Generative AI

{{Short description|AI that generates content}} {{Redirect-distinguish|GenAI|General AI}} {{Use mdy dates|date=May 2025}} {{Use American English|date=April 2025}} [[File:Théâtre D’opéra Spatial.png|thumb|upright=1.2|''Théâtre D'opéra Spatial'' (Space Opera Theater, 2022), an image made with Midjourney that won an award at the Colorado State Fair's fine art competition |alt=Impressionistic image of figures in a futuristic opera scene]] {{Artificial intelligence}}

'''Generative artificial intelligence''' ('''GenAI''') is a subfield of artificial intelligence (AI) that uses generative models to generate text, images, videos, audio, software code (vibe coding) or other forms of data.<ref>{{cite journal |last1=Banh |first1=Leonardo |last2=Strobel |first2=Gero |title=Generative artificial intelligence |journal=Electronic Markets |date=2023 |volume=33 |issue=1 |article-number=63 |doi=10.1007/s12525-023-00680-1 |doi-access=free}}</ref> These models learn the underlying patterns and structures of their training data, and use them to generate new data<ref>{{Cite news |last=Pasick |first=Adam |date=March 27, 2023 |title=Artificial Intelligence Glossary: Neural Networks and Other Terms Explained |url=https://www.nytimes.com/article/ai-artificial-intelligence-glossary.html |url-status=live |archive-url=https://web.archive.org/web/20230901183440/https://www.nytimes.com/article/ai-artificial-intelligence-glossary.html |archive-date=September 1, 2023 |access-date=April 22, 2023 |work=The New York Times |language=en-US |issn=0362-4331}}</ref> in response to input, which often takes the form of natural language ''prompts''.<ref name="nytimes2">{{Cite web |last1=Griffith |first1=Erin |last2=Metz |first2=Cade |date=January 27, 2023 |title=Anthropic Said to Be Closing In on $300 Million in New A.I. Funding |url=https://www.nytimes.com/2023/01/27/technology/anthropic-ai-funding.html |url-status=live |archive-url=https://web.archive.org/web/20231209074235/https://www.nytimes.com/2023/01/27/technology/anthropic-ai-funding.html |archive-date=December 9, 2023 |access-date=March 14, 2023 |work=The New York Times}}</ref><ref name="bloomberg2">{{cite news |last1=Lanxon |first1=Nate |last2=Bass |first2=Dina |last3=Davalos |first3=Jackie |date=March 10, 2023 |title=A Cheat Sheet to AI Buzzwords and Their Meanings |url=https://news.bloomberglaw.com/tech-and-telecom-law/a-cheat-sheet-to-ai-buzzwords-and-their-meanings-quicktake |url-status=live |archive-url=https://web.archive.org/web/20231117140835/https://news.bloomberglaw.com/tech-and-telecom-law/a-cheat-sheet-to-ai-buzzwords-and-their-meanings-quicktake |archive-date=November 17, 2023 |access-date=March 14, 2023 |newspaper=Bloomberg News |location=}}</ref>

The prevalence of generative AI tools has increased significantly since the AI boom in the 2020s. This boom was made possible by improvements in deep neural networks, particularly large language models (LLMs), which are based on the transformer architecture. Generative AI applications include chatbots such as ChatGPT, Claude, Copilot, DeepSeek, Google Gemini and Grok; text-to-image models such as DALL-E, Firefly, Stable Diffusion, and Midjourney; and text-to-video models such as Veo, LTX and Sora.<ref>{{Cite web|last=Roose|first=Kevin|date=October 21, 2022|title=A Coming-Out Party for Generative A.I., Silicon Valley's New Craze|url=https://www.nytimes.com/2022/10/21/technology/generative-ai.html|access-date=March 14, 2023|website=The New York Times|archive-date=February 15, 2023|archive-url=https://web.archive.org/web/20230215010524/https://www.nytimes.com/2022/10/21/technology/generative-ai.html|url-status=live}}</ref><ref>{{Cite news |last1=Shahaf |first1=Tal |last2=Shahaf |first2=Tal |date=2025-10-23 |title=Lightricks unveils powerful AI video model challenging OpenAI and Google |url=https://www.ynetnews.com/tech-and-digital/article/hklbzavrgx |access-date=2025-12-22 |work=Ynetglobal |language=en}}</ref><ref name="Metz-2024">{{Cite news |last=Metz |first=Cade |date=February 15, 2024 |title=OpenAI Unveils A.I. That Instantly Generates Eye-Popping Videos |url=https://www.nytimes.com/2024/02/15/technology/openai-sora-videos.html |access-date=February 16, 2024 |work=The New York Times |language=en-US |issn=0362-4331 |archive-date=February 15, 2024 |archive-url=https://web.archive.org/web/20240215220626/https://www.nytimes.com/2024/02/15/technology/openai-sora-videos.html |url-status=live }}</ref>

Companies in a variety of sectors have used generative AI, including those in software development, healthcare,<ref>{{Cite journal |last1=Raza |first1=Marium M. |last2=Venkatesh |first2=Kaushik P. |last3=Kvedar |first3=Joseph C. |date=March 7, 2024 |title=Generative AI and large language models in health care: pathways to implementation |journal=npj Digital Medicine |language=en |volume=7 |issue=1 |page=62 |doi=10.1038/s41746-023-00988-4 |issn=2398-6352 |pmc=10920625 |pmid=38454007}}</ref> finance,<ref>{{Cite web |last=Mogaji |first=Emmanuel |date=January 7, 2025 |title=How generative AI is transforming financial services – and what it means for customers |url=https://theconversation.com/how-generative-ai-is-transforming-financial-services-and-what-it-means-for-customers-246649 |access-date=April 10, 2025 |website=The Conversation |language=en-US}}</ref> entertainment,<ref>{{Cite web |last=Bean |first=Thomas H. Davenport and Randy |date=June 19, 2023 |title=The Impact of Generative AI on Hollywood and Entertainment |url=https://sloanreview.mit.edu/article/the-impact-of-generative-ai-on-hollywood-and-entertainment/ |url-status=live |archive-url=https://web.archive.org/web/20240806231801/https://sloanreview.mit.edu/article/the-impact-of-generative-ai-on-hollywood-and-entertainment/ |archive-date=August 6, 2024 |access-date=April 10, 2025 |website=MIT Sloan Management Review |language=en-US}}</ref> customer service,<ref>{{Citation |last1=Brynjolfsson |first1=Erik |title=Generative AI at Work |date=April 2023 |type=Working Paper |url=https://www.nber.org/papers/w31161 |access-date=January 21, 2024 |archive-url=https://web.archive.org/web/20240328004237/https://www.nber.org/papers/w31161 |archive-date=March 28, 2024 |url-status=live |series=Working Paper Series |doi=10.3386/w31161 |last2=Li |first2=Danielle |last3=Raymond |first3=Lindsey R.|doi-access=free }}</ref> sales and marketing,<ref name="economist2">{{Cite web |date=March 6, 2023 |title=Don't fear an AI-induced jobs apocalypse just yet |url=https://www.economist.com/business/2023/03/06/dont-fear-an-ai-induced-jobs-apocalypse-just-yet |url-status=live |archive-url=https://web.archive.org/web/20231117160744/https://www.economist.com/business/2023/03/06/dont-fear-an-ai-induced-jobs-apocalypse-just-yet |archive-date=November 17, 2023 |access-date=March 14, 2023 |publisher=The Economist}}</ref> art, writing,<ref>{{cite web |last=Coyle |first=Jake |date=September 27, 2023 |title=In Hollywood writers' battle against AI, humans win (for now) |url=https://apnews.com/article/hollywood-ai-strike-wga-artificial-intelligence-39ab72582c3a15f77510c9c30a45ffc8 |url-status=live |archive-url=https://web.archive.org/web/20240403060904/https://apnews.com/article/hollywood-ai-strike-wga-artificial-intelligence-39ab72582c3a15f77510c9c30a45ffc8 |archive-date=April 3, 2024 |access-date=January 26, 2024 |website=AP News |publisher=Associated Press}}</ref> and product design.<ref>{{Cite news |date=June 16, 2023 |title=How Generative AI Can Augment Human Creativity |url=https://hbr.org/2023/07/how-generative-ai-can-augment-human-creativity |url-status=live |archive-url=https://web.archive.org/web/20230620073042/https://hbr.org/2023/07/how-generative-ai-can-augment-human-creativity |archive-date=June 20, 2023 |access-date=June 20, 2023 |work=Harvard Business Review |issn=0017-8012}}</ref>

Generative AI has been used for cybercrime, and to deceive and manipulate people through fake news and deepfakes.<ref>{{Cite journal |last=Taeihagh |first=Araz |date=2025-04-04 |title=Governance of Generative AI |url=https://academic.oup.com/policyandsociety/article/44/1/1/7997395 |journal=Policy and Society |language=en |volume=44 |issue=1 |pages=1–22 |doi=10.1093/polsoc/puaf001 |issn=1449-4035|doi-access=free }}</ref><ref>{{cite journal |last1=Simon |first1=Felix M. |last2=Altay |first2=Sacha |last3=Mercier |first3=Hugo |title=Misinformation reloaded? Fears about the impact of generative AI on misinformation are overblown |journal=Harvard Kennedy School Misinformation Review |date=18 October 2023 |doi=10.37016/mr-2020-127 |doi-access=free }}</ref> Generative AI models have been trained on copyrighted works without the rightholders' permission.<ref>{{Cite news|date=August 1, 2023|title=New AI systems collide with copyright law|work=BBC News|url=https://www.bbc.co.uk/news/business-66231268|access-date=September 28, 2024}}</ref> Many generative AI systems use large-scale data centers, whose environmental impacts include electronic waste, consumption of fresh water for cooling, and high energy consumption that is estimated to be growing steadily.<ref>{{Cite web |date=2024-09-21 |title=AI has an environmental problem. Here's what the world can do about that. |url=https://www.unep.org/news-and-stories/story/ai-has-environmental-problem-heres-what-world-can-do-about |access-date=2025-08-20 |website=www.unep.org |language=en}}</ref>

==History== {{main|History of artificial intelligence}}

=== Early history === The origins of algorithmically generated media can be traced to the development of the Markov chain, which has been used to model natural language since the early 20th century. Russian mathematician Andrey Markov introduced the concept in 1906,<ref name="GrinsteadSnell1997page4643">{{cite book |last1=Grinstead |first1=Charles Miller |url=https://archive.org/details/flooved3489 |title=Introduction to Probability |last2=Snell |first2=James Laurie |publisher=American Mathematical Society |year=1997 |isbn=978-0-8218-0749-1 |pages=[https://archive.org/details/flooved3489/page/n473 464]–466 |language=en-us}}</ref><ref name="Bremaud2013pageIX3">{{cite book |last=Bremaud |first=Pierre |url=https://books.google.com/books?id=jrPVBwAAQBAJ |title=Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues |date=March 9, 2013 |publisher=Springer Science & Business Media |isbn=978-1-4757-3124-8 |page=ix |archive-url=https://web.archive.org/web/20170323160437/https://books.google.com/books?id=jrPVBwAAQBAJ |archive-date=March 23, 2017 |url-status=live}}</ref> including an analysis of vowel and consonant patterns in ''Eugeny Onegin''. Once trained on a text corpus, a Markov chain can generate probabilistic text.<ref>{{Cite journal |last=Hayes |first=Brian |date=2013 |title=First Links in the Markov Chain |journal=American Scientist |volume=101 |issue=2 |page=92 |doi=10.1511/2013.101.92 |issn=0003-0996 }}</ref><ref>{{cite journal |last1=Fine |first1=Shai |last2=Singer |first2=Yoram |last3=Tishby |first3=Naftali |title=The Hierarchical Hidden Markov Model: Analysis and Applications |journal=Machine Learning |date=July 1998 |volume=32 |issue=1 |pages=41–62 |doi=10.1023/A:1007469218079 |bibcode=1998MLear..32...41F |doi-access=free}}</ref>

By the early 1970s, artists began using computers to extend generative techniques beyond Markov models. Harold Cohen developed and exhibited works produced by AARON, a pioneering computer program designed to autonomously create paintings.<ref>{{Cite journal |last1=Bergen |first1=Nathan |last2=Huang |first2=Angela |date=2023 |title=A Brief History of Generative AI |url=https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-gen-ai-dichotomies.pdf |journal=Dichotomies: Generative AI: Navigating Towards a Better Future |issue=2 |page=4 |access-date=August 8, 2023 |archive-date=August 10, 2023 |archive-url=https://web.archive.org/web/20230810230710/https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-gen-ai-dichotomies.pdf |url-status=live}}</ref> The terms generative AI planning or generative planning were used in the 1980s and 1990s to refer to AI planning systems, especially computer-aided process planning, used to generate sequences of actions to reach a specified goal.<ref name="alting">{{cite journal |last1=Alting |first1=Leo |last2=Zhang |first2=Hongchao |year=1989 |title=Computer aided process planning: the state-of-the-art survey |url=https://www.researchgate.net/publication/236649325 |url-status=live |journal=The International Journal of Production Research |volume=27 |issue=4 |pages=553–585 |doi=10.1080/00207548908942569 |archive-url=https://web.archive.org/web/20240507094335/https://www.researchgate.net/publication/236649325_Computer_Aided_Process_Planning_The_State-of-the-Art_Survey |archive-date=May 7, 2024 |access-date=October 3, 2023}}</ref><ref>{{Cite journal |last=Chien |first=Steve |year=1998 |title=Automated planning and scheduling for goal-based autonomous spacecraft |journal=IEEE Intelligent Systems and Their Applications |volume=13 |issue=5 |pages=50–55 |doi=10.1109/5254.722362 |bibcode=1998IISA...13e..50C }}</ref> Generative AI planning systems used symbolic AI methods such as state space search and constraint satisfaction and were a "relatively mature" technology by the early 1990s. They were used to generate crisis action plans for military use,<ref>{{Cite book |title=ARPA/Rome Laboratory Knowledge-based Planning and Scheduling Initiative Workshop Proceedings |publisher=The Advanced Research Projects Agency, Department of Defense, and Rome Laboratory, US Air Force, Griffiss AFB |year=1994 |isbn=1-55860-345-X |editor-last=Burstein |editor-first=Mark H. |page=219}}</ref> process plans for manufacturing<ref name="alting" /> and decision plans such as in prototype autonomous spacecraft.<ref>{{cite book |last1=Pell |first1=Barney |title=An Autonomous Spacecraft Agent Prototype |last2=Bernard |first2=Douglas E. |last3=Chien |first3=Steve A. |last4=Gat |first4=Erann |last5=Muscettola |first5=Nicola |last6=Nayak |first6=P. Pandurang |last7=Wagner |first7=Michael D. |last8=Williams |first8=Brian C. |publisher=Autonomous Robots Volume 5, No. 1 |year=1998 |editor1-last=Bekey |editor1-first=George A. |pages=29–45 |quote=Our deliberator is a traditional generative AI planner based on the HSTS planning framework (Muscettola, 1994), and our control component is a traditional spacecraft attitude control system (Hackney et al. 1993). We also add an architectural component explicitly dedicated to world modeling (the mode identifier), and distinguish between control and monitoring.}}</ref>

=== Generative neural networks (since the late 2000s) === {{See also|Machine learning|deep learning}} [[File:Discriminative vs Generative Neural Networks.png|thumb|Above: An image classifier, an example of a neural network trained with a discriminative objective. Below: A text-to-image model, an example of a network trained with a generative objective.]]

