AI Insights
The impact of China’s artificial intelligence development on urban energy efficiency

Caglar, A. E., Gönenç, S. & Destek, M. A. Toward a sustainable environment within the framework of carbon neutrality scenarios: evidence from the novel Fourier-NARD approach. Sustain. Dev. 32, 6643–6655 (2024).
Caglar, A. E., Daştan, M., Ahmed, Z., Mert, M. & Avci, S. B. The synergy of renewable energy consumption, green technology, and environmental quality: designing < scp > sustainable development goals policies. Nat. Resour. Forum. (2024).
Wang, C. H. & Juo, W. An environmental policy of green intellectual capital: green innovation strategy for performance sustainability. Bus. Strat Env. 30, 3241–3254 (2021).
D’Adamo, I., Di Carlo, C., Gastaldi, M., Rossi, E. N. & Uricchio, A. F. Economic performance, environmental protection and social progress: A cluster analysis comparison towards sustainable development. Sustain. (Switz). 16, 5049–5049 (2024).
Ali, I., Rahaman, A., Ali, M. J. & Rahman, F. The growth–environment nexus amid geopolitical risks: cointegration and machine learning algorithm approaches. Discov. Sustain. 6, (2025).
Caglar, A. E., Daştan, M., Ahmed, Z., Mert, M. & Avci, S. B. A novel panel of European economies pursuing carbon neutrality: do current climate technology and renewable energy practices really pass through the Prism of sustainable development? Gondwana. Res. (2025).
Rasoulinezhad, E. & Taghizadeh-Hesary, F. Role of green finance in improving energy efficiency and renewable energy development. Energy Effic. 15, (2022).
Lee, C. C. & Lee, C. C. How does green finance affect green total factor productivity? Evidence from China. Energy Econ. 107, 105863 (2022).
Wang, J. & Hao, S. The Spatial impact of carbon trading on harmonious economic and environmental development: evidence from China. Environ. Geochem. Health. 45, 6495–6515 (2023).
Hong, Q., Cui, L. & Hong, P. The impact of carbon emissions trading on energy efficiency: evidence from quasi-experiment in china’s carbon emissions trading pilot. Energy Econ. 110, 106025–106025 (2022).
Chen, Z., Song, P. & Wang, B. Carbon emissions trading scheme, energy efficiency and rebound effect – Evidence from china’s provincial data. Energy Policy. 157, 112507–112507 (2021).
Song, M., Du, J. & Tan, K. H. Impact of fiscal decentralization on green total factor productivity. Int. J. Prod. Econ. 205, 359–367 (2018).
Liu, J., Cheng, Z. & Zhang, H. Does industrial agglomeration promote the increase of energy efficiency in china?? J. Clean. Prod. 164, 30–37 (2017).
Yuan, H., Feng, Y., Lee, C. C. & Cen, Y. How does manufacturing agglomeration affect green economic efficiency? Energy Econ. 92, 104944 (2020).
Ali, I., Islam, M. & Ceh, B. Assessing the impact of three emission (3E) parameters on environmental quality in canada: A provincial data analysis using the quantiles via moments approach. Int. J. Green. Energy. 1–19. (2024).
Jianda, W., Kangyin, D., Xiucheng, D. & Farhad, T. H. Assessing the digital economy and its carbon-mitigation effects: the case of China. Energy Econ. 113, (2022).
Rinku, N., Singh, N. G., Artificial intelligence in sustainable energy industry: status quo, challenges, and opportunities. J. Clean. Prod. 289, 234–237 (2023).
Viskovic, A., Franki, V. & Jevtic, D. Artificial Intelligence as a facilitator of the energy transition. In international convention on information and communication technology. Electron. Microelectron. 494–499. (2022).
Xue, Y., Tang, C., Wu, H., Liu, J. & Hao, Y. The emerging driving force of energy consumption in china: does digital economy development matter? Energy Policy. 165, 112997 (2022).
