In this exclusive edition of Counterpoint Conversations, Anshuman Saxena, Product Manager at Qualcomm, sits with Murtuza Ali, Senior Analyst at Counterpoint Research, to unpack the evolving landscape of ADAS and autonomous driving. The discussion highlights the strategic and technical foundations of Qualcomm’s approach to autonomy. Saxena details how Qualcomm’s Snapdragon RIDE platform is enabling this transformation by providing not just high-performance compute, but an integrated solution built around three critical pillars – hardware (SoC), data and simulation infrastructure, and AI software stacks.

The Interview

Key Takeaways from the Discussion:

From L2+ to L3 and L4: A Phased Approach

While L2+ systems currently support partial automation on highways, Qualcomm is actively working with OEMs to deploy L3 capabilities in controlled environments such as freeways, before scaling up to more complex urban scenarios with L4 capabilities. He emphasizes that each stage of automation must demonstrate clear value to both consumers and automakers, and Qualcomm's platforms are designed with this phased transition in mind.

Snapdragon RIDE Elite and Accelerated Adoption

Several leading automakers, including Mercedes and Li Auto, have already begun integrating Qualcomm’s Snapdragon RIDE Elite platform, which was announced in 2024, into their vehicle architectures. The commercial deployments could begin as early as next year – a rapid turnaround made possible by Qualcomm’s existing software ecosystem and cross-generational hardware compatibility.

Not Just Hardware: A Full-stack Platform Strategy

One of the core takeaways is Qualcomm’s shift from being a hardware provider to delivering full-stack, end-to-end automotive solutions. Automakers can choose to adopt just the compute layer or leverage Qualcomm’s support for data collection, pipeline automation, and AI stack development. This modular yet integrated approach enables OEMs at different stages of readiness to adopt autonomous capabilities efficiently.

Building Guardrails for AI-driven Autonomy

With the rise of end-to-end AI and foundation models in driving stacks, safety guardrails are of critical importance. These guardrails are implemented to ensure that AI-driven decisions in autonomous vehicles are explainable, robust, and compliant with formal safety protocols. Without these guardrails, even advanced architectures risk becoming black boxes – unacceptable in systems where safety is paramount.

Cloud-based Validation and the Role of Hyperscalers

Saxena also highlights Qualcomm’s strategic collaboration with hyperscalers like AWS to create cloud-based development environments. Through digital twins, virtual ECUs, and simulation platforms hosted in the cloud, Qualcomm enables its customers to test and validate driving stacks across millions of virtual miles – dramatically reducing development time and cost. This virtual workbench is central to Qualcomm’s goal of supporting fast, safe, and scalable deployment of autonomous systems.

Analyst Take:

The conversation presents a compelling picture of how Qualcomm is redefining its role in the automotive industry:

• Qualcomm’s pivot to a platform-centric model is resonating strongly with OEMs who are increasingly looking for integrated hardware-software solutions. The modularity of Snapdragon RIDE enables flexible adoption while maintaining high integration value.

• The emphasis on data and simulation pipelines is a competitive differentiator. While many chipmakers offer compute platforms, few provide robust infrastructure for end-to-end AI development, including data curation, validation, and simulation at scale.

• Safety-first innovation is more than a regulatory checkbox—it is embedded in Qualcomm’s technical stack. The guardrail concept demonstrates Qualcomm’s recognition that black-box AI cannot meet the trust thresholds required for consumer AV adoption.

• Time-to-market is a key advantage. By leveraging existing software stacks and supporting virtual validation via cloud environments, Qualcomm enables OEMs to compress traditional development timelines, which can often span five to seven years, into more agile, iterative cycles.

• Strategic partnerships with hyperscalers like AWS further amplify Qualcomm’s offering, allowing them to build cloud-native automotive development ecosystems – a crucial factor as the auto industry converges with software-defined development paradigms.

Watch the full Counterpoint conversation to learn more about how Qualcomm’s innovations are shaping the future of mobility and intelligent transport systems.