惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Engineering at Meta
Engineering at Meta
T
Threatpost
P
Palo Alto Networks Blog
NISL@THU
NISL@THU
O
OpenAI News
Project Zero
Project Zero
G
GRAHAM CLULEY
P
Privacy International News Feed
A
Arctic Wolf
Microsoft Azure Blog
Microsoft Azure Blog
H
Help Net Security
M
MIT News - Artificial intelligence
T
Threat Research - Cisco Blogs
S
Security @ Cisco Blogs
Google DeepMind News
Google DeepMind News
B
Blog RSS Feed
D
Docker
aimingoo的专栏
aimingoo的专栏
博客园 - 【当耐特】
N
Netflix TechBlog - Medium
云风的 BLOG
云风的 BLOG
雷峰网
雷峰网
W
WeLiveSecurity
P
Proofpoint News Feed
腾讯CDC
Cloudbric
Cloudbric
S
Secure Thoughts
C
Check Point Blog
博客园 - Franky
T
The Exploit Database - CXSecurity.com
T
Troy Hunt's Blog
GbyAI
GbyAI
Security Archives - TechRepublic
Security Archives - TechRepublic
Application and Cybersecurity Blog
Application and Cybersecurity Blog
月光博客
月光博客
C
Cyber Attacks, Cyber Crime and Cyber Security
I
Intezer
TaoSecurity Blog
TaoSecurity Blog
L
Lohrmann on Cybersecurity
V
Visual Studio Blog
F
Fortinet All Blogs
博客园 - 叶小钗
C
CXSECURITY Database RSS Feed - CXSecurity.com
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
Recorded Future
Recorded Future
C
Cisco Blogs
博客园 - 司徒正美
Stack Overflow Blog
Stack Overflow Blog
Y
Y Combinator Blog
Apple Machine Learning Research
Apple Machine Learning Research

