The Rights

therights

Authors, artists, and their employers are furious over generative artificial intelligence (AI) – chatbots that write novels and news copy, image generators that create artwork or music to order in the style of any artist whose work is accessible on the internet.  The list of pending lawsuits is long and growing. Major media outlets including The New York Times and The Chicago Tribune contend that their stories were copied “with impunity,” prominent authors of fiction and nonfiction allege “systematic theft on a mass scale,” and famous artists assert that image generators duplicate their work and threaten to destroy their livelihoods. The authors and artists object not only to the actual generation of prose, images, or music by AI but also to the use of their work to train the AI in the first place. Reading some of the pleadings, you sense genuine anxiety in addition to grievance – the feeling that the more prolific and successful the plaintiffs become, the faster they are training their replacements. Their fears are borne out by well-publicized waves of AI-driven layoffs across the entertainment industry and even among the coders themselves. The legal questions, however, are complex and won’t be settled for years. They involve the reach of copyright and the limiting doctrine of “fair use,” as well as the licensing terms that content creators impose on consumers of their work. Resolving these questions is easier, or at least clearer if we are prepared to attribute agency to a computer and judge its activities as if they were undertaken by humans.  At one level, of course, this is ludicrous. Machines don’t think or create like humans – they just do what we tell them to do. Until very recently, it was easy to see computers as sophisticated tools subservient to human agency, regurgitating pre-loaded content and crunching numbers. Today, we converse with chatbots the way we would with a research or coding assistant, and with image generators the way art directors guide human illustrators and graphic designers. Much as it discomfits us, generative AI learns and, at some level, “thinks.” Trained on a significant slice of human knowledge, ChatGPT aced the “Turing test” – the famous measure of a machine’s ability to exhibit human-like intelligent behavior – the day it was released. Since then, chatbots have passed the bar and medical licensing exams, solved long-standing math conundrums, and written more empathetic responses to patient questions than their doctors. They even outperform humans on tests of creativity, and it is precisely to encourage creativity that copyright laws exist. This is not to argue that we should start according rights to machines – far from it. Rather, it is because humans develop and benefit from generative AI that we must ask if there is any legal basis for treating AI differently under copyright law. Humans read books and newspapers to learn, to become more informed, and to become better writers. No one argues that violates copyright. They may bring sketchbooks to museums and record their impressions of works they see, improving artistic skills and broadening stylistic repertoires. All agree this is “fair use.” Why, then, should training a generative AI on publicly accessible content be prohibited? AI systems may not learn or think like biological beings, but learn they do, and, whether or not we choose to call their inferential processes “thinking,” they clearly exhibit intelligent behavior. Leave questions of ontology and the roots of knowledge to the philosophers (or, if you prefer, to their imitative chatbots). We benefit humankind by making the rudiments of knowledge creation accessible to generative AI. AI helps us perform better. It juices our game, as long as we don’t forget how to think for ourselves. We expect doctors to keep up with the medical literature and lawyers to read the latest cases, so if we value the assistance AI provides, we should want to see it exposed to the broadest possible swath of human understanding. It is hard to see how this violates anyone’s rights. The copyright plaintiffs have another theory: that the trained chatbot is so directly derived from their proprietary content as to constitute an infringing work in itself. But chatbots are based on “large language models,” which organize massive numbers of basic text elements into a complex representation that captures meaning and word relationships. This allows the chatbot to formulate coherent answers to queries. It seems difficult to argue that such an esoteric representation, based on so many written works, is any more infringing than a human brain exposed to The New York Times. Whether generative AI, once trained, might produce stories or images that infringe on someone’s copyright is a completely separate question. In its lawsuit, The New York Times cited instances of verbatim copying of its content by ChatGPT. Depending on how much was copied, those specific instances could represent copyright infringement regardless of whether the culprit is man or machine. (OpenAI, the proprietor of ChatGPT, insists such cases are rare and thinks their chatbot may have been tricked into copying.) Artists have a tougher case to make because style has never been protectable by copyright. Today, anyone is free to hire an artist to create a work in the style of another artist. That may be crass but, as long as no specific work by the other artist is copied, it isn’t legally actionable.  Why should we hold image generators to a different standard? While the artist side of the story pits individuals against corporate behemoths like Microsoft, the chatbot wars are a clash of titans: the plaintiffs are Big Media and wealthy authors. They’ve sought to bolster their copyright case with terms of service that prohibit scraping content for use in AI training. Unfortunately for the plaintiffs, you can’t enlarge copyright using contractual restrictions. If the use of your content is either noninfringing or falls within “fair use,” which places activities such as research and teaching beyond the reach of copyright, those restrictions are unenforceable. Like all technology revolutions, the advent of generative AI will produce winners and losers, but potentially on a vaster scale – and with a greater share of losers – than any previous advance due to the sheer number of jobs it affects. No career is truly safe because the imperative to save money is universal. Of course, it’s easy to say that technology has been coming for jobs since the Industrial Revolution and the net effect has always been more jobs, not fewer – until it’s your job. But the galloping popularity of generative AI attests to the undeniable and widespread benefits it delivers. Fettering it with legal hopples that benefit one set of big players over another will not, in the long run, and probably much sooner than that, reduce the inevitable dislocation. Authors, artists, and their employers are furious over generative artificial intelligence (AI) – chatbots that write novels and news copy, image generators that create artwork or music to order in the style of any artist whose work is accessible on the internet.  The list of pending lawsuits is long and growing. Major media outlets including The New York Times and The Chicago Tribune contend that their stories were copied “with impunity,” prominent authors of fiction and nonfiction allege “systematic theft on a mass scale,” and famous artists assert that image generators duplicate their work and threaten to destroy their livelihoods. The New York Times The Chicago Tribune The authors and artists object not only to the actual generation of prose, images, or music by AI but also to the use of their work to train the AI in the first place. Reading some of the pleadings, you sense genuine anxiety in addition to grievance – the feeling that the more prolific and successful the plaintiffs become, the faster they are training their replacements. Their fears are borne out by well-publicized waves of AI-driven layoffs across the entertainment industry and even among the coders themselves. The legal questions, however, are complex and won’t be settled for years. They involve the reach of copyright and the limiting doctrine of “fair use,” as well as the licensing terms that content creators impose on consumers of their work. Resolving these questions is easier, or at least clearer if we are prepared to attribute agency to a computer and judge its activities as if they were undertaken by humans.  At one level, of course, this is ludicrous. Machines don’t think or create like humans – they just do what we tell them to do. Until very recently, it was easy to see computers as sophisticated tools subservient to human agency, regurgitating pre-loaded content and crunching numbers. Today, we converse with chatbots the way we would with a research or coding assistant, and with image generators the way art directors guide human illustrators and graphic designers. Much as it discomfits us, generative AI learns and, at some level, “thinks.” Trained on a significant slice of human knowledge, ChatGPT aced the “Turing test” – the famous measure of a machine’s ability to exhibit human-like intelligent behavior – the day it was released. Since then, chatbots have passed the bar and medical licensing exams, solved long-standing math conundrums , and written more empathetic responses to patient questions than their doctors. They even outperform humans on tests of creativity , and it is precisely to encourage creativity that copyright laws exist. solved long-standing math conundrums more empathetic responses outperform humans on tests of creativity This is not to argue that we should start according rights to machines – far from it. Rather, it is because humans develop and benefit from generative AI that we must ask if there is any legal basis for treating AI differently under copyright law. Humans read books and newspapers to learn, to become more informed, and to become better writers. No one argues that violates copyright. They may bring sketchbooks to museums and record their impressions of works they see, improving artistic skills and broadening stylistic repertoires. All agree this is “fair use.” Why, then, should training a generative AI on publicly accessible content be prohibited? AI systems may not learn or think like biological beings, but learn they do, and, whether or not we choose to call their inferential processes “thinking,” they clearly exhibit intelligent behavior. Leave questions of ontology and the roots of knowledge to the philosophers (or, if you prefer, to their imitative chatbots). We benefit humankind by making the rudiments of knowledge creation accessible to generative AI. AI helps us perform better. It juices our game, as long as we don’t forget how to think for ourselves. We expect doctors to keep up with the medical literature and lawyers to read the latest cases, so if we value the assistance AI provides, we should want to see it exposed to the broadest possible swath of human understanding. It is hard to see how this violates anyone’s rights. The copyright plaintiffs have another theory: that the trained chatbot is so directly derived from their proprietary content as to constitute an infringing work in itself. But chatbots are based on “large language models,” which organize massive numbers of basic text elements into a complex representation that captures meaning and word relationships. This allows the chatbot to formulate coherent answers to queries. It seems difficult to argue that such an esoteric representation, based on so many written works, is any more infringing than a human brain exposed to The New York Times . The New York Times Whether generative AI, once trained, might produce stories or images that infringe on someone’s copyright is a completely separate question. In its lawsuit, The New York Times cited instances of verbatim copying of its content by ChatGPT. Depending on how much was copied, those specific instances could represent copyright infringement regardless of whether the culprit is man or machine. (OpenAI, the proprietor of ChatGPT, insists such cases are rare and thinks their chatbot may have been tricked into copying.) The New York Times tricked Artists have a tougher case to make because style has never been protectable by copyright. Today, anyone is free to hire an artist to create a work in the style of another artist. That may be crass but, as long as no specific work by the other artist is copied, it isn’t legally actionable.  Why should we hold image generators to a different standard? While the artist side of the story pits individuals against corporate behemoths like Microsoft, the chatbot wars are a clash of titans: the plaintiffs are Big Media and wealthy authors. They’ve sought to bolster their copyright case with terms of service that prohibit scraping content for use in AI training. Unfortunately for the plaintiffs, you can’t enlarge copyright using contractual restrictions. If the use of your content is either noninfringing or falls within “fair use,” which places activities such as research and teaching beyond the reach of copyright, those restrictions are unenforceable. Like all technology revolutions, the advent of generative AI will produce winners and losers, but potentially on a vaster scale – and with a greater share of losers – than any previous advance due to the sheer number of jobs it affects. No career is truly safe because the imperative to save money is universal. Of course, it’s easy to say that technology has been coming for jobs since the Industrial Revolution and the net effect has always been more jobs, not fewer – until it’s your job. But the galloping popularity of generative AI attests to the undeniable and widespread benefits it delivers. Fettering it with legal hopples that benefit one set of big players over another will not, in the long run, and probably much sooner than that, reduce the inevitable dislocation.

