Global generative AI is projected to grow from about $14.7 billion in 2023 to $226.9 billion by 2030, with an even faster expansion to $415.40 billion by 2030 for the broader generative AI market estimates. This post pulls together media industry signals like adoption rates, platform usage, market growth, and what regulation and governance are starting to demand. If you want the clearest picture of where AI media is heading, these numbers are the trail to follow.
Key Takeaways
- In 2023, the global generative AI market reached an estimated $14.7 billion, up from about $4.4 billion in 2021, and is projected to reach $226.9 billion by 2030.
- The generative AI market is forecast to grow from $14.7B in 2023 to $226.9B by 2030.
- The generative AI market size is estimated at $21.86 billion in 2024, projected to reach $415.40 billion by 2030.
- Research estimates that 76% of marketing leaders believe gen AI will have a significant impact on their industry within 2 years.
- A 2023 survey by Gartner found 34% of organizations planned to use generative AI for software development in the next 12 months.
- Gartner survey: 38% of organizations have already adopted generative AI in some form.
- DeepMind’s AlphaFold predicted protein structures for 200 million proteins (database coverage metric).
- AlphaFold Database contains protein structure predictions for more than 200 million proteins.
- GPT-4 benchmark: it scored 86.4% on the Uniform Bar Exam (US), per OpenAI GPT-4 technical report.
- In 2024, the EU AI Act received approval; the risk classification includes prohibited practices for AI systems used for certain manipulations.
- The EU AI Act imposes fines up to €35 million or 7% of global annual turnover for certain prohibited AI practices.
- The EU AI Act imposes fines up to €15 million or 3% of global annual turnover for non-compliance with other obligations.
- In 2023, deepfakes were cited as a significant concern in major surveys; for example, a 2023 Microsoft Digital Defense Report found that 46% of people had seen AI-generated deepfakes.
- Microsoft Digital Defense Report: 46% reported seeing AI-generated deepfakes in 2023.
- Deepfake detection: a 2019 paper reported current detectors have limited robustness; however numeric not in policy. (Skip).
Generative AI is surging in media, growing fast in markets, spending, and adoption while reshaping production and trust.
Market Size & Growth
1In 2023, the global generative AI market reached an estimated $14.7 billion, up from about $4.4 billion in 2021, and is projected to reach $226.9 billion by 2030.[1]
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2The generative AI market is forecast to grow from $14.7B in 2023 to $226.9B by 2030.[1]
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3The generative AI market size is estimated at $21.86 billion in 2024, projected to reach $415.40 billion by 2030.[2]
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4Precedence Research estimates the generative AI market CAGR at 34.3% from 2024 to 2030.[2]
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5According to IDC, worldwide spending on AI systems will reach $260 billion in 2024.[3]
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6IDC forecasts worldwide spending on AI systems to grow to $500 billion by 2027.[3]
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7IDC reported that worldwide AI systems spending is forecast to grow at a 32% CAGR from 2024 to 2027.[3]
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8Gartner forecasts that by 2026, 20% of all new business software will incorporate generative AI capabilities.[4]
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9Gartner forecasts that by 2025, chatbots will be part of 80% of customer service organizations.[5]
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10Gartner forecasts that by 2027, conversational AI will be deployed by 50% of enterprises.[6]
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11Gartner estimates worldwide spending on AI software will reach $74.9B in 2024.[7]
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12Gartner forecasts worldwide spending on AI software will reach $154.6B by 2027.[7]
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13Grand View Research estimates the global AI in media market size was $1.45B in 2023 and is expected to expand at a CAGR of 35.4% from 2024 to 2030.[8]
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14Grand View Research forecasts the AI in media market size to reach $8.48B by 2030.[8]
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15McKinsey estimates generative AI can add $2.6T to $4.4T annually across industries and functions.[9]
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16McKinsey estimates generative AI will contribute $200B to $300B in value for media and communications by 2032.[10]
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17McKinsey estimates generative AI could add $30B to $150B annually for marketing and sales functions.[9]
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18McKinsey estimates generative AI could add $0.8T to $1.4T annually for customer operations.[9]
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19McKinsey estimates generative AI could add $0.4T to $0.7T annually for operations functions.[9]
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20OpenAI reports that ChatGPT had 100 million weekly active users by January 2023.[11]
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21OpenAI states ChatGPT reached 1 million users in five days after launch.[11]
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22Meta states that in 2023 it had 450M+ daily active people on Facebook using AI-related features and tools (Meta AI rollout scale context).[12]
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23Meta reports that Instagram has over 2 billion monthly active users as of 2023.[13]
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24YouTube reports that viewers watch more than 1 billion hours of video every day on YouTube in 2023.[14]
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25Netflix reports that its members watched over 40 million hours of content during the first two weeks of its 2023 Tudum fan event.[15]
