The market for AI-related hardware and software is expected to grow between 40% and 55% annually, reaching between $780 billion and $990 billion by 2027, according to a new study.
The Bain & Company Technology Report 2024 also highlights three areas of opportunities – bigger models and larger data centres, enterprise and sovereign AI initiatives, and software efficiency and capabilities – which could enable the AI hardware and software market to come close to a trillion-dollar industry in the next three years.
Growing Workloads
AI workloads could grow 25-35% per year through 2027, Bain estimates. As AI scales, the need for computing power will radically expand the scale of large data centres over the next five to 10 years. AI will spur growth in data centres, from today’s 50–200 megawatts to more than a gigawatt, Bain reports.
This means that if large data centres cost between $1 billion and $4 billion today, they could cost between $10 billion and $25 billion five years from now. These changes are likely to have huge implications on the ecosystems that support data centres, including infrastructure engineering, power production, and cooling, as well as strain supply chains.
In addition to the need for more data centres, the AI-driven surge in demand for graphics processing units (GPUs) could increase total demand for certain upstream components by 30% or more by 2026, Bain predicts.
Sovereign AI Blocs
Another area that Bain says will add an additional layer of complexity for technology companies is the emergence of “sovereign” AI blocs. Governments worldwide — including Canada, France, India, Japan, and the UAE — are spending billions of dollars to subsidize sovereign AI. They’re investing in domestic computing infrastructure and AI models developed within their borders and trained on local data. As the sovereign AI push picks up steam, those who emerge as leaders will be based on several determining factors.
Efficient Software Development Needed
The arrival of generative AI has added pressure on software development companies to demonstrate greater efficiency. Generative AI appears to save about 10-15% of total software engineering time, according to Bain’s survey of more than 200 companies from across industries. However, most companies aren’t making the most of these savings, Bain found. When properly integrated, generative AI can significantly enhance efficiency in software development,” said Brahim Laaidi, partner in Bain’s Technology Practice in the Middle East. “However, achieving these improvements requires more than just deploying coding assistants. Engineering teams must adopt a comprehensive approach that includes advanced techniques like static analysis, a method of examining code without executing it to detect potential issues early, along with a focus on optimizing the entire software development lifecycle, from product management and refactoring to code reviews, testing, and build/release management.”