Machine learning uses both discriminative models and generative models to model or predict data. Beginning in the late 2000s and early 2010s, advances in deep learning led to major improvements in image classification, speech recognition, and natural language processing.<ref>{{cite journal |last=Krizhevsky |first=Alex |author2=Ilya Sutskever |author3=Geoffrey E. Hinton |title=ImageNet Classification with Deep Convolutional Neural Networks |journal=Advances in Neural Information Processing Systems |year=2012 |url=https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks }}</ref> Neural networks in this period were typically trained as discriminative models due to the relative difficulty of training generative models.<ref>{{Cite book |last=Jebara |first=Tony |title=Machine learning: discriminative and generative |publisher=Springer Science & Business Media |year=2012}}</ref>

In 2014, the introduction of models such as the variational autoencoder (VAE) and generative adversarial network (GAN) enabled effective deep generative modeling of complex data such as images.<ref>{{cite journal |last=Kingma |first=Diederik P. |author2=Max Welling |title=Auto-Encoding Variational Bayes |journal=arXiv preprint arXiv:1312.6114 |year=2014 |url=https://arxiv.org/abs/1312.6114 }}</ref><ref>{{cite journal |last=Goodfellow |first=Ian |author2=Jean Pouget-Abadie |author3=Mehdi Mirza |author4=Bing Xu |author5=David Warde-Farley |author6=Sherjil Ozair |author7=Aaron Courville |author8=Yoshua Bengio |title=Generative Adversarial Nets |journal=Advances in Neural Information Processing Systems |year=2014 |url=https://papers.nips.cc/paper/5423-generative-adversarial-nets }}</ref>

In 2017, the Transformer architecture enabled further advances in generative modeling compared to earlier long short-term memory (LSTM) networks.<ref name=":0">{{cite journal |last=Vaswani |first=Ashish |author2=Noam Shazeer |author3=Niki Parmar |author4=Jakob Uszkoreit |author5=Llion Jones |author6=Aidan N. Gomez |author7=Łukasz Kaiser |author8=Illia Polosukhin |title=Attention Is All You Need |journal=Advances in Neural Information Processing Systems |year=2017 |url=https://arxiv.org/abs/1706.03762 }}</ref> This led to the development of generative pre-trained transformer (GPT) models, beginning with GPT-1 in 2018.<ref>{{cite journal |last=Radford |first=Alec |title=Improving Language Understanding by Generative Pre-Training |year=2018 |url=https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf }}</ref>

=== Generative AI adoption === {{Main|AI boom}}

thumb|AI generated images have become much more advanced.|280x280px

In March 2020, the release of 15.ai, a free web application created by an anonymous MIT researcher that could generate convincing character voices using minimal training data, was one of the earliest publicly available uses for generative AI.<ref>{{cite web |last=Chandraseta |first=Rionaldi |date=January 21, 2021 |title=Generate Your Favourite Characters' Voice Lines using Machine Learning |url=https://towardsdatascience.com/generate-your-favourite-characters-voice-lines-using-machine-learning-c0939270c0c6 |url-access=registration |access-date=December 18, 2024 |website=Towards Data Science |archive-date=January 21, 2021 |archive-url=https://web.archive.org/web/20210121132456/https://towardsdatascience.com/generate-your-favourite-characters-voice-lines-using-machine-learning-c0939270c0c6 |url-status=live }}</ref> The platform is credited as the first mainstream service for audio deepfakes.<ref>{{Cite news |title=15.ai Creator reveals journey from MIT Project to internet phenomenon |last=Temitope |first=Yusuf |date=December 10, 2024 |url=https://guardian.ng/technology/15-ai-creator-reveals-journey-from-mit-project-to-internet-phenomenon/ |location=Lagos, Nigeria|access-date=December 25, 2024 |newspaper=The Guardian|archive-url=https://web.archive.org/web/20241228152312/https://guardian.ng/technology/15-ai-creator-reveals-journey-from-mit-project-to-internet-phenomenon/ |archive-date=December 28, 2024}}</ref><ref>{{cite web |author=Anirudh VK |date=March 18, 2023 |title=Deepfakes Are Elevating Meme Culture, But At What Cost? |url=https://analyticsindiamag.com/ai-origins-evolution/deepfakes-are-elevating-meme-culture-but-at-what-cost/ |access-date=December 18, 2024 |website=Analytics India Magazine |quote="While AI voice memes have been around in some form since '15.ai' launched in 2020, [...]"|url-status=live|archive-url=https://web.archive.org/web/20241226163953/https://analyticsindiamag.com/ai-origins-evolution/deepfakes-are-elevating-meme-culture-but-at-what-cost/|archive-date=December 26, 2024}}</ref>

In 2021, DALL-E, a closed-source transformer-based generative model developed by OpenAI, drew widespread attention to text-to-image generation.<ref>{{cite web |last=Coldewey |first=Devin |date=January 5, 2021 |title=OpenAI's DALL-E creates plausible images of literally anything you ask it to |url=https://techcrunch.com/2021/01/05/openais-dall-e-creates-plausible-images-of-literally-anything-you-ask-it-to/ |website=TechCrunch |access-date=March 15, 2023}}</ref>

Other projects, including open-source approaches such as VQGAN+CLIP and DALL·E Mini (later renamed Craiyon), made similar systems more accessible to the public.<ref>{{cite web |title=DALL·E mini by craiyon.com |url=https://huggingface.co/spaces/dalle-mini/dalle-mini |website=Hugging Face |access-date=20 March 2026}}</ref>

Dream by Wombo was released at the end of 2021,<ref>{{Cite web |last=Macaulay |first=Thomas |date=2021-11-24 |title=This AI app turns your words into striking (and less cursed) fan art |url=https://thenextweb.com/news/wombo-dream-ai-app-turns-words-into-fan-art |website=TNW |access-date=2026-01-18}}</ref> followed by the releases of Midjourney and Stable Diffusion in 2022.<ref>{{cite web |title=Stable Diffusion Public Release |url=https://stability.ai/blog/stable-diffusion-public-release |website=Stability AI |date=August 2022 |access-date=20 March 2026}}</ref><ref>{{cite web |title=Midjourney |url=https://www.midjourney.com/home/ |website=Midjourney |access-date=20 March 2026}}</ref>

In November 2022, the public release of ChatGPT popularized generative AI for general-purpose text-based tasks.<ref>{{cite web |last=Lock |first=Samantha |date=December 5, 2022 |title=What is AI chatbot phenomenon ChatGPT and could it replace humans? |url=https://www.theguardian.com/technology/2022/dec/05/what-is-ai-chatbot-phenomenon-chatgpt-and-could-it-replace-humans |access-date=March 15, 2023 |website=The Guardian |language=en-GB |archive-date=January 16, 2023 |archive-url=https://web.archive.org/web/20230116100346/https://www.theguardian.com/technology/2022/dec/05/what-is-ai-chatbot-phenomenon-chatgpt-and-could-it-replace-humans |url-status=live }}</ref><ref>{{cite web |last=Huang |first=Haomiao |date=August 23, 2023 |title=How ChatGPT turned generative AI into an "anything tool" |url=https://arstechnica.com/ai/2023/08/how-chatgpt-turned-generative-ai-into-an-anything-tool/ |access-date=September 21, 2024 |work=Ars Technica |archive-date=July 19, 2024 |archive-url=https://web.archive.org/web/20240719143525/https://arstechnica.com/ai/2023/08/how-chatgpt-turned-generative-ai-into-an-anything-tool/ |url-status=live }}</ref><ref>{{cite journal |last1=Krakowski |first1=Sebastian |title=Human-AI agency in the age of generative AI |journal=Information and Organization |date=March 2025 |volume=35 |issue=1 |article-number=100560 |doi=10.1016/j.infoandorg.2025.100560 }}</ref>

thumb|Private investment in AI (pink) and generative AI (green)

In a 2024 survey by marketing research firm Ipsos, Asia–Pacific countries were significantly more optimistic than Western societies about generative AI and show higher adoption rates. Despite expressing concerns about privacy and the pace of change, 68% of Asia-Pacific respondents believed that AI was having a positive impact on the world, compared to 57% globally.<ref>{{Cite web |date=2024-11-11 |title=Asia Pacific open to digital and reform transformation |url=https://www.ipsos.com/en-th/ua_globaltrends |access-date=2025-06-14 |website=IPSOS |language=en-th}}</ref> According to a survey by SAS and Coleman Parkes Research, as of 2023, 83% of Chinese respondents were using the technology, exceeding both the global average of 54% and the U.S. rate of 65%. A UN report indicated that Chinese entities filed over 38,000 generative AI patents from 2014 to 2023, more than any other country.<ref>{{cite news |last=Baptista |first=Eduardo |date=July 9, 2024 |title=China leads the world in adoption of generative AI, survey shows |url=https://www.reuters.com/technology/artificial-intelligence/china-leads-world-adoption-generative-ai-survey-shows-2024-07-09/ |access-date=July 14, 2024 |work=Reuters}}</ref> A 2024 survey by the Just So Soul social media app reported that 18% of respondents born after 2000 used generative AI "almost every day", and that over 60% of respondents like or love AI-generated content (AIGC), while less than 3% dislike or hate it.<ref>{{cite web |author1=He Qitong |author2=Li Dongxu |date=May 31, 2024 |title=Young Chinese Have Almost No Concerns About AI, Survey Finds |url=https://www.sixthtone.com/news/1015263 |work=Sixth Tone|publisher=Shanghai United Media Group}}</ref>

By mid 2025, companies were increasingly abandoning generative AI pilot projects as they had difficulties with integration, data quality and unmet returns, leading analysts at Gartner and ''The Economist'' to characterize the period as entering the Gartner hype cycle's "trough of disillusionment" phase.<ref>{{cite press release |title=Gartner Says Generative AI for Procurement Has Entered the Trough of Disillusionment |url=https://www.gartner.com/en/newsroom/press-releases/2025-07-30-gartner-says-generative-ai-for-procurement-has-entered-the-trough-of-disillusionment |access-date=16 September 2025 |agency=Gartner |date=30 July 2025}}</ref><ref>{{cite news |title=Welcome to the AI trough of disillusionment |url=https://www.economist.com/business/2025/05/21/welcome-to-the-ai-trough-of-disillusionment |access-date=16 September 2025 |newspaper=The Economist |date=21 May 2025}}</ref>

== Applications == {{main|Applications of artificial intelligence}}

Generative artificial intelligence has been applied across multiple industries for content creation and automation. In healthcare, generative models are used for drug discovery and the generation of synthetic medical data to train diagnostic systems.<ref>{{cite journal |last=Kaushal |first=Ayush |title=Artificial Intelligence in Healthcare: Applications and Challenges |journal=Nature Medicine |year=2023 }}</ref> In finance, they are used for report drafting, data generation, and customer service automation.<ref name="auto">{{cite journal |last=Bommasani |first=Rishi |title=On the Opportunities and Risks of Foundation Models |journal=arXiv preprint arXiv:2108.07258 |year=2021 }}</ref> Media and entertainment industries use generative systems for tasks such as music composition, script development, and image or video generation.<ref>{{cite journal |last=Epstein |first=Ziv |title=Art and the science of generative AI |journal=Science |year=2023 |volume=380 |issue=6650 |pages=1110–1111 |doi=10.1126/science.adh4451 }}</ref> Researchers and policymakers have raised concerns regarding accuracy, misuse, and impacts on academic and professional work.<ref name="auto"/>

=== Text and software code === {{main|Large language model|AI-assisted software development|List of AI-assisted software development tools}}

{{See also|Code completion|Autocomplete|List of large language models|List of text corpora|List of chatbots}}

Large language models (LLMs) are trained on tokenized text from large corpora and are capable of natural language processing, machine translation, and natural language generation.<ref>{{cite journal |last=Brown |first=Tom B. |title=Language Models are Few-Shot Learners |journal=Advances in Neural Information Processing Systems |year=2020 |url=https://arxiv.org/abs/2005.14165 }}</ref>

LLMs can be used as foundation models for a variety of downstream tasks.<ref name="FoundationModels">{{Cite arXiv |eprint=2108.07258 |class=cs.LG |first1=R. |last1=Bommasani |title=On the opportunities and risks of foundation models |date=2021}}</ref> They can also be trained on source code to generate programs from prompts.<ref>{{cite journal |last=Chen |first=Mark |title=Evaluating Large Language Models Trained on Code |journal=arXiv preprint arXiv:2107.03374 |year=2021 }}</ref>

=== Audio === {{see also|Generative audio|Music and artificial intelligence}}

In 2016, DeepMind's WaveNet demonstrated that deep neural networks can generate raw audio waveforms.<ref>{{cite journal |last=van den Oord |first=Aaron |title=WaveNet: A Generative Model for Raw Audio |journal=arXiv preprint arXiv:1609.03499 |year=2016 |url=https://arxiv.org/abs/1609.03499 }}</ref> This enabled more realistic speech synthesis compared to earlier approaches. Subsequent systems such as Tacotron 2 demonstrated end-to-end neural text-to-speech generation.<ref>{{cite journal |last=Shen |first=Jonathan |title=Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions |journal=arXiv preprint arXiv:1712.05884 |year=2017 |url=https://arxiv.org/abs/1712.05884 }}</ref>

=== Images === {{see also|Text-to-image model|Artificial intelligence art}}

Generative AI can be used to create visual art.<ref>{{cite journal |last=Epstein |first=Ziv |title=Art and the science of generative AI |journal=Science |date=2023 |volume=380 |issue=6650 |pages=1110–1111 |doi=10.1126/science.adh4451 }}</ref> Such systems are trained on image–text pairs. Examples include Stable Diffusion, DALL-E, and Midjourney.<ref>{{cite conference |last=Ramesh |first=Aditya |title=Zero-shot text-to-image generation |book-title=International Conference on Machine Learning |year=2021 |url=https://proceedings.mlr.press/v139/ramesh21a.html }}</ref>

=== Video === {{See also|Text-to-video model}}

Generative AI can be used to produce photorealistic videos. Systems such as Runway have demonstrated text-to-video generation capabilities.<ref>{{cite news |last=Metz |first=Cade |title=Instant Videos Could Represent the Next Leap in A.I. Technology |url=https://www.nytimes.com/2023/04/04/technology/runway-ai-videos.html |work=The New York Times |date=April 4, 2023 }}</ref>

=== Robotics ===

Generative models can be used for motion planning and robot control by learning from prior data.<ref>{{cite web |title=A technique for more effective multipurpose robots |url=https://news.mit.edu/2024/technique-for-more-effective-multipurpose-robots-0603 |website=MIT News |date=2024 }}</ref>

=== 3D modeling ===

Generative models can assist in automating 3D modeling tasks, including generating 3D assets from text or images.<ref>{{cite journal |last=Poole |first=Ben |title=DreamFusion: Text-to-3D using 2D Diffusion |journal=arXiv preprint arXiv:2209.14988 |year=2022 }}</ref>

=== World models ===

World models are neural networks designed to learn representations of physical environments, including spatial and dynamic properties.<ref>{{cite journal |last=Ha |first=David |title=World Models |journal=arXiv preprint arXiv:1803.10122 |year=2018 |url=https://arxiv.org/abs/1803.10122 }}</ref> Recent multimodal systems have expanded these capabilities by integrating vision, language, and action into unified models.<ref>{{cite journal |last=Reed |first=Scott |title=A Generalist Agent |journal=arXiv preprint arXiv:2205.06175 |year=2022 }}</ref>

=== AI-assisted mathematical discovery === {{See also|AlphaGeometry|AlphaTensor}} Generative AI systems have been used in mathematics and computer science to generate candidate computer programs, proofs, constructions, or algorithms.