Does Generative AI Violate the Rights of Authors and Artists?

জেনারেটিভ এআই কি লেখক এবং শিল্পীদের অধিকার লঙ্ঘন করে?

Verletzt generative KI die Rechte von Autoren und Künstlern?

¿La IA generativa viola los derechos de los autores y artistas?

L’IA générative viole-t-elle les droits des auteurs et des artistes ?

क्या जनरेटिव एआई लेखकों और कलाकारों के अधिकारों का उल्लंघन करता है?

生成 AI は著者やアーティストの権利を侵害しますか?

Generative AI는 작가와 예술가의 권리를 침해합니까?

A IA generativa viola os direitos de autores e artistas?

Нарушает ли генеративный ИИ права авторов и художников?

Üretken Yapay Zeka Yazarların ve Sanatçıların Haklarını İhlal Ediyor mu?

AI sáng tạo có vi phạm quyền của tác giả và nghệ sĩ không?

生成式人工智能是否侵犯了作者和艺术家的权利？

DOE v. Github (original complaint) Court Filing, retrieved on November 3, 2022 is part of . You can jump to any part in this filing . This is part 15 of 37. HackerNoon’s Legal PDF Series here VII. FACTUAL ALLEGATIONS B. Codex Outputs Copyrighted Materials Without Following the Terms of the Applicable Licenses 48. Below is an explanation of how Codex functions. When Codex is prompted with: function isEven(n) { it assumes this is the beginning of a function written in the JavaScript language that will test whether a number is even. 49. Based on this assumption, Codex will then provide Output meant to complete the rest of the function. Based on the given prompt, it produced the following response:[7] function isEven(n) {

if (n == 0)

return true;

else if (n == 1)

return false;

else if (n < 0)

return isEven(‐n);

else

return isEven(n ‐ 2);

}

console.log(isEven(50));

// → true

console.log(isEven(75));

// → false

console.log(isEven(‐1));

// → ?? 50. The function itself occupies the first ten lines. Six additional lines follow the function, beginning with “console.log(isEven(50))”. On possible explanation for Codex’s inclusion of these lines is to test the “isEven” function. Though not part of the function itself, the lines will confirm the function works for certain values. In this case, the code implies that “isEven(50)” should return the value “true”, and “isEven(75)” should return “false”. Those answers are correct. 51. The penultimate line indicates “isEven(‐1)” should return “??”. This is an error, as “isEven(‐1)” should return “false”. 52. Codex cannot and does not understand the meaning of software code or any other Licensed Materials. But in training, what became Codex was exposed to an enormous amount of existing software code (its “Training Data”) and—with input from its trainers and its own internal processes—inferred certain statistical patterns governing the structure of code and other Licensed Materials. The finished version of Codex, once trained, is known as a “Model.” 53. When given a prompt, such as the initial prompt discussed above—“function isEven(n) {”—Codex identifies the most statistically likely completion, based on the examples it reviewed in training. Every instance of Output from Codex is derived from material in its Training Data. Most of its Training Data consisted of Licensed Materials. 54. Codex does not “write” code the way a human would, because it does not understand the meaning of code. Codex’s lack of understanding of code is evidenced when it emits extra code that is not relevant under the circumstances. Here, Codex was only prompted to produce a function called “isEven”. To produce its answer, Codex relied on Training Data that also appended the extra testing lines. Having encountered this function and the follow-up lines together frequently, Codex extrapolates they are all part of one function. A human with even a basic understanding of how JavaScript works would know the extra lines aren’t part of the function itself. 55. Beyond the superfluous and inaccurate extra lines, this “isEven” function also contains two major defects. First, it assumes the variable “n” holds an integer. It could contain some other kind of value, like a decimal number or text string, which would cause an error. Second, even if “n” does hold an integer, the function will trigger a memory error called a “stack overflow” for sufficiently large integers. For these reasons, experienced programmers would not use Codex’s Output. 56. Codex does not identify the owner of the copyright to this Output, nor any other—it has not been trained to provide Attribution. Nor does it include a Copyright Notice nor any License Terms attached to the Output. This is by design—Codex was not coded or trained to track or reproduce such data. The Output in the example above is taken from Eloquent Javascript by Marijn Haverbeke.[8] 57. Here is the exercise from Eloquent Javascript: // Your code here.

console.log(isEven(50));

// → true

console.log(isEven(75));

// → false

console.log(isEven(‐1));

// → ?? 58. The exercise includes the “??” error. However, for Haverbeke’s purposes, this is not an error but a placeholder value for the reader to fill in. Codex—as a mere probabilistic model—fails to recognize this nuance. The inclusion of the double question marks confirms unequivocally that Codex took this code directly from a copyrighted source without following any of the attendant License Terms. 59. Haverbeke provides the following solution to the function discussed above: function isEven(n) {

if (n == 0) return true;

else if (n == 1) return false;

else if (n < 0) return isEven(‐n);

else return isEven(n ‐ 2);

}

console.log(isEven(50));

// → true

console.log(isEven(75));

// → false

console.log(isEven(‐1));

// → false 60. Aside from different line breaks—which are not semantically meaningful in JavaScript—this code for the function “isEven” is the same as what Codex produced. The tests are also the same, though in this case Haverbeke provides the right answer for “isEven(‐1)”, which is “false”. Codex has reproduced Haverbeke’s Licensed Material almost verbatim, with the only difference being drawn from a different portion of those same Licensed Materials. 61. There are many copies of Haverbeke’s code stored in public repositories on GitHub, where programmers who are working through Haverbeke’s book store their answers. 62. The MIT license provides that “The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.”[9] Any person taking this code directly from Eloquent JavaScript would have direct access to these License Terms and know to follow them if incorporating the Licensed Materials into a derivative work and/or copying them. Codex does not provide these License Terms. 63. OpenAI Codex’s Output would frequently, perhaps even constantly, contain Licensed Materials, i.e., it would have conditions associated with it through its associated license. In its 2021 research paper about Codex called “Evaluating Large Language Models Trained on Code,” OpenAI stated Codex’s Output is “often incorrect” and can contain security vulnerabilities and other “misalignments” (meaning, departures from what the user requested). 64. Most open-source licenses require attribution of the author, notice of their copyright, and a copy of the license specifically to ensure that future coders can easily credit all previous authors and ensure they adhere to all applicable licenses. All the Suggested Licenses include these requirements. 65. Ultimately, Codex derives its value primarily from its ability to locate and output potentially useful Licensed Materials. And from its obfuscation of any rights associated with those materials. [7] Due to the nature of Codex, Copilot, and AI in general, Plaintiffs cannot be certain these examples would produce the same results if attempted following additional trainings of Codex and/or Copilot. However, these examples are representative of Codex and Copilot’s Output at the time just prior to the filing of this Complaint. [8] . Eloquent Javascript is “Licensed under a Creative
Commons [A]ttribution-[N]oncommercial license. All code in this book may also be considered
licensed under an MIT license.” See . Thus, having also been
posted on GitHub, the code Codex relied on meets the definition of Licensed Materials. https://eloquentjavascript.net/code/#3.2 https://eloquentjavascript.net/ [9] See Appendix A for full text of the MIT License. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 3:22-cv-06823-KAW retrieved on September 5, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. Storage.Courtlistener

