By Michael Entner-Gómez | Digital Transformation Officer | Entner Consulting Group, LLC.
The automotive industry is undergoing a seismic shift, driven by advancements in Software-Defined Vehicles (SDVs), Electric Vehicles (EVs), and cutting-edge technologies. Last year (2022), I published a popular series of LinkedIn articles with predictions about the automotive industry. Let’s go back and review where we stand with regard to those predictions.
The Rise of SDVs and EVs
The Backbone of SDVs: RTOS and BlackBerry QNX
Prediction: The importance of RTOS in SDVs was emphasized, with BlackBerry QNX expected to lead the market.
Outcome: This prediction was accurate. BlackBerry QNX has significantly expanded its reach, now embedded in over 235 million vehicles, solidifying its leadership in the SDV (Software-Defined Vehicle) sector. Despite this impressive market penetration, BlackBerry continues to face profitability challenges. With a market capitalization of $2.2 billion, it remains a potential target for acquisition. This situation reflects the company's strong market presence but also highlights its financial vulnerabilities in a competitive industry landscape.
Cloud-Native Architecture: The Next Automotive Revolution
Prediction: A shift towards cloud-native architecture in automotive was advocated.
Outcome: The automotive software industry is witnessing a significant shift towards cloud-native environments, a transition greatly influenced by AWS. This move signals the industry's acceptance of advanced cloud-based technologies. While most Original Equipment Manufacturers (OEMs) and Tier-1 companies are actively exploring cloud-native applications, there's a general lack of deep understanding in this area. This gap indicates both an opportunity for growth and a challenge in fully leveraging the potential of cloud-native solutions in automotive software development.
OEMs: Navigating Holistic SDV Solutions
Prediction: OEMs were expected to struggle with the transition to software-defined vehicles.
Outcome: This prediction has been validated. Currently, the Software-Defined Vehicle (SDV) sector lacks a cohesive, fully articulated architecture. Existing efforts, mostly by consortiums, tackle only fragments of the issue without a unified vision that addresses all key aspects of SDV. Entner Consulting Group aims to bridge this gap with its AutoEDGE v1.0, positioning it as a 'north star' in the industry. This initiative is set to provide much-needed guidance and structure in the SDV landscape, offering a comprehensive framework that the industry has been missing.
The Global Market Dynamics
EV Market: The Rise of Chinese and Indian Manufacturers
Prediction: Chinese and Indian manufacturers were predicted to dominate the EV market.
Outcome: This prediction has come true. Chinese electric vehicle (EV) manufacturers are exceeding sales expectations, demonstrating substantial growth and an increasingly dominant presence in the global EV market, as reported by several sources. Conversely, India seems to be missing a crucial opportunity to expand beyond its domestic market. The potential for India to excel in producing robust, low-cost vehicles is significant, yet this potential remains largely untapped. This contrast underscores the dynamic nature of the EV industry, where some are capitalizing on market opportunities while others are yet to fully seize their growth prospects.
AWS: A New Player in Automotive
Prediction: AWS was predicted to dominate in areas like data management and AI in automotive.
Outcome: AWS has emerged as a significant force in the automotive industry, offering a wide array of services tailored for automotive applications. Their growing influence in this sector is notable. However, there's a concern that AWS may be losing momentum due to its strategy of attempting to displace rather than collaborate with smaller, innovative players. This approach is a common challenge for AWS. To maintain and enhance its market position, AWS could benefit from embracing and supporting these innovative entities, fostering a more collaborative ecosystem rather than overshadowing these crucial players in the automotive space.
Innovation and Technology Integration
Tier-1 Companies in Automotive Software Market
Prediction: Tier-1 companies were not expected to dominate the holistic SDV software market.
Outcome: Partially confirmed. The automotive software market is evolving, emphasizing collaboration over the traditional dominance of Tier-1 companies, as noted by multiple consulting firms. Aptiv's acquisition of Wind River illustrates this trend but raises concerns. Wind River, while operating independently, may not fully integrate its valuable assets into Aptiv's broader automotive focus. This could limit the potential synergistic impact in solutioning. Meanwhile, Continental's CAEdge platform seems to have hit a roadblock with Andrea Ketzer's move to AWS. Despite ongoing collaboration with AWS, the synergy and competitive dynamics are unclear, with potential overlaps in their market ambitions. Other Tier-1 companies are also moving in this direction, but none have shown noteworthy developments.
Open Source, AI, and Data Management
The Debate Over Open Source in Automotive Systems
Prediction: There will be great debate around the use of open source software in mission-critical automotive systems.
Outcome: The automotive industry is still assessing the advantages and challenges of integrating open source software into critical systems. With various players in this arena, it's questionable whether relying on commercially available software versions is cost-effective, as the expenditure could be substantial. A more strategic approach for OEMs, Tier-1s, and related companies might be to develop and support their own branded software releases. Alternately, securing significant discounts with commercial vendors like RedHat and Canonical could be viable. The landscape is evolving rapidly, and it's anticipated that open source software will eventually constitute a major portion, possibly up to 90%, of automotive software.
AI: Transforming Automotive Data Management
Prediction: AI was predicted to be crucial in managing data from multiple sources in SDVs.