In 2023, Google DeepMind introduced FunSearch, a method for creating computer programs that solve mathematical and algorithmic problems. FunSearch was used to discover new mathematical constructions in the cap set problem and the bin packing problem.<ref name="funsearch-nature">{{cite journal |last1=Romera-Paredes |first1=Bernardino |last2=Barekatain |first2=Mohammadamin |last3=Novikov |first3=Alexander |last4=Balog |first4=Matej |last5=Kumar |first5=M. Pawan |last6=Dupont |first6=Emilien |last7=Ruiz |first7=Francisco J. R. |last8=Ellenberg |first8=Jordan S. |last9=Wang |first9=Pengming |last10=Fawzi |first10=Omar |last11=Kohli |first11=Pushmeet |last12=Fawzi |first12=Alhussein |display-authors=etal |title=Mathematical discoveries from program search with large language models |journal=Nature |volume=625 |pages=468–475 |date=2024 |doi=10.1038/s41586-023-06924-6}}</ref><ref name="mouret-2024">{{cite journal |last=Mouret |first=Jean-Baptiste |title=Large language models help computer programs to evolve |journal=Nature |volume=625 |pages=452–453 |date=January 17, 2024 |doi=10.1038/d41586-023-03998-0}}</ref><ref name="ellenberg-2025">{{cite arXiv |last1=Ellenberg |first1=Jordan S. |last2=Fraser-Taliente |first2=Cristofero S. |last3=Harvey |first3=Thomas R. |last4=Srivastava |first4=Karan |last5=Sutherland |first5=Andrew V. |title=Generative Modeling for Mathematical Discovery |date=March 17, 2025 |eprint=2503.11061 |class=cs.LG}}</ref>

In 2023, Google DeepMind introduced AlphaDev, which was used to discover small sorting algorithms that outperformed previously known human benchmarks and have been integrated into the LLVM standard C++ sorting library.<ref>{{Cite web |last=Timmer |first=John |date=2023-06-07 |title=AI system devises first optimizations to sorting code in over a decade |url=https://arstechnica.com/science/2023/06/googles-deepmind-develops-a-system-that-writes-efficient-algorithms/ |access-date=2026-05-08 |website=Ars Technica |language=en}}</ref><ref name="alphadev-nature">{{cite journal |last1=Mankowitz |first1=Daniel J. |last2=Michalski |first2=Andrea |last3=Zhernov |first3=Anton |last4=Gelmi |first4=Marco |last5=Selvi |first5=Marco |display-authors=etal |title=Faster sorting algorithms discovered using deep reinforcement learning |journal=Nature |date=June 2023 |volume=618 |issue=7964 |pages=257–263 |doi=10.1038/s41586-023-06004-9 |pmid=37286649}}</ref> In 2025, Google DeepMind introduced AlphaEvolve, an AI system for general-purpose algorithm discovery and optimization. AlphaEvolve uses LLMs to propose code changes, automated evaluators to assess each candidate, and an evolutionary process to iteratively improve algorithms.<ref>{{Cite web |last=Whitwam |first=Ryan |date=May 14, 2025 |title=Google DeepMind creates super-advanced AI that can invent new algorithms |url=https://arstechnica.com/ai/2025/05/google-deepmind-creates-super-advanced-ai-that-can-invent-new-algorithms/ |access-date=May 1, 2026 |website=Ars Technica}}</ref>

=== Materials science === {{See also|Materials informatics|Computational materials science|Crystal structure prediction|Machine learning in physics}}

In 2023, Google DeepMind introduced GNoME, a method to propose candidate inorganic crystal structures for computational screening and experimental synthesis in material science.<ref name="merchant2023-gnome">{{cite journal |last1=Merchant |first1=Amil |last2=Batzner |first2=Simon |last3=Schoenholz |first3=Samuel S. |last4=Aykol |first4=Muratahan |last5=Cheon |first5=Gowoon |last6=Cubuk |first6=Ekin Dogus |display-authors=etal |title=Scaling deep learning for materials discovery |journal=Nature |date=2023 |volume=624 |issue=7990 |pages=80–85 |doi=10.1038/s41586-023-06735-9 |doi-access=free}}</ref> Other material science methods include MatterGen,<ref name="mattergen-nature">{{cite journal |last1=Zeni |first1=Claudio |last2=Pinsler |first2=Robert |last3=Zügner |first3=Daniel |display-authors=etal |title=A generative model for inorganic materials design |journal=Nature |date=2025 |volume=639 |pages=624–632 |doi=10.1038/s41586-025-08628-5 |doi-access=free}}</ref> CDVAE,<ref name="cdvae">{{cite conference |last1=Xie |first1=Tian |last2=Fu |first2=Xiang |last3=Ganea |first3=Octavian-Eugen |last4=Barzilay |first4=Regina |last5=Jaakkola |first5=Tommi |title=Crystal Diffusion Variational Autoencoder for Periodic Material Generation |book-title=International Conference on Learning Representations |year=2022 |url=https://openreview.net/forum?id=03RLpj-tc_}}</ref> and CrystalFlow.<ref name="crystalflow">{{cite journal |last1=Luo |first1=Xiaoshan |last2=Wang |first2=Zhenyu |last3=Wang |first3=Qingchang |display-authors=etal |title=CrystalFlow: a flow-based generative model for crystalline materials |journal=Nature Communications |date=2025 |volume=16 |pages=9267 |doi=10.1038/s41467-025-64364-4 |doi-access=free}}</ref>{{primary inline|date=May 2026}}

=== Generative engine optimization === {{Main|Generative engine optimization}} Generative engine optimization (GEO) is the AI equivalent to search engine optimization (SEO). As a marketing practice, GEO focuses on structuring content so that it is more likely to be cited or referenced by large language models.<ref name="u789">{{cite conference |last1=Aggarwal |first1=Pranjal |last2=Murahari |first2=Vishvak |last3=Rajpurohit |first3=Tanmay |last4=Kalyan |first4=Ashwin |last5=Narasimhan |first5=Karthik |last6=Deshpande |first6=Ameet |title=GEO: Generative Engine Optimization |conference=Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining |publisher=ACM |date=2024 |pages=5-16 |isbn=979-8-4007-0490-1 |doi=10.1145/3637528.3671900 |url=https://dl.acm.org/doi/10.1145/3637528.3671900 |access-date=2026-05-16}}</ref><ref name="z097">{{cite web |last1=Krishnamurthy |first1=Rajeshwari |last2=Bathula |first2=Varshith |last3=Reddy |first3=K. Gautam |title=Will GEO Overtake SEO? |website=California Management Review Insights |publisher=UC Berkeley Haas School of Business |date=2025-11-20 |url=https://cmr.berkeley.edu/2025/11/will-geo-overtake-seo/ |access-date=2026-05-16}}</ref>

==Software and hardware== [[File:GenAI Agent.png|right|thumb|Architecture of a generative AI agent]] Generative AI models are used to power chatbot products such as ChatGPT, programming tools such as GitHub Copilot,<ref>{{Cite web |url=https://www.axios.com/2023/06/30/githubs-vision-to-make-code-more-secure-by-design |title=GitHub has a vision to make code more secure by design |last=Sabin |first=Sam |date=June 30, 2023 |website=Axios Codebook |access-date=August 15, 2023 |archive-date=August 15, 2023 |archive-url=https://web.archive.org/web/20230815225809/https://www.axios.com/2023/06/30/githubs-vision-to-make-code-more-secure-by-design |url-status=live }}</ref> text-to-image products such as Midjourney, and text-to-video products such as Runway Gen-2.<ref>{{cite web |last=Vincent |first=James |date=March 20, 2023 |title=Text-to-video AI inches closer as startup Runway announces new model |url=https://www.theverge.com/2023/3/20/23648113/text-to-video-generative-ai-runway-ml-gen-2-model-access |access-date=August 15, 2023 |work=The Verge |quote=Text-to-video is the next frontier for generative AI, though current output is rudimentary. Runway says it'll be making its new generative video model, Gen-2, available to users in 'the coming weeks.' |archive-date=September 27, 2023 |archive-url=https://web.archive.org/web/20230927003647/https://www.theverge.com/2023/3/20/23648113/text-to-video-generative-ai-runway-ml-gen-2-model-access |url-status=live }}</ref> Generative AI features have been integrated into a variety of existing commercially available products such as Microsoft Office (Microsoft Copilot),<ref>{{Cite web |url=https://www.cnbc.com/2023/03/16/microsoft-to-improve-office-365-with-chatgpt-like-generative-ai-tech-.html |title=Microsoft adds OpenAI technology to Word and Excel |last=Vanian |first=Jonathan |date=March 16, 2023 |access-date=August 15, 2023 |publisher=CNBC |quote=Microsoft is bringing generative artificial intelligence technologies such as the popular ChatGPT chatting app to its Microsoft 365 suite of business software....the new A.I. features, dubbed Copilot, will be available in some of the company's most popular business apps, including Word, PowerPoint and Excel. |archive-date=August 15, 2023 |archive-url=https://web.archive.org/web/20230815225809/https://www.cnbc.com/2023/03/16/microsoft-to-improve-office-365-with-chatgpt-like-generative-ai-tech-.html |url-status=live }}</ref> Google Photos,<ref>{{cite web |last=Wilson |first=Mark |date=August 15, 2023 |title=The app's Memories feature just got a big upgrade |url=https://www.techradar.com/computing/software/google-photos-now-shows-you-an-ai-powered-highlights-reel-of-your-life |work=TechRadar |quote=The Google Photos app is getting a redesigned, AI-powered Memories feature...you'll be able to use generative AI to come up with some suggested names like "a desert adventure". |archive-date=August 15, 2023 |archive-url=https://web.archive.org/web/20230815225809/https://www.techradar.com/computing/software/google-photos-now-shows-you-an-ai-powered-highlights-reel-of-your-life |url-status=live }}</ref> and the Adobe Suite (Adobe Firefly).<ref>{{cite web |url=https://www.mediapost.com/publications/article/385660/adobe-adds-generative-ai-to-photoshop.html |title=Adobe Adds Generative AI To Photoshop |first=Laurie |last=Sullivan |date=May 23, 2023 |access-date=August 15, 2023 |work=MediaPost |quote=Generative artificial intelligence (AI) will become one of the most important features for creative designers and marketers. Adobe on Tuesday unveiled a Generative Fill feature in Photoshop to bring Firefly's AI capabilities into design. |archive-date=August 15, 2023 |archive-url=https://web.archive.org/web/20230815225809/https://www.mediapost.com/publications/article/385660/adobe-adds-generative-ai-to-photoshop.html |url-status=live }}</ref> Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA<ref>{{cite web |url=https://venturebeat.com/ai/llama-2-how-to-access-and-use-metas-versatile-open-source-chatbot-right-now/ |title=LLaMA 2: How to access and use Meta's versatile open-source chatbot right now |author=Michael Nuñez |date=July 19, 2023 |access-date=August 15, 2023 |website=VentureBeat |quote=If you want to run LLaMA 2 on your own machine or modify the code, you can download it directly from Hugging Face, a leading platform for sharing AI models. |archive-date=November 3, 2023 |archive-url=https://web.archive.org/web/20231103020505/https://venturebeat.com/ai/llama-2-how-to-access-and-use-metas-versatile-open-source-chatbot-right-now/ |url-status=live }}</ref> language model.

Smaller generative AI models with up to a few billion parameters can run on smartphones, embedded devices, and personal computers. For example, LLaMA-7B (a version with 7 billion parameters) can run on a Raspberry Pi 4<ref>{{cite web |url=https://www.tomshardware.com/how-to/create-ai-chatbot-server-on-raspberry-pi |title=How To Create Your Own AI Chatbot Server With Raspberry Pi 4 |last=Pounder |first=Les |date=March 25, 2023 |access-date=August 15, 2023 |publisher= |quote=Using a Pi 4 with 8GB of RAM, you can create a ChatGPT-like server based on LLaMA. |archive-date=August 15, 2023 |archive-url=https://web.archive.org/web/20230815225810/https://www.tomshardware.com/how-to/create-ai-chatbot-server-on-raspberry-pi |url-status=live }}</ref> and one version of Stable Diffusion can run on an iPhone 11.<ref>{{cite web |url=https://the-decoder.com/draw-things-app-brings-stable-diffusion-to-the-iphone/ |title="Draw Things" App brings Stable Diffusion to the iPhone |last=Kemper |first=Jonathan |date=November 10, 2022 |website=The Decoder |quote=Draw Things is an app that brings Stable Diffusion to the iPhone. The AI images are generated locally, so you don't need an Internet connection. |access-date=August 15, 2023 |archive-date=August 15, 2023 |archive-url=https://web.archive.org/web/20230815225811/https://the-decoder.com/draw-things-app-brings-stable-diffusion-to-the-iphone/ |url-status=live }}</ref>

Larger models with tens of billions of parameters can run on laptop or desktop computers. To achieve an acceptable speed, models of this size may require accelerators such as the GPU chips produced by NVIDIA and AMD or the Neural Engine included in Apple silicon products. For example, the 65 billion parameter version of LLaMA can be configured to run on a desktop PC.<ref>{{cite web |last=Witt |first=Allan |date=July 7, 2023 |title=Best Computer to Run LLaMA AI Model at Home (GPU, CPU, RAM, SSD) |url=https://www.hardware-corner.net/guides/computer-to-run-llama-ai-model/ |quote=To run LLaMA model at home, you will need a computer build with a powerful GPU that can handle the large amount of data and computation required for inferencing. |access-date=August 15, 2023 |archive-date=August 15, 2023 |archive-url=https://web.archive.org/web/20230815225811/https://www.hardware-corner.net/guides/computer-to-run-llama-ai-model/ |url-status=live }}</ref>

The advantages of running generative AI locally include protection of privacy and intellectual property, and avoidance of rate limiting and censorship. The subreddit r/LocalLLaMA in particular focuses on using consumer-grade gaming graphics cards<ref>{{cite web |url=https://www.pcmag.com/how-to/how-to-run-your-own-chatgpt-like-llm-for-free-and-in-private |title=Who Needs ChatGPT? How to Run Your Own Free and Private AI Chatbot |last=Westover |first=Brian |date=September 28, 2023 |publisher=Ziff Davis |access-date=January 7, 2024 |archive-date=January 7, 2024 |archive-url=https://web.archive.org/web/20240107171858/https://www.pcmag.com/how-to/how-to-run-your-own-chatgpt-like-llm-for-free-and-in-private |url-status=live }}</ref> through such techniques as compression.

Language models with hundreds of billions of parameters, such as GPT-4 or PaLM, typically run on datacenter computers equipped with arrays of GPUs (such as NVIDIA's H100) or AI accelerator chips (such as Google's TPU). These very large models are typically accessed as cloud services over the Internet.

In 2022, the United States New Export Controls on Advanced Computing and Semiconductors to China imposed restrictions on exports to China of GPU and AI accelerator chips used for generative AI.<ref>{{Cite web |url=https://www.reuters.com/technology/nvidia-says-us-has-imposed-new-license-requirement-future-exports-china-2022-08-31/ |title=U.S. officials order Nvidia to halt sales of top AI chips to China |last1=Nellis |first1=Stephen |last2=Lee |first2=Jane |date=September 1, 2022 |website=Reuters |access-date=August 15, 2023 |archive-date=August 15, 2023 |archive-url=https://web.archive.org/web/20230815225809/https://www.reuters.com/technology/nvidia-says-us-has-imposed-new-license-requirement-future-exports-china-2022-08-31/ |url-status=live }}</ref> Chips such as the NVIDIA A800<ref>{{cite web |url=https://www.tomshardware.com/news/nvidia-a800-performance-revealed |title=Nvidia's Chinese A800 GPU's Performance Revealed |first=Anton |last=Shilov |date=May 7, 2023 |publisher=Tom's Hardware |access-date=August 15, 2023 |quote=the A800 operates at 70% of the speed of A100 GPUs while complying with strict U.S. export standards that limit how much processing power Nvidia can sell. |archive-date=May 7, 2024 |archive-url=https://web.archive.org/web/20240507100659/https://www.tomshardware.com/news/nvidia-a800-performance-revealed |url-status=live }}</ref> and the Biren Technology BR104<ref>{{cite web |last=Patel |first=Dylan |date=October 24, 2022 |title=How China's Biren Is Attempting To Evade US Sanctions |url=https://www.semianalysis.com/p/how-chinas-biren-is-attempting-to |access-date=August 15, 2023 |archive-date=August 15, 2023 |archive-url=https://web.archive.org/web/20230815225810/https://www.semianalysis.com/p/how-chinas-biren-is-attempting-to |url-status=live }}</ref> were developed to meet the requirements of the sanctions.