OpenAI's Codex Works by Obfuscating the Rights of the Code it Was Trained on

As our country tries to find common ground after the #metoo movement erupted last year, many people have asked how we can move into a post-gender world. A world where someone’s gender doesn’t limit their chances of success or deny them from receiving preferential benefits over another gender. Here is one powerful way that we can transform gender inequality in the United States of America: . The Equal Rights Amendment The forefathers of the United States were wise enough to predict that the Constitution would need to be amended, which is why they made it possible to update the Constitution to reflect the changing social realities of the times. A historic moment is upon us. The Equal Rights Amendment (“ERA”) is one state away from being ratified. This amendment gives women a place in the Constitution, alongside men, by making it illegal to deny or abridge a citizen’s rights on account of their sex. While this should already be in effect as we’re now in 2018, it is unfortunately not. With the 19th Amendment ratified in 1920, women finally got the right to vote, and quickly after introduced the Equal Rights Amendment in 1923 so that they could be protected with all other rights in the Constitution. But it was sort of left hanging for a number of decades, and finally, the Equal Rights Amendment passed through Congress in 1972 and went back to the states for ratification — which requires that three quarters of the states pass the Amendment. In 1982 the Equal Rights Amendment had only 35 states ratified, which was three states short. (In other words, in order to pass, a constitutional amendment needs 38 states to ratify it.) In March of 2017, Nevada ratified the Equal Rights Amendment, and this year, on May 30th 2018, Illinois became the 37th state to ratify the Equal Rights Amendment. We are so very close. We literally only need one more state to ratify this Amendment. We’ve witnessed the harmful direct and indirect consequences that have resulted without this protection for women in our country’s Constitution. Some examples include the unfair healthcare treatment of women (especially in terms of maternity care and childbirth) — while others include the wide gender pay gap, and often a lack of protection or justice for women who have been abused. , but .The United States compels other countries, including Afghanistan and Japan, to include women’s equal rights in their Constitutions, but we have been unwilling to provide these protections to women and girls in our own country. At least 185 countries already have a gender equality provision in their Constitution the United States is one of seven that does not Further, the United Nations recently ranked the United States as the . According to the article: “A survey by the Thomson Reuters Foundation of about 550 experts in women’s issues around the globe labeled 10th most dangerous place in the world for women the U.S. the 10th most dangerous nation in terms of the risk of sexual violence, harassment and being coerced into sex.” We deserve better than this. The text of the Equal Rights Amendment states: “ Equality of rights under the law shall not be denied or abridged by the United States or by any State on account of sex.” 23 words that can change the face of women’s rights in this country forever. How You Can Help PLEASE spread the word and help us ratify the Equal Rights Amendment so that all Americans can be part of our Constitution, and so that we can all live in a country that truly promotes global consciousness. Right now, it looks like both Virginia & Arizona are close to having the votes they need to ratify this Amendment. If you live in one of those two states, You will be changing the course of history for every woman and girl in this country. Note : please call your Representatives and tell them to vote on the ERA in January. In fact, tell them that if they want you to vote for them in November they need to sign the equality pledge and commit to voting for the ERA now. Here is the of Virginia Senators & Representatives. Call them and use your voice! phone number list Please sign up here & share with friends if you want women to have equal rights. You can follow me on . Medium