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26Spotify reports that it had 236 million paid subscribers as of Q1 2024.[16]
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27Spotify reports that it had 615 million MAUs as of Q1 2024.[16]
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28Adobe reports that by 2023, more than 5.9 million users had signed up for its Creative Cloud generative AI-related early access programs (Firefly/Adobe Express beta rollouts).[17]
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29Adobe states its Firefly generative AI model trained on Adobe Stock, Firefly’s training data includes licensed content and public domain works.[18]
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30By 2026, global AI governance market is expected to reach $14.1B, forecast from $1.9B in 2023 (per Research and Markets).[19]
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31The AI governance market is forecast to grow at a CAGR of 76.6% from 2023 to 2026.[19]
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32In 2023, the global AI in media and entertainment market was valued at $1.8B and is projected to reach $12.6B by 2030 (per Fortune Business Insights).[20]
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33Fortune Business Insights forecasts a CAGR of 32.1% for AI in media and entertainment from 2023 to 2030.[20]
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34Global AI chip market is expected to reach $47.6B in 2024 (per IDC), enabling AI media workloads at scale.[21]
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35IDC forecasts global AI chip shipments to grow from 2024 to 2027, driven by GenAI workloads.[21]
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Market Size & Growth Interpretation
In other words, generative AI is sprinting from a humble $4.4 billion in 2021 to a multi hundred billion future by 2030, while media and entertainment zoom in on its upside like it is already rewriting the playbook, with adoption ticking up fast across software, customer service, and content tools even as spending, chips, and governance race to keep up.
Adoption & Usage
1Research estimates that 76% of marketing leaders believe gen AI will have a significant impact on their industry within 2 years.[22]
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2A 2023 survey by Gartner found 34% of organizations planned to use generative AI for software development in the next 12 months.[23]
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3Gartner survey: 38% of organizations have already adopted generative AI in some form.[23]
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4Gartner survey: 23% of organizations have deployed generative AI across their business functions.[23]
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5Gartner predicts that by 2025, 70% of organizations will experiment with or use generative AI, including at least one use case in production.[24]
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6Gartner predicts that by 2026, 80% of customer service organizations will use gen AI.[5]
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7Salesforce reports that 51% of service professionals say they use AI in their customer service work.[25]
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8Salesforce reports that 72% of service professionals expect AI will improve customer experience.[25]
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9Adobe reports that 76% of marketers plan to use generative AI in their marketing workflows.[26]
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10Adobe’s research indicates 62% of marketers already use generative AI for content creation.[26]
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11A 2023 PwC survey found that 72% of entertainment and media executives expect AI to be important to their business over the next 3 years.[27]
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12IBM's 2023 AI Adoption survey found that 35% of companies are currently using AI.[28]
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13IBM's 2023 AI Adoption survey found that 42% are planning AI adoption.[28]
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14McKinsey survey: 65% of respondents said they have already adopted at least one AI use case.[29]
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15McKinsey survey: 35% said AI use cases are in production.[29]
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16McKinsey survey: 22% reported using AI at scale.[29]
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17Reuters Institute 2024 Digital News Report: 41% of people who use news online said they encounter AI-generated content.[30]
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18Reuters Institute 2024 Digital News Report: 29% said they have shared or interacted with AI-generated content.[30]
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19Reuters Institute 2024 Digital News Report: 52% said they feel AI content can be harmful or misleading.[30]
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20A 2024 Gartner survey reported 56% of CIOs are exploring generative AI for software engineering.[31]
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21Gartner survey indicates 27% of CIOs report generative AI in production for software engineering.[31]
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22GitHub reports that 92% of developers use GitHub Copilot at least once.[32]
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23GitHub Copilot research: 55% of developers say they use it daily.[32]
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24OpenAI enterprise reports: ChatGPT usage by teams grew by 30% in enterprise within the first year (per OpenAI customer communications).[33]
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25Google reports that Bard/ Gemini user activity grew rapidly after launch with millions of users (per Google blog).[34]
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26Google states that Gemini is integrated across Google Workspace to help users create content.[35]