Liu, Z. et al. Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: challenges and future perspectives. Energy AI. 10, 100195–100195 (2022).
Hussain, M., Yang, S., Maqsood, U. S. & Zahid, R. M. A. Tapping into the green potential: the power of artificial intelligence adoption in corporate green innovation drive. Bus. Strat Env. 33, 4375–4396 (2024).
Farzaneh, H. et al. Artificial intelligence evolution in smart buildings for energy efficiency. Appl. Sci. 11, 763 (2021).
Shahbaz, M., Wang, J., Dong, K. & Zhao, J. The impact of digital economy on energy transition across the globe: the mediating role of government governance. Renew. Sustain. Energy Rev. 166, 112620–112620 (2022).
Yi, M., Liu, Y., Sheng, M. S. & Wen, L. Effects of digital economy on carbon emission reduction: new evidence from China. Energy Policy. 171, 113271 (2022).
Li, X., Li, S., Cao, J. & Spulbar, A. C. Does artificial intelligence improve energy efficiency? Evidence from provincial data in China. Energy Econ. 108149–108149. (2024).
Zhang, L. et al. Digital economy, energy efficiency, and carbon emissions: evidence from provincial panel data in China. Sci. Total Environ. 852, 158403–158403 (2022).
Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A. & De Felice, F. Artificial intelligence and machine learning applications in smart production: progress, trends, and directions. Sustain. (Switz). 12, 492 (2020).
Wei, W. et al. Embodied greenhouse gas emissions from Building china’s large-scale power transmission infrastructure. Nat. Sustain. 4, 739–747 (2021).
Dong, K., Sun, R., Hochman, G. & Li, H. Energy intensity and energy conservation potential in china: A regional comparison perspective. Energy 155, 782–795 (2018).
Du, L. & Lin, W. Does the application of industrial robots overcome the Solow paradox? Evidence from China. Technol. Soc. 68, 101932 (2022).
Cheng, H., Jia, R., Li, D. & Li, H. The rise of robots in China. J. Econ. Perspect. 33, 71–88 (2019).
Tao, W., Weng, S., Chen, X., ALHussan, F. B. & Song, M. Artificial intelligence-driven transformations in low-carbon energy structure: evidence from China. Energy Econ. 136, 107719 (2024).
Caglar, A. E., Avci, S. B., Gökçe, N. & Destek, M. A. A sustainable study of competitive industrial performance amidst environmental quality: new insight from novel fourier perspective. J. Environ. Manage. 366, 121843 (2024).
Lu, J. & Li, H. Can digital technology innovation promote total factor energy efficiency? Firm-level evidence from China. Energy 293, 130682–130682 (2024).
Luo, S. et al. Digitalization and sustainable development: how could digital economy development improve green innovation in china?? Bus. Strat Environ. 32, 1847–1871 (2023).
Pan, W., Xie, T., Wang, Z. & Ma, L. Digital economy: an innovation driver for total factor productivity. J. Bus. Res. 139, 303–311 (2022).
Lyu, Y., Wang, W., Wu, Y. & Zhang, J. How does digital economy affect green total factor productivity? Evidence from China. Sci. Total Environ. 857, 159428 (2023).
Hanafizadeh, P. & Nik, M. R. H. Configuration of data monetization: A review of literature with thematic analysis. Glob J. Flex. Syst. Manag. 21, 17–34 (2019).
Jackson, I., Ivanov, D., Dolgui, A. & Namdar, J. Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation. Int. J. Prod. Res. 62, 6120–6145 (2024).
Mitra, R., Saha, P. & Kumar Tiwari, M. Sales forecasting of a food and beverage company using deep clustering frameworks. Int. J. Prod. Res. 62, 3320–3332 (2023).
Wu, J., Zhang, Z. & Zhou, S. X. Credit rating prediction through supply chains: A machine learning approach. Prod. Oper. Manag. 31, 1613–1629 (2022).