Forbes - CIO Network

Ralliant’s Amir Kazmi On Wiring AI Into Critical Infrastructure Nvidia Buys Kumo AI To Bring AI Predictions To Business Data Anthropic's Fable 5 AI Model Offers More Power At A Higher Price Argentina Wants To Let AI Own Companies. Here’s What That Means The AI Conversation CEOs Are Not Having Out Loud Moneyball Meets AI: How The New York Jets Are Charting An AI Future How Anthropic, OpenAI and Nvidia Are Driving the AI Economy Wall Street Is About To Test AI's Trillion-Dollar Valuations The VPN Risk Too Many Companies Ignore The Agentic Enterprise Got A Major Upgrade This Summer. OpenAI, Anthropic And The $1 Trillion Question: Who Really Wins From AI? Trump's AI Evaluations Order: Right Policy, Unfinished Governance Trump's AI Order Creates A New Test For Frontier AI—And Public Trust Microsoft Build 2026 Reveals the Future of AI, Data and ERP Artificial Intelligence Positioned To Disrupt $5 Trillion Industry Healthcare CIOs Should Take Note Of Copilot Health Innovation At The Pace Of AI Requires A Different Corporate Metabolism How Expedia Is Reinventing Travel Through AI And Agentic Design The AI Risks CISOs Aren’t Talking About Enough Prat Vemana On Leading Technology, Product And AI Innovation At Target AI Spurs A Cultural Shift In A 1,000-Developer Insurance Company Rewiring Omnicom’s Operating Model For AI At Scale 4 AI Strategy Questions Every Executive Needs To Drive ROI Building A Retail Platform Across Iconic American Brands Why AI Likely Means More Work For Humans AI Flattening Organizations Is The Latest Chapter In A Continuing Story OpenAI And Anthropic Are Testing Two Very Different AI Business Models Why Nvidia Needs More Than GPUs To Win The AI Infrastructure Race Google Wants Gemini To Become The Operating Layer For AI Tokenomics 101: Cost Of Getting Work Done (Not The Cost Of Tokens). AI Security Threats Coming From Outside And Inside, And Few Are Ready The AI Trade Is Moving Beyond GPUs AI Turns Solo Workers Into Departments And VCs Are Paying Attention Employee’s AI Shortcut Triggers SEC Filing — Boards, Take Note Transforming Wealth Management Using AI At Citi Uber Burns Its 2026 AI Budget In Four Months On Claude Code The Cyber Resilience Standard Every Hospital CIO Must Meet AI Layoffs Are A Substitute For A Strategy The Last Competitive Advantage In Software Isn't Software Knowledge Management, The Tech World’s Step Child, May Be AI’s Salvation The AI Governance Talent Gap Is Smaller Than It Looks Capgemini Warns CEOs: Physical AI Can No Longer Be Ignored AI Opens Work Opportunities — We Just Can’t Imagine Them Yet Friendly Chatbots Make More Mistakes — And Annoy Your Customers More From Information Provider To AI Partner: Thomson Reuters’ Next Chapter AI Is Breaking Silicon Valley’s Global Playbook AI Transformation Of An Internet Era Success: The SurveyMonkey Story Could The Musk V. Altman Trial Change The AI Race? At Least 18% of Jobs Face Major AI Risk, OpenAI Economist Predicts As Musk Takes OpenAI To Court, Its $130 Billion Philanthropy Bet Faces A Trial OpenAI Publishes 5 Principles For Its AGI Push How Hearst Is Using Data And AI To Transform A 140-Year-Old Business 6 Employee Critiques About Their Companies’ AI Practices AI Boosts Productivity — And Fears Of Layoffs, Anthropic Study Finds How Mythos’ Vulnerability Apocalypse Will Play Out Alleged Claude Mythos Breach Raises Questions About AI Security Consumers Warm Up To AI, Will Trust Follow? Stop Cleaning Your Data. Start Finding The Signal. Architecture: A Question At The Core Of AI In The Enterprise Why Healthcare AI Still Struggles To Deliver QClaw Goes Global. The Agent Built Itself In 5 Days Apple’s Tim Cook Exit Hides A $4 Trillion Agentic AI Power Move AI’s Missing Link Is Accountability Can A Startup Turn Night Into Day Using Space Mirrors? Why Sam Altman’s Warning About A Big Cyberattack In 2026 Is Overblown Most Employees Are Learning AI By Osmosis These Days OpenAI GPT-5.4-Cyber — The Security Of Tomorrow Or A PR Response To Claude Mythos? UF Health Names Healthcare Vet Craig Richardville As New Tech Leader Allbirds Ran Toward AI And The Stock Surged 800% Lisa Davis Is Doing Something About Being The Only Woman In The Room AI May Be Running Out Of Data, Stanford Report Warns Is The Cult Of ‘Tokenmaxxing’Just Another Fad Or The New Normal? Inside Syngenta’s AI Driven Approach To Modern Agriculture Forget Bigger Models, Neuromorphic AI Thinks Like A Human Brain CoreWeave Becomes AI's Landlord With Meta And Anthropic Deals AI Slop Is Real. Your Adoption Strategy May Be Making It Worse. Cloud Investments Not Keeping Up With AI With AI, Job Searches And Recruiting May Be Less Onerous, Hopefully The One AI Question Boards Should Stop Asking Their CEOs Turner Construction Appoints Former GE Aerospace Exec As CIO Ignore The Doom Talk: AI’s Real Value Only Arises When Humans Step Up China’s Grassroots OpenClaw Is Rewriting The Global Agentic AI Race Anthropic–Pentagon Dispute Brings A Turning Point For The AI Industry AI Delivering Value And ROI, But Think Twice Before You Cut March 31 Is World Backup Day. Here’s How To Protect Your Data Now AI Doesn’t Fix Systems — It Exposes Them The Healthcare Rule CIOs Shouldn’t Overlook AI: The Cybersecurity Crisis That Vendors Love Where Digital And Robot-Based AI Agents Now Prevail Quantum Computing’s Next Major Breakthrough May Come From Australia 6 Ways To Rise Above An Increasingly AI-Saturated World The Real Shift Is Not AI Tools. It Is Workflow Ownership We Trust AI Over Our Own Brains, Research Finds Pravina Ladva On How Swiss Re Uses Data And AI To Build Resilience We’re Still Only Seeing AI’s First-Order Effects, Former Tesla Head States Why China Is Winning The Open Source AI Race AI Doesn’t Own The Customer Yet. Here’s How Retailers Can Keep It That Way Shobhit Varshney Of Citi On Scaling AI With Purpose And Discipline How AI Is Transforming Patient Health At Genentech Agentic AI Reshapes Nvidia Strategy Beyond GPUs At GTC
AI’s Data Surge Demands Action In A New Battle Over Creator Rights
Dr. Jonathan Reichental · 2026-04-30 · via Forbes - CIO Network
The rise of AI is reshaping how content is used, valued, and protected.

The rise of AI is reshaping how content is used, valued, and protected.

getty

Every time you ask an AI chatbot a question, the response is shaped by vast datasets sourced across the internet. This material includes both licensed content and publicly available data from open systems and databases. Items behind paywalls and other protections don’t typically get ingested by the crawlers that seek out material to train AI, although exceptions sometimes occur.

However, in an increasingly digital world, copyrighted items such as books, music, and videos often find themselves used in the training of large language models (LLM) that power popular chatbots, whether inadvertently or because it exists in accessible systems.

Research suggests that a significant portion of creators’ original work has made it into the training of an LLM without permission. This content is being metaphorically remixed and delivered to consumers in their favorite conversational agent without credit or compensation to the creator.