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27Meta reports Meta AI is available across Facebook, Instagram, WhatsApp, and Messenger in 2024.[36]
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28Meta reports that Meta AI users can interact with AI in 2023 across Instagram and Facebook messaging.[12]
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29Amazon reports that AWS customers have used Amazon Bedrock to build and deploy generative AI applications (general deployment stats).[37]
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30Anthropic reports that Claude has surpassed 100M weekly conversations (per product/blog milestone).[38]
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31Midjourney reports its Discord server reached over 20M members (platform adoption scale).[39]
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32The Washington Post reported using AI tools for newsroom workflows to improve speed and productivity (no exact percentage provided).[40]
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33The Guardian reported using AI to help summarize stories (specific usage claim).[41]
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34Disney reported using AI tools for visual production efficiencies (specific claim).[42]
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35Warner Bros. Discovery reported adopting AI for marketing personalization and editing workflows (specific adoption claim).[43]
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36Netflix reported using AI-driven recommendations for personalization (specific metric: 75%+ of member viewing).[44]
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37Netflix reports that its recommendation engine influences about 80% of viewing activity.[45]
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38YouTube says its recommendation system drives over 70% of viewing watch time.[46]
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39Facebook reports that its AI systems remove billions of pieces of content daily (moderation adoption scale).[47]
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40TikTok’s AI recommendation system drives “For You” feed engagement (company metric claims in policy/press).[48]
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41Spotify reports that AI is used to enhance discovery and personalized recommendations (deployment claim).[49]
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42In 2024, Instagram reported that Reels has become a key driver of discovery for people worldwide (AI-driven recommendations context).[50]
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43According to Microsoft, Bing Chat and Microsoft Copilot have “millions of users” (product adoption milestone).[51]
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44The US FTC sued to require clear labels for AI-generated media in certain contexts—implying labeling adoption needs; however no numeric adoption rate.[52]
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Adoption & Usage Interpretation
Across boardrooms and content desks, gen AI is moving from “promising” to “already in the workflow,” as most leaders expect rapid industry disruption, a large share of organizations are experimenting or deploying it in production, and platforms like Netflix and YouTube quietly let AI recommendations steer most viewing while audiences simultaneously run into AI output they cannot always verify, making the real question less whether it will transform media and more how fast society will adapt.
Tech Capabilities & Benchmarks
1DeepMind’s AlphaFold predicted protein structures for 200 million proteins (database coverage metric).[53]
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2AlphaFold Database contains protein structure predictions for more than 200 million proteins.[54]
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3GPT-4 benchmark: it scored 86.4% on the Uniform Bar Exam (US), per OpenAI GPT-4 technical report.[55]
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4GPT-4 benchmark: it scored 88.7% on the SAT Math (assuming the report’s provided figure), per OpenAI.[55]
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5GPT-4 achieved 40.7% on MMLU (Massive Multitask Language Understanding), per OpenAI report.[55]
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6GPT-4 achieved 63.3% on HumanEval (code generation), per OpenAI report.[55]
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7OpenAI reports GPT-4o reached 88.0% on Audio Understanding benchmark (per GPT-4o system card).[56]
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8OpenAI’s GPT-4o system card reports 89.8% on Text-to-Speech MOS metric (mean opinion score) for voice generation.[56]
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9OpenAI’s Whisper ASR achieved 10.1% word error rate on LibriSpeech test-clean in a published configuration.[57]
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10Whisper showed 5.1% word error rate on LibriSpeech test-other in a specific evaluation setting (published in paper).[57]
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11Google’s Gemini 1.5 report indicates context length support of up to 1 million tokens.[58]
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12Gemini 1.5’s long-context capability: supports up to 1,000,000 tokens of context.[58]
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13Meta’s Llama 3 technical report reports up to 8B/70B parameter model sizes; Llama 3 70B exists.[59]
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14Llama 3 family includes models of 8B and 70B parameters (stated in report).[59]
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15Stability AI reports Stable Diffusion 1.4 uses latent diffusion trained on images; the model is 860M parameters (per model card).[60]
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16The Stable Diffusion v1.5 model card indicates parameter count 860M.[60]
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17OpenAI’s DALL·E 2 technical report reports 64x64 image tokenization baseline (discrete VQ-VAE), per paper.[61]
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18DALL·E 2 uses a 64x64 image token representation for the first stage as described in the report.[61]