Chien, C. F., Lin, Y. S. & Lin, S. K. Deep reinforcement learning for selecting demand forecast models to empower industry 3.5 and an empirical study for a semiconductor component distributor. Int. J. Prod. Res. 58, 2784–2804 (2020).
Brooks, R. A. Intelligence without representation. Artif. Intell. 47, 139–159 (1991).
Raees, N. The effect of ventilation and economizer on energy consumptions for air source heat pumps in schools. Am. J. Eng. Appl. Sci. 7, 58–65 (2014).
Zhu, S. et al. Intelligent computing: the latest advances, challenges, and future. Intell. Comput. 2, (2023).
Liu, J., Qian, Y., Yang, Y. & Yang, Z. Can artificial intelligence improve the energy efficiency of manufacturing companies? Evidence from China. IJERPH 19 (2022).
Li, P., Yang, J., Islam, M. A., Ren, S. Making AI less ‘Thirsty’: Uncovering and addressing the secret water footprint of AI models. arXiv 2304.03271 (2023).
Zhou, X., Zhou, D., Wang, Q. & Su, B. How information and communication technology drives carbon emissions: A sector-level analysis for China. Energy Econ. 81, 380–392 (2019).
Li, Z. & Wang, J. The dynamic impact of digital economy on carbon emission reduction: evidence City-level empirical data in China. J. Clean. Prod. 351, 131570–131570 (2022).
Diamantoulakis, P. D., Kapinas, V. M. & Karagiannidis, G. K. Big data analytics for dynamic energy management in smart grids. Big Data Res. 2, 94–101 (2015).
Pawanr, S. & Gupta, K. A. Review on recent advances in the energy efficiency of machining processes for sustainability. Energies 17, 3659–3659 (2024).
Balakrishnan, D., Sharma, P., Bora, B. J. & Dizge, N. Harnessing biomass energy: advancements through machine learning and AI applications for sustainability and efficiency. Chem. Eng. Res. Des. 191, 193–205 (2024).
Mahmood, S. et al. Integrating machine and deep learning technologies in green buildings for enhanced energy efficiency and environmental sustainability. Sci. Rep. 14, (2024).
Villarreal, J. A. S., Mendoza, V. S., Acosta, J. A. N. & Ruiz, E. R. Energy consumption outlier detection with AI models in modern cities: a case study from north-eastern Mexico. Algorithms 17, 322–322 (2024).
Wang, E. Z., Lee, C. C. & Li, Y. Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries. Energy Econ. 105, 105748 (2022).
Lin, B. & Xu, C. The effects of industrial robots on firm energy intensity: from the perspective of technological innovation and electrification. Technol. Forecast. Soc. Chang. 203, 123373–123373 (2024).
Wang, Y., Zhao, W. & Ma, X. The Spatial spillover impact of artificial intelligence on energy efficiency: empirical evidence from 278 Chinese cities. Energy 312, 133497 (2024).
Acemoglu, D., Autor, D., Dorn, D., Hanson, G. H. & Price, B. Return of the Solow paradox?? IT, productivity, and employment in US manufacturing. am. Econ. Rev. 104, 394–399 (2014).
Barbieri, N., Marzucchi, A. & Rizzo, U. Knowledge sources and impacts on subsequent inventions: do green technologies differ from non-green ones? Res. Policy. 49, 103901–103901 (2019).
Ouyang, X., Li, Q. & Du, K. How does environmental regulation promote technological innovations in the industrial sector? Evidence from Chinese provincial panel data. Energy Policy. 139, 111310–111310 (2020).
Jenne, C. A. & Cattell, R. K. Structural change and energy efficiency in industry. Energy Econ. 5, 114–123 (1983).
Hu, L., Yuan, W., Jiang, J., Ma, T. & Zhu, S. Asymmetric effects of industrial structure rationalization on carbon emissions: evidence from Thirty Chinese provinces. J. Clean. Prod. 428, 139347–139347 (2023).