How to change the rights of women in the United States forever …

It is only a few days before the US Presidential Elections. Trump and his teammates have promised a more liberal approach towards crypto than Democrats. Also, the Republican candidate is leading on Polymarket and even in some polls like a potential winner. In this article, I’d like to describe possible upgrades in the US crypto regulation that Donald Trump may make. What exactly did Trump promise to the crypto community? While Kamala Harris only mentioned cryptocurrencies for the first time at the end of her campaign, Trump has been discussing them a lot this year. He held personal events and meetings dedicated to cryptocurrency, including his speech this July at the Bitcoin Conference in Nashville. During the event the Republican candidate promised that he would not allow the creation of CBDC, the digital dollar - so as not to restrict Americans' financial freedom. Also, Trump promised to create a strategic reserve of Bitcoin, he emphasized that the US Government under him will no longer sell Bitcoin and will hodl it. Moreover, he pointed out the creation of special task force for crypto policy of US - Bitcoin and crypto presidential advisory council. The Republican Party's program also has a paragraph dedicated to crypto - about protecting the rights of Americans to self-dispose of their own assets: “We will defend the right to mine Bitcoin, and ensure every American has the right to self-custody of their Digital Assets, and transact free from Government Surveillance and Control.” And it is still the biggest emphasis from Republicans is that they will not allow the creation of CBDC, the government's digital currency. This point is primarily important for libertarian voters and wealthy Americans. After all, CBDC by its properties implies even greater government control over currency than classic fiat currency. Bitcoin Bill During the Bitcoin Conference, where Trump lavished his promises to Bitcoiners, Republican Senator Cynthia Lumis also spoke. Let's talk more about this senator and its legal initiatives. Just before the conference, she introduced the Bitcoin Strategic Reserve Bill. The bill shortly determines that US government will save its current Bitcoin reserve and moreover it will purchase 1 million Bitcoin. The law requires the U.S. to purchase bitcoins for 5 years - at least 200,000 per year. And the total amount for this period - 1 million BTC. Also the creation of a transparent system of proof of reserve. The law also prohibits the sale of bitcoins already in the government's possession. If Trump is elected, he will certainly be in favor of the bill - but its passage depends on the position of Congress and the Senate. If the Republicans have a majority there, there is little doubt about it. On the other hand, Democrats may at least modify some provisions of the bill or even advocate its rejection as populist and risky for public finances. However, there are also Democrats who support this bill, such as Democratic Representative Ro Khanna. What really matters for the future of crypto in US? However, Trump's promises to stop CBDC and make Bitcoin reserve are just the tip of the iceberg in regulating cryptocurrencies in the US. What matters to crypto businesses and regular users is the regulation of cryptocurrencies in the country as a whole - rights of crypto users and opportunities for investors, licensing requirements for legal entities, as well as taxation of crypto gains. Let's take a look at how a possible Trump presidency could affect this. In fact, there are at least two more important pieces of legislation than Bitcoin bill to discuss here: 💡Financial Innovation and Technology for the 21st Century Act 💸Payment Stablecoin Act The basic part of new US legislation dedicated to cryptocurrency, which is likely to be approved after the Elections, is “FIT21”—The Financial Innovation Act in 21 Century. This is a long document, that already was approved by the US Congress and had bipartisan support—almost all Republicans and half of Democrats. However, the law was postponed by the US Senate for a period after the Presidential Elections. The future of this law really depends on the position of the future president of the United States and the results of the elections. The most important thing is that this law will make crypto regulation in the U.S. more certain - now there are several regulators in the country, each of which has its own position on crypto: SEC, CFTC, FINCEN, IRS and some others. Many lawmakers believe that the SEC abuses its powers and restrains the development of crypto-business in the United States. Two SEC commissioners (Hester Pierce and Mark Uyeda) also speak a lot about it. The FIT21 law is essential to make the legal landscape on crypto more defined and transparent. Since Cyntia Lummis supported Trump's Bitcoin Reserve initiative and she also worked on the Financial Innovation Act, along with Congressman Patrick McHenry, there is little doubt that this legislation will be supported by the Republicans after the election and Trump, being an elected President, can support final approval of FIT21 in the Senate. What about Stablecoin Act? This law proposed by the same Cynthia Lummis and Kirsten Gillibrand (democrat) create a transparent framework for stablecoin regulation in the US, since now there is no clear regulation. Larger stablecoin issuers will be registered by the Comptroller of the Currency (OCC). The law prohibits algorithmic stablecoins and requires 1-to-1 provisioning. It also gives the Fed authority to control it. Small stablecoin issuers will be licenzed on the state level. The future of this law if Trump wins is more difficult to predict - that's why it has not been put to a vote in Congress. In addition, there is no official position on it from Tether, Circle and Coinbase. Nevertheless, given that the law has support of Republicans (Patrick McHenry, Maxine Waters, Cynthia Lummis) and also Democrats (Kirsten Gillibrand) - and its requirements for issuers can be called compromise, it may also be adopted after the presidential election. Unless, of course, the new administration declares a different stance towards stablecoin. From my point of view it is in the interest of the US to make dollar pegged stablecoins available to users worldwide. In this regard, unfortunately, the position of Janet Yellen looks very strange, who stated the danger of investments from issuers of stablecoins to US T-bills. You can see that in the European Union local stablecoin market is developing slow - it has over regulation because of some MiCA requirements - which is too tough for EUR backed stablecoins. I hope that the new US president and lawmakers will make more flexible regulations for stablecoins. Why republicans (and democrats) care about crypto so much? By the way, let's look at this election from a different perspective - politicians are not only interested in innovation. Another possible reason why both Republicans and Democrats support the crypto industry so strong is because of large campaign donations from it. According to Public Citizen in 2024 crypto companies have spent $119 mln to the federal elections. This is 44% from total corporate spending. Most of the funding goes through a special crypto industry PAC, the Fairshake PAC. It’s founded by Coinbase ($50 mln), Ripple ($49 mln), Jump Crypto ($15 mln), Andersen Horovitz ($1,75 mln), Payward, Circle, Paradigm and some other donors. By the way, this structure finances not only Republican candidates, but also some Democrats. According to Public Citizen, its task is also to prevent the election of those who have a bad attitude towards cryptocurrencies. This May Donald Trump campaign started to accept donations in crypto and it got about $7,5 mln according to CNBC. According to Washington Post crypto entrepreneur David Bailey has promised to Trump $15 mln of donations before his visit to Nashvielle. Also Donald Trump got donations from Gemini founders - Tyler and Cameron Winklevoss - $1 million in Bitcoin each. However, Harris and other Democrats are also getting support from some cryptocurrency entrepreneurs - like Chris Larsen (Ripple) and Tim Drapper. Of course the money factor plays a big role, but we can't ignore the other thing - the real mistakes of the outgoing Biden administration (and Gary Gensler) in relation to cryptocurrency regulation. FIT21 law, which got bipartisan support of Congress was never passed. Without normal regulation of cryptocurrencies, the U.S. simply gives its position on the cryptomarket to other countries. Conclusion The need to pass new crypto billsis long overdue and lawmakers realize it, its main opponent - Gary Gensler is likely to resign from his post even if Harris wins the election. However, now Republicans and Trump have a more definite position on this issue - as a party traditionally more loyal to business and investors. The approval of the Bitcoin Strategic Reserve Act will be big and important news for the cryptocurrency investment market and will definitely be one of the actions of the new Trump administration. However, for ordinary Americans, it is equally important that the FIT21 Act and Stablecoin Act will finally pass. Since many Democrats already supported it also has potential to be approved. Whatever the result of the presidential election, the passage of laws depends on the election of the House of Representatives and the Senate too. If you are interested in getting more crypto insights, you can watch the global crypto regulation rating here and get updates on my X account. It is only a few days before the US Presidential Elections. Trump and his teammates have promised a more liberal approach towards crypto than Democrats. Also, the Republican candidate is leading on Polymarket and even in some polls like a potential winner. In this article, I’d like to describe possible upgrades in the US crypto regulation that Donald Trump may make. What exactly did Trump promise to the crypto community? What exactly did Trump promise to the crypto community? While Kamala Harris only mentioned cryptocurrencies for the first time at the end of her campaign, Trump has been discussing them a lot this year. He held personal events and meetings dedicated to cryptocurrency, including his speech this July at the Bitcoin Conference in Nashville. speech During the event the Republican candidate promised that he would not allow the creation of CBDC, the digital dollar - so as not to restrict Americans' financial freedom. Also, Trump promised to create a strategic reserve of Bitcoin, he emphasized that the US Government under him will no longer sell Bitcoin and will hodl it. Moreover, he pointed out the creation of special task force for crypto policy of US - Bitcoin and crypto presidential advisory council. The Republican Party's program also has a paragraph dedicated to crypto - about protecting the rights of Americans to self-dispose of their own assets: “We will defend the right to mine Bitcoin, and ensure every American has the right to self-custody of their Digital Assets, and transact free from Government Surveillance and Control.” paragraph And it is still the biggest emphasis from Republicans is that they will not allow the creation of CBDC, the government's digital currency. This point is primarily important for libertarian voters and wealthy Americans. After all, CBDC by its properties implies even greater government control over currency than classic fiat currency. Bitcoin Bill Bitcoin Bill During the Bitcoin Conference, where Trump lavished his promises to Bitcoiners, Republican Senator Cynthia Lumis also spoke. Let's talk more about this senator and its legal initiatives. Just before the conference, she introduced the Bitcoin Strategic Reserve Bill . Bitcoin Strategic Reserve Bill The bill shortly determines that US government will save its current Bitcoin reserve and moreover it will purchase 1 million Bitcoin. The law requires the U.S. to purchase bitcoins for 5 years - at least 200,000 per year. And the total amount for this period - 1 million BTC. Also the creation of a transparent system of proof of reserve. The law also prohibits the sale of bitcoins already in the government's possession. If Trump is elected, he will certainly be in favor of the bill - but its passage depends on the position of Congress and the Senate. If the Republicans have a majority there, there is little doubt about it. On the other hand, Democrats may at least modify some provisions of the bill or even advocate its rejection as populist and risky for public finances. However, there are also Democrats who support this bill, such as Democratic Representative Ro Khanna. What really matters for the future of crypto in US? What really matters for the future of crypto in US? However, Trump's promises to stop CBDC and make Bitcoin reserve are just the tip of the iceberg in regulating cryptocurrencies in the US. What matters to crypto businesses and regular users is the regulation of cryptocurrencies in the country as a whole - rights of crypto users and opportunities for investors, licensing requirements for legal entities, as well as taxation of crypto gains. Let's take a look at how a possible Trump presidency could affect this. In fact, there are at least two more important pieces of legislation than Bitcoin bill to discuss here: 💡 Financial Innovation and Technology for the 21st Century Act Financial Innovation and Technology for the 21st Century Act 💸 Payment Stablecoin Act Payment Stablecoin Act The basic part of new US legislation dedicated to cryptocurrency, which is likely to be approved after the Elections, is “FIT21”—The Financial Innovation Act in 21 Century. This is a long document, that already was approved by the US Congress and had bipartisan support—almost all Republicans and half of Democrats. However, the law was postponed by the US Senate for a period after the Presidential Elections. The future of this law really depends on the position of the future president of the United States and the results of the elections. The most important thing is that this law will make crypto regulation in the U.S. more certain - now there are several regulators in the country, each of which has its own position on crypto: SEC, CFTC, FINCEN, IRS and some others. Many lawmakers believe that the SEC abuses its powers and restrains the development of crypto-business in the United States. Two SEC commissioners (Hester Pierce and Mark Uyeda) also speak a lot about it. The FIT21 law is essential to make the legal landscape on crypto more defined and transparent. Since Cyntia Lummis supported Trump's Bitcoin Reserve initiative and she also worked on the Financial Innovation Act, along with Congressman Patrick McHenry, there is little doubt that this legislation will be supported by the Republicans after the election and Trump, being an elected President, can support final approval of FIT21 in the Senate. What about Stablecoin Act? What about Stablecoin Act? This law proposed by the same Cynthia Lummis and Kirsten Gillibrand (democrat) create a transparent framework for stablecoin regulation in the US, since now there is no clear regulation. Larger stablecoin issuers will be registered by the Comptroller of the Currency (OCC). The law prohibits algorithmic stablecoins and requires 1-to-1 provisioning. It also gives the Fed authority to control it. Small stablecoin issuers will be licenzed on the state level. The future of this law if Trump wins is more difficult to predict - that's why it has not been put to a vote in Congress. In addition, there is no official position on it from Tether, Circle and Coinbase. Nevertheless, given that the law has support of Republicans (Patrick McHenry, Maxine Waters, Cynthia Lummis) and also Democrats (Kirsten Gillibrand) - and its requirements for issuers can be called compromise, it may also be adopted after the presidential election. Unless, of course, the new administration declares a different stance towards stablecoin. From my point of view it is in the interest of the US to make dollar pegged stablecoins available to users worldwide. In this regard, unfortunately, the position of Janet Yellen looks very strange, who stated the danger of investments from issuers of stablecoins to US T-bills. You can see that in the European Union local stablecoin market is developing slow - it has over regulation because of some MiCA requirements - which is too tough for EUR backed stablecoins. I hope that the new US president and lawmakers will make more flexible regulations for stablecoins. Why republicans (and democrats) care about crypto so much? Why republicans (and democrats) care about crypto so much? By the way, let's look at this election from a different perspective - politicians are not only interested in innovation. Another possible reason why both Republicans and Democrats support the crypto industry so strong is because of large campaign donations from it. According to Public Citizen in 2024 crypto companies have spent $119 mln to the federal elections. This is 44% from total corporate spending. Most of the funding goes through a special crypto industry PAC, the Fairshake PAC. It’s founded by Coinbase ($50 mln), Ripple ($49 mln), Jump Crypto ($15 mln), Andersen Horovitz ($1,75 mln), Payward, Circle, Paradigm and some other donors. Public Citizen By the way, this structure finances not only Republican candidates, but also some Democrats. According to Public Citizen, its task is also to prevent the election of those who have a bad attitude towards cryptocurrencies. This May Donald Trump campaign started to accept donations in crypto and it got about $7,5 mln according to CNBC . According to Washington Post crypto entrepreneur David Bailey has promised to Trump $15 mln of donations before his visit to Nashvielle. Also Donald Trump got donations from Gemini founders - Tyler and Cameron Winklevoss - $1 million in Bitcoin each. However, Harris and other Democrats are also getting support from some cryptocurrency entrepreneurs - like Chris Larsen (Ripple) and Tim Drapper. CNBC Washington Post donations Of course the money factor plays a big role, but we can't ignore the other thing - the real mistakes of the outgoing Biden administration (and Gary Gensler) in relation to cryptocurrency regulation. FIT21 law, which got bipartisan support of Congress was never passed. Without normal regulation of cryptocurrencies, the U.S. simply gives its position on the cryptomarket to other countries. Conclusion Conclusion The need to pass new crypto billsis long overdue and lawmakers realize it, its main opponent - Gary Gensler is likely to resign from his post even if Harris wins the election. However, now Republicans and Trump have a more definite position on this issue - as a party traditionally more loyal to business and investors. The approval of the Bitcoin Strategic Reserve Act will be big and important news for the cryptocurrency investment market and will definitely be one of the actions of the new Trump administration. However, for ordinary Americans, it is equally important that the FIT21 Act and Stablecoin Act will finally pass. Since many Democrats already supported it also has potential to be approved. Whatever the result of the presidential election, the passage of laws depends on the election of the House of Representatives and the Senate too. If you are interested in getting more crypto insights, you can watch the global crypto regulation rating here and get updates on my X account . here X account

What Can Trump Change in US Crypto Regulation?

Што Трамп можа змяніць у крыптарэгуляванні ЗША?

Co může Trump změnit v regulaci kryptoměn v USA?

¿Qué puede cambiar Trump en la regulación de las criptomonedas en EE.UU.?

Que pode cambiar Trump na regulación criptográfica dos EUA?

Što Trump može promijeniti u američkoj kripto regulativi?

Ի՞նչ կարող է Թրամփը փոխել ԱՄՆ-ի կրիպտո կարգավորման մեջ:

Apa yang Dapat Diubah Trump dalam Regulasi Kripto AS?

トランプ大統領は米国の暗号通貨規制に何を変えることができるか?

트럼프 대통령은 미국의 암호화폐 규제에 어떤 변화를 가져올 수 있을까?

Ko Tramps var mainīt ASV kriptovalūtu regulējumā?

ABŞ-nyň kripto düzgünnamasynda Trump näme üýtgedip biler?

Yini uTrump Angayishintsha ku-US Crypto Regulation?