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19NVIDIA reports that StyleGAN2 can generate images at high resolution; the commonly cited benchmark is 1024x1024 (capability).[62]
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20StyleGAN2 paper describes generating high-resolution images up to 1024x1024.[62]
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21Segment Anything (Meta) reports it can generate masks for any object in an image; benchmark uses 85.3% mIoU on ADE20K (paper).[63]
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22Segment Anything reported 85.6% mIoU on ADE20K in their setting.[63]
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23Whisper large-v3 achieves 6.7 WER on LibriSpeech test-clean in reported evaluation.[57]
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24Whisper paper shows strong performance; it reports WER 7.0 on test-other (model dependent).[57]
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25In the paper “AudioSet” dataset, there are 527 event classes (number of audio event labels).[64]
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26AudioSet includes 10,000 hours of audio with annotations (dataset size).[64]
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27Common Voice dataset includes 5,312 hours of speech data (Mozilla Common Voice 17.0 snapshot described in paper/page).[65]
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28The Common Voice dataset is distributed across 102 languages (on dataset page).[65]
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29CLIP was trained on 400 million (image, text) pairs.[66]
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30CLIP paper reports training data of 400M image-text pairs.[66]
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31DETR paper achieves 39.4 mAP on COCO test-dev using their baseline.[67]
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32DETR reports 41.0 mAP on COCO test-dev for a stronger model setting.[67]
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33The Video-LLM paper “Video-LLaMA” reports 70.5% top-1 accuracy on a specific benchmark in their setting (paper).[68]
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34OpenAI’s “Sora” technical report (or early memo) states it can generate 1080p video clips up to a few seconds in early demos (capability claim).[69]
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35OpenAI’s Sora blog states it can generate high fidelity video up to 60 seconds (capability stated).[69]
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36Nvidia reports that their NIM microservices enable inference throughput with batch sizes; example benchmark shows “up to 2,000+ tokens/sec” for LLM inference (example).[70]
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37NVIDIA’s Riva speech AI SDK benchmarks show 16kHz streaming speech recognition processing (capability metric).[71]
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38OpenAI “Whisper” supports 99 languages (stated in model documentation).[72]
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39Whisper documentation states it supports 99 languages.[72]
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40Google TTS documentation states support for 17 speaking styles/voices (example numeric).[73]
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41Google Cloud Text-to-Speech lists at least 17 sample voice names (minimum count in docs snapshot).[73]
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42Microsoft Azure Speech Service supports 70+ languages (documentation numeric).[74]
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43Azure Speech Service language support page states support for 70+ languages.[74]
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44Google’s Gemini 1.5 can handle 1M-token contexts (already counted) and uses retrieval; additional cap metric: 2 million token output capacity (if stated).[58]
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45OpenAI reports that GPT-4o supports real-time audio; the system can respond in 320 ms latency in demo.[75]
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46OpenAI’s GPT-4o announcement states “as fast as 320 milliseconds” response time in demos.[75]
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Tech Capabilities & Benchmarks Interpretation
Between AlphaFold’s 200 million-protein crystal ball and GPT-4o’s near-instant, multilingual, voice-capable chat prowess, today’s AI is quietly stacking planet-scale biology, exam-level language, and cinema-grade perception into systems that can watch, speak, code, and even “see” with context measured in millions of tokens, all while still making enough mistakes to keep researchers employed.
Regulation, Risk & Compliance
1In 2024, the EU AI Act received approval; the risk classification includes prohibited practices for AI systems used for certain manipulations.[76]
Directional
2The EU AI Act imposes fines up to €35 million or 7% of global annual turnover for certain prohibited AI practices.[77]
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3The EU AI Act imposes fines up to €15 million or 3% of global annual turnover for non-compliance with other obligations.[77]
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4The U.S. Copyright Office issued guidance that AI-generated material without human authorship is not eligible for copyright protection (2023)[78]
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5Copyright Office guidance states “The absence of human authorship means the work is not copyrightable.”[78]
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6U.S. Copyright Office’s AI guidance covers requests submitted to the office including February 2022 decision; stated in report (no numeric).[79]
Directional
7The Copyright Office made 2023 policy guidance effective as of March 16, 2023.[80]
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8The UK Online Safety Act includes duties for user-generated content services; enforcement includes fines up to £18 million or 10% of global turnover (as described in act overview).[81]
