The rise of technologies such as the Internet of Things (IoT), autonomous vehicles, smart cameras, etc. is generating lots of big data. The volume of data in 2022 was 97ZB and is doubling every 2-3 years. This is leading to unprecedented growth in energy consumption and costs needed for data processing. Sending raw data for remote processing on centralized nodes is limited in terms of speed and bandwidth, and even next-gen tech like 5G or 6G will be insufficient to cope with this growth. Processing data at the Edge, where it's generated, requires increasing power efficiency by several orders of magnitude. However, the use of general-purpose digital processors based on von Neumann architecture is limited, with optimization possibilities nearing natural limits. A new class of chips, neuromorphic hardware, is needed to execute AI algorithms like Deep Learning at high speed, low energy consumption, endurance, and scalability. MultiSpin.AI’s vision is to improve neuromorphic computing by increasing the energy efficiency and processing speed by at least three orders of magnitude over digital computing and >10x compared to the most advanced neuromorphic devices to reach an unparalleled 2,000 Tera operations per second per watt (TOPS/W). To achieve this, MultiSpin.AI will develop an AI co-processor based on a crossbar of multi-level magnetic tunnel junctions (M2TJ) cells/ n-ary state cells. The use of multi-level M2TJs reduces the number of cells, simplifies circuity, and reduces the number of digital-to-analog conversions (DAC) at the input of the crossbar, and analog-to-digital conversions at the crossbar output. The combined effect is realising much higher energy efficiency and faster AI inference at the Edge. This breakthrough will help provide a significant impact by enabling transformative applications like autonomous vehicles, robots, and medical devices and help strengthen strategic autonomy for the EU chips industry and reduce CO2 emissions from AI inference.