AI Chips for Edge Applications 2026-2036: Technologies, Markets, Forecasts

The global AI chips market for edge devices will exceed US$80 billion by 2036, with the largest applications by market size being automotive and AI smartphones. Artificial Intelligence (AI) is already displaying significant transformative potential across a number of different applications, from fraud detection in high-frequency trading to the use of generative AI as a significant time-saver for the preparation of written documentation, as well as a creative prompt. While the use of semiconductor chips with neural network architectures (these architectures being especially well-equipped in handling machine learning workloads, machine learning being an integral facet to functioning AI) is prevalent within data centers, it is at the edge where significant opportunity for adoption of AI lies. The benefits to end-users of providing a greater array of functionalities to edge devices, as well as, in certain applications, being able to fully outsource human-hours to intelligent systems, is significant. AI has already found its way into the flagship smartphones of the world's leading designers and is set to be rolled out across several different devices, from passenger vehicles to humanoid robots.

 

IDTechEx has published a market report that offers unique independent insights into the global edge AI chip technology landscape and corresponding markets. The report contains a comprehensive analysis of players involved with AI chip design for edge devices, as well as a detailed assessment of technology innovations and market dynamics. The market analysis and forecasts focus on total revenue, with granular forecasts that are segmented by geography (China, Europe, US, and Rest of World) and application (automotive, humanoid robots, AI smartphones, AI laptops, edge sensors for predictive maintenance).

 

The report presents an analysis of data and insights from key players, and it builds on IDTechEx's expertise in the semiconductor, computing and electronics sectors.

 

This research delivers valuable insights for:

 

Computing can be segmented by where computation takes place within the network (i.e. within the cloud or at the edge of the network). This report focuses on specialized chips deployed at the edge for AI and machine learning applications.

 

The differentiation between edge and cloud computing environments is not a trivial one, as each environment has its own requirements and capabilities. An edge computing environment is one in which computations are performed on a device - usually the same device on which the data is created - that is at the edge of the network (and, therefore, close to the user). This contrasts with cloud or data center computing, which is at the center of the network. Such edge devices include cars, cameras, laptops, mobile phones, autonomous vehicles, etc. Computation is carried out close to the user, at the edge of the network where the data is located. Given this definition of edge computing, edge AI is therefore the deployment of AI applications at the edge of the network. The benefits of running AI applications on edge devices include not having to send data back and forth between the cloud and the edge device to carry out the computation; as such, edge devices running AI algorithms can make decisions quickly without needing a connection to the internet or the cloud. Given that many edge devices run on a power cell, AI chips used for such edge devices need to have lower power consumption than within data centers, in order to be able to run effectively on these devices. This results in typically simpler algorithms being deployed, that don't require as much power.

Despite being predicted to exceed US$80 billion by 2036, the substantial growth of the edge AI market over the coming decade will not be straightforward. This is due to the saturation and stop-start nature of certain markets that have already employed AI architectures in their incumbent chipsets, and where rigorous testing is necessary prior to high volume rollout, respectively. For example, the smartphone market has already begun to saturate. Though premiumization of smartphones continues (where the percentage share of total smartphones sold given over to premium smartphones increases year-on-year), where AI revenue increases as more premium smartphones are sold. Because these smartphones incorporate AI coprocessing in their chipsets, it is expected that this will itself begin to saturate over the next ten years.

 

IDTechEx forecasts consumer electronics (AI smartphones and AI PCs) and automotive (autonomous driving and intelligent cockpit functions) to be the largest markets for edge AI chips. Source: IDTechEx's report "AI Chips for Edge Applications 2026-2036: Technologies, Markets, Forecasts".

 

Higher degrees of autonomy, as defined by the Society of Automotive Engineers (SAE) from levels 0 (no automation) to 5 (full automation) is a megatrend in the automotive sector. Robotaxis continue to expand into new cities globally, while an increasing number of private vehicles will have autonomous features. In 2026, a shift from SAE level 2+ (hands off, eyes on), to level 3 (conditional eyes off) pushes responsibility of the vehicle from the driver to the OEM in some scenarios. Edge AI capabilities will therefore be greater for such vehicles to guarantee reliable, consistent, and safe behavior, or OEMs could face legal issues. Furthermore, intelligent cockpits will require further AI compute, which can be integrated onto a separate chip, or combined with autonomous driving and ADAS (advanced driver assistance systems) on a single chip.

 

As of 2026, humanoid robots are gaining more traction and beginning to see scaling and deployments, particularly on automotive manufacturing floors. While the automotive industry is where deployments will start, over the next decade IDTechEx is expecting to see deployments in more open, challenging environments, such as for patrolling, surveillance, and households.

 

 

In parallel to the overall growth of the humanoid robots market, IDTechEx expects the required AI compute per robot to increase significantly, as they are assigned more challenging tasks from the typical picking, placing, and other logistics work deployed by current humanoid robots on manufacturing floors.

 

As of January 2026, every major smartphone OEM has AI enabled features on its flagship phones, ranging from generative photo editing to personalized content creation. IDTechEx forecasts the AI chips for smartphones market to dominate the overall edge AI chip market, with AI chips becoming standard in flagship phones and more common in mid-range phones. Mid-range phones will gradually eat into market share of budget phones, as manufacturers will push for higher-range phones to maintain margins as the cost of leading edge hardware on the smallest process nodes continues to increase.

 

IDTechEx defines AI PCs as those with dedicated AI chips as part of the system-on-chip (SoC) with performance exceeding 40 TOPS (tera-operations per second). In 2025, this was an emerging market, with less than 10% of new PC sales fitting this definition. With leading manufacturers such as Lenovo and Apple committing to a greater proportion of AI PC sales, IDTechEx expects the majority of new PC sales to be AI PCs by the early 2030s.

 

Edge AI for predictive maintenance is quickly becoming a topic of focus for major sensor suppliers such as Bosch, as well as start-ups. By running machine learning methods locally on the sensor, systems can predict when maintenance and repair is required before it actually happens, yielding a significant increase in uptime and potential money savings. The AI compute required is typically much lower than in autonomous vehicles or AI PCs, for example, and will therefore be less expensive. With an increasing number of smart factories expected over the next ten years, more MEMS and IMU sensors will be embedded with AI capabilities on the edge.

This report provides critical market intelligence concerning AI hardware at the edge, particularly chips used for accelerating machine learning workloads. This includes:

 

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