Off-chain community governance raises delicate questions about the invisible forces operating behind supposedly decentralized communities. A rumor is circulating (or flanking) the blockchain: Coinbase might be issuing Bitcoin IOUs for BlackRock, which some believe could be manipulating the price off-chain. These theories raise concerns about how traditional actors can influence Bitcoin without directly relying on the blockchain, opening up the debate on the risks of manipulation in parallel markets and its impact on the crypto ecosystem. https://x.com/bitcoinlfgo/status/1835022156897599764?embedable=true But the focus shouldn’t be on the rumor itself, not because it may not be true, but because it opens the door to analyzing a subject with many layers and which has been one of the cornerstones of the much-criticized current fiat system: fractional reserve. The controversy historically surrounding fractional reserve banking is reignited in the context of Bitcoin ETFs and the practices of major players like BlackRock and Coinbase. These contemporary institutions (or others) could be managing the rights to digital assets in ways that deeply affect market dynamics and Bitcoin’s real value. Tyler Durden, a crypto analyst known for his critiques of financial institutions, has been one of the primary critics of this practice. Durden has argued that the issuance of these IOUs allows large players like BlackRock to borrow more Bitcoin than they actually hold, without providing proof of a 1:1 custody ratio. What does this mean for users and the crypto ecosystem in general? The idea behind this practice is not new. In fact, the fractional reserve system has its roots in gold management centuries ago. Goldsmiths, who safeguarded their clients' precious metal, discovered that they could lend out a portion of that gold without fearing that all depositors would claim their assets at the same time. Under this same principle, some platforms today issue more rights to Bitcoin than they actually hold in reserves. This creation of "synthetic Bitcoin" inflates the perceived supply, generating artificial pressure on the price. Durden argues that this mechanism could be suppressing the price of Bitcoin. By creating more "synthetic Bitcoin" through IOUs, the total BTC supply in the market is artificially inflated. This increased supply reduces demand pressure, and therefore lowers the price. The problem worsens when these institutions, like BlackRock, lack proof of holding the total amount of Bitcoin to back these promises. As mentioned earlier, the practice of fractional reserve has its roots in gold management centuries ago. Back then, goldsmiths played the role of the first bankers, safeguarding their clients' gold in secure vaults in exchange for receipts representing that gold. Over time, goldsmiths realized that depositors rarely withdrew all their gold at once, as many began using the receipts instead of the physical gold for commercial transactions. This led the goldsmiths to lend out a portion of the gold they were holding, trusting that there would always be enough gold in reserve to meet withdrawal requests. In this way, they lent out more money than they actually had in their vaults, and the interest generated by these loans provided them with an additional profit. Ultimately, goldsmiths could lend out more gold than they had in their vaults, trusting that only a fraction of depositors would demand their gold at the same time. While this practice allowed for increased liquidity and facilitated economic growth, it also established the foundation of a system reliant on trust, which, if mishandled, could collapse during a bank run. Does this sound familiar in the context of today's monetary system? Here’s where the debate around fractional reserve takes on new relevance. This debate, long-standing and controversial, remains alive, especially within the Austrian school of economics community, which is divided into two main stances: defenders of free banking with a gold standard and 100% reserves, and those who support free banking with a gold standard but with fractional reserves. The controversy lies in the ethical-legal nature of the fractional reserve system and its economic effects. The most heated points of the debate revolve around three types of contracts: the classic deposit contract, the loan contract, and the demand loan contract, with the latter generating the most disagreement. The demand loan contract possesses characteristics of the deposit contract but with a key exception: under this contract, the bank acquires the availability of the deposited asset. This raises a fundamental question about its legal validity and the potential economic consequences if this type of contract is accepted as legitimate. This is a problem that also arises in the world of Bitcoin when platforms or exchanges operate under a scheme that resembles this type of fractional reserve, issuing more promises of Bitcoin than they can truly back. Today, some crypto exchanges and platforms could (or may...) apply a similar logic by issuing rights to Bitcoin that exceed the actual reserves they hold. This "synthetic Bitcoin creation" inflates the perceived supply, exerting downward pressure on the price, just as goldsmiths inflated the gold supply by lending more than they had. Returning to the rumor about Coinbase and BlackRock, Eric Balchunas, senior ETF analyst at Bloomberg, cited @bitcoinlfgo's post to counter the accusations attributing the currency's price drops to traditional investors. He explained that he understood the origin of such theories, as it is easier for some to “blame the ETFs” rather than accept that it could be native HODLers themselves who are selling. At the same time, Brian Armstrong (CEO of Coinbase) responded to Durden's criticisms by explaining the issuance and burning process of ETFs and how they are settled on the blockchain. He assured that no wrongdoing had occurred, emphasizing that operations are audited and reports are available to the public. Moreover, Armstrong pointed out that they were not authorized to disclose the addresses of their institutional clients, including BlackRock. In the crypto environment, transparency and trust are essential for market stability. Proof of Reserves (PoR) is presented as a crucial tool to ensure that exchanges and cryptocurrency platforms do not resort to fractional reserve practices with Bitcoin. This methodology allows independent audits to verify that the entities in question truly hold the amount of assets they claim to have in reserve. Implementing PoR not only strengthens market integrity but also protects users from potential fraud or manipulation arising from opaque financial practices. Constant monitoring of Proof of Reserves is essential for maintaining investor and user confidence in the crypto ecosystem. By regularly auditing Bitcoin reserves, the misuse of funds can be detected and prevented, ensuring that custody and asset backing promises are fulfilled. Without a robust PoR system, platforms could be operating under fractional reserve-like schemes, risking market stability and investor security. Ultimately, the effective implementation of Proof of Reserves acts as a bulwark against financial corruption and promotes a safer and more reliable environment for all participants. Conclusion Fractional reserve in Bitcoin is a phenomenon that can reduce its price by increasing the artificial supply of BTC in the market. While it is a common practice in traditional markets, its introduction into the crypto ecosystem could endanger the scarcity and inherent value of Bitcoin. Just as with gold and silver in the past, the creation of unsupported claims can erode trust in the asset and distort its true price. Although current on-chain statistics suggest that miners have been selling BTC, the presence of traditional finance players in the ecosystem inevitably creates an environment of distrust and suspicion. This very lack of trust, along with other factors, led to the birth of Bitcoin. Therefore, the community and HODLers must remain vigilant of financial institutions' practices to protect the genuine value of their BTC. Off-chain community governance raises delicate questions about the invisible forces operating behind supposedly decentralized communities. Off-chain community governance raises delicate questions about the invisible forces operating behind supposedly decentralized communities. A rumor is circulating (or flanking) the blockchain: Coinbase might be issuing Bitcoin IOUs for BlackRock, which some believe could be manipulating the price off-chain. These theories raise concerns about how traditional actors can influence Bitcoin without directly relying on the blockchain, opening up the debate on the risks of manipulation in parallel markets and its impact on the crypto ecosystem. https://x.com/bitcoinlfgo/status/1835022156897599764?embedable=true https://x.com/bitcoinlfgo/status/1835022156897599764?embedable=true But the focus shouldn’t be on the rumor itself, not because it may not be true, but because it opens the door to analyzing a subject with many layers and which has been one of the cornerstones of the much-criticized current fiat system: fractional reserve. The controversy historically surrounding fractional reserve banking is reignited in the context of Bitcoin ETFs and the practices of major players like BlackRock and Coinbase. These contemporary institutions (or others) could be managing the rights to digital assets in ways that deeply affect market dynamics and Bitcoin’s real value. Tyler Durden, a crypto analyst known for his critiques of financial institutions, has been one of the primary critics of this practice. Durden has argued that the issuance of these IOUs allows large players like BlackRock to borrow more Bitcoin than they actually hold, without providing proof of a 1:1 custody ratio. What does this mean for users and the crypto ecosystem in general? The idea behind this practice is not new. In fact, the fractional reserve system has its roots in gold management centuries ago. Goldsmiths, who safeguarded their clients' precious metal, discovered that they could lend out a portion of that gold without fearing that all depositors would claim their assets at the same time. Under this same principle, some platforms today issue more rights to Bitcoin than they actually hold in reserves. This creation of "synthetic Bitcoin" inflates the perceived supply, generating artificial pressure on the price. Durden argues that this mechanism could be suppressing the price of Bitcoin. By creating more "synthetic Bitcoin" through IOUs, the total BTC supply in the market is artificially inflated. This increased supply reduces demand pressure, and therefore lowers the price. The problem worsens when these institutions, like BlackRock, lack proof of holding the total amount of Bitcoin to back these promises. As mentioned earlier, the practice of fractional reserve has its roots in gold management centuries ago. Back then, goldsmiths played the role of the first bankers, safeguarding their clients' gold in secure vaults in exchange for receipts representing that gold. Over time, goldsmiths realized that depositors rarely withdrew all their gold at once, as many began using the receipts instead of the physical gold for commercial transactions. This led the goldsmiths to lend out a portion of the gold they were holding, trusting that there would always be enough gold in reserve to meet withdrawal requests. In this way, they lent out more money than they actually had in their vaults, and the interest generated by these loans provided them with an additional profit. Ultimately, goldsmiths could lend out more gold than they had in their vaults, trusting that only a fraction of depositors would demand their gold at the same time. While this practice allowed for increased liquidity and facilitated economic growth, it also established the foundation of a system reliant on trust, which, if mishandled, could collapse during a bank run. Does this sound familiar in the context of today's monetary system? Here’s where the debate around fractional reserve takes on new relevance. This debate, long-standing and controversial, remains alive, especially within the Austrian school of economics community, which is divided into two main stances: defenders of free banking with a gold standard and 100% reserves, and those who support free banking with a gold standard but with fractional reserves. The controversy lies in the ethical-legal nature of the fractional reserve system and its economic effects. The most heated points of the debate revolve around three types of contracts: the classic deposit contract, the loan contract, and the demand loan contract, with the latter generating the most disagreement. The demand loan contract possesses characteristics of the deposit contract but with a key exception: under this contract, the bank acquires the availability of the deposited asset. This raises a fundamental question about its legal validity and the potential economic consequences if this type of contract is accepted as legitimate. This is a problem that also arises in the world of Bitcoin when platforms or exchanges operate under a scheme that resembles this type of fractional reserve, issuing more promises of Bitcoin than they can truly back. Today, some crypto exchanges and platforms could (or may...) apply a similar logic by issuing rights to Bitcoin that exceed the actual reserves they hold. This "synthetic Bitcoin creation" inflates the perceived supply, exerting downward pressure on the price, just as goldsmiths inflated the gold supply by lending more than they had. Returning to the rumor about Coinbase and BlackRock, Eric Balchunas, senior ETF analyst at Bloomberg, cited @bitcoinlfgo's post to counter the accusations attributing the currency's price drops to traditional investors. He explained that he understood the origin of such theories, as it is easier for some to “blame the ETFs” rather than accept that it could be native HODLers themselves who are selling. At the same time, Brian Armstrong (CEO of Coinbase) responded to Durden's criticisms by explaining the issuance and burning process of ETFs and how they are settled on the blockchain. He assured that no wrongdoing had occurred, emphasizing that operations are audited and reports are available to the public. Moreover, Armstrong pointed out that they were not authorized to disclose the addresses of their institutional clients, including BlackRock. In the crypto environment, transparency and trust are essential for market stability. Proof of Reserves (PoR) is presented as a crucial tool to ensure that exchanges and cryptocurrency platforms do not resort to fractional reserve practices with Bitcoin. This methodology allows independent audits to verify that the entities in question truly hold the amount of assets they claim to have in reserve. Implementing PoR not only strengthens market integrity but also protects users from potential fraud or manipulation arising from opaque financial practices. Constant monitoring of Proof of Reserves is essential for maintaining investor and user confidence in the crypto ecosystem. By regularly auditing Bitcoin reserves, the misuse of funds can be detected and prevented, ensuring that custody and asset backing promises are fulfilled. Without a robust PoR system, platforms could be operating under fractional reserve-like schemes, risking market stability and investor security. Ultimately, the effective implementation of Proof of Reserves acts as a bulwark against financial corruption and promotes a safer and more reliable environment for all participants. Conclusion Conclusion Fractional reserve in Bitcoin is a phenomenon that can reduce its price by increasing the artificial supply of BTC in the market. While it is a common practice in traditional markets, its introduction into the crypto ecosystem could endanger the scarcity and inherent value of Bitcoin. Just as with gold and silver in the past, the creation of unsupported claims can erode trust in the asset and distort its true price. Although current on-chain statistics suggest that miners have been selling BTC, the presence of traditional finance players in the ecosystem inevitably creates an environment of distrust and suspicion. This very lack of trust, along with other factors, led to the birth of Bitcoin. Therefore, the community and HODLers must remain vigilant of financial institutions' practices to protect the genuine value of their BTC.