It’s not a new issue, but it appears to be getting worse for creatives as recent high-profile legal rulings have been more favorable to the AI companies.

For Creators, All Is Not Lost

One expert believes creators still have agency and the time to act is now.

Eric R. Burgess, CEO and co-founder of Credtent, argues it’s an American ideal that content creators should be able to consent to how their work is used and be compensated for it. He is convinced, without deliberate and urgent action, it’s a principle that is rapidly eroding in the age of LLMs.

For Burgess, content development and protection is something he knows a lot about. He’s been a creative technologist for around 30 years and has worked with Disney and other big names in designing and producing games and enterprise software.

Together with Dr. Galen Buckwalter, one of the founding scientists of eHarmony, they created Credtent, a set of services to support content creators and help AI companies adhere to responsible practices when it comes to copyrighted material.

They offer an alternative to the legal path, one that is often too expensive for most writers and creatives.

Eric R. Burgess, CEO and Co-founder of Credtent.

CredTent

A New Model For Managing AI Content Rights

If a person suspects their material is being used without permission, Credtent will make a request to the AI provider to remove it, recognizing there is complexity here and it may not always be possible to “untrain” the LLM in someone’s content.

Responses and results take time and have been somewhat unpredictable, but early indications suggest some positive movement from the big AI players. Credtent is also now deploying better tools to make it easier for all parties to monitor and manage requests.

One outcome is that a provider will deny that they’ve ingested the copyrighted material in question. Burgess and his team have the technical chops to perform some smart prompting and other approaches that may provide indicative evidence that content has indeed been used in the training of an LLM.

Burgess emphasizes he’s not anti-AI. In fact, he wants AI to succeed for everyone’s advantage. To this end, he offers to value and license copyrighted content to the LLM providers for an annual fee. He sees it as a win-win for creators and the AI industry.

As a recent example, the owners of the content of the Ed Sullivan Show approached Burgess to help determine how much it was worth and how to best monetize it in the era of AI. Done right, everyone benefits, from the content owners, to the LLM and end-users.

If a creator decides not to have their content be part of developing an LLM, he believes AI companies should respect their decision to not be part of the AI economy.

At the center of this debate is a familiar legal question.

The Fair Use Debate

The dominant argument right now for giving the LLMs a free pass to use copyrighted material is that the content they ingest and use meets the criteria for fair use. This means they have the right to use content without permission of its owners based on four factors: 1) the purpose and character of use, 2) the nature of the copyrighted work, 3) the amount of the work copied, and 4) the effect of use on the existing and potential market.

In a legal case, an AI company doesn’t need to prevail in all of the factors to win a fair use determination.

These companies can also maintain that an LLM does not typically produce output that is verbatim of the original content.

In a 2025 case where Anthropic, one of the largest generative AI providers, consumed a vast library of digital books to train their LLM, the judge ruled in their favor citing the fair use doctrine. However, it was a split decision, because the judge also held them liable for illegally downloading millions of pirated books. It was an important case because it demonstrated that it matters both how content is acquired and how it is used, and it showcased the issues ahead as AI copyright cases begin to stack up.

On the topic of fair use, Burgess and his team have a different opinion. They believe fair use is a concept typically associated with educational purposes and when a big tech company processes copyrighted content without consent, sells it and makes money, that’s the definition of unfair use.

In addition, he challenges the assumption that remixed facts can always remain facts. That’s a trust issue and unless resolved, it will continue to be an area of concern for serious uses of generative AI.

Burgess thinks the LLMs that can deliver the most trustworthy content, which, for example, can be validated to be from a human expert, will come out ahead in the generative AI race.

Options For Creators And AI Companies

For creators that wonder whether their content has been used to train an LLM, they should begin their research with their publisher, if applicable. For authors, for example, their publisher may have a clause in their contracts that gives them permission to license their work for AI training.

If content owners want to seek a legal path, they should have a strong argument that unlicensed use of their work is harming its value and is resulting in tangible financial losses. This position is gaining most traction in the court system which will continue to evolve as judges, and the law catches up with the scope, nature, and impact of AI.

Many other options will emerge in the months and years ahead for both creators and AI companies to find amicable resolutions, such as those provided by Burgess and the team at Credtent.

An Unsettled Landscape

For now, AI companies are getting a break from judges as they lean towards a favorable view of fair use. But they must be diligent in how copyrighted works are acquired and used for training their LLMs. In the coming months they may not be so lucky. Law firms are just getting started in their pursuit of major copyright infringements. LLMs may find themselves choosing between licensing and litigation.

These are early days, and the path ahead looks increasingly complex. It may bring a windfall for lawyers, but it will be costly for both creators and AI companies. At the very least, new options are beginning to emerge for those seeking to protect their work.