Mobileye Global Inc.

Greetings and welcome to the Mobileye fourth quarter and full year 2025 earnings call. At this time, all participants are on a listen-only mode. A brief question and answer session will follow the formal presentation. If anyone should require operator assistance during the conference, please press star zero on your telephone keypad. As a reminder, this conference is being recorded. It is now my pleasure to introduce your host, Dan Gals. Mr. Gals, please go ahead.

Thank you, Donna. Hello, everyone. Welcome to a mobilized fourth quarter and full year 2025 earnings conference call for the period ending December 27th, 2025. Please note that today's discussion contains forward-looking statements based on the business environment as we currently see it. Such statements involve risks and uncertainties. Please refer to the accompanying press release, which includes additional information on the specific factors that could cause actual results to differ materially. Additionally, on this call, we will refer to both GAAP and non-GAAP figures. A reconciliation of GAAP to non-GAAP financial measures is provided in our posted earnings release. Joining us on the call today, as usual, are Professor Amnon Shashua, Mobilize CEO and President, Moran Shemesh, Mobilize CFO, and Nimrod Nihishtan, Mobilize EVP of Business Development and Strategy. Thanks, and now I'll turn the call over to Amnon. Thank you, Amnon.

Hello, everyone, and thank you for joining our earnings call. As I look back on 2025, there are a number of meaningful positives to highlight, both for our company and the industry. In a very uncertain geopolitical environment, demand for our products came in higher than expected throughout 2025, demonstrating the resilience of the auto industry and our product offerings. Results were quite strong, with revenue up 15%, adjusted operating profit up 45% and operating cash flow up more than 50%. The industry began to clarify the structure and features of the next generation of ADAS for mass market vehicles. Several forces are coming together here. Demand for incremental safety, demand for convenience in the form of highway hands-off driving, and the need to consolidate the technology on a single ECU to keep the system's cost low. Mobileye's IQ6 Hi chip is very well positioned, and we won the first two major programs with two of the biggest fixed OEMs in the world. Waymo's commercialization provided a number of supporting proof points on consumer acceptance and demand for autonomous mobility services. This led to a major uptick in demand signals from transportation network companies and public transport groups, which led to an expansion of expected volume through our Volkswagen ecosystem to 100,000 units by 2033. We are now one year closer to the launch of our advanced products with the Volkswagen Group. We expect the first major public milestone to be removal of the safety drivers in Moya's Robotaxi fleet in 2026. We are implementing a unique first think, slow think structure to our advanced products that we believe accelerates both precision and scalability. This includes novel technologies like vision language semantic action models and artificial community intelligence. And finally, Mobileye took a decisive step to expand its footprint into the humanoid robotics field with acquisition of Menti Robotics. Menti has achieved a fully vertically integrated, low cost, highly capable robot that has a clear path to commercialization into the structured environments of industrial and logistics services fields and with its distinctive technology to cater to unstructured environments like home use cases. Aside from our 2025 results, we detailed all of these areas in my CES talk on January 6th. I encourage anyone with an interest in Mobileye or just physical AI in general to make sure to view that presentation. Turning to guidance, Moran will spend some time on it, but I'll address it briefly. We're encouraged by the volume growth we are expecting despite global auto production that's expected to be flattish again. And while we don't expect the volume levels of Q1 to be sustained throughout the year, it's a strong signal for the year and all the flows for Q1 has been rising for the last month or two. Turning to technology. At CS, I talked a bit about the debate around approach, specifically this concept of data in, commands out, which is a false debate because no legitimate actors in our field are actually doing that. There's always a need for structure and architecture, and everyone's architectures have evolved, given advancements in AI over the last few years, including ours. We introduced two new innovations that are accelerating our path to precision, scalable autonomous vehicles. One is artificial community intelligence, referred to as ACI. This is a simulation concept using a self-play reinforcement learning technique that we're using to train our planning engine, also known as driving policy. This is the first ever productization of a technique proposed in academic research. A strong motivation for ACI is that the sample complexity for planning is much higher than for perception because the multi-agent nature of driving or actions that you take will impact the actions of other world users. Therefore, the amount of data one needs to collect could be unwieldy even for large data collection fleets. As a solution, we have created simulators that can achieve one billion hours of training overnight. Mobileye has unique advantages here since our REM maps, which cover much of the globe, can be used as a realistic and diverse baseline structure for the training. The other advantage is we have developed sophisticated sim-to-real techniques that have the required understanding of the noise model of our perception engine when transferring the driving policy to the real world. That sim-to-real technology is also very relevant to humanoid robotics and will be a key area of technology sharing between Mobileye and Minty. We also introduced a fast-think, slow-think concept that utilizes specialized vision language models to provide contextual information and to address robustness to vehicle decision making. This is not necessarily about safety. It's more about understanding the semantics of complex scenes. For example, a scene where a policeman signals that the road he would like to take is blocked. The safety layer ensures that we won't hit the policeman, but we also need to understand the scene, figure out that we shouldn't try to overtake the policeman, but rather we should either wait or take a different route. This is what slow thinking gives. Since this is not safety critical, the contextual information can be inputted into the system at a lower frequency than perception, which is typically analyzed at 10 frames per second. Structuring our architecture with fast think and slow think components saves compute and even brings use of cloud-based compute into the pictures. As a result, we can put a very sophisticated VLM on the in-car compute, but call on much bigger VLMs in the cloud when the situation warrants. This has very positive effects on the mean time between intervention metrics, but can also eventually lead to scalability benefits in terms of cars per teleoperator, as the VLM can replace a human teleoperator in many cases. Turning briefly to our announced acquisition of Minty Robotics, Most of the AI that humans are using every day is in the digital world. The two main applications of AI in the physical world are autonomous vehicles and robotics. It makes sense for these two expressions of physical AI to be together because there's a great deal of technology overlap. Both extensively use computer vision and control, fast, slow thinking concepts, make heavy use of VLMs, and extensive simple real-life techniques. Menti itself, compared to other companies we evaluated, has a superior combination of strengths, including a high level of vertical integration, a pure AI approach with the ability to demonstrate high-level capabilities with no teleoperation, a design strategy that results in an optimized cost versus usefulness ratio, and above all, a distinctive AI technique to do continuous on-the-job learning from passive demonstrations. a truly practical approach to capitalize on the most near-term industrial and logistics markets, and then expand to more challenging markets over time. We believe access to mobilized tools, simulation, and data training infrastructure will accelerate Minty's development. And the number of technologies developed for robots, such as self-play simulation and sim-to-real techniques, can also bolster mobilized AV development. Finally, There is potential for catalysts as we continue to demonstrate the strong capabilities of the Menti robot and execute on customer proof-of-concept work in the near term. I'll now turn the call over to Moran.