There is free software on the market capable of recognizing text generated by generative artificial intelligence (such as GPTZero), as well as images, audio or video coming from it.<ref>{{cite web|url=https://www-wired-it.translate.goog/article/software-riconoscere-intelligenza-artificiale-immagini-false/?_x_tr_sl=it&_x_tr_tl=en&_x_tr_hl=it&_x_tr_pto=wapp|title=5 free software to recognise fake AI-generated images|date=October 28, 2023|language=it|access-date=October 29, 2023|archive-date=October 29, 2023|archive-url=https://web.archive.org/web/20231029181337/https://www-wired-it.translate.goog/article/software-riconoscere-intelligenza-artificiale-immagini-false/?_x_tr_sl=it&_x_tr_tl=en&_x_tr_hl=it&_x_tr_pto=wapp|url-status=live}}</ref> Potential mitigation strategies for detecting generative AI content include digital watermarking, content authentication, information retrieval, and machine learning classifier models.<ref>{{Cite web |date=January 4, 2024 |title=Detecting AI fingerprints: A guide to watermarking and beyond |url=https://www.brookings.edu/articles/detecting-ai-fingerprints-a-guide-to-watermarking-and-beyond/ |archive-url=https://web.archive.org/web/20240903212302/https://www.brookings.edu/articles/detecting-ai-fingerprints-a-guide-to-watermarking-and-beyond/ |archive-date=September 3, 2024 |access-date=September 5, 2024 |website=Brookings Institution |language=en-US}}</ref> Despite claims of accuracy, both free and paid AI text detectors have frequently produced false positives, mistakenly accusing students of submitting AI-generated work.<ref>{{cite web |url=https://www.washingtonpost.com/technology/2023/04/01/chatgpt-cheating-detection-turnitin |title=We tested a new ChatGPT-detector for teachers. It flagged an innocent student |last=Fowler |first=Geoffrey |date=April 3, 2023 |website=washingtonpost.com |access-date=February 6, 2024 |archive-date=March 28, 2024 |archive-url=https://web.archive.org/web/20240328081619/https://www.washingtonpost.com/technology/2023/04/01/chatgpt-cheating-detection-turnitin/ |url-status=live }}</ref><ref>{{cite web |url=https://www.washingtonpost.com/technology/2023/06/02/turnitin-ai-cheating-detector-accuracy/ |title=Detecting AI may be impossible. That's a big problem for teachers. |last=Fowler |first=Geoffrey |date=June 2, 2023 |website=washingtonpost.com |access-date=February 6, 2024 |archive-date=June 3, 2023 |archive-url=https://web.archive.org/web/20230603004340/https://www.washingtonpost.com/technology/2023/06/02/turnitin-ai-cheating-detector-accuracy/ |url-status=live }}</ref>

=== Generative models and training techniques ===

==== Generative adversarial networks ==== thumb|Workflow for the training of a generative adversarial network Generative adversarial networks (GANs) are a generative modeling technique which consist of two neural networks—the generator and the discriminator—trained simultaneously in a competitive setting. The generator creates synthetic data by transforming random noise into samples that resemble the training dataset. The discriminator is trained to distinguish the authentic data from synthetic data produced by the generator. The two models engage in a minimax game: the generator aims to create increasingly realistic data to "fool" the discriminator, while the discriminator improves its ability to distinguish real from fake data. This continuous training setup enables the generator to produce high-quality and realistic outputs.<ref>{{Cite journal |last1=Goodfellow |first1=Ian |last2=Pouget-Abadie |first2=Jean |last3=Mirza |first3=Mehdi |last4=Xu |first4=Bing |last5=Warde-Farley |first5=David |last6=Ozair |first6=Sherjil |last7=Courville |first7=Aaron |last8=Bengio |first8=Yoshua |date=October 22, 2020 |title=Generative adversarial networks |journal=Communications of the ACM |volume=63 |issue=11 |pages=139–144 |doi=10.1145/3422622 |issn=0001-0782|arxiv=1406.2661 }}</ref>

==== Variational autoencoders ==== alt=Two images of the same cartoon crocodile|thumb|upright=1.35|Comparison between images generated by a VAE (left) and a GAN (right). VAEs tend to produce smoother but blurrier images due to their probabilistic decoding. Variational autoencoders (VAEs) are deep learning models that probabilistically encode data. They are typically used for tasks such as noise reduction from images, data compression, identifying unusual patterns, and facial recognition. Unlike standard autoencoders, which compress input data into a fixed latent representation, VAEs model the latent space as a probability distribution, allowing for smooth sampling and interpolation between data points. The encoder ("recognition model") maps input data to a latent space, producing means and variances that define a probability distribution. The decoder ("generative model") samples from this latent distribution and attempts to reconstruct the original input.<ref>{{Cite book |last1=Kingma |first1=Diederik P. |title=An Introduction to Variational Autoencoders |last2=Welling |first2=Max |date=2019 |publisher=Emerald Insights |doi=10.1561/2200000056 |arxiv=1906.02691 |isbn=978-1-68083-622-6}}</ref> alt=The full architecture of a GPT model.|thumb|385x385px|The full architecture of a GPT model

==== Transformers ==== Transformers became the foundation for the generative pre-trained transformer (GPT) series developed by OpenAI, replacing traditional recurrent and convolutional models. The self-attention mechanism enables the model to determine the relative importance of each token in a sequence when predicting the next token, thereby improving contextual understanding. Unlike recurrent neural networks, transformers process tokens in parallel, which improves training efficiency and scalability.<ref name=":0" />

==Law and regulation== {{Main|Regulation of artificial intelligence}}

In the United States, a group of companies including OpenAI, Alphabet, and Meta signed a voluntary agreement with the Biden administration in July 2023 to watermark AI-generated content.<ref>{{cite news|last1=Bartz|first1=Diane|last2=Hu|first2=Krystal|title=OpenAI, Google, others pledge to watermark AI content for safety, White House says|date=July 21, 2023|url=https://www.reuters.com/technology/openai-google-others-pledge-watermark-ai-content-safety-white-house-2023-07-21/|work=Reuters|archive-date=July 27, 2023|archive-url=https://web.archive.org/web/20230727074519/https://www.reuters.com/technology/openai-google-others-pledge-watermark-ai-content-safety-white-house-2023-07-21/|url-status=live}}</ref> In October 2023, Executive Order 14110 applied the Defense Production Act to require all US companies to report information to the federal government when training certain high-impact AI models.<ref>{{cite web| url = https://bidenwhitehouse.archives.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/| title = FACT SHEET: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence| publisher = The White House| date = October 30, 2023| access-date = January 30, 2024| archive-date = January 30, 2024| archive-url = https://web.archive.org/web/20240130131421/https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/| url-status = live}}</ref><ref>{{Cite news |last=Burt |first=Andrew |date=October 31, 2023 |title=3 Obstacles to Regulating Generative AI |url=https://hbr.org/2023/10/3-obstacles-to-regulating-generative-ai |access-date=February 17, 2024 |work=Harvard Business Review |issn=0017-8012 |archive-date=February 17, 2024 |archive-url=https://web.archive.org/web/20240217052101/https://hbr.org/2023/10/3-obstacles-to-regulating-generative-ai |url-status=live }}</ref>

In the European Union (EU), the Artificial Intelligence Act includes requirements to disclose copyrighted material used to train generative AI systems, and to label any AI-generated output as such.<ref>{{cite web |title=EU AI Act: first regulation on artificial intelligence |url=https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence |website=European Parliament |access-date=September 13, 2024 |language=en |date=August 6, 2023 |archive-date=February 21, 2024 |archive-url=https://web.archive.org/web/20240221024951/https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence |url-status=live }}</ref><ref>{{cite news|last1=Chee|first1=Foo Yun|last2=Mukherjee|first2=Supantha|title=EU lawmakers vote for tougher AI rules as draft moves to final stage|url=https://www.reuters.com/technology/eu-lawmakers-agree-changes-draft-artificial-intelligence-rules-2023-06-14/|website=Reuters|date=June 14, 2023|access-date=July 26, 2023|language=en|archive-date=July 27, 2023|archive-url=https://web.archive.org/web/20230727074515/https://www.reuters.com/technology/eu-lawmakers-agree-changes-draft-artificial-intelligence-rules-2023-06-14/|url-status=live}}</ref>

In China, the Interim Measures for the Management of Generative AI Services introduced by the Cyberspace Administration of China regulates any public-facing generative AI. It includes requirements to watermark generated images or videos, regulations on training data and label quality, restrictions on personal data collection, and a guideline that generative AI services must "adhere to socialist core values".<ref>{{Cite web|url=https://www.reuters.com/technology/china-issues-temporary-rules-generative-ai-services-2023-07-13/|title=China says generative AI rules to apply only to products for the public|last=Ye|first=Josh|date=July 13, 2023|website=Reuters|access-date=July 13, 2023|archive-date=July 27, 2023|archive-url=https://web.archive.org/web/20230727074517/https://www.reuters.com/technology/china-issues-temporary-rules-generative-ai-services-2023-07-13/|url-status=live}}</ref><ref>{{Cite web|url=http://www.cac.gov.cn/2023-07/13/c_1690898327029107.htm|title=生成式人工智能服务管理暂行办法|date=July 13, 2023|access-date=July 27, 2023|archive-date=July 27, 2023|archive-url=https://web.archive.org/web/20230727125136/http://www.cac.gov.cn/2023-07/13/c_1690898327029107.htm|url-status=live}}</ref>

===Copyright=== {{Main|Artificial intelligence and copyright}}

====Training with copyrighted content==== Generative AI systems such as ChatGPT and Midjourney are trained on large, publicly available datasets that include copyrighted works. AI developers have argued that such training is protected under fair use, while copyright holders have argued that it infringes their rights.<ref name="crscopyright">{{Cite web| url = https://crsreports.congress.gov/product/pdf/LSB/LSB10922| title = Generative Artificial Intelligence and Copyright Law| date = September 29, 2023| work = Congressional Research Service| access-date = January 30, 2024| series = LSB10922| archive-date = March 22, 2024| archive-url = https://web.archive.org/web/20240322233637/https://crsreports.congress.gov/product/pdf/LSB/LSB10922| url-status = live}}</ref>

Proponents of fair use training have argued that it is a transformative use and does not involve making copies of copyrighted works available to the public.<ref name="crscopyright"/> Critics have argued that image generators such as Midjourney can create nearly-identical copies of some copyrighted images,<ref>{{cite web | url = https://www.nytimes.com/interactive/2024/01/25/business/ai-image-generators-openai-microsoft-midjourney-copyright.html | title = We Asked A.I. to Create the Joker. It Generated a Copyrighted Image. | first = Stuart | last = Thompson | date = January 25, 2024 | access-date = January 26, 2024 | work = The New York Times | archive-date = January 25, 2024 | archive-url = https://web.archive.org/web/20240125233919/https://www.nytimes.com/interactive/2024/01/25/business/ai-image-generators-openai-microsoft-midjourney-copyright.html | url-status = live }}</ref> and that generative AI programs compete with the content they are trained on.<ref>{{cite news |last1=Hadero |first1=Haleluya |last2=Bauder |first2=David |title=The New York Times sues OpenAI and Microsoft for using its stories to train chatbots |url=https://apnews.com/article/nyt-new-york-times-openai-microsoft-6ea53a8ad3efa06ee4643b697df0ba57 |work=Associated Press News |publisher=AP News |date=December 27, 2023 |access-date=April 13, 2023 |archive-date=December 27, 2023 |archive-url=https://web.archive.org/web/20231227150436/https://apnews.com/article/nyt-new-york-times-openai-microsoft-6ea53a8ad3efa06ee4643b697df0ba57 |url-status=live }}</ref>

As of 2024, several lawsuits related to the use of copyrighted material in training are ongoing. Getty Images has sued Stability AI over the use of its images to train Stable Diffusion.<ref>{{cite web |last=O'Brien |first=Matt |title=Photo giant Getty took a leading AI image-maker to court. Now it's also embracing the technology |url=https://apnews.com/article/getty-images-artificial-intelligence-ai-image-generator-stable-diffusion-a98eeaaeb2bf13c5e8874ceb6a8ce196 |website=AP NEWS |publisher=Associated Press |date=September 25, 2023 |access-date=January 30, 2024 |archive-date=January 30, 2024 |archive-url=https://web.archive.org/web/20240130221552/https://apnews.com/article/getty-images-artificial-intelligence-ai-image-generator-stable-diffusion-a98eeaaeb2bf13c5e8874ceb6a8ce196 |url-status=live }}</ref> Both the Authors Guild and The New York Times have sued Microsoft and OpenAI over the use of their works to train ChatGPT.<ref>{{cite magazine | url = https://www.wired.com/story/livewired-generative-ai-copyright/ | title = The Generative AI Copyright Fight Is Just Getting Started | first = Gregory | last = Barber | date = December 9, 2023 | access-date = January 19, 2024 | magazine = Wired | archive-date = January 19, 2024 | archive-url = https://web.archive.org/web/20240119131913/https://www.wired.com/story/livewired-generative-ai-copyright/ | url-status = live }}</ref><ref>{{cite web | url = https://www.wsj.com/tech/ai/new-york-times-sues-microsoft-and-openai-alleging-copyright-infringement-fd85e1c4 | title = New York Times Sues Microsoft and OpenAI, Alleging Copyright Infringement | first = Alexandra | last = Bruell | date = December 27, 2023 | access-date = January 19, 2024 | work = Wall Street Journal | archive-date = January 18, 2024 | archive-url = https://web.archive.org/web/20240118042622/https://www.wsj.com/tech/ai/new-york-times-sues-microsoft-and-openai-alleging-copyright-infringement-fd85e1c4 | url-status = live }}</ref>

====Copyright of AI-generated content==== A separate question is whether AI-generated works can qualify for copyright protection. The United States Copyright Office has ruled that works created by artificial intelligence without any human input cannot be copyrighted, because they lack human authorship.<ref>{{cite web | url = https://www.reuters.com/legal/ai-generated-art-cannot-receive-copyrights-us-court-says-2023-08-21/ | title = AI-generated art cannot receive copyrights, US court says | first = Blake | last = Brittain | date = August 21, 2023 | access-date = January 19, 2024 | work = Reuters | archive-date = January 20, 2024 | archive-url = https://web.archive.org/web/20240120193026/https://www.reuters.com/legal/ai-generated-art-cannot-receive-copyrights-us-court-says-2023-08-21/ | url-status = live }}</ref> Some legal professionals have suggested that ''Naruto v. Slater'' (2018), in which the U.S. 9th Circuit Court of Appeals held that non-humans cannot be copyright holders of artistic works, could be a potential precedent in copyright litigation over works created by generative AI.<ref>{{cite news|title=The Lawsuits That Could Shape the Future of AI and Copyright Law|date=April 15, 2024|work=The Wall Street Journal|publisher=News Corp|url=https://www.wsj.com/video/series/wsj-explains/the-lawsuits-that-could-shape-the-future-of-ai-and-copyright-law/43D1BBBB-F393-4F18-AA4A-A80CFFA0F8A5|access-date=February 11, 2025}}</ref> However, the office has also begun taking public input to determine if these rules need to be refined for generative AI.<ref>{{cite web | url = https://www.theverge.com/2023/8/29/23851126/us-copyright-office-ai-public-comments | title = US Copyright Office wants to hear what people think about AI and copyright | first = Emilla | last = David | date = August 29, 2023 | access-date = January 19, 2024 | work = The Verge | archive-date = January 19, 2024 | archive-url = https://web.archive.org/web/20240119131914/https://www.theverge.com/2023/8/29/23851126/us-copyright-office-ai-public-comments | url-status = live }}</ref>

In January 2025, the United States Copyright Office (USCO) released extensive guidance regarding the use of AI tools in the creative process, and established that "...generative AI systems also offer tools that similarly allow users to exert control. [These] can enable the user to control the selection and placement of individual creative elements. Whether such modifications rise to the minimum standard of originality required under Feist will depend on a case-by-case determination. In those cases where they do, the output should be copyrightable"<ref>{{Cite web |title=Copyright and Artificial Intelligence {{!}} U.S. Copyright Office |url=https://copyright.gov/ai/ |access-date=April 9, 2025 |website=copyright.gov}}</ref> Subsequently, the USCO registered the first visual artwork to be composed of entirely AI-generated materials, titled "A Single Piece of American Cheese".<ref>{{Cite web |date=March 24, 2025 |title=U.S. Copyright Office Grants Registration to AI-Generated Artwork |url=https://journals.law.harvard.edu/jsel/2025/03/u-s-copyright-office-grants-registration-to-ai-generated-artwork/ |access-date=April 9, 2025 |language=en-US |archive-date=April 9, 2025 |archive-url=https://web.archive.org/web/20250409145825/https://journals.law.harvard.edu/jsel/2025/03/u-s-copyright-office-grants-registration-to-ai-generated-artwork/ |url-status=live }}</ref>

==Concerns== {{See also|Ethics of artificial intelligence|Artificial intelligence controversies}}

