The same way the internet revolutionized our society, the rise of Artificial Intelligence (AI) that can learn without the need of explicit instructions is transforming our life. AI uses brain inspired neural network algorithms powered by computers. However, these central processing units (CPU) are extremely energy inefficient at implementing these tasks. This represents a major bottleneck for energy efficient, scalable and portable AI systems. Reducing the energy consumption of the massively dense interconnects in existing CPUs needed to emulate complex brain functions is a major challenge. ChipAI aims at developing a nanoscale photonics-enabled technology capable of deliver compact, high-bandwidth and energy efficiency CPUs using optically interconnected spiking neuron-like sources and detectors. ChipAI will pursue its main goal through the exploitation of Resonant Tunnelling (RT) semiconductor nanostructures embedded in sub-wavelength metal cavities, with dimensions 100 times smaller over conventional devices, for efficient light confinement, emission and detection. Key elements developed are non-linear RT nanoscale lasers, LEDs, detectors, and synaptic optical links on silicon substrates to make an economically viable technology. This platform will be able to fire and detect neuron-like light-spiking (pulsed) signals at rates 1 billion times faster than biological neurons (>10 GHz per spike rates) and requiring ultralow energy (<10 fJ). This radically new architecture will be tested for spike-encoding information processing towards validation for use in artificial neural networks. This will enable the development of real-time and offline portable AI and neuromorphic (brain-like) CPUs. In perspective, ChipAI will not only lay the foundations of the new field of neuromorphic optical computing, as will enable new non-AI functional applications in biosensing, imaging and many other fields where masses of cheap miniaturized pulsed sources and detectors are needed.