2025年技术、媒体与电信（TMT）预测报告

Deloitte predicts 2025 will be a “gap year” forgen AI and the TMT sector, marked by eightcritical gaps that need to be bridged for today’spotential to be realized ARTICLE16 MINUTE READ As we look ahead to 2025 and beyond, it's clear that TMT is on the verge of a signicant leap forward,largely powered by rapid generative AI adoption. But to get there, the industry will need to close gaps,including: balancing gen AI infrastructure investments with monetization, addressing gender disparities ingen AI usage, managing the energy consumption of gen AI data centers, tackling trust concerns surroundingdeepfake content, discovering how best to use gen AI in media and gaming, and harnessing the power ofgen AI agents to manage and act in real time. Further gaps exist in streaming video and cloud spending.Plus, there are non-gap predictions, around new smartphones and PCs with gen AI chips on them, newstadiums and other sports infrastructure leveling up the fan experience, and telco consolidation, specicallyof wireless players. Overcoming these hurdles will be important to help businesses and industries thrive. This blank space is caused due to formatting limitations. Text continues on next page. Closing the gap for a brighter future We are at a pivotal moment in the history of human invention. Future generations will certainly look back on the choices wemake today. Deloie’s prediction that 2025 will be a “gap year” for generative AI underscores this signicant inection point.These gaps—spanning infrastructure investment, gender disparities, energy consumption, trust decits, and the capabilities ofgen AI agents—are not just challenges for the industry but societal imperatives. How we collectively address these gaps willdene the legacy we create. Beyond gen AI, advancements in cloud computing and telecommunications are expected to bring unprecedented eciencies,new business models, and augmented consumer experiences. Investments in sports infrastructure and the increasingprominence of women’s sports can act as catalysts for economic and social development. These trends reinforce the industry’srole in fostering innovation that enhances businesses, consumers, and broader communities. The 2025 TMT Predictions represent opportunities to create lasting impact. By navigating the path forward with trust, inclusivity,and sustainability at the forefront, industry advancements can benet not only the current generation, but all those who follow.Together, we can rise to the occasion and bridge the gap to a brighter tomorrow. -Lara Abrash, chair, Deloitte US Eight gaps that mark 2025 as a “gap year” for TMT 1.The gen AI infrastructure and monetization gap. As we predicted last year, companies are spending tensof billions of dollars on chips and further hundreds of billions to build gen AI data centers for trainingand inference of gen AI models. While some companies oering gen AI enterprise software are seeingincremental revenues, the investment is 10 times (or more) higher than the return, at least for now. Thosespending the most might suggest that the risk of underinvesting in gen AI is higher than the risk ofoverinvesting. But the gap persists and seems to be widening. 2.The gen AI data center electricity and sustainability gap.Proposed gen AI data centers requireunprecedented amounts of power, preferably low carbon, which is creating a gap between their needsand the capacities of electrical grids, and companies’ sustainability targets. Much is being done to close itby hyperscalers, chip companies, and utilities around the world, but the gap is expected to remain in2025. 3.The gen AI gender gap. Women are less likely than men to use gen AI tools for both work and play.Some of this is due to lack of trust, but women’s usage of gen AI is expected to catch up to men’s usage… within the year in some markets. 4.The gen AI deepfake trust gap.The proliferation of deepfake gen AI content (images, video, and audio)is making it harder for consumers, as a society, to trust their own eyes and ears. That gap needs to bebridged by the gen AI ecosystem comprehensively and immutably labeling gen AI content, as well asreliably and accurately detecting fake images in real time. The marginal cost of creating convincing deepfakes is falling, and the cost of detection needs to fall at an equivalent pace to help close the gap. 5.The studio gen AI usage gap:Many expect large studios to be using gen AI for content production, andsome are, but there is a gap between those expectations and reality. Many are cautious about challenges with intellectual property inherent to generative content, but they are keen to gain enterprise capabilitiesthat can reduce time, lower costs, and expand their reach. 6.The autonomous gen AI agent gap.The prospect of autonomous bots that can consistently and reliablycomplete discrete tasks and orchestrate entire workows is tantalizing. Agentic AI pilots are launching in2024—will they reach widespread ad