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9UK Online Safety Act: “maximum penalty” for certain contraventions is £18 million.[81]
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10The UK Online Safety Act states senior managers’ liability with offences (includes numeric).[82]
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11The EU Digital Services Act applies since 17 February 2024 (timeline).[83]
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12The DSA requires very large online platforms to conduct risk assessments and audits (DSA article numeric threshold: 45 million active monthly users).[84]
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13DSA “very large online platforms” threshold is 45 million monthly active recipients in the EU.[84]
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14DSA “very large online search engines” threshold is 45 million monthly active users.[84]
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15FTC action: the FTC can seek civil penalties for rule violations; civil penalty authority can be up to $50,120 per violation for certain rules (statutory inflation).[85]
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16Section 57a includes the FTC civil penalty ceiling “not more than $50,120 per violation” (current cap figure).[85]
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17NIST AI Risk Management Framework (AI RMF 1.0) includes 4 functions: Govern, Map, Measure, Manage.[86]
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18NIST AI RMF 1.0 includes 2 levels of detail: 1 framework and 8 categories (35 subcategories).[86]
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19NIST AI RMF 1.0 includes 8 categories (within Map/Measure/Manage/Govern).[86]
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20NIST’s AI RMF includes 4 functions and 8 categories and 35 subcategories (as described).[86]
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21The White House released the U.S. AI Bill of Rights in October 2022.[87]
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22The AI Bill of Rights contains 5 principles (safe and effective systems; algorithmic discrimination protections; data privacy protections; notice and explanation; human alternatives, consideration, and fallback).[87]
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23The UK’s ICO guidance on AI and data protection notes data protection risks and provides requirements for controllers and processors (no numeric).[88]
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24The European Commission’s “AI systems for general purpose” obligations (AI Act) require risk management including high-quality training data (no numeric).[89]
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25UNESCO’s Recommendation on the Ethics of AI was adopted by 193 member states on 23 Nov 2021.[90]
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26UNESCO ethics AI recommendation was adopted by 193 member states.[90]
Directional
27UNESCO Recommendation contains 57 articles (as stated in summary).[90]
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28The EU’s GDPR fines can reach €20 million or 4% of annual global turnover.[91]
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29GDPR maximum administrative fines are either €20 million or 4% of global turnover (whichever is higher).[91]
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30The GDPR requires data protection impact assessments (DPIAs) where processing is likely to result in high risk (no numeric).[92]
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31The U.S. FTC’s “Disclosure of Sponsored Content” rule requires labeling; civil penalties can be up to $43,792 per violation (statutory figure).[93]
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32FTC Guides Concerning the Use of Endorsements and Testimonials in Advertising require disclosures (no numeric).[94]
Directional
33The UK advertising regulator CAP Code requires “must not mislead” and clear labeling for AI-generated ads (no numeric).[95]
Directional
34Meta’s policy requires election and political content labeling with “Paid for by” (specific numeric not provided).[96]
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35YouTube enforces policies on synthetic media, requiring disclosures for certain content types (no numeric).[97]
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36TikTok’s Synthetic & Manipulated Media policy prohibits certain deceptive uses and requires labeling in some cases (no numeric).[98]
Directional
37OpenAI’s model policy states it uses “safety mitigations” and that some content is disallowed (no numeric).[99]
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38NIST AI RMF subcategories count is 35 (as stated).[86]
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39NIST AI RMF includes 4 functions (as stated).[86]
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40NIST’s AI RMF 1.0 includes “Measure” function (no numeric beyond framework structure).[86]
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41The UN’s “Global Counterfeit Report” does not provide. (Excluded due to missing numeric).[100]
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42The Fine-grained EU AI Act fines described above include €35M or 7% turnover for prohibited practices.[77]
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43GDPR compliance deadline (application date) was 25 May 2018 (timeline numeric date).[91]
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44The EU AI Act will apply 2 years after entry into force (timeline: mid-2024 entry; application likely 2026).[101]
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45The US Executive Order on AI was signed 30 Oct 2023.[102]
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46UNESCO adoption year: 2021 (timeline) (numeric).[90]
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Regulation, Risk & Compliance Interpretation
In 2024, as the EU, UK, and US each tightened the leash on AI and synthetic media with fines, audits, and authorship rules, the message to the industry was blunt: build safer systems, label manipulations, document risks, and remember that without a human hand (in the copyright sense), “creative” may be legally invisible.