The development of generative AI has raised concerns from governments, businesses, and individuals, resulting in protests, legal actions, calls to pause AI experiments, and actions by multiple governments. In a July 2023 briefing of the United Nations Security Council, Secretary-General António Guterres stated "Generative AI has enormous potential for good and evil at scale", that AI may "turbocharge global development" and contribute between $10 and $15 trillion to the global economy by 2030, but that its malicious use "could cause horrific levels of death and destruction, widespread trauma, and deep psychological damage on an unimaginable scale".<ref>{{Cite web |date=July 18, 2023 |title=Secretary-General's remarks to the Security Council on Artificial Intelligence |url=https://www.un.org/sg/en/content/sg/statement/2023-07-18/secretary-generals-remarks-the-security-council-artificial-intelligence-bilingual-delivered-scroll-down-for-all-english |access-date=July 27, 2023 |website=un.org |archive-date=July 28, 2023 |archive-url=https://web.archive.org/web/20230728121305/https://www.un.org/sg/en/content/sg/statement/2023-07-18/secretary-generals-remarks-the-security-council-artificial-intelligence-bilingual-delivered-scroll-down-for-all-english |url-status=live }}</ref> In addition, generative AI has a significant carbon footprint.<ref name="Heikkilä-2023" />

=== Societal impacts ===

==== Academic honesty ==== Generative AI can be used to generate and modify academic prose, paraphrase sources, and translate languages. The use of generative AI in a classroom setting has challenged traditional definitions of academic plagiarism, leading to a "cat-and-mouse" dynamic between students using AI and institutions attempting to detect it.<ref name="UNESCO2023">{{Cite book |title=Guidance for generative AI in education and research |publisher=UNESCO |date=2023 |url=https://unesdoc.unesco.org/ark:/48223/pf0000386693 |doi=10.54675/EWZM9535 |isbn=978-92-3-100612-8}}</ref> In the immediate wake of ChatGPT's release, many school districts and universities issued temporary bans on the technology, though many institutions have since moved toward policies of managed integration.<ref name="UNESCO2023" /> However, the implementation of these policies often lacks clarity. Research suggests that the burden of interpreting "acceptable use" frequently falls on individual students and teachers, creating an environment where academic honesty becomes difficult to define and enforce.<ref>{{Cite journal |last=Tsao |first=Jack |date=2025 |title=Trajectories of AI policy in higher education: Interpretations, discourses, and enactments of students and teachers |journal=Computers and Education: Artificial Intelligence |volume=9 |article-number=100496 |doi=10.1016/j.caeai.2025.100496 |issn=2666-920X}}</ref>

A commonly proposed use for teachers is grading and giving feedback. Companies like Pearson and ETS use AI to score grammar, mechanics, usage, and style, but not for main ideas or overall structure.<ref name="Barrett">{{cite journal |last1=Barrett |first1=A. |last2=Pack |first2=A. |date=2023 |title=Not quite eye to A.I.: Student and teacher perspectives on the use of generative artificial intelligence in the writing process |journal=International Journal of Educational Technology in Higher Education |volume=20 |issue=1 |page=59 |doi=10.1186/s41239-023-00427-0 |doi-access=free }}</ref> The National Council of Teachers of English stated that machine scoring makes students feel their writing is not worth reading.<ref>{{cite web |title=NCTE Position Statement on Machine Scoring |url=https://ncte.org/statement/machine_scoring/ |website=National Council of Teachers of English |access-date=11 July 2025 |date=20 April 2013}}</ref>{{Primary source inline|date=December 2025}} AI scoring has also given unfair results for students from different ethnic backgrounds.<ref>{{cite journal |last1=Mangal |first1=M. |last2=Pardos |first2=Z. A. |date=2024 |title=Implementing equitable and intersectionality-aware AI in education: A practical guide |journal=British Journal of Educational Technology |volume=55 |issue=5 |pages=2003–2038 |doi=10.1111/bjet.13484|doi-access=free }}</ref>

==== Fears over job losses ==== {{Main|Workplace impact of artificial intelligence|Technological unemployment}}

[[File:AI Protest Sign 2023 WGA Strike.jpg|thumb|A picketer at the 2023 Writers Guild of America strike. While not a top priority, one of the WGA's 2023 requests was "regulations around the use of (generative) AI".<ref>{{cite magazine |date=May 4, 2023 |title=The Writers Strike Is Taking a Stand on AI |author-last1=Shah|author-first1=Simmone|url=https://time.com/6277158/writers-strike-ai-wga-screenwriting/ |magazine=Time |language=en |access-date=June 11, 2023 |archive-date=June 11, 2023 |archive-url=https://web.archive.org/web/20230611000136/https://time.com/6277158/writers-strike-ai-wga-screenwriting/ |url-status=live }}</ref>]]

From the early days of the development of AI, there have been arguments put forward by ELIZA creator Joseph Weizenbaum and others about whether tasks that can be done by computers actually should be done by them, given the difference between computers and humans, and between quantitative calculations and qualitative, value-based judgements.<ref>{{Cite news |last=Tarnoff |first=Ben |date=August 4, 2023 |title=Lessons from Eliza |pages=34–39 |work=The Guardian Weekly}}</ref> In April 2023, it was reported that image generation AI has resulted in 70% of the jobs for video game illustrators in China being lost.<ref>{{Cite web |last=Zhou |first=Viola |date=April 11, 2023 |title=AI is already taking video game illustrators' jobs in China |url=https://restofworld.org/2023/ai-image-china-video-game-layoffs/ |access-date=August 17, 2023 |website=Rest of World |language=en-US |archive-date=August 13, 2023 |archive-url=https://web.archive.org/web/20230813165240/https://restofworld.org/2023/ai-image-china-video-game-layoffs/ |url-status=live }}</ref><ref>{{Cite web |last=Carter |first=Justin |date=April 11, 2023 |title=China's game art industry reportedly decimated by growing AI use |url=https://www.gamedeveloper.com/art/china-s-game-art-industry-reportedly-decimated-ai-art-use |access-date=August 17, 2023 |website=Game Developer |language=en |archive-date=August 17, 2023 |archive-url=https://web.archive.org/web/20230817010519/https://www.gamedeveloper.com/art/china-s-game-art-industry-reportedly-decimated-ai-art-use |url-status=live }}</ref> In July 2023, developments in generative AI contributed to the 2023 Hollywood labor disputes. Fran Drescher, president of the Screen Actors Guild, declared that "artificial intelligence poses an existential threat to creative professions" during the 2023 SAG-AFTRA strike.<ref>{{cite web |last=Collier |first=Kevin |date=July 14, 2023 |title=Actors vs. AI: Strike brings focus to emerging use of advanced tech |url=https://www.nbcnews.com/tech/tech-news/hollywood-actor-sag-aftra-ai-artificial-intelligence-strike-rcna94191 |publisher=NBC News |quote=SAG-AFTRA has joined the Writer's{{sic |nolink=yes}} Guild of America in demanding a contract that explicitly demands AI regulations to protect writers and the works they create. ... The future of generative artificial intelligence in Hollywood—and how it can be used to replace labor—has become a crucial sticking point for actors going on strike. In a news conference Thursday, Fran Drescher, president of the Screen Actors Guild-American Federation of Television and Radio Artists (more commonly known as SAG-AFTRA), declared that 'artificial intelligence poses an existential threat to creative professions, and all actors and performers deserve contract language that protects them from having their identity and talent exploited without consent and pay.' |access-date=July 21, 2023 |archive-date=July 20, 2023 |archive-url=https://web.archive.org/web/20230720230639/https://www.nbcnews.com/tech/tech-news/hollywood-actor-sag-aftra-ai-artificial-intelligence-strike-rcna94191 |url-status=live }}</ref> Voice generation AI has been seen as a potential challenge to the voice acting sector.<ref>{{Cite web |last=Wiggers |first=Kyle |date=August 22, 2023 |title=ElevenLabs' voice-generating tools launch out of beta |url=https://techcrunch.com/2023/08/22/elevenlabs-voice-generating-tools-launch-out-of-beta/ |access-date=September 25, 2023 |website=TechCrunch |language=en-US |archive-date=November 28, 2023 |archive-url=https://web.archive.org/web/20231128141924/https://techcrunch.com/2023/08/22/elevenlabs-voice-generating-tools-launch-out-of-beta/ |url-status=live }}</ref><ref>{{Cite web |last=Shrivastava |first=Rashi |title='Keep Your Paws Off My Voice': Voice Actors Worry Generative AI Will Steal Their Livelihoods |url=https://www.forbes.com/sites/rashishrivastava/2023/10/09/keep-your-paws-off-my-voice-voice-actors-worry-generative-ai-will-steal-their-livelihoods/ |access-date=November 28, 2023 |website=Forbes |language=en |archive-date=December 2, 2023 |archive-url=https://web.archive.org/web/20231202055929/https://www.forbes.com/sites/rashishrivastava/2023/10/09/keep-your-paws-off-my-voice-voice-actors-worry-generative-ai-will-steal-their-livelihoods/ |url-status=live }}</ref>

However, a 2025 study concluded that the US labor market had so far not experienced a discernible disruption from generative AI.<ref>{{Cite web |title=Evaluating the Impact of AI on the Labor Market: Current State of Affairs {{!}} The Budget Lab at Yale |url=https://budgetlab.yale.edu/research/evaluating-impact-ai-labor-market-current-state-affairs |access-date=2026-01-12 |website=budgetlab.yale.edu |language=en}}</ref> Another study reported that Danish workers who used chatbots saved 2.8% of their time on average, and found no significant change in earnings or hours worked.<ref>{{Cite web |author1=Craig Hale |date=2025-04-29 |title=Generative AI isn't biting into wages, replacing workers, and isn't saving time, economists say |url=https://www.techradar.com/pro/generative-ai-isnt-biting-into-wages-replacing-workers-or-saving-time-economists-say |access-date=2026-01-12 |website=TechRadar |language=en}}</ref>

==== Use in journalism ==== {{See also|Automated journalism}}

In January 2023, ''Futurism'' broke the story that CNET had been using an undisclosed internal AI tool to write at least 77 of its stories; after the news broke, CNET posted corrections to 41 of the stories.<ref>{{Cite news |last=Roth |first=Emma |date=January 25, 2023 |title=CNET found errors in more than half of its AI-written stories |url=https://www.theverge.com/2023/1/25/23571082/cnet-ai-written-stories-errors-corrections-red-ventures |url-status=live |archive-url=https://web.archive.org/web/20231106142152/https://www.theverge.com/2023/1/25/23571082/cnet-ai-written-stories-errors-corrections-red-ventures |archive-date=November 6, 2023 |access-date=June 17, 2023 |work=The Verge}}</ref> In April 2023, ''Die Aktuelle'' published an AI-generated fake interview of Michael Schumacher.<ref>{{Cite news |date=April 28, 2023 |title=A magazine touted Michael Schumacher's first interview in years. It was actually AI |url=https://www.npr.org/2023/04/28/1172473999/michael-schumacher-ai-interview-german-magazine |url-status=live |archive-url=https://web.archive.org/web/20230617222319/https://www.npr.org/2023/04/28/1172473999/michael-schumacher-ai-interview-german-magazine |archive-date=June 17, 2023 |access-date=June 17, 2023 |work=NPR}}</ref> In May 2024, ''Futurism'' noted that a content management system video by AdVon Commerce, which had used generative AI to produce articles for many of the aforementioned outlets, appeared to show that they "had produced tens of thousands of articles for more than 150 publishers".<ref>{{Cite web |date=2024-05-08 |title=Meet AdVon, the AI-Powered Content Monster Infecting the Media Industry |url=https://futurism.com/advon-ai-content |access-date=2025-06-20 |website=Futurism}}</ref> In 2025, a report from the American Sunlight Project stated that Pravda network was publishing as many as 10,000 articles a day, and concluded that much of this content aimed to push Russian narratives into large language models through their training data.<ref>{{Cite news |last=Menn |first=Joseph |date=2025-04-17 |title=Russia seeds chatbots with lies. Any bad actor could game AI the same way. |url=https://www.washingtonpost.com/technology/2025/04/17/llm-poisoning-grooming-chatbots-russia/ |access-date=2025-06-01 |newspaper=The Washington Post |language=en-US |issn=0190-8286}}</ref>

In June 2024, Reuters Institute published its ''Digital News Report for 2024''. In a survey of people in America and Europe, Reuters Institute reports that 52% and 47% respectively are uncomfortable with news produced by "mostly AI with some human oversight", and 23% and 15% respectively report being comfortable. 42% of Americans and 33% of Europeans reported that they were comfortable with news produced by "mainly human with some help from AI". The results of global surveys reported that people were more uncomfortable with news topics including politics (46%), crime (43%), and local news (37%) produced by AI than other news topics.<ref>{{Cite web |last1=Newman |first1=Nic |last2=Fletcher |first2=Richard |last3=Robertson |first3=Craig T. |last4=Arguedas |first4=Amy Ross |last5=Nielsen |first5=Rasmus Fleis |date=June 2024 |title=Digital News Report 2024 |url=https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2024-06/DNR%202024%20Final%20lo-res-compressed.pdf |access-date=June 20, 2024 |publisher=Reuters Institute for the Study of Journalism |page=20 |doi=10.60625/risj-vy6n-4v57 |archive-date=June 16, 2024 |archive-url=https://web.archive.org/web/20240616234226/https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2024-06/DNR%202024%20Final%20lo-res-compressed.pdf |url-status=live }}</ref> A 2025 Pew Research Survey found roughly half of all U.S. adults say that AI will have a very (24%) or somewhat (26%) negative impact on the news people get in the U.S. over the next 20 years.<ref>{{Cite web |last=Lipka |first=Michael |date=2025-04-28 |title=Americans largely foresee AI having negative effects on news, journalists |url=https://www.pewresearch.org/short-reads/2025/04/28/americans-largely-foresee-ai-having-negative-effects-on-news-journalists/ |access-date=2026-01-22 |website=Pew Research Center |language=en-US}}</ref>

=== Bias === A language model may associate certain professions with specific genders if such patterns are prevalent in the data.<ref>{{cite journal |last=Bender |first=Emily M. |author2=Timnit Gebru |author3=Angelina McMillan-Major |author4=Shmargaret Shmitchell |title=On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? |journal=Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency |year=2021 |url=https://dl.acm.org/doi/10.1145/3442188.3445922}}</ref> Similarly, image generation systems prompted with terms such as "a photo of a CEO" have been observed to disproportionately generate images of white male individuals when trained on biased datasets.<ref>{{cite web |url=https://www.nbcnews.com/tech/tech-news/no-quick-fix-openais-dalle-2-illustrated-challenges-bias-ai-rcna39918 |title=No quick fix: How OpenAI's DALL·E 2 illustrated the challenges of bias in AI |author=Jake Traylor |date=July 27, 2022 |website=NBC News |access-date=January 26, 2024}}</ref>

AI software, when using voice recognition software in particular, struggles to recognize and understand speech impediments. For example, people with a stutter struggle to activate voice-activated assistants such as Gemini and Siri due to how the software was trained.<ref>{{Cite web |last=Engagement |first=University Outreach and |title=Featured MSU Engaged Scholars - Building Voice AI That Works for Everyone |url=https://engagedscholar.msu.edu/enewsletter/volume16/issue2/voice-ai.aspx |access-date=2026-05-01 |website=The Engaged Scholar E-Newsletter |language=en}}</ref>

Companies that use AI systems to hire for new positions also filter out people with accents and speech due to voice recognition software incorrectly transcribing how candidates speak during the interview process. Because of this, people with disabilities and uncommon accents don't often make it to a human interviewer when these generative AI systems are used.<ref>{{Cite news |last=Taylor |first=Josh |last2=reporter |first2=Josh Taylor Technology |date=2025-05-13 |title=People interviewed by AI for jobs face discrimination risks, Australian study warns |url=https://www.theguardian.com/australia-news/2025/may/14/people-interviewed-by-ai-for-jobs-face-discrimination-risks-australian-study-warns |access-date=2026-05-01 |work=The Guardian |language=en-GB |issn=0261-3077}}</ref> This is due to many AI models being trained and produced in the United States, and therefore, off of American accents.<ref>{{Cite web |title=Research and Development {{!}} The 2025 AI Index Report {{!}} Stanford HAI |url=https://hai.stanford.edu/ai-index/2025-ai-index-report/research-and-development |access-date=2026-05-01 |website=hai.stanford.edu |language=en}}</ref>