Xue, L. et al. Impacts of industrial structure adjustment, upgrade and coordination on energy efficiency: empirical research based on the extended STIRPAT model. Energy Strategy Rev. 43, 100911 (2022).
Li, B., Jiang, F., Xia, H. & Pan, J. Under the background of AI application, research on the impact of science and technology innovation and industrial structure upgrading on the sustainable and High-Quality development of regional economies. Sustain. (Switz). 14, 11331 (2022).
Su, Y. & Fan, Q. Renewable energy technology innovation, industrial structure upgrading and green development from the perspective of china’s provinces. Technol. Forecast. Soc. Chang. 180, 121727–121727 (2022).
Du, K., Cheng, Y. & Yao, X. Environmental regulation, green technology innovation, and industrial structure upgrading: the road to the green transformation of Chinese cities. Energy Econ. 98, 105247–105247 (2021).
Yu, H. et al. How does green technology innovation influence industrial structure? Evidence of heterogeneous environmental regulation effects. Environ. Dev. Sustain. 26, 17875–17903 (2023).
Peng, H., Shen, N., Ying, H. & Wang, Q. Can environmental regulation directly promote green innovation behavior?—— based on situation of industrial agglomeration. J. Clean. Prod. 314, 128044 (2021).
Chen, L., Li, W., Yuan, K. & Zhang, X. Can informal environmental regulation promote industrial structure upgrading? Evidence from China. Appl. Econ. 54, 2161–2180 (2021).
Huang, S. & Ge, J. Are there heterogeneities in environmental risks among different types of resource-based cities in china?? Assessment based on environmental risk field approach. Int. J. Disaster Risk Reduct. 104810–104810. (2024).
Wang, K., Chen, X. & Wang, C. The impact of sustainable development planning in resource-based cities on corporate ESG–Evidence from China. Energy Econ. 127, 107087 (2023).
Jiang, Z., Yuan, C. & Xu, J. The impact of digital government on energy sustainability: empirical evidence from prefecture-level cities in China. Technol. Forecast. Soc. Chang. 209, 123776–123776 (2024).
Lu, S., Zhang, W., Yu, J. & Li, J. The identification of spatial evolution stage of resource-based cities and its development characteristics. Acta Geogr. Sin. 75 2180–2191 (2020).
Wang, L. & Shao, J. Digital economy, entrepreneurship and energy efficiency. Energy 269, 126801–126801 (2023).
Wu, Y., Shi, K., Chen, Z., Liu, S. & Chang, Z. Developing improved Time-Series DMSP-OLS-Like data (1992–2019) in China by integrating DMSP-OLS and SNPP-VIIRS. IEEE Trans. Geosci. Remote Sens. 60, 1–14 (2021).
Lin, Y. & Cheung, A. Climate policy uncertainty and energy transition: evidence from prefecture-level cities in China. Energy Econ. 107938–107938 (2024).
Renshaw, E. F. Energy efficiency and the slump in labour productivity in the USA. Energy Econ. 3, 36–42 (1981).
Wilson, B., Trieu, L. H. & Bowen, B. Energy efficiency trends in Australia. Energy Policy. 22, 287–295 (1994).
Charnes, A., Cooper, W. W. & Rhodes, E. Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2, 429–444 (1978).
Li, M. J. & Tao, W. Q. Review of methodologies and Polices for evaluation of energy efficiency in high energy-consuming industry. Appl. Energy. 187, 203–215 (2017).
Muhammad, S., Pan, Y., Agha, M. H., Umar, M. & Chen, S. Industrial structure, energy intensity and environmental efficiency across developed and developing economies: the intermediary role of primary, secondary and tertiary industry. Energy 247, 123576–123576 (2022).
Tone, K. A strange case of the cost and allocative efficiencies in DEA. J. Oper. Res. Soc. 53, 1225–1231 (2002).
