Explore AI product economics with Google AI's Vikas Kansal. Learn how GPU costs and unit economics are changing the software playbook for Gemini 3.1 and NotebookLM.
The traditional software playbook is breaking because the marginal cost of a free user is no longer zero; every interaction burns cash because those GPUs are firing every single time a user hits 'Enter.' To survive, you have to move beyond gating features and start gating the intensity of use and the outcomes provided.
Summarize this
Vikas Kansal is a leader in product management for Google AI, where he oversees some of the most successful consumer subscription bundles in history. His work involves managing high-profile AI tools such as Gemini 3.1, NotebookLM, and Veo3. In this discussion, he provides a personal account of the unique challenges faced when building and scaling massive AI products while balancing user experience with the literal reality of infrastructure demands.
In the traditional software playbook, serving additional users cost essentially nothing once the code was built. However, the new world of AI product management is different because every user interaction burns cash. Every time a user hits enter, GPUs fire and electricity meters spin, meaning that serving AI models has a significant and literal cost. This shift forces product leaders to rethink unit economics to avoid bankrupting the business while trying to scale.
Vikas Kansal points out a paradox where the free versions of AI tools are becoming exceptionally powerful, often outperforming humans at tasks like writing Python code or drafting marketing copy. This creates a tension for product managers who want to provide a magical experience to hook users without incurring unsustainable costs. Balancing the high value of these free tools against the expensive GPU power required to run them is a central challenge in modern AI business models.
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