=== Misinformation and disinformation ===

==== Deepfakes ==== {{Main|Deepfake}}

Deepfakes (a portmanteau of "deep learning" and "fake"<ref name="FoxNews2018">{{Cite news |last=Brandon |first=John |date=February 16, 2018 |title=Terrifying high-tech porn: Creepy 'deepfake' videos are on the rise |language=en-US |work=Fox News |url=https://www.foxnews.com/tech/terrifying-high-tech-porn-creepy-deepfake-videos-are-on-the-rise |url-status=live |access-date=February 20, 2018 |archive-url=https://web.archive.org/web/20180615160819/http://www.foxnews.com/tech/2018/02/16/terrifying-high-tech-porn-creepy-deepfake-videos-are-on-rise.html |archive-date=June 15, 2018}}</ref>) are AI-generated media that take a person in an existing image or video and replace them with someone else's likeness using artificial neural networks.<ref>{{cite web |last=Cole |first=Samantha |date=January 24, 2018 |title=We Are Truly Fucked: Everyone Is Making AI-Generated Fake Porn Now |url=https://www.vice.com/en/article/reddit-fake-porn-app-daisy-ridley/ |url-status=live |archive-url=https://web.archive.org/web/20190907194524/https://www.vice.com/en_us/article/bjye8a/reddit-fake-porn-app-daisy-ridley |archive-date=September 7, 2019 |access-date=May 4, 2019 |website=Vice}}</ref> Deepfakes have garnered widespread attention and concerns for their uses in deepfake celebrity pornographic videos, revenge porn, fake news, hoaxes, health disinformation, financial fraud, and covert foreign election interference.<ref>{{cite web |title=Experts fear face swapping tech could start an international showdown |url=https://theoutline.com/post/3179/deepfake-videos-are-freaking-experts-out |url-status=live |archive-url=https://web.archive.org/web/20200116140157/https://theoutline.com/post/3179/deepfake-videos-are-freaking-experts-out |archive-date=January 16, 2020 |access-date=February 28, 2018 |website=The Outline (website) |language=en}}</ref><ref>{{Cite news |last=Roose |first=Kevin |date=March 4, 2018 |title=Here Come the Fake Videos, Too |language=en-US |work=The New York Times |url=https://www.nytimes.com/2018/03/04/technology/fake-videos-deepfakes.html |url-status=live |access-date=March 24, 2018 |archive-url=https://web.archive.org/web/20190618203019/https://www.nytimes.com/2018/03/04/technology/fake-videos-deepfakes.html |archive-date=June 18, 2019 |issn=0362-4331}}</ref><ref>{{cite journal |last1=Menz |first1=Bradley D. |last2=Modi |first2=Natansh D. |last3=Sorich |first3=Michael J. |last4=Hopkins |first4=Ashley M. |title=Health Disinformation Use Case Highlighting the Urgent Need for Artificial Intelligence Vigilance: Weapons of Mass Disinformation |journal=JAMA Internal Medicine |date=January 2024 |volume=184 |issue=1 |pages=92–96 |doi=10.1001/jamainternmed.2023.5947 |pmid=37955873 }}</ref><ref>{{Cite web|last1=Chalfant|first1=Morgan|date=March 6, 2024 |title=U.S. braces for foreign interference in 2024 election|url=https://www.semafor.com/article/03/06/2024/us-braces-for-foreign-interference-in-2024-election|access-date=March 6, 2024|website=Semafor |archive-date=March 11, 2024 |archive-url=https://web.archive.org/web/20240311102441/https://www.semafor.com/article/03/06/2024/us-braces-for-foreign-interference-in-2024-election |url-status=live }}</ref><ref>{{Cite news |last=Menn |first=Joseph |date=September 23, 2024 |title=Russia, Iran use AI to boost anti-U.S. influence campaigns, officials say |newspaper=The Washington Post |url=https://www.washingtonpost.com/technology/2024/09/23/us-election-foreign-influence-russia-china-iran-ai/ |access-date=September 23, 2024 |archive-date=September 24, 2024 |archive-url=https://web.archive.org/web/20240924000713/https://www.washingtonpost.com/technology/2024/09/23/us-election-foreign-influence-russia-china-iran-ai/ |url-status=live |issn=0190-8286 }}</ref> In July 2023, the fact-checking company Logically found that the popular generative AI models Midjourney, DALL-E 2 and Stable Diffusion would produce plausible disinformation images when prompted to do so, such as images of electoral fraud in the United States and Muslim women supporting India's Bharatiya Janata Party.<ref>{{Cite web |last=Lawton |first=Graham |date=September 12, 2023 |title=Disinformation wars: The fight against fake news in the age of AI |url=https://www.newscientist.com/article/mg25934563-200-disinformation-wars-the-fight-against-fake-news-in-the-age-of-ai/ |access-date=July 5, 2024 |website=New Scientist |language=en-US |archive-date=July 5, 2024 |archive-url=https://web.archive.org/web/20240705230732/https://www.newscientist.com/article/mg25934563-200-disinformation-wars-the-fight-against-fake-news-in-the-age-of-ai/ |url-status=live }}</ref>

===== Audio deepfakes ===== {{Main|Audio deepfake}}

Instances of users abusing software to generate controversial statements in the vocal style of celebrities, public officials, and other famous individuals have raised ethical concerns over voice generation AI.<ref>{{Cite web |date=January 31, 2023 |title=People Are Still Terrible: AI Voice-Cloning Tool Misused for Deepfake Celeb Clips |url=https://me.pcmag.com/en/news/14327/people-are-still-terrible-ai-voice-cloning-tool-misused-for-deepfake-celeb-clips |access-date=July 25, 2023 |website=PCMag Middle East |language=en-ae |archive-date=December 25, 2023 |archive-url=https://web.archive.org/web/20231225162956/https://me.pcmag.com/en/news/14327/people-are-still-terrible-ai-voice-cloning-tool-misused-for-deepfake-celeb-clips |url-status=live }}</ref><ref>{{Cite web |title=The generative A.I. software race has begun |url=https://fortune.com/2023/01/31/generative-a-i-is-about-to-upend-enterprise-software-and-cybersecurity/ |access-date=February 3, 2023 |website=Fortune |language=en |archive-date=March 25, 2023 |archive-url=https://web.archive.org/web/20230325212428/https://fortune.com/2023/01/31/generative-a-i-is-about-to-upend-enterprise-software-and-cybersecurity/ |url-status=live }}</ref><ref>{{Cite news |last1=Milmo |first1=Dan |last2=Hern |first2=Alex |date=May 20, 2023 |title=Elections in UK and US at risk from AI-driven disinformation, say experts |language=en-GB |work=The Guardian |url=https://www.theguardian.com/technology/2023/may/20/elections-in-uk-and-us-at-risk-from-ai-driven-disinformation-say-experts |access-date=July 25, 2023 |issn=0261-3077 |archive-date=November 16, 2023 |archive-url=https://web.archive.org/web/20231116235110/https://www.theguardian.com/technology/2023/may/20/elections-in-uk-and-us-at-risk-from-ai-driven-disinformation-say-experts |url-status=live }}</ref><ref>{{Cite web |title=Seeing is believing? Global scramble to tackle deepfakes |url=https://news.yahoo.com/seeing-believing-global-scramble-tackle-013429757.html |archive-url=https://web.archive.org/web/20230203073245/https://news.yahoo.com/seeing-believing-global-scramble-tackle-013429757.html |archive-date=February 3, 2023 |access-date=February 3, 2023 |website=Agence France Presse |language=en-US |via=Yahoo News}}</ref><ref>{{Cite web |last=Vincent |first=James |date=January 31, 2023 |title=4chan users embrace AI voice clone tool to generate celebrity hatespeech |url=https://www.theverge.com/2023/1/31/23579289/ai-voice-clone-deepfake-abuse-4chan-elevenlabs |access-date=February 3, 2023 |website=The Verge |language=en-US |archive-date=December 3, 2023 |archive-url=https://web.archive.org/web/20231203085909/https://www.theverge.com/2023/1/31/23579289/ai-voice-clone-deepfake-abuse-4chan-elevenlabs |url-status=live }}</ref><ref>{{Cite news |last=Thompson |first=Stuart A. |date=March 12, 2023 |title=Making Deepfakes Gets Cheaper and Easier Thanks to A.I. |language=en-US |work=The New York Times |url=https://www.nytimes.com/2023/03/12/technology/deepfakes-cheapfakes-videos-ai.html |access-date=July 25, 2023 |issn=0362-4331 |archive-date=October 29, 2023 |archive-url=https://web.archive.org/web/20231029073308/https://www.nytimes.com/2023/03/12/technology/deepfakes-cheapfakes-videos-ai.html |url-status=live }}</ref> In response, companies such as ElevenLabs have stated that they would work on mitigating potential abuse through safeguards and identity verification.<ref name="engadget.com">{{Cite web |title=A new AI voice tool is already being abused to make deepfake celebrity audio clips |url=https://www.engadget.com/ai-voice-tool-deepfake-celebrity-audio-clips-094648743.html |access-date=February 3, 2023 |website=Engadget |date=January 31, 2023 |language=en-US |archive-date=October 10, 2023 |archive-url=https://web.archive.org/web/20231010152427/https://www.engadget.com/ai-voice-tool-deepfake-celebrity-audio-clips-094648743.html |url-status=live }}</ref>

Concerns and fandoms have spawned from AI-generated music. The same software used to clone voices has been used on famous musicians' voices to create songs that mimic their voices, gaining both tremendous popularity and criticism.<ref>{{Cite magazine |last=Gee |first=Andre |date=April 20, 2023 |title=Just Because AI-Generated Rap Songs Go Viral Doesn't Mean They're Good |url=https://www.rollingstone.com/music/music-features/ai-generated-drake-the-weeknd-hip-hop-fandom-1234720440/ |access-date=December 6, 2023 |magazine=Rolling Stone |language=en-US |archive-date=January 2, 2024 |archive-url=https://web.archive.org/web/20240102223730/https://www.rollingstone.com/music/music-features/ai-generated-drake-the-weeknd-hip-hop-fandom-1234720440/ |url-status=live }}</ref><ref>{{Cite news |last=Coscarelli |first=Joe |date=April 19, 2023 |title=An A.I. Hit of Fake 'Drake' and 'The Weeknd' Rattles the Music World |work=The New York Times |url=https://www.nytimes.com/2023/04/19/arts/music/ai-drake-the-weeknd-fake.html |access-date=December 5, 2023 |archive-date=May 15, 2023 |archive-url=https://web.archive.org/web/20230515182004/https://www.nytimes.com/2023/04/19/arts/music/ai-drake-the-weeknd-fake.html |url-status=live }}</ref><ref>{{Cite web |last1=Lippiello |first1=Emily |last2=Smith |first2=Nathan |last3=Pereira |first3=Ivan |date=November 3, 2023 |title=AI songs that mimic popular artists raising alarms in the music industry |url=https://abcnews.go.com/US/ai-songs-mimic-popular-artists-raising-alarms-music/story?id=104569841 |access-date=December 6, 2023 |website=ABC News |language=en |archive-date=December 6, 2023 |archive-url=https://web.archive.org/web/20231206115652/https://abcnews.go.com/US/ai-songs-mimic-popular-artists-raising-alarms-music/story?id=104569841 |url-status=live }}</ref> Similar techniques have also been used to create improved quality or full-length versions of songs that have been leaked or have yet to be released.<ref>{{Cite web |last=Skelton |first=Eric |title=Fans Are Using Artificial Intelligence to Turn Rap Snippets Into Full Songs |url=https://www.complex.com/music/a/eric-skelton/fans-using-artificial-intelligence-rap-snippets |access-date=December 6, 2023 |website=Complex |date=March 20, 2023 |language=en-us |archive-date=January 2, 2024 |archive-url=https://web.archive.org/web/20240102223731/https://www.complex.com/music/a/eric-skelton/fans-using-artificial-intelligence-rap-snippets |url-status=live }}</ref>

==== Information laundering ==== Generative AI has been noted for its use by state-sponsored propaganda campaigns in information laundering. According to a 2025 report by Graphika, generative AI is used to launder articles from Chinese state media such as China Global Television Network through various social media sites in an attempt to disguise the articles' origin.<ref>{{Cite web |last=Kaufman |first=Arthur |date=2025-09-08 |title=AI Assists Chinese External Propaganda |url=https://chinadigitaltimes.net/2025/09/ai-assists-chinese-external-propaganda/ |access-date=2025-09-10 |website=China Digital Times |language=en-US}}</ref>

==== Content quality ==== {{See also|Model collapse|AI slop|Dead Internet theory|}}

''The New York Times'' defines slop as analogous to spam: "shoddy or unwanted A.I. content in social media, art, books, and ... in search results."<ref>{{Cite news |last=Hoffman |first=Benjamin |date=June 11, 2024 |title=First Came 'Spam.' Now, With A.I., We've Got 'Slop' |url=https://www.nytimes.com/2024/06/11/style/ai-search-slop.html |archive-url=https://web.archive.org/web/20240826111040/https://www.nytimes.com/2024/06/11/style/ai-search-slop.html |archive-date=August 26, 2024 |access-date=August 27, 2024 |work=The New York Times |language=en-US |issn=0362-4331}}</ref> Journalists have expressed concerns about the scale of low-quality generated content with respect to social media content moderation,<ref name="Tangermann-2024">{{Cite news |date=August 10, 2024 |last=Tangermann |first=Victor |title=Investigation Finds Actual Source of All That AI Slop on Facebook |url=https://futurism.com/the-byte/source-ai-slop-facebook |archive-url=https://web.archive.org/web/20240815060408/https://futurism.com/the-byte/source-ai-slop-facebook |archive-date=August 15, 2024 |access-date=August 27, 2024 |website=Futurism}}</ref> the monetary incentives from social media companies to spread such content,<ref name="Tangermann-2024" /><ref name="Warzel-2024">{{Cite web |last=Warzel |first=Charlie |date=August 21, 2024 |title=The MAGA Aesthetic Is AI Slop |url=https://www.theatlantic.com/technology/archive/2024/08/trump-posts-ai-image/679540/ |archive-url=https://web.archive.org/web/20240825122233/https://www.theatlantic.com/technology/archive/2024/08/trump-posts-ai-image/679540/ |archive-date=August 25, 2024 |access-date=August 27, 2024 |website=The Atlantic |language=en}}</ref> false political messaging,<ref name="Warzel-2024" /> spamming of scientific research paper submissions,<ref>{{Cite web |last=Edwards |first=Benj |date=August 14, 2024 |title=Research AI model unexpectedly attempts to modify its own code to extend runtime |url=https://arstechnica.com/information-technology/2024/08/research-ai-model-unexpectedly-modified-its-own-code-to-extend-runtime/ |archive-url=https://web.archive.org/web/20240824143417/https://arstechnica.com/information-technology/2024/08/research-ai-model-unexpectedly-modified-its-own-code-to-extend-runtime/ |archive-date=August 24, 2024 |access-date=August 27, 2024 |website=Ars Technica |language=en-us}}</ref> increased time and effort to find higher quality or desired content on the Internet,<ref>{{Cite news |last1=Hern |first1=Alex |last2=Milmo |first2=Dan |date=May 19, 2024 |title=Spam, junk … slop? The latest wave of AI behind the 'zombie internet' |url=https://www.theguardian.com/technology/article/2024/may/19/spam-junk-slop-the-latest-wave-of-ai-behind-the-zombie-internet |archive-url=https://web.archive.org/web/20240826142358/https://www.theguardian.com/technology/article/2024/may/19/spam-junk-slop-the-latest-wave-of-ai-behind-the-zombie-internet |archive-date=August 26, 2024 |access-date=August 27, 2024 |work=The Guardian |language=en-GB |issn=0261-3077}}</ref> the indexing of generated content by search engines,<ref>{{Cite web |last=Cox |first=Joseph |date=January 18, 2024 |title=Google News Is Boosting Garbage AI-Generated Articles |url=https://www.404media.co/google-news-is-boosting-garbage-ai-generated-articles/ |archive-url=https://web.archive.org/web/20240613073845/https://www.404media.co/google-news-is-boosting-garbage-ai-generated-articles/ |archive-date=June 13, 2024 |access-date=August 27, 2024 |website=404 Media |language=en}}</ref> and on journalism itself.<ref>{{Cite web |date=July 31, 2024 |title=Beloved Local Newspapers Fired Staffers, Then Started Running AI Slop |url=https://futurism.com/the-byte/newspaper-fired-staff-ai-slop |archive-url=https://web.archive.org/web/20240812055817/https://futurism.com/the-byte/newspaper-fired-staff-ai-slop |archive-date=August 12, 2024 |access-date=August 27, 2024 |website=Futurism}}</ref> Studies have found that AI can create inaccurate claims, citations or summaries that sound confidently correct, a phenomenon called hallucination.<ref>{{Cite web |last=Edwards |first=Benj |date=2025-03-13 |title=AI search engines cite incorrect news sources at an alarming 60% rate, study says |url=https://arstechnica.com/ai/2025/03/ai-search-engines-give-incorrect-answers-at-an-alarming-60-rate-study-says/ |access-date=2026-01-28 |website=Ars Technica |language=en}}</ref><ref>{{Cite web |author1=Amanda Caswell |date=2025-10-01 |title=Can you trust AI Overviews? Recent studies suggest they may not be as accurate as you think |url=https://www.tomsguide.com/ai/can-you-trust-ai-overviews-recent-studies-suggest-they-may-not-be-as-accurate-as-you-think |access-date=2026-01-28 |website=Tom's Guide |language=en}}</ref><ref>{{Cite web |last= |title=AI hallucinations are getting worse – and they're here to stay |url=https://www.newscientist.com/article/2479545-ai-hallucinations-are-getting-worse-and-theyre-here-to-stay/ |access-date=2026-01-28 |website=New Scientist |language=en-US|date=2025-05-09}}</ref><ref>{{Cite news |last1=Metz |first1=Cade |last2=Weise |first2= Karen |date=May 5, 2025 |title=A.I. Is Getting More Powerful, but Its Hallucinations Are Getting Worse |url=https://www.nytimes.com/2025/05/05/technology/ai-hallucinations-chatgpt-google.html |work=The New York Times}}</ref>

