AI has taken the world by storm in recent months as the technology, which was largely a work in progress, hit the masses thanks to the ChatGPT breakthrough.

If the AI frenzy is currently mainly about the first step in machine learning (training), the second step (inference), is slowly but surely becoming the key for AI’s mass adoption and is likely to become another massive growth driver for the Tech industry in the next couple of years as most organizations roll out AI services and apps.

AI is indeed the underlying and necessary base for many new technologies including autonomous driving, robotics or the Internet of Things and the increasing complexity of training deep learning models is having a profound impact on the cloud computing architecture.

Hyperscalers are notably redesigning their cloud infrastructures with data transfer speed and energy consumption optimization in mind.

Longer term, quantum computing emerges as the next step in high-performance computing.

As AI is expected to open up new revenue opportunities and bring major efficiencies, it should gradually become pervasive and provide a material boost to GDP over coming years.

A multi-year investment cycle is then in sight to train AI and deliver AI services to customers at scale, an obvious positive for semiconductors, software and data center providers.

Industrial and consumer goods companies should also have the opportunity to leverage their installed base of IoT products and devices (cars, robots, industrial machines…) to offer a wide range of connected digital services (self driving, remote monitoring and maintenance…) and over-the-air options through subscription-based models.


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