The Weekly Dispatch

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Michael Bernzweig, Founder, Software Oasis

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This edition looks at three silent breakers in the AI era: pricing, people data, and product validation. Lava’s Mitchell Jones shows why flat seat pricing collapses when a few agents or heavy users drive most of your compute costs—and how usage-native billing keeps margins and value aligned.

Illoominus founder Noelle London tackles the HR side, turning scattered people systems into an AI-ready intelligence layer so leaders answer talent questions in minutes, not weeks.

Esfera Studio’s Ricardo Tosatto then reimagines prototyping, using AI to jump from pixels to functional flows that expose real risks and opportunities earlier.

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Rethinking SaaS Pricing for the AI Era with Lava

Mitchell Jones, founder and CEO of Lava, is building next‑generation payments infrastructure so AI‑native products and agents can be priced on real usage, protect margins, and handle both getting paid and paying for services—something legacy SaaS billing and payment stacks were never designed to do. AI introduces significant, usage‑driven costs that can’t be safely averaged away under a flat seat model, especially when a handful of heavy users or agents consume far more compute than others. At the same time, seat‑based pricing stops reflecting real value as agents replace and augment human users: the better the automation gets, the fewer seats are needed, leaving revenue out of sync with outcomes.

Making People Data AI-Ready with Illoominus

Noelle London, founder and CEO of Illoominus, helps mid‑market organizations turn scattered HR systems and messy people data into a unified, AI‑ready employee intelligence layer—so leaders can answer strategic questions about talent, performance, and retention in minutes instead of waiting weeks for one‑off reports. In many mid‑market organizations, simple questions about headcount, tenure, performance, or attrition force HR leaders to file IT tickets and wait weeks for custom queries, by which time the data is already stale. Illoominus replaces that pattern with a self‑serve layer over existing HR systems, so people leaders can explore trends—like “Are high performers leaving faster in certain teams?”—without going through a data‑science bottleneck.

The New Era of AI-Accelerated Prototyping with Esfera Studio

Ricardo Tosatto, COO of Esfera Studio, is helping founders move from pixel‑perfect mockups to AI‑accelerated, functional prototypes—so product teams can validate real user flows in days, not months, and free designers to solve user problems instead of pushing pixels. In the traditional model, teams invest heavily in high‑fidelity Figma flows—button states, design systems, pixel‑perfect layouts—only to discover feasibility and cost issues once development begins. Ricardo highlights how AI‑powered tooling lets you jump more quickly to working MVPs, replacing static simulations with live, data‑connected prototypes that can be put in front of users, investors, and stakeholders early.

Across revenue, talent, and product, the pattern is the same: static abstractions are breaking. Teams that price on real usage, see their workforce clearly, and test real interactions instead of pretty mockups will adapt faster as AI rewrites costs and expectations.

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💡 Final Thoughts

The next edition lands Tuesday, diving deeper into how to retrofit pricing, people analytics, and product workflows for an AI-native landscape.

P.S. Reply with the one place—pricing, people data, or prototyping—where guesswork still has the biggest say in your roadmap.

Best regards,

Michael Bernzweig
Founder, Software Oasis
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