The shift to AI makes multi-sensor arrays (including LiDAR) more valuable. Unlike older rules-based systems where data fusion was complex, AI models benefit directly from more diverse input data. This improves the training of the core driving model, making a multi-sensor approach with increasingly cheap LiDAR more beneficial.

The neural nets powering autonomous vehicles are highly generalizable, with 80-90% of the underlying software being directly applicable to other verticals like trucking. A company's long-term value lies in its scaled driving data and core AI competency, not its initial target market.

Instead of building its own capital-intensive robotaxi fleet, Waive's go-to-market strategy is to sell its autonomous driving stack to major auto manufacturers. This software-centric approach allows them to leverage the scale, distribution, and hardware infrastructure of established OEMs to reach millions of consumers.

By eschewing expensive LiDAR, Tesla lowers production costs, enabling massive fleet deployment. This scale generates exponentially more real-world driving data than competitors like Waymo, creating a data advantage that will likely lead to market dominance in autonomous intelligence.

The AI's ability to handle novel situations isn't just an emergent property of scale. Waive actively trains "world models," which are internal generative simulators. This enables the AI to reason about what might happen next, leading to sophisticated behaviors like nudging into intersections or slowing in fog.

Waive integrates Vision-Language-Action models (VLAs) to create a conversational interface for the car. This allows users to talk to the AI chauffeur ("drive faster") and provides engineers with a powerful introspection tool to ask the system why it made a certain decision, demystifying its reasoning.

Instead of building its own AV tech or committing to one exclusive partner, Lyft is embracing a 'polyamorous' approach by working with multiple AV companies like Waymo, May Mobility, and Baidu. This de-risks their strategy, positioning them as an open platform that can integrate the best technology as it emerges, rather than betting on a single winner.

Waive's core strategy is generalization. By training a single, large AI on diverse global data, vehicles, and sensor sets, they can adapt to new cars and countries in months, not years. This avoids the AV 1.0 pitfall of building bespoke, infrastructure-heavy solutions for each new market.

A key learning from working with auto manufacturers is the desire for brand differentiation through driving personality. Waive can tailor its AI's behavior—from "helpfully assertive" to comfortably cautious—to match a brand's specific identity. This transforms the AI from a utility into a core part of the product experience.