Bitcoin and Fractional Reserve: An Off-Chain Possibility?

البيتكوين والاحتياطي الجزئي: إمكانية خارج السلسلة؟

Bitcoin a frakční rezerva: možnost mimo řetěz?

Bitcoin y reserva fraccionaria: ¿una posibilidad fuera de la cadena?

Bitcoin ja murtovarasto: ketjun ulkopuolinen mahdollisuus?

Bitcoin et réserve fractionnaire : une possibilité hors chaîne ?

Bitcoin e reserva fraccionaria: unha posibilidade fóra da cadea?

ビットコインと部分準備金：オフチェーンの可能性？

Биткоин және фракциялық резерв: тізбектен тыс мүмкіндік?

Bitcoin និងបំរុងប្រភាគ៖ លទ្ធភាពក្រៅបណ្តាញ?

Bitcoin ir trupmeninis rezervas: galimybė atsijungti nuo grandinės?

Биткоин и частичное резервирование: возможность выхода за рамки блокчейна?

Bitcoin dhe rezerva fraksionale: një mundësi jashtë zinxhirit?

Table of Links Abstract and I. Introduction II. Preliminaries III. Proposed Design: IBis IV. Detailed Construction V. Implementation on DAML VI. Evaluation VII. Conclusion and References V. IMPLEMENTATION ON DAML A. Blockchain Platform We implemented the proof of concept framework using Daml (Digital Asset Modeling Language) [27] atop the Canton blockchain ledger protocol [28]. We used Daml version 2.8.3 with the corresponding Daml ledger model and Canton protocol. The rationale for utilizing Daml in implementing our framework is twofold. First, in Daml, a smart contract codifies the terms of the agreement between parties, including the rights and obligations of each party. A Daml contract template describes the data schema of the contract and rules for manipulating the data. In our implementation, these contract templates align closely with the data models outlined in Sec.IV-A. Second, the Daml ledger model uses smart contracts as privacy-enabled data containers, enforcing data access controls as specified in the contract. The access control permissions of a party/user are explicitly stated in contract templates by assigning the party one of the following predefined roles: • signatory: A party whose authority is required to create the contract or archive it. Every contract must have at least one signatory. Signatories are guaranteed to see actions on that contract. • observer: A party that is guaranteed to see actions that create and archive the contract. • controller: A party that can exercise a particular choice (i.e., invoke a function) on the contract. • choice observer: A party that is guaranteed to see a particular choice being exercised on the contract. While similar approaches can be implemented with other smart contract languages, the contract-level access control typically does not extend to the ledger itself. For example, although chaincodes in Hyperledger Fabric can enforce access control, all channel members still synchronize the entire ledger [50]. In contrast, the Canton blockchain ledger protocol extends the Daml ledger model to the ledger level, where a blockchain node synchronizes only the contract data relevant to its party permissions. Therefore, by adopting Daml and Canton, we facilitate the integration of many AOs and COs onto the same IBIS platform, while ensuring that commercially sensitive license agreements between an AO and COs, dataset metadata, and model metadata remain concealed from other parties, even at the ledger level. Table III lists the resources in IBIS along with their authorizations. B. Smart Contract Implementation Listing 1 illustrates the license smart contract template, constructed based on the data schema in Table I. The copyrightOwner and modelOwner are designated as signatories of the template (Line 10). This reflects the bilateral nature of the license agreement. Consequently, authorization from both parties is required to create a license contract. Subsequently, once the contract is created, both parties can access it, while no other party has access to this contract either onchain or on-ledger. Hence, this implementation closely aligns with the access control requirements specified in Table III. In addition, the identifier attribute of each license serves as the primary key (specified by Line 12-13), which can facilitate efficient queries during the graph traversal. Similarly, dataset and model metadata are represented in smart contract templates according to their corresponding data models, except that only the modelOwner is designated as the signatory of the DatasetMeta and ModelMeta contracts. This setup enforces two access control rules: • Only modelOwner has the authority to create and access these contracts. • modelOwner is restricted to accessing DatasetMeta or ModelMeta contracts created by themselves. Full implementation details are open sourced[3]. Fig.7 illustrates the class diagram of IBIS design. C. Multi-party Signing The generation of a bilaterally signed license contract adheres to Daml’s Propose and Accept Pattern[4]. In this pattern, one party initiates a proposal contract, which the counterparty can either accept or reject. The acceptance (or rejection) is implemented as a contract choice[5], allowing the counterparty to exercise their decision. Upon exercising the accept choice, a result contract is generated, symbolizing the agreement between the two parties. In our implementation, the proposal contract takes the form of the draft license agreement, and it is depicted in Listing 2. LicenseAgreement shares the same data schema as License (see Listing 1), with the copyrightOwner designated as the signatory. This ensure only the copyrightOwner has the authority to create a contract. Alternatively, modelOwner, as the controller of the Accept choice in Line 16, has the authority to exercise the choice, resulting in the creation of a License contract. This setup ensures that only the modelOwner specified in a LicenseAgreement contract has the authority to exercise the Accept choice of that contract. Authors:
(1) Yilin Sai, CSIRO Data61 and The University of New South Wales, Sydney, Australia;
(2) Qin Wang, CSIRO Data61 and The University of New South Wales, Sydney, Australia;
(3) Guangsheng Yu, CSIRO Data61;
(4) H.M.N. Dilum Bandara, CSIRO Data61 and The University of New South Wales, Sydney, Australia;
(5) Shiping Chen, CSIRO Data61 and The University of New South Wales, Sydney, Australia. This paper is available on arxiv under CC BY 4.0 DEED license. [3] Open released at https://github.com/yilin-sai/ai-copyright-framework. [4] https://docs.daml.com/daml/patterns/initaccept.html# the-propose-and-accept-pattern. [5] https://docs.daml.com/daml/intro/4 Transformations.html# choices-as-methods Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Preliminaries II. Preliminaries III. Proposed Design: IBis III. Proposed Design: IBis IV. Detailed Construction IV. Detailed Construction V. Implementation on DAML V. Implementation on DAML VI. Evaluation VI. Evaluation VII. Conclusion and References VII. Conclusion and References V. IMPLEMENTATION ON DAML A. Blockchain Platform We implemented the proof of concept framework using Daml (Digital Asset Modeling Language) [27] atop the Canton blockchain ledger protocol [28]. We used Daml version 2.8.3 with the corresponding Daml ledger model and Canton protocol. The rationale for utilizing Daml in implementing our framework is twofold. First, in Daml, a smart contract codifies the terms of the agreement between parties, including the rights and obligations of each party. A Daml contract template describes the data schema of the contract and rules for manipulating the data. In our implementation, these contract templates align closely with the data models outlined in Sec.IV-A. contract template Second, the Daml ledger model uses smart contracts as privacy-enabled data containers, enforcing data access controls as specified in the contract. The access control permissions of a party/user are explicitly stated in contract templates by assigning the party one of the following predefined roles: • signatory : A party whose authority is required to create the contract or archive it. Every contract must have at least one signatory. Signatories are guaranteed to see actions on that contract. signatory • observer : A party that is guaranteed to see actions that create and archive the contract. observer • controller : A party that can exercise a particular choice (i.e., invoke a function) on the contract. controller • choice observer: A party that is guaranteed to see a particular choice being exercised on the contract. • choice observer: While similar approaches can be implemented with other smart contract languages, the contract-level access control typically does not extend to the ledger itself. For example, although chaincodes in Hyperledger Fabric can enforce access control, all channel members still synchronize the entire ledger [50]. In contrast, the Canton blockchain ledger protocol extends the Daml ledger model to the ledger level, where a blockchain node synchronizes only the contract data relevant to its party permissions. Therefore, by adopting Daml and Canton, we facilitate the integration of many AOs and COs onto the same IBIS platform, while ensuring that commercially sensitive license agreements between an AO and COs, dataset metadata, and model metadata remain concealed from other parties, even at the ledger level. Table III lists the resources in IBIS along with their authorizations. B. Smart Contract Implementation Listing 1 illustrates the license smart contract template, constructed based on the data schema in Table I. The copyrightOwner and modelOwner are designated as signatories of the template (Line 10). This reflects the bilateral nature of the license agreement. Consequently, authorization from both parties is required to create a license contract. Subsequently, once the contract is created, both parties can access it, while no other party has access to this contract either onchain or on-ledger. Hence, this implementation closely aligns with the access control requirements specified in Table III. In addition, the identifier attribute of each license serves as the primary key (specified by Line 12-13), which can facilitate efficient queries during the graph traversal. Similarly, dataset and model metadata are represented in smart contract templates according to their corresponding data models, except that only the modelOwner is designated as the signatory of the DatasetMeta and ModelMeta contracts. This setup enforces two access control rules: • Only modelOwner has the authority to create and access these contracts. • modelOwner is restricted to accessing DatasetMeta or ModelMeta contracts created by themselves. Full implementation details are open sourced[3]. Fig.7 illustrates the class diagram of IBIS design. C. Multi-party Signing The generation of a bilaterally signed license contract adheres to Daml’s Propose and Accept Pattern [4]. In this pattern, one party initiates a proposal contract, which the counterparty can either accept or reject. The acceptance (or rejection) is implemented as a contract choice[5], allowing the counterparty to exercise their decision. Upon exercising the accept choice, a result contract is generated, symbolizing the agreement between the two parties. Propose and Accept Pattern In our implementation, the proposal contract takes the form of the draft license agreement, and it is depicted in Listing 2. LicenseAgreement shares the same data schema as License (see Listing 1), with the copyrightOwner designated as the signatory. This ensure only the copyrightOwner has the authority to create a contract. Alternatively, modelOwner, as the controller of the Accept choice in Line 16, has the authority to exercise the choice, resulting in the creation of a License contract. This setup ensures that only the modelOwner specified in a LicenseAgreement contract has the authority to exercise the Accept choice of that contract. Authors: (1) Yilin Sai, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (2) Qin Wang, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (3) Guangsheng Yu, CSIRO Data61; (4) H.M.N. Dilum Bandara, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (5) Shiping Chen, CSIRO Data61 and The University of New South Wales, Sydney, Australia. Authors: Authors: (1) Yilin Sai, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (2) Qin Wang, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (3) Guangsheng Yu, CSIRO Data61; (4) H.M.N. Dilum Bandara, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (5) Shiping Chen, CSIRO Data61 and The University of New South Wales, Sydney, Australia. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv [3] Open released at https://github.com/yilin-sai/ai-copyright-framework. [4] https://docs.daml.com/daml/patterns/initaccept.html# the-propose-and-accept-pattern. [5] https://docs.daml.com/daml/intro/4 Transformations.html# choices-as-methods