Outcome: No validating outcome yet. The role of artificial intelligence (AI) in data management within Software-Defined Vehicles (SDVs) is still under development, with well-defined AI use cases yet to emerge. The industry is currently grappling with "AI fatigue," struggling to stay focused on actionable AI applications. Despite this uncertainty, the potential impact of AI in this field is significant. As developments continue, it remains to be seen which companies or technologies will lead in this space. I am actively involved in this area and will provide updates as the landscape of AI in SDVs progresses.
The Challenge of Creating Digital Twins
Prediction: The creation of digital twins was expected to be a high-cost endeavor but essential for advanced automotive systems.
Outcome: No validating outcome yet. The automotive industry's advancement in developing and utilizing digital twins (DT) is an ongoing process. While we seem to have moved past the initial hype surrounding DT, the industry has yet to effectively envision and tackle revolutionary use cases. Cost remains a significant barrier, and the sophisticated models required for automotive simulations are either still under development or not yet available for implementation. This indicates a phase of transition and exploration in the practical application of digital twins in the automotive sector.
Data Monetization: A Complex Task for OEMs
Prediction: OEMs were advised to leave data monetization to experts due to its complexity.
Outcome: No validating outcome yet. OEMs' approach to data monetization is a developing area, but they are facing backlash due to mishandling of data. This highlights their inexperience in this domain and suggests that their initial visions for monetizing data may not be feasible as planned. A more viable strategy could be to collaborate with external entities specialized in data management outside the automotive sector, allowing OEMs to benefit financially without directly handling the data. This approach could offer a more effective and less controversial path to data monetization.
Infrastructure, Pricing, and Autonomous Technology
The Business of EV Charging Infrastructure
Prediction: Charging infrastructure for EVs was predicted to become a significant business.
Outcome: No validating outcome yet. The EV charging infrastructure sector is experiencing rapid shifts, with varying investment levels and government support. Apart from Tesla, few are effectively monetizing this area, leading to a widespread inadequacy in infrastructure. A slowdown in the development of EV charging facilities could negatively impact related businesses, as evidenced by the emerging trend of equipment discounts for businesses and consumers. This situation creates a challenging cycle: insufficient charging infrastructure hampers EV sales, and the slow pace of EV adoption, in turn, affects the expansion and profitability of charging infrastructure.
The Need for Reduced EV Prices
Prediction: A reduction in EV prices was emphasized for widespread adoption.
Outcome: No validating outcome yet. The trend in EV pricing and its impact on adoption rates remains a key area of focus. If the prices match ICE we may see a tipping point for increased sales. Otherwise I believe EV sales will slow, although they will grow in market share over the long run. Huge discounts and subsidies will help move the needle, but growing economic concerns are weighing heavily on this sector.
Autonomous Vehicles: A Slow Progress
Prediction: The progress towards fully autonomous vehicles was expected to be slower than anticipated.
Outcome: The correlation between electric vehicle (EV) pricing and adoption rates is crucial. A key turning point could be reached if EV prices become competitive with internal combustion engine (ICE) vehicles, potentially accelerating sales. However, if prices remain high, the growth in EV sales might slow, even though market share is expected to increase over time. Significant discounts and subsidies could stimulate sales, but the sector is currently facing challenges due to broader economic concerns, which might affect consumer purchasing decisions and the overall pace of EV adoption.
The Industry's Missed Opportunities and Future Pathways
Missed Opportunities in In-Vehicle Computing
Prediction: Major tech companies like Intel and AMD were criticized for missing out on in-vehicle high-performance computing.
Outcome: No validating outcome yet. The roles of tech companies in the realm of ARM and RISC-V architectures are still in a state of evolution. However, there is a noticeable acceleration in the development and deployment of both ARM and RISC-V technologies. This trend indicates a growing interest and investment in these architectures, suggesting a shift towards more advanced and efficient processing solutions in the automotive industry. Still nothing from Intel or AMD.
The CI/CD Pipeline Dream in Automotive
Prediction: A fully integrated CI/CD pipeline in automotive was seen as a challenge.
Outcome: No validating outcome yet. The integration of Continuous Integration/Continuous Deployment (CI/CD) pipelines in the automotive sector is an ongoing and complex process. The current landscape features highly fragmented pipelines. Specialty solutions in this area are often overpriced and incomplete, which hinders widespread adoption, especially for a comprehensive Software-Defined Vehicle (SDV) solution. Progress in this area is likely, but it requires resilience and commitment, as it's not an endeavor for the faint-hearted. The path to streamlined and effective CI/CD pipelines in automotive remains a challenging but necessary pursuit.
Current State of SDV Efforts
Prediction: Most OEMs and Tier-1s were taking a car-centric approach to SDV development.
Outcome: No validating outcome yet. The strategy for Software-Defined Vehicle (SDV) development is continually evolving, with the industry increasingly acknowledging the need for integrated approaches. However, there's still a predominant focus on the car-centric aspect of SDVs. This approach often overlooks the broader, holistic requirements that extend beyond in-vehicle systems, crucial for the comprehensive development and implementation of SDVs. Addressing these non-vehicle aspects is essential to fully realize the potential of SDVs.
Conclusion: Reflecting on Predictions and Reality
As we work through these predictions and their real-world outcomes, it's evident that the automotive industry's landscape is rapidly evolving. The insights offered in my original posts have largely materialized, underscoring the value of foresight in this fast-paced sector. This journey through predictions and outcomes invites a broader dialogue among industry experts, companies, and enthusiasts. Together, we can continue to shape the future of mobility and automotive technology.