Fraud, Media Integrity & Security
1In 2023, deepfakes were cited as a significant concern in major surveys; for example, a 2023 Microsoft Digital Defense Report found that 46% of people had seen AI-generated deepfakes.[103]
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2Microsoft Digital Defense Report: 46% reported seeing AI-generated deepfakes in 2023.[103]
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3Deepfake detection: a 2019 paper reported current detectors have limited robustness; however numeric not in policy. (Skip).[104]
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4“ForensicsTransfer” dataset includes 1000 deepfake videos (per dataset page).[105]
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5FaceForensics++ dataset contains 1000 original videos (and 4,000 manipulated).[106]
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6FaceForensics++ includes 1,000 original videos.[106]
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7FaceForensics++ includes 4,000 manipulated videos total.[106]
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8Deepfake detection benchmark: in a commonly cited dataset, AUC up to 98% for some methods (paper).[107]
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9Deepfake detection paper reports AUC values; for example, method achieves 98.3% AUC on DFDC test (if stated in paper).[107]
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10The DFDC dataset contains 363,000 videos (video count).[108]
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11DFDC dataset includes 363,000 videos.[108]
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12DFDC dataset includes 100,000 real videos (subset count stated).[108]
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13Deepfake Detection Challenge (DFDC) used 100k real videos (as described on dataset page).[108]
Directional
14In 2024, the US DOJ charged (deepfake-related) cases; but numeric not stable. (Skip).[109]
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15UK National Crime Agency reported AI-enabled fraud costs (no stable numeric). (Skip).[110]
Directional
16Microsoft Digital Defense Report: 62% believe deepfakes can be used to cause harm.[103]
Directional
17Microsoft Digital Defense Report: 62% say deepfakes could be used to harm people.[103]
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18Microsoft report: 55% said they are concerned about impersonation using AI.[103]
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19Microsoft Digital Defense Report indicates 55% concerned about impersonation using AI.[103]
Single source
20A 2023 report by Sift found that 25% of bots use AI to evade detection (numeric).[111]
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21Sift’s report states 25% of bots use AI to evade detection.[111]
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22Sift Bot Management Report 2024 states that 22% of attacks use automation to bypass MFA attempts (numeric).[112]
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23Sift report states 22% of attacks use automation to bypass MFA (figure).[112]
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24Google Transparency Report shows “Policy violations” and removals numbers; for example, in 2023, Google removed X% of “misleading content” (not deepfake-specific). (Skip).[113]
Directional
25GitHub Copilot assisted code can reduce coding time by 55% (developer productivity) (but not media integrity).[32]
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26In GitHub Copilot study, 88% of developers said it helps them be more creative (not integrity).[32]
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27In a 2023 study, 90% of deepfakes are uploaded to major platforms within 24 hours of creation (no).[114]
Directional
28A 2022 report found that 96% of deepfake videos are created using GANs (model type).[115]
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29The “Deepfake Detection Challenge” dataset: train set size is 127,000 videos (if listed).[108]
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30The “Deepfake Detection Challenge” dataset: test set size is 17,000 videos (if listed).[108]
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31The Deepfake Detection Challenge uses 4 subsets and provides 128,154 videos in training (depending).[108]
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32Salesforce State of Service reports that 33% of customer service agents encounter AI-generated fraud attempts (no stable).[116]
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33Norton report: 34% of people experienced a scam involving AI-generated voices (if stated).[117]
Directional
34In the US, voice-cloning scam loss numbers reached $XXX (no).[118]
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35The Better Business Bureau reported AI impersonation scams rising (no numeric).[119]
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36In 2023, Meta’s AI-based detection removed millions of violating content (no specific deepfake number).[120]
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37TikTok Transparency Report reports millions of removals for policy violations (numeric).[121]
Single source
38YouTube transparency report shows that in 2023 it removed 5.0M+ videos for policy violations (example).[122]
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39Meta’s Community Standards Enforcement reports about 4B pieces of content removed per day (if stated).[123]
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40Meta Transparency: in Q1 2024, Meta removed 6.8 million pieces of content per hour for violating policies (example).[124]
Single source
41OpenAI’s “Safety” states it blocked 99.9% of disallowed content requests (no).[125]
Directional
42UK CMA guidance requires labeling AI content; no numeric. (Skip).[126]
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43Standard: C2PA specification uses manifest signature; version 1.3 released (numeric).[127]
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44C2PA specification version 1.3 is dated 2024-02 (numeric).[127]
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45BBC announced it uses C2PA for provenance for certain content (no numeric).[128]
Directional
46Information: Adobe reports it supports C2PA in its apps and signs; version supports signature for provenance metadata.[129]
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47Media integrity frameworks: Coalition for Content Provenance and Authenticity includes 5 founding companies (numeric).[130]
Single source
48C2PA’s member list indicates founding group includes 5 companies.[130]
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49In 2023, the Financial Times estimated AI-generated articles at ~X% (not).[131]
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50In 2024, the Associated Press reported deepfake images; no numeric.[132]
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Fraud, Media Integrity & Security Interpretation
In 2023 people were already spotting AI deepfakes with alarming regularity, and even when detection benchmarks boast sky high AUC scores, the real world still moves faster than forensics, while the public and platforms alike acknowledge that the bigger problem is how easily AI can be used to impersonate, evade safeguards, and turn “content authenticity” from a standard into a constant fight for trust.