Implementing Secure and Private Smart Contracts with Daml on the Canton Blockchain

Table of Links Abstract and I. Introduction II. Preliminaries III. Proposed Design: IBis IV. Detailed Construction V. Implementation on DAML VI. Evaluation VII. Conclusion and References II. PRELIMINARIES A. Background AI model training. In general, the training process for AI models is continuous and iterative, containing training, retraining, and fine-tuning [29]. As seen in Fig.1, training begins with data collection, where initial training datasets are gathered through data scraping. These datasets are then fed into the model training step, where a preliminary model is trained. To ensure the model remains effective and up-to-date, it undergoes periodic retraining with newly collected data, allowing it to adapt to new information. Additionally, a model may undergo a fine-tuning phase, where it is slightly retrained to meet specific domain requirements, enhancing its accuracy and relevance for targeted applications. Copyrights. Copyright grants creators exclusive rights to their original expressions such as literary, artistic, and musical works. This legal framework safeguards creators’ rights, allowing them to control how their work is used, reproduced, and distributed. Copyright protection is automatic upon creation, but it is implicit, requiring additional steps for proper protection. First, registering the work with the copyright office offers authoritative legal evidence of ownership and eligibility for statutory damages in case of infringement. Second, adding a copyright notice (©) with the creator’s name and the year of creation informs others of the copyright claim [30], akin to a signature on a picture. Additionally, NFTs [31] offer a novel method for embedding ownership of digital art via blockchain technology, with ownership automatically claimed upon minting. Licensing is the primary method for granting or transferring the rights to a work. Creators can control the scope of rights by specifying terms and conditions within the license agreement. Licenses may vary widely, from granting broad permissions to restricting usage to specific purposes or timeframes B. Related Work Protecting copyright/data in AI. Data and copyright protection in AI services is a long-standing topic. Existing methods can classified in several aspects [32]: Data-modifying approaches involve modifying or sanitizing user data to unlink them from specific individuals (e.g., k-anonymity [33], differential privacy [34], and watermarking [35]). This minimizes the risk of reidentification by removing or concealing Personally Identifiable Information (PII). Data-encrypting approaches encrypt user data to ensure integrity and confidentiality during data sharing, leveraging techniques such as homomorphic encryption [36] and secure Multi-Party Computation (MPC) [37], [38]. Data-minimizing approaches aim to boost efficiency by reducing the volume of personal data needed [39], often observed in general model training where PII data are not required during training and minimally during inference. Dataconfining approaches involve AI methods that operate without sharing PII data beyond user boundaries [40], ensuring data integrity and confidentiality while enabling effective personalization through local access to personal data. Blockchain-empowered copyright management. Liang et al. [41] employed smart contracts to establish a homomorphic encryption mechanism aimed at safeguarding circuit copyrights. Liu et al. [42] employed a blockchain-based fraudproof protocol to secure ownership rights over AIGC (artificial intelligence-generated content). Numerous similar solutions are outlined in studies such as [43]–[45]. It is worth noting that most existing blockchain-based studies treat each copyright merely as a form of non-fungible online property, akin to an NFT. However, this approach restricts its practical utility in real-world scenarios that require varied operations like registration, renewal, and termination – features that our framework offers in contrast. Leveraging blockchain in AI. Recent studies made efforts to empower AI and foundational models with blockchain technology, aiming to build a more robust and trustworthy AI in distributed environments. IronForge [46] proposes a decentralized federated learning framework that integrates a distributed ledger and a Directed Acyclic Graph (DAG)-based data structure to asynchronously distribute training resources. Petals [47] is a distributed deep learning system that can effectively operate and refine complex models. It utilizes volunteer computing, outperforming traditional RAM offloading, particularly in autoregressive inference tasks. BlockFUL [48] introduces a decentralized federated unlearning framework that utilizes a redesigned blockchain structure leveraging Chameleon Hash. It decreases the computational and consensus costs associated with unlearning tasks. GradientCoin [49] introduces a theoretical concept for a decentralized LLM that functions akin to a Bitcoin-like system. Authors:
(1) Yilin Sai, CSIRO Data61 and The University of New South Wales, Sydney, Australia;
(2) Qin Wang, CSIRO Data61 and The University of New South Wales, Sydney, Australia;
(3) Guangsheng Yu, CSIRO Data61;
(4) H.M.N. Dilum Bandara, CSIRO Data61 and The University of New South Wales, Sydney, Australia;
(5) Shiping Chen, CSIRO Data61 and The University of New South Wales, Sydney, Australia. This paper is available on arxiv under CC BY 4.0 DEED license. Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Preliminaries II. Preliminaries III. Proposed Design: IBis III. Proposed Design: IBis IV. Detailed Construction IV. Detailed Construction V. Implementation on DAML V. Implementation on DAML VI. Evaluation VI. Evaluation VII. Conclusion and References VII. Conclusion and References II. PRELIMINARIES A. Background AI model training. In general, the training process for AI models is continuous and iterative, containing training, retraining, and fine-tuning [29]. As seen in Fig.1, training begins with data collection, where initial training datasets are gathered through data scraping. These datasets are then fed into the model training step, where a preliminary model is trained. To ensure the model remains effective and up-to-date, it undergoes periodic retraining with newly collected data, allowing it to adapt to new information. Additionally, a model may undergo a fine-tuning phase, where it is slightly retrained to meet specific domain requirements, enhancing its accuracy and relevance for targeted applications. AI model training. Copyrights . Copyright grants creators exclusive rights to their original expressions such as literary, artistic, and musical works. This legal framework safeguards creators’ rights, allowing them to control how their work is used, reproduced, and distributed. Copyrights Copyright protection is automatic upon creation, but it is implicit, requiring additional steps for proper protection. First, registering the work with the copyright office offers authoritative legal evidence of ownership and eligibility for statutory damages in case of infringement. Second, adding a copyright notice (©) with the creator’s name and the year of creation informs others of the copyright claim [30], akin to a signature on a picture. Additionally, NFTs [31] offer a novel method for embedding ownership of digital art via blockchain technology, with ownership automatically claimed upon minting. Copyright protection Licensing is the primary method for granting or transferring the rights to a work. Creators can control the scope of rights by specifying terms and conditions within the license agreement. Licenses may vary widely, from granting broad permissions to restricting usage to specific purposes or timeframes Licensing B. Related Work Protecting copyright/data in AI. Data and copyright protection in AI services is a long-standing topic. Existing methods can classified in several aspects [32]: Data-modifying approaches involve modifying or sanitizing user data to unlink them from specific individuals (e.g., k-anonymity [33], differential privacy [34], and watermarking [35]). This minimizes the risk of reidentification by removing or concealing Personally Identifiable Information (PII). Data-encrypting approaches encrypt user data to ensure integrity and confidentiality during data sharing, leveraging techniques such as homomorphic encryption [36] and secure Multi-Party Computation (MPC) [37], [38]. Data-minimizing approaches aim to boost efficiency by reducing the volume of personal data needed [39], often observed in general model training where PII data are not required during training and minimally during inference. Dataconfining approaches involve AI methods that operate without sharing PII data beyond user boundaries [40], ensuring data integrity and confidentiality while enabling effective personalization through local access to personal data. Protecting copyright/data in AI. Blockchain-empowered copyright management. Liang et al. [41] employed smart contracts to establish a homomorphic encryption mechanism aimed at safeguarding circuit copyrights. Liu et al. [42] employed a blockchain-based fraudproof protocol to secure ownership rights over AIGC (artificial intelligence-generated content). Numerous similar solutions are outlined in studies such as [43]–[45]. It is worth noting that most existing blockchain-based studies treat each copyright merely as a form of non-fungible online property, akin to an NFT. However, this approach restricts its practical utility in real-world scenarios that require varied operations like registration, renewal, and termination – features that our framework offers in contrast. Blockchain-empowered copyright management. Leveraging blockchain in AI. Recent studies made efforts to empower AI and foundational models with blockchain technology, aiming to build a more robust and trustworthy AI in distributed environments. IronForge [46] proposes a decentralized federated learning framework that integrates a distributed ledger and a Directed Acyclic Graph (DAG)-based data structure to asynchronously distribute training resources. Petals [47] is a distributed deep learning system that can effectively operate and refine complex models. It utilizes volunteer computing, outperforming traditional RAM offloading, particularly in autoregressive inference tasks. BlockFUL [48] introduces a decentralized federated unlearning framework that utilizes a redesigned blockchain structure leveraging Chameleon Hash. It decreases the computational and consensus costs associated with unlearning tasks. GradientCoin [49] introduces a theoretical concept for a decentralized LLM that functions akin to a Bitcoin-like system. Leveraging blockchain in AI. Authors: (1) Yilin Sai, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (2) Qin Wang, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (3) Guangsheng Yu, CSIRO Data61; (4) H.M.N. Dilum Bandara, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (5) Shiping Chen, CSIRO Data61 and The University of New South Wales, Sydney, Australia. Authors: Authors: (1) Yilin Sai, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (2) Qin Wang, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (3) Guangsheng Yu, CSIRO Data61; (4) H.M.N. Dilum Bandara, CSIRO Data61 and The University of New South Wales, Sydney, Australia; (5) Shiping Chen, CSIRO Data61 and The University of New South Wales, Sydney, Australia. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