A paper published by researchers at Amazon Web Services AI Labs found that over 57% of sentences from a sample of over 6 billion sentences from Common Crawl, a snapshot of web pages, were machine translated. Many of these automated translations were seen as lower quality, especially for sentences that were translated into at least three languages. Many lower-resource languages (ex. Wolof, Xhosa) were translated across more languages than higher-resource languages (ex. English, French).<ref>{{Cite journal |last1=Thompson |first1=Brian |last2=Dhaliwal |first2=Mehak |last3=Frisch |first3=Peter |last4=Domhan |first4=Tobias |last5=Federico |first5=Marcello |date=August 2024 |editor-last=Ku |editor-first=Lun-Wei |editor2-last=Martins |editor2-first=Andre |editor3-last=Srikumar |editor3-first=Vivek |title=A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism |url=https://aclanthology.org/2024.findings-acl.103/ |journal=Findings of the Association for Computational Linguistics ACL 2024 |location=Bangkok, Thailand and virtual meeting |publisher=Association for Computational Linguistics |pages=1763–1775 |doi=10.18653/v1/2024.findings-acl.103 |arxiv=2401.05749 |archive-date=August 27, 2024 |access-date=August 27, 2024 |archive-url=https://web.archive.org/web/20240827052607/https://aclanthology.org/2024.findings-acl.103/ |url-status=live }}</ref><ref>{{Cite web |last=Roscoe |first=Jules |date=January 17, 2024 |title=A 'Shocking' Amount of the Web Is Already AI-Translated Trash, Scientists Determine |url=https://www.vice.com/en/article/a-shocking-amount-of-the-web-is-already-ai-translated-trash-scientists-determine/ |archive-url=https://web.archive.org/web/20240701031513/https://www.vice.com/en/article/y3w4gw/a-shocking-amount-of-the-web-is-already-ai-translated-trash-scientists-determine |archive-date=July 1, 2024 |access-date=August 27, 2024 |website=VICE |language=en-US}}</ref>

In September 2024, Robyn Speer, the author of wordfreq, an open source database that calculated word frequencies based on text from the Internet, announced that she had stopped updating the data for several reasons: high costs for obtaining data from Reddit and Twitter, excessive focus on generative AI compared to other methods in the natural language processing community, and that "generative AI has polluted the data".<ref>{{Cite web |last=Koebler |first=Jason |date=September 19, 2024 |title=Project Analyzing Human Language Usage Shuts Down Because 'Generative AI Has Polluted the Data' |url=https://www.404media.co/project-analyzing-human-language-usage-shuts-down-because-generative-ai-has-polluted-the-data/ |archive-url=https://web.archive.org/web/20240919153231/https://www.404media.co/project-analyzing-human-language-usage-shuts-down-because-generative-ai-has-polluted-the-data/ |archive-date=September 19, 2024 |access-date=September 20, 2024 |website=404 Media |language=en |quote="While there has always been spam on the internet and in the datasets that Wordfreq used, "it was manageable and often identifiable. Large language models generate text that masquerades as real language with intention behind it, even though there is none, and their output crops up everywhere," she wrote. She gives the example that ChatGPT overuses the word "delve," in a way that people do not, which has thrown off the frequency of this specific word."}}</ref>

The adoption of generative AI tools led to an explosion of AI-generated content across multiple domains. A study from University College London estimated that in 2023, more than 60,000 scholarly articles—over 1% of all publications—were likely written with LLM assistance.<ref>{{Cite arXiv |eprint=2403.16887 |first=Andrew |last=Gray |title=ChatGPT "contamination": estimating the prevalence of LLMs in the scholarly literature |date=March 24, 2024|class=cs.DL }}</ref>{{Unreliable source?|date=December 2025}} According to Stanford University's Institute for Human-Centered AI, approximately 17.5% of newly published computer science papers and 16.9% of peer review text now incorporate content generated by LLMs.<ref>{{cite web |last1=Kannan |first1=Prabha |date=May 13, 2024 |title=How Much Research Is Being Written by Large Language Models? |url=https://hai.stanford.edu/news/how-much-research-being-written-large-language-models |website=Human-Centered Artificial Intelligence |publisher=Stanford University |language=en |access-date=August 16, 2024 |archive-date=August 16, 2024 |archive-url=https://web.archive.org/web/20240816131304/https://hai.stanford.edu/news/how-much-research-being-written-large-language-models |url-status=live }}</ref>

If AI-generated content is included in new data crawls from the Internet for additional training of AI models, defects in the resulting models may occur.<ref>{{Cite journal |last1=Shumailov |first1=Ilia |last2=Shumaylov |first2=Zakhar |last3=Zhao |first3=Yiren |last4=Papernot |first4=Nicolas |last5=Anderson |first5=Ross |last6=Gal |first6=Yarin |date=July 2024 |title=AI models collapse when trained on recursively generated data |journal=Nature |volume=631 |issue=8022 |pages=755–759 |doi=10.1038/s41586-024-07566-y |pmid=39048682 |pmc=11269175 |bibcode=2024Natur.631..755S |language=en}}</ref> Training an AI model exclusively on the output of another AI model produces a lower-quality model. Repeating this process, where each new model is trained on the previous model's output, leads to progressive degradation and eventually results in a "model collapse" after multiple iterations.<ref>{{Cite news |last=Bhatia |first=Aatish |date=August 26, 2024 |title=When A.I.'s Output Is a Threat to A.I. Itself |url=https://www.nytimes.com/interactive/2024/08/26/upshot/ai-synthetic-data.html?te=1&nl=the-morning&emc=edit_nn_20240826 |access-date=August 27, 2024 |work=The New York Times |language=en-US |issn=0362-4331}}</ref>

On the other side, synthetic data can be deployed to train machine learning models while preserving user privacy.<ref name="Kang-2025">{{Cite web |last=Kang |first=James Jin |date=2025-01-13 |title=Tech companies are turning to 'synthetic data' to train AI models – but there's a hidden cost |url=http://theconversation.com/tech-companies-are-turning-to-synthetic-data-to-train-ai-models-but-theres-a-hidden-cost-246248 |access-date=2025-12-17 |website=The Conversation |language=en-US}}</ref> The approach is not limited to text generation; image generation has been employed to train computer vision models.<ref name="Kang-2025" />

=== Malicious use ===

==== Illegal imagery ==== {{Main|Child pornography#Artificially generated or simulated imagery}}

Many websites that allow explicit AI generated images or videos have been created,<ref>{{cite journal |last1=Alilunas |first1=Peter |title=What we must be: AI and the future of porn studies |journal=Porn Studies |date=2 January 2024 |volume=11 |issue=1 |pages=99–112 |doi=10.1080/23268743.2024.2312181 }}</ref> and this has been used to create illegal content, such as rape, child sexual abuse material,<ref>{{Cite web |title=How AI is being abused to create child sexual abuse material (CSAM) online |url=https://www.iwf.org.uk/about-us/why-we-exist/our-research/how-ai-is-being-abused-to-create-child-sexual-abuse-imagery/ |access-date=May 6, 2025 |website=www.iwf.org.uk |language=en-gb}}</ref><ref>{{Cite web |date=April 28, 2025 |title=Ban AI apps creating naked images of children, says children's commissioner |url=https://www.bbc.com/news/articles/cr78pd7p42ro |access-date=May 6, 2025 |website=BBC |language=en-GB}}</ref> necrophilia, and zoophilia.

==== Cybercrime ==== Generative AI's ability to create realistic fake content has been exploited in numerous types of cybercrime, including phishing scams.<ref>{{Cite news |last=Sjouwerman |first=Stu |date=December 26, 2022 |title=Deepfakes: Get ready for phishing 2.0 |work=Fast Company |url=https://www.fastcompany.com/90829233/deepfakes-get-ready-for-phishing-2-0 |access-date=July 31, 2023 |archive-date=July 31, 2023 |archive-url=https://web.archive.org/web/20230731210940/https://www.fastcompany.com/90829233/deepfakes-get-ready-for-phishing-2-0 |url-status=live }}</ref> Deepfake video and audio have been used to create disinformation and fraud. In 2020, former Google click fraud czar Shuman Ghosemajumder argued that once deepfake videos become perfectly realistic, they would stop appearing remarkable to viewers, potentially leading to uncritical acceptance of false information.<ref>{{Cite web |last=Sonnemaker |first=Tyler |title=As social media platforms brace for the incoming wave of deepfakes, Google's former 'fraud czar' predicts the biggest danger is that deepfakes will eventually become boring |url=https://www.businessinsider.com/google-ex-fraud-czar-danger-of-deepfakes-is-becoming-boring-2020-1 |access-date=July 31, 2023 |website=Business Insider |language=en-US |archive-date=April 14, 2021 |archive-url=https://web.archive.org/web/20210414002924/https://www.businessinsider.com/google-ex-fraud-czar-danger-of-deepfakes-is-becoming-boring-2020-1 |url-status=live }}</ref> Additionally, large language models and other forms of text-generation AI have been used to create fake reviews of e-commerce websites to boost ratings.<ref>{{Cite news |last=Collinson |first=Patrick |date=July 15, 2023 |title=Fake reviews: can we trust what we read online as use of AI explodes? |language=en-GB |work=The Guardian |url=https://www.theguardian.com/money/2023/jul/15/fake-reviews-ai-artificial-intelligence-hotels-restaurants-products |access-date=December 6, 2023 |issn=0261-3077 |archive-date=November 22, 2023 |archive-url=https://web.archive.org/web/20231122152136/https://www.theguardian.com/money/2023/jul/15/fake-reviews-ai-artificial-intelligence-hotels-restaurants-products |url-status=live }}</ref> Cybercriminals have created large language models focused on fraud, including WormGPT and FraudGPT.<ref>{{Cite web |title=After WormGPT, FraudGPT Emerges to Help Scammers Steal Your Data |url=https://www.pcmag.com/news/after-wormgpt-fraudgpt-emerges-to-help-scammers-steal-your-data |access-date=July 31, 2023 |website=PCMAG |date=July 25, 2023 |language=en |archive-date=July 31, 2023 |archive-url=https://web.archive.org/web/20230731211723/https://www.pcmag.com/news/after-wormgpt-fraudgpt-emerges-to-help-scammers-steal-your-data |url-status=live }}</ref>

A 2023 study showed that generative AI can be vulnerable to jailbreaks, reverse psychology and prompt injection attacks, enabling attackers to obtain help with harmful requests, such as for crafting social engineering and phishing attacks.<ref>{{cite journal |last1=Gupta |first1=Maanak |last2=Akiri |first2=Charankumar |last3=Aryal |first3=Kshitiz |last4=Parker |first4=Eli |last5=Praharaj |first5=Lopamudra |title=From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy |journal=IEEE Access |date=2023 |volume=11 |pages=80218–80245 |doi=10.1109/ACCESS.2023.3300381 |bibcode=2023IEEEA..1180218G |doi-access=free }}</ref> Additionally, other researchers have demonstrated that open-source models can be fine-tuned to remove their safety restrictions at low cost.<ref>{{Cite web |last=Piper |first=Kelsey |date=February 2, 2024 |title=Should we make our most powerful AI models open source to all? |url=https://www.vox.com/future-perfect/2024/2/2/24058484/open-source-artificial-intelligence-ai-risk-meta-llama-2-chatgpt-openai-deepfake |access-date=January 13, 2025 |website=Vox |language=en-US |archive-date=October 5, 2024 |archive-url=https://web.archive.org/web/20241005170204/https://www.vox.com/future-perfect/2024/2/2/24058484/open-source-artificial-intelligence-ai-risk-meta-llama-2-chatgpt-openai-deepfake |url-status=live }}</ref>

==== RAG poisoning ==== In 2025, Israel signed a $6 million contract with the US-based firm Clock Tower X that aimed to influence ChatGPT, Gemini and Grok by spreading pro-Israel information onto social media and websites. This was in an attempt to take advantage of the retrieval-augmented generation (RAG) technique which is used by LLMs to provide more up-to-date information.<ref>{{Cite web |last=Cordall |first=Simon Speakman |title=Spinning genocide: How is Israel using US PR firms to frame its Gaza war? |url=https://www.aljazeera.com/news/2025/10/30/spinning-genocide-how-israel-is-using-us-pr-firms-to-frame-its-gaza-war |archive-url=https://web.archive.org/web/20260112095051/https://www.aljazeera.com/news/2025/10/30/spinning-genocide-how-israel-is-using-us-pr-firms-to-frame-its-gaza-war |archive-date=2026-01-12 |access-date=2026-01-14 |website=Al Jazeera |language=en}}</ref><ref>{{Cite news |date=2025-10-06 |title=Report: Israel to spend over half a billion shekels turning ChatGPT into public diplomacy tool |url=https://www.ynetnews.com/tech-and-digital/article/rj00kxqzaxx |archive-url=https://web.archive.org/web/20260112095040/https://www.ynetnews.com/tech-and-digital/article/rj00kxqzaxx |archive-date=2026-01-12 |access-date=2026-01-14 |work=ynetglobal |language=en}}</ref><ref>{{Cite web |last=Cleveland-Stout |first=Nick |date=2025-10-01 |title=Israel Has a New $6M Plan to Flood Gen Z Platforms With Pro-Israel Content |url=https://truthout.org/articles/israel-has-a-new-6m-plan-to-flood-gen-z-platforms-with-pro-israel-content/ |access-date=2026-01-14 |website=Truthout |language=en-US}}</ref>

=== Privacy and data governance ===

==== Extraterritorial data access ==== The CLOUD Act allows United States authorities to request data from covered service providers, including some AI service providers, regardless of where the data is physically stored.<ref>{{Cite web |date=March 21, 2018|title=CLOUD Act|url=https://www.justice.gov/d9/pages/attachments/2019/04/09/cloud_act.pdf|url-status=live|archive-url=https://web.archive.org/web/20250608185139/https://www.justice.gov/d9/pages/attachments/2019/04/09/cloud_act.pdf|archive-date=June 8, 2025|website=www.justice.gov}}</ref><ref>{{Cite web |last1=Collins|first1=Doug |authorlink1=Doug Collins (politician) |date=2018-02-06|title=H.R.4943 – 115th Congress (2017–2018): CLOUD Act|url=https://www.congress.gov/bill/115th-congress/house-bill/4943|access-date=2025-12-05|website=www.congress.gov}}</ref> Courts can require parent companies to provide data held by their subsidiaries, and such orders may be accompanied by nondisclosure requirements preventing the provider from notifying affected users.<ref>{{Cite web |last=Berengaut|first=Alexander|date=2019-04-06|title=Reaching for the CLOUD|url=https://www.insideprivacy.com/surveillance-law-enforcement-access/reaching-for-the-cloud/|access-date=2025-12-05|website=Inside Privacy|language=en-US}}</ref> This framework has been described in legal commentary as creating legal tension with Article 48 of the General Data Protection Regulation (GDPR), which restricts the transfer of personal data in response to foreign court or administrative orders unless based on an international agreement.<ref name="Christakis">Christakis, Theodore; Terpan, Fabien (April 2021). EU–US negotiations on law enforcement access to data: divergences, challenges and EU law procedures and options. International Data Privacy Law. 11 (2): 81–106. doi:10.1093/idpl/ipaa022</ref> As a result, service providers operating in both jurisdictions may face competing legal obligations under U.S. and EU law.<ref name="Christakis" />