Tone, K. A slacks-based measure of super-efficiency in data envelopment analysis. Eur. J. Oper. Res. 143, 32–41 (2002).
Acemoglu, D. & Restrepo, P. Robots and jobs: evidence from US labor markets. J. Political Econ. 128, 2188–2244 (2020).
Beaudry, P., Doms, M. & Lewis, E. Should the personal computer be considered a technological revolution?? Evidence from U.S. Metropolitan areas. J. Polit. Econ. 118, 988–1036 (2010).
Mann, K. & Püttmann, L. Benign effects of automation: new evidence from patent texts. Rev. Econ. Stat. 105, 562–579 (2021).
Autor, D., Chin, C., Salomons, A. & Seegmiller, B. New frontiers: the origins and content of new work, 1940–2018. Q. J. Econ. 139, 1399–1465 (2024).
Henderson, J. V. Marshall’s scale economies. J. Urban Econ. 53, 1–28 (2003).
Xiong, M., Li, W., Xian, B. T. S. & Yang, A. Digital inclusive finance and enterprise innovation—Empirical evidence from Chinese listed companies. J. Innov. Knowl. 8, 100321 (2023).
Kathuria, V. Informal regulation of pollution in a developing country: evidence from India. Ecol. Econ. 63, 403–417 (2007).
Pargal, S. & Wheeler, D. Informal regulation of industrial pollution in developing countries: evidence from Indonesia. J. Political Econ. 104, 1314–1327 (1996).
Jia, R., Shao, S. & Yang, L. High-speed rail and CO2 emissions in urban china: A Spatial difference-in-differences approach. Energy Econ. 99, 105271–105271 (2021).
Luan, F., Yang, X., Chen, Y. & Regis, P. J. Industrial robots and air environment: A moderated mediation model of population density and energy consumption. Sustain. Prod. Consum. 30, 870–888 (2022).
Shi, D. & Li, S. Emissions trading system and energy use efficiency: Measurements and empirical evidence for cities at and above the prefecture level. China Industrial Economics 5–23 (2020).
Goldsmith-Pinkham, P., Sorkin, I. & Swift, H. Bartik instruments: what, when, why, and how. am. Econ. Rev. 110, 2586–2624 (2020).
Borusyak, K., Hull, P. & Jaravel, X. Quasi-experimental shift-share research designs. Rev. Econ. Stud. 89, 181–213 (2021).
Lee, C. C., Fang, Y., Quan, S. & Li, X. Leveraging the power of artificial intelligence toward the energy transition: the key role of the digital economy. Energy Econ. 135, 107654 (2024).
AI Insights
Cisco’s WebexOne Event Spotlights Global AI Brands and Ryan Reynolds, Acclaimed Actor, Film Producer, and Entrepreneur

Customer speakers include CarShield Founder, President and COO Steve Proetz; Topgolf Director of Global Technology Delivery Doug Klausen; GetixHealth CTO David Stuart; HD Supply Vice President of IT Emil DiMotta III and more, along with Cisco partners and leaders
SAN JOSE, Calif., Sept. 15 2025 — Cisco (NASDAQ: CSCO) today announced its luminary customers and partners headlining WebexOne, Cisco’s annual AI Collaboration and Customer Experience event, taking plance from September 28 – October 1, 2025 in San Diego. This year, executives from top global brands will take the stage to highlight how Cisco is addressing today’s demands for AI-powered innovations for the employee and customer experience.
WHO: Webex by Cisco, a leader in powering employee and customer experience solutions with AI, is hosting its annual signature event, WebexOne.
WHAT: The multiday event will explore trending topics shaping today’s workforce across generative AI, customer experience, and conferencing and office tech. WebexOne will feature the latest innovations from Cisco, executive-led sessions on product and strategy news, and customer conversations with inspiring leaders from the world’s leading brands.
