Every year, McKinsey & Company surveys the technological horizon and names the innovations poised to matter most for business leaders. Its newly released Technology Trends Outlook 2026, now in its sixth edition, tracks 14 trends across three broad categories: the AI revolution, compute and connectivity frontiers, and cutting-edge engineering. The message this year is unmistakable: technology is stepping off the screen and into the physical world.
AI gets hands and a body
Four of the 14 trends are explicitly AI-focused, but the report argues AI is quietly powering nearly all the others too. Agentic software development and agentic AI both newly expanded categories this year describe systems that no longer answer questions but complete entire tasks end-to-end, working alongside human employees rather than simply assisting them. McKinsey calls this the shift from AI that talks to AI that acts.
That shift extends into the physical realm as well. Robots are being trained to understand and navigate unpredictable environments, autonomous vehicles are making real-time driving decisions, and “dark factories” are producing goods with no humans on-site. The report frames this as “physical AI” arriving first in manufacturing and logistics, before eventually reaching hospitals, farms, and city infrastructure.
Innovation is outrunning our ability to absorb it
A recurring theme in the report is speed and its costs. In biopharma, AI can now generate thousands of viable drug candidates in the time it once took to produce a handful. Still, human bottlenecks like lab validation and regulatory review haven’t kept pace. In cybersecurity, the report notes that more than three-quarters of vulnerabilities are now discovered only after an exploit already exists, meaning AI has compressed the defenders’ window to nearly zero even as it also helps them respond faster.
Power, chips, and the new infrastructure race
The most sobering thread running through the report is energy. AI infrastructure spending doubled in a single year, and McKinsey projects that U.S. data centers running AI workloads alone could consume as much electricity by 2030 as the entire state of California does today. The bottleneck, the report notes, isn’t hyperscalers’ ambition; it’s the physical supply chain, with transformer lead times stretching past two years and thousands of gigawatts of energy projects stuck waiting for grid connections worldwide.
This energy crunch is also reshaping the chip industry. Tech giants including Amazon, Google, Meta, and Microsoft are increasingly co-designing custom, application-specific silicon rather than relying on general-purpose chips, chasing efficiency gains as inference running trained models at scale overtakes training as AI’s dominant computing workload.
Where the money is flowing
McKinsey’s investment data offers a forward-looking signal of conviction. Five trends agentic software development, AI infrastructure, AI for scientific discovery, the future of space technologies, and the future of robotics are on pace to more than double their 2026 investment compared with 2025.
The bottom line
McKinsey’s core takeaway isn’t a prediction of any single winning technology, but a warning about pace: no organization can deploy AI at scale without upskilling its workforce, modernizing legacy systems, and securing energy and compute as fiercely as it secures talent. As the report puts it, leaders who understand the patterns behind this change will be positioned to shape it rather than react to it.