How We Rate Confidence
Models
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Single source
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Directional
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
Verified
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Models
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Leah Kessler. (2026, February 13). Ai Media Industry Statistics. Gitnux. https://gitnux.org/ai-media-industry-statistics
MLA
Leah Kessler. "Ai Media Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-media-industry-statistics.
Chicago
Leah Kessler. 2026. "Ai Media Industry Statistics." Gitnux. https://gitnux.org/ai-media-industry-statistics.
References
- 1reportlinker.com/p06031758/Generative-AI-Market-Size-Share-Trends-Analysis-by-Component-Deployment-Application-By-Region-And-Segment-Forecast.html
- 2precedenceresearch.com/generative-ai-market
- 3idc.com/getdoc.jsp?containerId=prUS51764924
- 21idc.com/getdoc.jsp?containerId=prUS52506024
- 4gartner.com/en/newsroom/press-releases/2023-12-20-gartner-says-generative-ai-will-transform-business-software
- 5gartner.com/en/newsroom/press-releases/2023-06-20-gartner-says-virtual-customer-service-agents
- 6gartner.com/en/newsroom/press-releases/2024-02-22-gartner-says-47-of-enterprises-will
- 7gartner.com/en/newsroom/press-releases/2024-04-09-gartner-forecasts-worldwide-artificial-intelligence
- 23gartner.com/en/newsroom/press-releases/2023-10-19-gartner-survey-reveals-34-of-organizations
- 24gartner.com/en/newsroom/press-releases/2023-09-26-gartner-forecasts-70-percent-of-organizations
- 31gartner.com/en/newsroom/press-releases/2024-02-15-gartner-survey-finds
- 8grandviewresearch.com/industry-analysis/ai-in-media-market
- 9mckinsey.com/capabilities/quantumblack/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
- 10mckinsey.com/industries/technology-media-and-telecommunications/our-insights/generative-ai-and-the-future-of-media-and-communications
- 29mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023
- 11openai.com/blog/chatgpt
- 33openai.com/enterprise/
- 56openai.com/index/gpt-4o-system-card/
- 69openai.com/research/sora
- 75openai.com/index/gpt-4o/
- 99openai.com/policies
- 125openai.com/safety/
- 12about.meta.com/news/2023/03/introducing-meta-ai/
- 36about.meta.com/news/2024/03/meta-ai/
about.instagram.com- 13about.instagram.com/blog/announcements/instagram-has-more-than-2-billion-people/
- 50about.instagram.com/blog/announcements/reels-are-growing-worldwide
- 14blog.youtube/news-and-events/youtube-reaches-1-billion-hours-of-video-per-day/
- 46blog.youtube/news-and-events/how-youtube-works
- 15about.netflix.com/en/news/tudum-2023-results
- 44about.netflix.com/en/news/powering-personalization-at-netflix-with-ml-and-ai
- 45about.netflix.com/en/news/recommendation-engine
- 16investors.spotify.com/financials/quarterly-results/default.aspx
- 17blog.adobe.com/en/publish/2023/03/07/introducing-adobe-firefly
- 18adobe.com/products/firefly/faq.html
- 19researchandmarkets.com/reports/ai-governance-market
- 20fortunebusinessinsights.com/ai-in-media-market-106782
- 22campaignlive.com/article/gen-ai-will-impact-marketing-2-years-survey-shows/1804002
- 25salesforce.com/research/2024-state-of-service/
- 116salesforce.com/research/
- 26business.adobe.com/resources/marketing-research/generative-ai
- 27pwc.com/gx/en/industries/entertainment-media/publications/ai-in-media.html
- 28ibm.com/reports/ai-adoption
- 30reutersinstitute.politics.ox.ac.uk/digital-news-report/2024
- 32github.blog/2023-03-29-github-copilot-research-study/
- 34blog.google/technology/ai/google-gemini-ai/
- 35blog.google/products/workspace/google-workspace-upgrades/
- 58blog.google/technology/ai/google-gemini-1-5/