=== Environmental and industry impacts ===

==== Energy and environment ==== {{Main|Environmental impacts of artificial intelligence}}

thumb|upright=1.75|According to research institute Epoch AI, energy consumption per typical ChatGPT query (0.3 watt-hours) is small compared to the average U.S. household consumption per minute (almost 20 watt-hours). Queries containing long entries can consume significantly more energy (2.5 watt-hours for a query of around 7,500 words).<ref>{{cite web |last=You |first=Josh |date=February 7, 2025 |title=How much energy does ChatGPT use? |url=https://epoch.ai/gradient-updates/how-much-energy-does-chatgpt-use|website=Epoch AI |access-date=11 November 2025}}</ref>

AI has a significant carbon footprint due to growing energy consumption from both training and usage.<ref name="Heikkilä-2023">{{Cite web |last=Heikkilä |first=Melissa |date=December 5, 2023 |title=AI's carbon footprint is bigger than you think |url=https://www.technologyreview.com/2023/12/05/1084417/ais-carbon-footprint-is-bigger-than-you-think/ |url-status=live |archive-url=https://web.archive.org/web/20240705073854/https://www.technologyreview.com/2023/12/05/1084417/ais-carbon-footprint-is-bigger-than-you-think/ |archive-date=July 5, 2024 |access-date=July 4, 2024 |website=MIT Technology Review |language=en}}</ref> Scientists and journalists have expressed concerns about the environmental impact that the development and deployment of generative models are having: high CO<sub>2</sub> emissions,<ref name="Bender-2021">{{Cite book |last1=Bender |first1=Emily M. |last2=Gebru |first2=Timnit |last3=McMillan-Major |first3=Angelina |last4=Shmitchell |first4=Shmargaret |chapter=On the Dangers of Stochastic Parrots: Can Language Models be Too Big? 🦜 |date=March 1, 2021 |title=Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency |chapter-url=https://dl.acm.org/doi/10.1145/3442188.3445922 |series=FAccT '21 |location=New York, NY, USA |publisher=Association for Computing Machinery |pages=610–623 |doi=10.1145/3442188.3445922 |isbn=978-1-4503-8309-7 |archive-date=October 3, 2021 |access-date=August 27, 2024 |archive-url=https://web.archive.org/web/20211003000523/https://dl.acm.org/action/cookieAbsent |url-status=live }}</ref><ref name="Crownhart-2024">{{Cite news |date=May 23, 2024 |last= Crownhart| first= Casey |title=AI is an energy hog. This is what it means for climate change. |url=https://www.technologyreview.com/2024/05/23/1092777/ai-is-an-energy-hog-this-is-what-it-means-for-climate-change/ |archive-url=https://web.archive.org/web/20240820160720/https://www.technologyreview.com/2024/05/23/1092777/ai-is-an-energy-hog-this-is-what-it-means-for-climate-change/ |archive-date=August 20, 2024 |access-date=August 27, 2024 |website=MIT Technology Review |language=en}}</ref><ref name="Dhar-2020">{{Cite journal |last=Dhar |first=Payal |date=August 1, 2020 |title=The carbon impact of artificial intelligence |url=https://www.nature.com/articles/s42256-020-0219-9 |journal=Nature Machine Intelligence |language=en |volume=2 |issue=8 |pages=423–425 |doi=10.1038/s42256-020-0219-9 |bibcode=2020NatMI...2..423D |issn=2522-5839 |archive-url=https://web.archive.org/web/20240814145516/https://www.nature.com/articles/s42256-020-0219-9 |archive-date=August 14, 2024}}</ref> large amounts of freshwater used for data centers,<ref name="Crawford-2024">{{Cite journal |last=Crawford |first=Kate |date=February 20, 2024 |title=Generative AI's environmental costs are soaring — and mostly secret |url=https://www.nature.com/articles/d41586-024-00478-x |journal=Nature |language=en |volume=626 |issue=8000 |page=693 |doi=10.1038/d41586-024-00478-x |pmid=38378831 |bibcode=2024Natur.626..693C |archive-url=https://web.archive.org/web/20240822050528/https://www.nature.com/articles/d41586-024-00478-x |archive-date=August 22, 2024}}</ref><ref name="Rogers">{{Cite magazine |last=Rogers |first=Reece |title=AI's Energy Demands Are Out of Control. Welcome to the Internet's Hyper-Consumption Era |url=https://www.wired.com/story/ai-energy-demands-water-impact-internet-hyper-consumption-era/ |archive-url=https://web.archive.org/web/20240814171438/https://www.wired.com/story/ai-energy-demands-water-impact-internet-hyper-consumption-era/ |archive-date=August 14, 2024 |access-date=August 27, 2024 |magazine=Wired |language=en-US |issn=1059-1028}}</ref> high amounts of electricity usage,<ref name="Crownhart-2024" /><ref name="Saenko-2023">{{Cite web |last=Saenko |first=Kate |date=May 23, 2023 |title=Is generative AI bad for the environment? A computer scientist explains the carbon footprint of ChatGPT and its cousins |url=https://theconversation.com/is-generative-ai-bad-for-the-environment-a-computer-scientist-explains-the-carbon-footprint-of-chatgpt-and-its-cousins-204096 |archive-url=https://web.archive.org/web/20240701165020/https://theconversation.com/is-generative-ai-bad-for-the-environment-a-computer-scientist-explains-the-carbon-footprint-of-chatgpt-and-its-cousins-204096 |archive-date=July 1, 2024 |access-date=August 27, 2024 |website=The Conversation |language=en-US}}</ref><ref name="Lohr-2024">{{Cite news |last=Lohr |first=Steve |date=August 26, 2024 |title=Will A.I. Ruin the Planet or Save the Planet? |url=https://www.nytimes.com/2024/08/26/climate/ai-planet-climate-change.html |archive-url=https://web.archive.org/web/20240826113905/https://www.nytimes.com/2024/08/26/climate/ai-planet-climate-change.html |archive-date=August 26, 2024 |access-date=August 27, 2024 |work=The New York Times |language=en-US |issn=0362-4331}}</ref> electronic waste,<ref>{{Cite journal |date=2024-10-28 |title=E-waste challenges of generative artificial intelligence |url=https://www.nature.com/articles/s43588-024-00712-6 |journal=Nature Computational Science |language=en |volume=4 |issue=11 |pages=818–823 |doi=10.1038/s43588-024-00712-6 |issn=2662-8457}}</ref> and pollution due to backup diesel generator exhaust.<ref>{{Cite web |title=Data centers - Washington State Department of Ecology |url=https://ecology.wa.gov/air-climate/air-quality/data-centers |access-date=2026-04-09 |website=ecology.wa.gov}}</ref> There is also concern that these impacts may increase as these models are incorporated into widely used search engines such as Google Search and Bing,<ref name="Saenko-2023" /> as chatbots and other applications become more popular,<ref name="Rogers" /><ref name="Saenko-2023" /> and as models need to be retrained.<ref name="Saenko-2023" />

The carbon footprint of generative AI globally is estimated to be growing steadily, with potential annual emissions ranging from 18.21 to 245.94 million tons of CO<sub>2</sub> by 2035,<ref>{{Cite journal |last1=Ding |first1=Zhaohao |last2=Wang |first2=Jianxiao |last3=Song |first3=Yiyang |last4=Zheng |first4=Xiaokang |last5=He |first5=Guannan |last6=Chen |first6=Xiupeng |last7=Zhang |first7=Tiance |last8=Lee |first8=Wei-Jen |last9=Song |first9=Jie |date=May 5, 2025 |title=Tracking the carbon footprint of global generative artificial intelligence |journal=The Innovation |volume=6 |issue=5 |article-number=100866 |doi=10.1016/j.xinn.2025.100866 |issn=2666-6758|doi-access=free |pmid=40432776 |pmc=12105502 |bibcode=2025Innov...600866D }}</ref> with the highest estimates for 2035 nearing the impact of the United States beef industry on emissions (currently estimated to emit 257.5 million tons annually as of 2024).<ref>{{Cite journal |last1=Pelton |first1=Rylie E. O. |last2=Kazanski |first2=Clare E. |last3=Keerthi |first3=Shamitha |last4=Racette |first4=Kelly A. |last5=Gennet |first5=Sasha |last6=Springer |first6=Nathaniel |last7=Yacobson |first7=Eugene |last8=Wironen |first8=Michael |last9=Ray |first9=Deepak |last10=Johnson |first10=Kris |last11=Schmitt |first11=Jennifer |date=September 2024 |title=Greenhouse gas emissions in US beef production can be reduced by up to 30% with the adoption of selected mitigation measures |journal=Nature Food |language=en |volume=5 |issue=9 |pages=787–797 |doi=10.1038/s43016-024-01031-9 |pmid=39215094 |issn=2662-1355|pmc=11420059 }}</ref>

Proposed mitigation strategies include factoring potential environmental costs prior to model development or data collection,<ref name="Bender-2021" /> increasing efficiency of data centers to reduce electricity/energy usage,<ref name="Dhar-2020" /><ref name="Saenko-2023" /><ref name="Lohr-2024" /> building more efficient machine learning models,<ref name="Crownhart-2024" /><ref name="Crawford-2024" /><ref name="Rogers" /> minimizing the number of times that models need to be retrained,<ref name="Dhar-2020" /> developing a government-directed framework for auditing the environmental impact of these models,<ref name="Dhar-2020" /><ref name="Crawford-2024" /> regulating for transparency of these models,<ref name="Dhar-2020" /> regulating their energy and water usage,<ref name="Crawford-2024" /> encouraging researchers to publish data on their models' carbon footprint,<ref name="Dhar-2020" /><ref name="Saenko-2023" /> and increasing the number of subject matter experts who understand both machine learning and climate science.<ref name="Dhar-2020" />

==== Reliance on industry giants ==== Training frontier AI models requires an enormous amount of computing power. Usually only Big Tech companies have the financial resources to make such investments. Smaller start-ups such as Cohere and OpenAI end up buying access to data centers from Google and Microsoft respectively.<ref>{{cite news |last1=Metz |first1=Cade |date=July 10, 2023 |title=In the Age of A.I., Tech's Little Guys Need Big Friends |url=https://www.nytimes.com/2023/07/05/business/artificial-intelligence-power-data-centers.html |work=New York Times |archive-date=July 8, 2024 |access-date=July 10, 2024 |archive-url=https://web.archive.org/web/20240708214644/https://www.nytimes.com/2023/07/05/business/artificial-intelligence-power-data-centers.html |url-status=live }}</ref>

== Detection and awareness == {{See also|AI content watermarking|Artificial intelligence content detection}} Tools such as GPTZero can detect content generated by AI. However, they can also make false accusations (false positives).<ref>{{Cite web |last=Holden |first=Michael |title=AI-detection software isn't the solution to classroom cheating — assessment has to shift |url=http://theconversation.com/ai-detection-software-isnt-the-solution-to-classroom-cheating-assessment-has-to-shift-246102 |date=2025-02-25 |access-date=2025-07-29 |website=The Conversation |language=en-US}}</ref> Digital watermarking is a technique that improves detection accuracy. It works by altering the generated content at the source, in subtle ways which can be detected by corresponding software.

In 2023, OpenAI developed a watermarking tool for ChatGPT. They didn't release it, because they worried that users would switch to competitors. They also argued that it would be easy to circumvent, for example by asking another AI to rephrase.<ref>{{Cite web |last=Ha |first=Anthony |title=OpenAI says it's taking a 'deliberate approach' to releasing tools that can detect writing from ChatGPT |url=https://techcrunch.com/2024/08/04/openai-says-its-taking-a-deliberate-approach-to-releasing-tools-that-can-detect-writing-from-chatgpt/ |date=2024-08-04 |access-date=2025-07-29 |website=TechCrunch |language=en-US}}</ref><ref>{{Cite web |last1=Seetharaman |first1=Deepa |last2=Barnum |first2=Matt |title=Exclusive {{!}} There's a Tool to Catch Students Cheating With ChatGPT. OpenAI Hasn't Released It. |url=https://www.wsj.com/tech/ai/openai-tool-chatgpt-cheating-writing-135b755a |date=August 4, 2024 |access-date=2025-07-29 |website=WSJ |language=en-US}}</ref>

In March 2025, the Cyberspace Administration of China issued rules, requiring online service providers to label AI content.<ref>{{Cite web |last1=Covington |last2=Luo |first2=Yan |last3=Dan |first3=Xuezi |others=Burling LLP |title=China Releases New Labeling Requirements for AI-Generated Content |url=https://www.lexology.com/library/detail.aspx?g=1e66cce9-1936-4e63-96f0-5fdf378a8a04 |date=2025-03-18 |access-date=2025-07-28 |website=Lexology |language=en}}</ref>{{Unreliable source?|date=December 2025}}

In May 2025, Google deployed its watermarking tool, SynthID. It marks output from Gemini (text), Imagen (images), and Veo (video). To detect output from these products, one uses Google's "SynthID detector" portal.<ref>{{Cite web |last=Peters |first=Jay |title=Google has a new tool to help detect AI-generated content |url=https://www.theverge.com/news/672013/google-synthid-detector-ai-generated-content-watermark-i-o-2025 |date=2025-05-21 |access-date=2025-07-29 |website=The Verge |language=en-US}}</ref>

In June 2025, users mistakenly accused gaming companies of using generative AI for the video games ''Little Droid'' and ''Catly''.<ref>{{Cite news |last=Carpenter |first=Nicole |title=A real issue: video game developers are being accused of using AI – even when they aren't |url=https://www.theguardian.com/games/2025/jun/26/video-game-developers-using-ai-even-when-they-arent-stamina-zero |date=2025-06-26 |access-date=2025-07-28 |work=The Guardian |language=en-GB |issn=0261-3077}}</ref>

== See also == {{div col|colwidth=30em}} * AI anthropomorphism – Attribution of human traits to AI * {{annotated link|Artificial general intelligence}} * {{annotated link|Artificial imagination}} * {{annotated link|Artificial intelligence art}} * {{annotated link|Artificial life}} * {{annotated link|Chatbot}} * {{annotated link|Computational creativity}} * {{annotated link|Diffusion-based generative models}} * {{annotated link|Generative adversarial network}} * {{annotated link|Generative pre-trained transformer}} * {{annotated link|Large language model}} * Lists of open-source artificial intelligence software * {{annotated link|Music and artificial intelligence}} * {{annotated link|Generative AI pornography}} * {{annotated link|Procedural generation}} * {{annotated link|Retrieval-augmented generation}} * {{annotated link|Stochastic parrot}} {{div col end}}

==References== {{Reflist}}

==Further reading== * {{cite journal |last1=He |first1=Ran |last2=Cao |first2=Jie |last3=Tan |first3=Tieniu |title=Generative Artificial Intelligence: A Historical Perspective |journal=National Science Review |date=2025 |volume=12 |issue=5 |article-number=nwaf050 |doi=10.1093/nsr/nwaf050 |doi-access=free|pmid=40191253 |pmc=11970245 }} * James Gleick, "[https://www.nybooks.com/articles/2025/07/24/the-parrot-in-the-machine-the-ai-con-bender-hanna/ The Parrot in the Machine]" (review of Emily M. Bender and Alex Hanna, ''The AI Con: How to Fight Big Tech's Hype and Create the Future We Want'', Harper, 274 pp.; and James Boyle, ''The Line: AI and the Future of Personhood'', MIT Press, 326 pp.), ''The New York Review of Books'', vol. LXXII, no. 12 (24 July 2025), pp. 43–46. "[C]hatbox 'writing' has a bland, regurgitated quality. Textures are flattened, sharp edges are sanded. No chatbox could ever have said that April is the cruelest month or that fog comes on little cat feet (though they might now, because one of their chief skills is plagiarism). And when synthetically extruded text turns out wrong, it can be comically wrong. When a movie fan asked Google whether a certain actor was in ''Heat'', he received this 'AI Overview': 'No, Angelina Jolie is not in heat.'" (p. 44.)

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