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Inside the AI race: Energy use, agents and the real impact on work

Inside the AI race: Energy use, agents and the real impact on work

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 Reporting from the front line of the artificial intelligence revolution, Time magazine reporter Harry Booth has a unique perspective on the technology moving markets and transforming business. The London-based University of Auckland graduate has been part of Time’s team of AI reporters for the last 16 months, his byline regularly appearing in the pages of the iconic news magazine. This week’s episode of The Business of Tech podcast features an in-depth conversation with Booth, who gave me a tour of how AI is reshaping the world of work, explained the technology’s breakneck pace of development, looming questions over its energy use, and the critical signals to watch as 2026 approaches. Is AI really cleaning up? Despite dire predictions that white-collar jobs would be decimated, Booth finds reality to be more complex and, in some ways, more sobering. In areas like translation, seasoned professionals aren’t being replaced outright. Instead, their roles have shifted. Translators Booth interviewed are now tasked with correcting AI-generated text – a role rebranded as “AI cleanup” – which brings downward pressure on rates without necessarily delivering true productivity gains. Surprisingly, fixing flawed machine translation can take as long as translating from scratch, eroding job satisfaction and earnings for skilled workers.​ The same story, Booth notes, is playing out in other “canary in the coal mine” sectors. A frequently cited study found that software engineers using AI coding assistants believed their workload to be 20% faster. But empirical measurement showed a 20% slowdown. This suggests productivity impacts are far from settled, with AI often under-delivering unless carefully tailored to fit the workflow.​ From assistants to agents Much has been made in the past year of the rise of “AI agents” – systems that operate independently and can execute multi-step tasks, not just answer queries. “We’re seeing the emergence of agentic AI — these aren’t just chatbots, but systems that can carry out tasks, fetch data, and increasingly do things in the world on our behalf,” Booth told me. He believes we’re still in the early innings. Some AI can now complete longer software engineering tasks. The length of time an AI system can work independently has roughly doubled every four to seven months. If that trend holds, Booth suggests we could see agents capable of a full workday by 2027. However, today’s agents remain far from being true digital employees. Meaningful productivity gains only appear when companies design AI tools that address specific, high-value pain points using both language models and smart software engineering.​ Energy, infrastructure, and the next bottleneck On the infrastructure side, AI’s growing thirst for energy is emerging as a defining challenge. Far from being a personal moral issue (a single AI prompt’s carbon footprint is tiny, Booth points out), energy is a strategic concern for the giants racing to train ever-larger models. “AI isn’t a climate disaster at the individual level, but as companies multiply their data centres, the real bottleneck for development is shifting – from talent and chips to energy itself,” he said. With global electricity production growing slowly and massive datacenter builds underway, companies are securing long-term energy deals – sometimes using the rhetoric of AI’s needs as justification for keeping older, dirtier power sources online.​ But Booth also highlights a surprising upside: the same AI giants are pouring fresh capital into clean-energy tech, particularly nuclear fusion. Projects previously imagined as decades away are suddenly within striking distance. Fusion investment has exploded from US$2 billion to $15 billion in just three years, with players like OpenAI, Google, and Softbank on board. New Zealand’s own OpenStar is part of this story, pursuing commercial fusion with techniques borrowed from the scrappy world of startups. While a fusion-powered data centre is still years away, the influx of funding is credibly accelerating commercial viability, with some experts predicting net-positive fusion within a decade.​ What Harry Booth is watching in 2026 As AI accelerates, Booth will keep his investigative lens focused on several fronts in 2026: Will the time horizon, how long an AI agent can independently operate, keep doubling at today’s pace? How will new training techniques, like direct observation of professionals and ever-more-complex simulation environments, impact AI capability? Will scaling models with ever-greater compute keep delivering breakthroughs, or are diminishing returns setting in? Most importantly, can the infrastructure, both silicon and power, keep up? Can the effort to make AI safer and more transparent move as quickly as the technology itself?​ Tune in to this week’s The Business of Tech to hear the full conversation with Harry Booth, ...
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