-
Featured Brands and Customers: More than 50 Webex customers and partners will speak at WebexOne, including Conagra Brands, Kennedy Space Center, Brightli and more. All will address how they’re partnering with Cisco to revolutionize customer experiences and collaboration with AI.
-
Luminary Speakers: Ryan Reynolds, acclaimed Actor, film Producer, and Entrepreneur, will be the closing keynote. Ryan will explore the art of creative leadership, storytelling, and innovation across entertainment, business, and beyond. Deepu Talla, Vice President of Robotics and Edge AI at NVIDIA, will offer a visionary look at the new era of AI, highlighting the transformative possibilities ahead.
-
Inspiring Cisco Leaders: Cisco executives, including Jeetu Patel, President and Chief Product Officer, Anurag Dhingra, SVP & GM of Cisco Collaboration, Aruna Ravichandran, SVP and Chief Marketing & Customer Officer, and others will take the stage to discuss Cisco’s vision for artificial intelligence, customer experience, and collaboration. They will also showcase the latest technology revolutionizing the future of work and customer experience, and discuss how they integrate with Cisco’s broader product portfolio.
All attendees will also have the option to attend a training program that offers hands-on demos, 200+ hours of learning from 82 classes and labs, and 100+ breakout sessions featuring top customers and Cisco speakers.
Cisco will also announce its fourth-annual Webex Customer Award winners at the event.
WHEN:
September 28 – October 1, 2025, beginning at 9 a.m. PT
WHERE:
In-person: Marriott Marquis, San Diego Marina
Broadcast virtually: Using the Webex Events app
For press interested in behind-the-scenes exclusive access onsite at WebexOne, please contact Webex PR at webexpr@external.cisco.com. For general registration, please visit the link here.
AI Insights
Darwin Awards For AI Celebrate Epic Artificial Intelligence Fails

As the AI Darwin Awards prove, some AI ideas turn out to be far less bright than they seem.
getty
Not every artificial intelligence breakthrough is destined to change the world. Some are destined to make you wonder “With all this so-called intelligence flooding our lives, how could anyone think that was a smart idea?” That’s the spirit behind the AI Darwin Awards, which recognize the most spectacularly misguided uses of the technology. Submissions are open now.
Reads an introduction to the growing list of nominees, which include legal briefs replete with fictional court cases, fake books by real writers and an Airbnb host manipulating images with AI to make it appear a guest owed money for damages:
“Behold, this year’s remarkable collection of visionaries who looked at the cutting edge of artificial intelligence and thought, ‘Hold my venture capital.’ Each nominee has demonstrated an extraordinary commitment to the principle that if something can go catastrophically wrong with AI, it probably will — and they’re here to prove it.”
A software developer named Pete — who asked that his last name not be used to protect his privacy — launched the AI Darwin Awards last month, mostly as a joke, but also as a cheeky reminder that humans ultimately decide how technology gets deployed.
Don’t Blame The Chainsaw
“Artificial intelligence is just a tool — like a chainsaw, nuclear reactor or particularly aggressive blender,” reads the website for the awards. “It’s not the chainsaw’s fault when someone decides to juggle it at a dinner party.
“We celebrate the humans who looked at powerful AI systems and thought, ‘You know what this needs? Less testing, more ambition, and definitely no safety protocols!’ These visionaries remind us that human creativity in finding new ways to endanger ourselves knows no bounds.”
The AI Darwin Awards are not affiliated with the original Darwin Awards, which famously call out people who, through extraordinarily foolish choices, “protect our gene pool by making the ultimate sacrifice of their own lives.” Now that we let machines make dumb decisions for us too, it’s only fair they get their own awards.
Who Will Take The Crown?
Among the contenders for the inaugural AI Darwin Awards winner are the lawyers who defended MyPillow CEO Mike Lindell in a defamation lawsuit. They submitted an AI-generated brief with almost 30 defective citations, misquotes and references to completely fictional court cases. A federal judge fined the attorneys for their misstep, saying they violated a federal law requiring that lawyers certify court filings are grounded in the actual law.
