Defining outcome-based pricing

In this episode of Get Paid, we speak to Dave Kellogg about the future of the software industry, with a specific focus on the shift from traditional SaaS models to AI and outcome-based pricing. Kellogg, a seasoned CEO, founder, Executive in Residence at Balderton Capital, and CFO Whisperer, with a background of leadership at Host Analytics (now Planful), MarkLogic, Salesforce.com, and Business Objects, shares his expert insights on the three laws of pricing. He emphasizes that while delivering higher value justifies raising prices, the maximum price is always capped by customer perception of value and the competitive landscape. Together, we explore the inherent challenges and opportunities of implementing outcome-based pricing, particularly the need for predictability, and how creative deal structures, such as including rollover credits and multi-year contracts, can reduce customer friction and increase adoption.

We also talk about the operational implications of this transformation, with particular focus on the complex issue of sales commission structures in an outcome-based environment. Kellogg evaluates the balance between incentivizing initial sales and aligning variable compensation with actual value delivery, suggesting a model that pays part upfront and connects the rest to successful customer outcomes over time. We also explore the changing financial metrics for AI-native companies, where rising computation costs might temporarily reduce gross margins. Kellogg challenges the common focus on margin percentage by highlighting that dollar-value gross profit is the key measure of business value and predicting that efficiency improvements in AI models will eventually lead to healthier gross margins.

Finally, we discuss the major shift in talent acquisition and go-to-market strategies. We analyze the rise of the "Forward Deployed Engineer" (FDE) as a key role in product development, especially for early-stage, AI-driven companies where the product is still being heavily refined based on real-world use cases - a necessity in a platform-focused world. Kellogg recommends that new AI startups focus on hiring a small, elite team of AI-native engineers and seek go-to-market hires with proven experience in navigating competitive, greenfield markets. He often favors those who excel in aggressive, "uncomfortable" sales environments over those who are comfortable only with established market leadership.

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