The market for AI-related hardware and software is expected to grow between 40 per cent and 55 per cent annually, reaching between $780 billion and $990 billion by 2027, new research released by Global Consultancy Bain & Company showed.
Bain’s new report —fifth annual Global Technology Report —provides insights on the new waves of growth in the technology sector as a result of disruptions from the fast-changing Artificial Intelligence (AI) advancements.
Three areas of opportunities – bigger models and larger data centres, enterprise and sovereign AI initiatives, and software efficiency and capabilities – could enable the AI hardware and software market to come close to a trillion-dollar industry in the next three years.
“Generative AI is the prime mover of the current wave of change, but it is complicated by post-globalization shifts and the need to adapt business processes to deliver value. Companies are moving beyond the experimentation phase and are beginning to scale generative AI across the enterprise. As they do, CIOs will need to maintain production-grade AI solutions that will enable companies to adapt to a landscape that is quickly shifting. Essentially, they need to adopt an ‘AI everywhere’ approach,” David Crawford, Chairman of Bain’s Global Technology Practice said.
As AI scales, so will data centres
AI workloads could grow 25 per cent to 35 per cent 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, according to Bain report.
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 expected 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 per cent or more by 2026, Bain predicts.
Just as the pandemic created a surge in PC demand, surging demand for AI computing power will strain supply chains for data centre chips, personal computers, and smartphones.
These trends, when paired with geopolitical tensions, could trigger the next shortage of semiconductors, Bain warns. If data centre demand for current-generation GPUs were to double by 2026, not only would suppliers of key components need to increase their output, but makers of chip packaging components would need to nearly triple their production capacity to keep up with demand.
Emergence of sovereign AI
Another area that Bain says will add an additional layer of complexity for technology companies is the emergence of “sovereign” AI blocs. The post-globalization movement in technology is spreading from the pandemic-era chip shortage to current data, security, and AI privacy concerns. Governments worldwide—including Canada, France, India, Japan, and the United Arab Emirates— 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.
“Establishing successful sovereign AI ecosystems will be time-consuming and incredibly expensive,” Anne Hoecker, head of Bain’s Global Technology practice, said. “While less complex in some ways than building semiconductor fabs, these projects require more than securing local subsidies. Hyperscalers and other big tech firms may continue to invest in localized AI operations that will ensure significant competitive advantages.”
More efficient software
The arrival of generative AI has added pressure on software development companies to demonstrate greater efficiency. Generative AI appears to save about 10 per cent to 15 per cent 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.
M&A in tech becomes more unpredictable
Bain’s research shows that persistent regulatory obstacles have prompted tech companies to shift their M&A activity away from deals intended to capture scale and toward deals intended to acquire access to new capabilities, products, or markets—which Bain refers to as “scope deals.”
From 2015 to 2018, the percentage of tech industry scope deals increased from 50 per cent to 80 per cent, holding steady ever since. Over the past six years, scope deals have accounted for nearly 80 per cent of all tech industry M&A. That’s a bigger share than in most other industries. Bain’s research shows that tech is still heavily scrutinized and there’s no sign that the popularity of tech-scope deals will give way to a return to massive-scale deals any time soon. If anything, M&A in the industry has become more unpredictable, Bain concluded.
Other topics covered in this year’s Bain report include areas where generative AI is already delivering, and why some software companies are seeing drops in customer success.