As the operational cost of autonomous vehicles plummets, the business model will shift from fare-based revenue to advertising. By leveraging user data and AI like Grok, the car becomes a platform for hyper-targeted ads and commerce recommendations. This could eventually make rides free for consumers willing to engage with advertisers.

The biggest hurdle for enterprise AI adoption is uncertainty. A dedicated "lab" environment allows brands to experiment safely with partners like Microsoft. This lets them pressure-test AI applications, fine-tune models on their data, and build confidence before deploying at scale, addressing fears of losing control over data and brand voice.

Despite their power, premium offers are a poor starting point for new ventures without established credibility. Use free or discounted 'foot-in-the-door' offers to prove your value and build a reputation, then transition to a premium model. This approach de-risks customer acquisition when you're an unknown entity.

Traditional SaaS companies are trapped by their per-seat pricing model. Their own AI agents, if successful, would reduce the number of human seats needed, cannibalizing their core revenue. AI-native startups exploit this by using value-based pricing (e.g., tasks completed), aligning their success with customer automation goals.

AI agent platforms are typically priced by usage, not seats, making initial costs low. Instead of a top-down mandate for one tool, leaders should encourage teams to expense and experiment with several options. The best solution for the team will emerge organically through use.

To land its first skeptical customers like Drada, Merge offered its platform for free for two months without a contract. This de-risked the decision for the customer and allowed Merge to prove its product's value and the team's responsiveness before asking for a financial commitment.

Instead of ad-hoc pilots, structure them to quantify value across three pillars: incremental revenue (e.g., reduced churn), tangible cost savings (e.g., FTE reduction), and opportunity costs (e.g., freed-up productivity). This builds a solid, co-created business case for monetization.

Standard SaaS pricing fails for agentic products because high usage becomes a cost center. Avoid the trap of profiting from non-use. Instead, implement a hybrid model with a fixed base and usage-based overages, or, ideally, tie pricing directly to measurable outcomes generated by the AI.

The "DoorDash Problem" posits that AI agents could reduce service platforms like Uber and Airbnb to mere commodity providers. By abstracting away the user interface, agents eliminate crucial revenue streams like ads, loyalty programs, and upsells. This shifts the customer relationship to the AI, eroding the core business model of the App Store economy's biggest winners.