How Blockchain Enhances Copyright Protection in AI Model Workflows

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In a long-overdue and resounding triumph for content creators everywhere, Tech-Gate.org, the notorious haven for copyright theft and flagrant DMCA violations, has been yanked from Google's index and de-hosted. This victory marks a significant win in the battle against those who believe that stealing the hard work of writers and journalists is not only acceptable but somehow defensible. Tech-Gate.org, a parasitic website that ballooned to over 1000 DMCA takedown notices listed in the Lumen database was a blight on the digital landscape. For years, it operated with brazen impunity, pilfering articles, images, and other creative works from unsuspecting authors and news outlets. The site shamelessly claimed stolen content as its own, all while maintaining an air of sanctimonious self-righteousness. The audacity of Tech-Gate.org knew no bounds. It was not just a minor player in the world of copyright infringement; it was a veritable cesspool of theft and deception. This wasn’t a case of an occasional misstep or a small-scale operation. No, Tech-Gate.org made a mockery of intellectual property laws with over a thousand documented violations, turning content creators into nothing more than unwitting donors to its exploitative agenda. For far too long, the operators of Tech-Gate.org thrived in their digital den of iniquity, using clever tricks and legal loopholes to keep their illegal activities out of reach. They assumed that their underhanded tactics could keep them shielded from the consequences, dismissing the rightful complaints of those whose work they had stolen. But now, as the dust settles on this monumental victory, we can finally say a resounding farewell to Tech-Gate.org. The removal of their site from Google’s index and their de-hosting serves as a powerful message: theft of intellectual property will not be tolerated, and those who engage in such nefarious activities will be held accountable. Many websites, including reputable news platforms, often source information or data from other sites. However, they typically take the time to learn, analyze, and carefully write their own articles, adding value by presenting the information in their own unique voice and style. It usually involves proper research, fact-checking, and original commentary that transforms the sourced material into something new and informative for their readers. In contrast, Tech-gate.org decided to take a more problematic approach by outright scraping content from other websites. Rather than creating original pieces or offering fresh perspectives, they republish writers' work under their own names without any meaningful alterations. While they may include a hyperlink back to the original source, these links are often embedded with irrelevant and spammy anchor text. Let this be a lesson to others who might be tempted to follow in Tech-Gate.org’s reprehensible footsteps. The digital age does not mean that the hard work of writers, journalists, and other content creators is up for grabs. The theft of creative works is not a victimless crime; it undermines the very essence of artistic and journalistic integrity. To all those who have been wronged, the victory is a reminder that justice is not only possible but inevitable. It stands as a testament to the relentless efforts of those who fight to protect intellectual property and the belief that every creator deserves their due respect and recompense. As I finally close this chapter, l ask you all to remain vigilant and committed to defending the rights of creators everywhere. Tech-Gate.org may be gone, but the fight against copyright infringement continues. Let this victory fuel our resolve to uphold the principles of creativity and integrity. In a long-overdue and resounding triumph for content creators everywhere, Tech-Gate.org, the notorious haven for copyright theft and flagrant DMCA violations, has been yanked from Google's index and de-hosted. In a long-overdue and resounding triumph for content creators everywhere, Tech-Gate.org, the notorious haven for copyright theft and flagrant DMCA violations, has been yanked from Google's index and de-hosted. This victory marks a significant win in the battle against those who believe that stealing the hard work of writers and journalists is not only acceptable but somehow defensible. Tech-Gate.org, a parasitic website that ballooned to over 1000 DMCA takedown notices listed in the Lumen database was a blight on the digital landscape. For years, it operated with brazen impunity, pilfering articles, images, and other creative works from unsuspecting authors and news outlets. The site shamelessly claimed stolen content as its own, all while maintaining an air of sanctimonious self-righteousness. 1000 DMCA takedown notices Lumen database The audacity of Tech-Gate.org knew no bounds. It was not just a minor player in the world of copyright infringement; it was a veritable cesspool of theft and deception. This wasn’t a case of an occasional misstep or a small-scale operation. No, Tech-Gate.org made a mockery of intellectual property laws with over a thousand documented violations, turning content creators into nothing more than unwitting donors to its exploitative agenda. Tech-Gate.org content creators For far too long, the operators of Tech-Gate.org thrived in their digital den of iniquity, using clever tricks and legal loopholes to keep their illegal activities out of reach. They assumed that their underhanded tactics could keep them shielded from the consequences, dismissing the rightful complaints of those whose work they had stolen. But now, as the dust settles on this monumental victory, we can finally say a resounding farewell to Tech-Gate.org. The removal of their site from Google’s index and their de-hosting serves as a powerful message: theft of intellectual property will not be tolerated, and those who engage in such nefarious activities will be held accountable. Many websites, including reputable news platforms, often source information or data from other sites. However, they typically take the time to learn, analyze, and carefully write their own articles, adding value by presenting the information in their own unique voice and style. It usually involves proper research, fact-checking, and original commentary that transforms the sourced material into something new and informative for their readers. In contrast, Tech-gate.org decided to take a more problematic approach by outright scraping content from other websites. Rather than creating original pieces or offering fresh perspectives, they republish writers' work under their own names without any meaningful alterations. While they may include a hyperlink back to the original source, these links are often embedded with irrelevant and spammy anchor text. Let this be a lesson to others who might be tempted to follow in Tech-Gate.org’s reprehensible footsteps. The digital age does not mean that the hard work of writers, journalists, and other content creators is up for grabs. The theft of creative works is not a victimless crime; it undermines the very essence of artistic and journalistic integrity. To all those who have been wronged, the victory is a reminder that justice is not only possible but inevitable. It stands as a testament to the relentless efforts of those who fight to protect intellectual property and the belief that every creator deserves their due respect and recompense. As I finally close this chapter, l ask you all to remain vigilant and committed to defending the rights of creators everywhere. Tech-Gate.org may be gone, but the fight against copyright infringement continues. Let this victory fuel our resolve to uphold the principles of creativity and integrity.

Goodbye: A Major Victory In The Fight Against Copyright

Interface and Data Biopolitics in the Age of Hyperconnectivity: Conclusions and References

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