that he spent a decade at Google (working on the Chrome web platform and Search) and led corporate strategy at Stripe. Our conversation was centered around the concept of emergence: why the most durable systems grow from the bottom up, and what that means for product design, org culture, and the future of technology - especially AI.

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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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