Another nominee: the AI-generated summer reading list published earlier this year by the Chicago Sun Times and The Philadelphia Inquirer that contained fake books by real authors. “WTAF. I did not write a book called Boiling Point,” one of those authors, Rebecca Makkai, posted to BlueSky. Another writer, Min Jin Lee, also felt the need to issue a clarification.
“I have not written and will not be writing a novel called Nightshare Market,” the Pachinko author wrote on X. “Thank you.”
Then there’s the executive producer at Xbox Games Studios who suggested scores of newly laid-off employees should turn to chatbots for emotional support after losing their jobs, an idea that did not go over well.
“Suggesting that people process job loss trauma through chatbot conversations represents either breathtaking tone-deafness or groundbreaking faith in AI therapy — likely both,” the submission reads.
What Inspired The AI Darwin Awards?
The creator of the awards, who lives in Melbourne, Australia, and has worked in software for three decades, said he frequently uses large language models, including to craft the irreverent text for the AI Darwin Awards website. “It takes a lot of steering from myself to give it the desired tone, but the vast majority of actual content, probably 99%, is all the work of my LLM minions,” he said in an interview.
Pete got the idea for the awards as he and co-workers shared their experiences with AI on Slack. “Occasionally someone would post the latest AI blunder of the day and we’d all have either a good chuckle, or eye-roll or both,” he said.
The awards sit somewhere between reality and satire.
“AI will mean lots of good things for us all and it will mean lots of bad things,” the contest’s creator said. “We just need to work out how to try and increase the good and decrease the bad. In fact, our first task is to identify both the good and the bad. Hopefully the AI Darwin Awards can be a small part of that by highlighting some of the ‘bad.’”
He plans to invite the public to vote on candidates in January, with the winner to be announced in February.
For those who’d rather not win an AI Darwin Award, the site includes a handy guide for how for avoiding the dubious distinction. It includes these tips: “Test your AI systems in safe environments before deploying them globally,” “consider hiring humans for tasks that require empathy, creativity or basic common sense” and “ask ‘What’s the worst that could happen?’ and then actually think about the answer.”
AI Insights
Redefining speed: The AI revolution in clinical decision-making

Clinicians need one main thing: More time
As the EHR and data collection have become more robust, clinicians are spending more time on paperwork and administration. The American Medical Association conducted surveys in 2024 and found that physicians spent an average of 13 hours on indirect patient care (order entry, documentation, lab interpretation) and over seven hours on administrative tasks (prior authorization, insurance forms, meetings). On top of patient care, this meant a 57.8-hour workweek.
Ultimately, clinicians need more time with their patients and less time taking notes. They need more time to understand complex cases and less time spent searching for information. Information overload is also a challenge: Medical knowledge is doubling every 73 days, and patients are increasingly relying on multiple medications. It also takes an average of 17 years between clinical discovery and changing practice based on evidence—clinicians need efficient ways to stay updated in their area of expertise.
AI can produce time savings that add up
We’re seeing a revolution in how artificial intelligence (AI) can support them. As AI is introduced further into healthcare administrative work and clinical settings, there are opportunities for clinicians to be more productive and meaningful with their time.
When we look at how AI-enabled features can save time for clinicians, the amazing thing is that it’s not massive blocks of time—like 5 or 10 minutes. It’s 10 seconds on a task, or 30 seconds here, or 45 seconds there. And the clinicians we speak with are so happy about it. AI can help speed up the little things—the couple of clicks saved—and over time, that can make a huge difference. It’s multiple moments of small savings that add up to these meaningful productivity gains.