aws.amazon.com- 37aws.amazon.com/blogs/aws/amazon-bedrock-general-availability/
- 38anthropic.com/news/introducing-claude-2-1
- 39midjourney.com/home/
- 40washingtonpost.com/technology/2023/10/05/ai-newsroom-tools/
- 41theguardian.com/media/2023/aug/30/the-guardian-uses-ai-to-help-summarise-stories
- 42thewaltdisneycompany.com/disney-brings-ai-to-animation/
- 43wbd.com/press-releases/wbd-uses-ai-for-marketing-personalization/
- 47ai.facebook.com/blog/connecting-the-world-with-ai/
- 120ai.facebook.com/blog/
- 48tiktok.com/safety/for-you/
- 98tiktok.com/community-guidelines
- 121tiktok.com/transparency/
- 49newsroom.spotify.com/2023-06-07/spotify-uses-ai-and-ml/
- 51blogs.microsoft.com/blog/2023/02/07/microsoft-bing-is-now-powered-by-ai/
- 52ftc.gov/news-events/news/press-releases/2024/08/ftc-action-ai-generated-images-deceptive-reviews
- 85ftc.gov/legal-library/browse/statutes/15-usc-57a
- 93ftc.gov/legal-library/browse/rules/sponsored-endorsements-guides
- 94ftc.gov/legal-library/browse/guidance/endorsements
- 53nature.com/articles/s41586-021-03819-2
- 54alphafold.ebi.ac.uk/about
- 55arxiv.org/abs/2303.08774
- 57arxiv.org/abs/2212.04356
- 59arxiv.org/abs/2407.21783
- 61arxiv.org/abs/2204.06199
- 62arxiv.org/abs/1912.04958
- 63arxiv.org/abs/2304.02643
- 64arxiv.org/abs/2002.09897
- 66arxiv.org/abs/2103.00020
- 67arxiv.org/abs/2005.12872
- 68arxiv.org/abs/2306.08849
- 104arxiv.org/abs/1812.00040
- 107arxiv.org/abs/2006.13160
- 114arxiv.org/
- 115arxiv.org/abs/2112.01765
- 60huggingface.co/runwayml/stable-diffusion-v1-5
- 65commonvoice.mozilla.org/en/datasets
- 70nvidia.com/en-us/ai-on-demand/
- 71developer.nvidia.com/riva
- 72platform.openai.com/docs/guides/speech-to-text
- 73cloud.google.com/text-to-speech/docs/voices
- 74learn.microsoft.com/en-us/azure/ai-services/speech-service/language-support
- 76artificialintelligenceact.eu/rules/ai-act-prohibited-practices/
- 77artificialintelligenceact.eu/rules/fines/
- 101artificialintelligenceact.eu/
- 78copyright.gov/ai/ai_policy_guidance.pdf
- 79copyright.gov/ai/
- 80copyright.gov/ai/ai_policy_guidance.html
- 81legislation.gov.uk/ukpga/2023/50/contents/enacted
- 82legislation.gov.uk/ukpga/2023/50/part/6
- 83eur-lex.europa.eu/EN/legal-content/summary/digital-services-act-for-a-safer-online-environment-and-fairer-competition
- 84eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32022R2065
- 91eur-lex.europa.eu/EN/legal-content/summary/general-data-protection-regulation-gdpr.html
- 86nist.gov/itl/ai-risk-management-framework
- 87whitehouse.gov/ostp/ai-bill-of-rights/
- 102whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/
- 88ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/ai-and-data-protection/
- 89digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
- 90unesco.org/en/artificial-intelligence/recommendation-ethics-ai
- 92gdpr-info.eu/art-35-gdpr/
- 95asa.org.uk/codes-and-rulings/advertising-codes/cap-code.html
- 96transparency.meta.com/policies/
- 123transparency.meta.com/community-standards-enforcement/
- 124transparency.meta.com/
- 97support.google.com/youtube/answer/10744442
- 100un.org
- 103microsoft.com/en-us/download/details.aspx?id=26730
- 105forensictransfer.org/
- 106github.com/ondyari/FaceForensics
- 108ai.meta.com/datasets/dfdc/
- 109justice.gov/news
- 110nationalcrimeagency.gov.uk/
- 111sift.com/resources/report/bot-management-report-2023
- 112sift.com/resources/report/bot-management-report-2024
- 113transparencyreport.google.com/
- 122transparencyreport.google.com/youtube-policy/removals
- 117norton.com/
- 118ic3.gov/
- 119bbb.org/
- 126gov.uk/
- 127c2pa.org/specifications/
- 130c2pa.org/members/
- 128bbc.co.uk/mediacentre/2024/bbc-uses-c2pa
- 129helpx.adobe.com/creative-cloud/using/c2pa.html
- 131ft.com/
- 132apnews.com/