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NewsMaterial2 min readDisconfirmAI Editorial

Bardia Shahali, VP of Sales at Granola: Why AI Infrastructure and Application Layers Require Different Sales Organizations

Bardia Shahali, who built the sales team at Fireworks AI (growing its revenue from approximately $3 million to $400 million) and currently serves as Vice President of Sales at Granola, explained in Tomasz Tunguz's Office Hours program the differences in sales organizations across two tiers of the AI industry. The infrastructure layer features single deals reaching tens of millions of dollars with extremely short cycles but intense competition, requiring FDEs and SEs to partner with AEs to deliver highly complex products. In contrast, the application layer involves AEs and CSMs delivering software similar to previous-generation SaaS, but with faster adoption and expansion. Because model capabilities reach new heights approximately every 45 days, Fireworks has shifted its incentive plan from annual to quarterly.

2026-10-22 08:00 GMT+8

The AI industry comprises two markets: infrastructure and applications. Bardia Shahali, Vice President of Sales at Granola, discussed the differences in building sales organizations for these two markets on Tomasz Tunguz's Office Hours program. He has built teams at both levels—first constructing the sales team at Fireworks AI, where he grew revenue from roughly $3 million to $400 million, and now leading application-layer sales at Granola.

At the infrastructure layer, deals can be very large and close very quickly. Shahali noted that at Fireworks, customers are AI-native companies growing at extraordinary rates, with individual deals reaching tens of millions of dollars; an eight-figure contract can be closed within two weeks. However, the cost is fierce competition—every inference service provider chases the same clients, with higher stakes and denser competitors. In this environment, the infrastructure layer requires a different team configuration: FDEs (forward-deployed engineers) and SEs (sales engineers) must work alongside AEs (account executives) because they are delivering a highly complex product.

The logic differs at the application layer. Granola builds software products for end users, and its sales structure resembles previous-generation SaaS: AEs collaborate with CSMs (customer success managers) to deliver solutions, but productivity, adoption, and expansion speeds are typically higher and faster.

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This dynamic environment imposes new requirements on organizational management. Shahali mentioned that Fireworks previously set sales incentive plans annually but has now switched to quarterly adjustments because product iteration is too rapid—model capabilities reach a new peak approximately every 45 days, causing the competitive landscape to shift quickly. Annual planning cannot keep pace with this rhythm. He also pointed out a common risk: if a sales team is too small, it lacks the capacity for outbound outreach and market expansion.

It should be noted that these observations stem from Shahali’s firsthand experience at two companies and are limited in sample size. The revenue figures for Fireworks AI are self-reported and have not been independently audited; whether Granola’s application-layer sales model represents the broader AI application sector remains to be verified by more practice. Nevertheless, the differences between the infrastructure and application layers in terms of deal size, cycle length, competitive density, and team structure provide a useful analytical framework for understanding how AI companies operate commercially: the same sales methodology cannot be applied to both layers.

Sources

  • tomtunguz.comTomasz Tunguz↗
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