So, as we find ways to further integrate UpToDate into the workflow, this is what we think about: Finding those extra moments that matter. Getting clinical information closer to the provider so they don’t have to open extra applications for decision-making. We’re looking for multiple ways to get evidence and clinical intelligence streamlined throughout the care experience and into the EHR, presenting tremendous opportunities for time savings.
The opportunities are plentiful. How can ambient and note-taking technology link to the relevant evidence-based clinical content for quick reference? How could patient interactions with chatbots ahead of a clinic visit prep the provider with relevant evidence in advance? Identifying innovative partners that can work alongside us in ambient solutions, documentation, chatbots, and more can help bring content and evidence closer to clinicians and save those seconds over time.
Time savings can bring new clinical opportunities
What can clinicians do with that saved time? Some have been concerned that GenAI tools will deteriorate clinical decision-making skills—our recent Future Ready Healthcare report showed that 57% of respondents share these concerns. But I like to think about the opportunities created through those time savings: How can AI help open up space for deeper critical thinking?
With AI saving time and supporting smaller tasks, the first thing it can do is alleviate some of the administrative burden, which is already happening. It can also expand critical thinking opportunities and provide space to consider challenges in healthcare that historically we haven’t had time to solve. It can “re-humanize medical practice” in a way that provides professional fulfillment and allows clinicians to spend more time as caregivers, rather than note-takers. When these efforts are scaled across the workforce, it can result in productivity gains and operational efficiencies across an enterprise.
AI tools need to be grounded in expert-driven evidence
As we rapidly move into the AI era, it’s easy to find tools that seem to give faster answers, especially among generative AI (GenAI) tools. But are they grounded in evidence and industry recommendations?
Keeping expert clinicians in the loop is critical—if you’ve trusted UpToDate for a while, you’ll know this is our position. Our clinical decision support is grounded not just in evidence but in the recommendations of over 7,600 clinical practitioners and experts who curate content as new evidence emerges, and provide graded recommendations to help guide decision-making, even when the conditions are gray. Relying on clinical recommendations curated by human experts keeps the information and care guidance current and relevant. As AI is layered on top of these human-generated recommendations, clinicians can start finding information more efficiently—saving precious seconds with each patient.
We know this expertise matters. A 2024 Wolters Kluwer Health survey of US physicians showed they were overall positive about the prospects of GenAI in clinical settings; however, 91% said they would have to know the materials the AI was trained on were created by doctors and medical experts in order to trust it. They also overwhelmingly wanted (89%) the technology vendor to be transparent about where the information came from, who created it, and how it was sourced.
The UpToDate, you know and trust, is entering a new era, which is in line with Bud Rose’s vision for a consultative conversation with clinical experts. And we’re just getting started—join us in helping shape the next wave of healthcare innovation.
Read our vision for the future of healthcare and explore our perspectives on AI in clinical content.
-
Business2 weeks ago
The Guardian view on Trump and the Fed: independence is no substitute for accountability | Editorial
-
Tools & Platforms1 month ago
Building Trust in Military AI Starts with Opening the Black Box – War on the Rocks
-
Ethics & Policy2 months ago
SDAIA Supports Saudi Arabia’s Leadership in Shaping Global AI Ethics, Policy, and Research – وكالة الأنباء السعودية
-
Events & Conferences4 months ago
Journey to 1000 models: Scaling Instagram’s recommendation system
-
Jobs & Careers3 months ago
Mumbai-based Perplexity Alternative Has 60k+ Users Without Funding
-
Podcasts & Talks2 months ago
Happy 4th of July! 🎆 Made with Veo 3 in Gemini
-
Education3 months ago
VEX Robotics launches AI-powered classroom robotics system
-
Education2 months ago
Macron says UK and France have duty to tackle illegal migration ‘with humanity, solidarity and firmness’ – UK politics live | Politics
-
Podcasts & Talks2 months ago
OpenAI 🤝 @teamganassi
-
Funding & Business3 months ago
Kayak and Expedia race to build AI travel agents that turn social posts into itineraries