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Wall Street Journal Notification 8/9

The Wall Street Journal released an __article __claiming that the U.S. has launched an investigation against Tether, the issuer of the world’s most popular stablecoin USDT. The article claimed that Tether was investigated for the alleged use of its stablecoin in illicit action, including sanctions evasion and money laundering. Importantly, the WSJ noted that the U.S. was considering imposing sanctions on Tether. Not surprisingly, Tether rushed to deny allegations, calling the WSJ article “irresponsible reporting”. Tether confirmed that it had “no knowledge of any such investigations into the company.”  The issuer of USDT also added that it had “extensive dealings with law enforcement to crack down on bad actors.” WSJ articles often have a major impact on financial market dynamics, so USDT faced a serious test after the article was released. In the short term, the USDT found itself under pressure and temporarily lost its peg to the U.S. dollar. However, it should be noted that the sell-off was not strong and USDT has quickly recovered, highlighting its ability to overcome FUD. Previously, the usual FUD on Tether was mostly focused on the company’s reserves. Typically, analysts or other talking heads claimed that Tether did not have enough reserves to support the stablecoin and was basically printing money out of thin air. These claims were issued every year and none of them was confirmed. Now that the capitalization of USDT exceeds $120 billion, few people take claims about Tether’s “insufficient reserves” seriously. Tether’s business has reached a huge scale and the company’s executives have no reason to risk their freedom as they are already making tons of money. Apparently, the time has come for a new type of FUD on Tether. The stablecoin is accused of being used by illicit actors all over the world. How serious is the potential threat? It’s not the first time that WSJ has issued FUD on Tether. In recent years, WSJ accused Tether of insufficient reserves, weak management, and even the falsification of documents. Meanwhile, the capitalization of USDT continued to grow together with its role in the crypto world. None of the allegations has been confirmed. It is impossible to confirm or deny WSJ allegations without access to insider information, but history tells us that WSJ reporting on Tether should be taken with a grain of salt. What happens if WSJ reporting is accurate and the investigation against Tether is underway? At this point, it looks like the most probable outcome in this scenario is the tightening of compliance in USDT and a series of actions against illicit actors. U.S. sanctions on Tether do not look like the most likely outcome of the potential investigation against the company. Tether has grown into a significant player in the U.S. debt market. Sanctions against the company would force it to sell its holdings, triggering a strong sell-off in the U.S. debt markets and leading to rising interest rates. The economic damage made by such a move would be too big to ignore. At this point, the markets do not believe in such a scenario. Bitcoin and other cryptos (which will certainly suffer sell-offs if the U.S. imposes sanctions on Tether) have been steadily moving higher in recent weeks, so traders bet that WSJ issued just another FUD on Tether. The Wall Street Journal released an __ article __claiming that the U.S. has launched an investigation against Tether, the issuer of the world’s most popular stablecoin USDT . article USDT The article claimed that Tether was investigated for the alleged use of its stablecoin in illicit action, including sanctions evasion and money laundering. Importantly, the WSJ noted that the U.S. was considering imposing sanctions on Tether. Not surprisingly, Tether rushed to deny allegations, calling the WSJ article “irresponsible reporting”. Tether confirmed that it had “no knowledge of any such investigations into the company.”  The issuer of USDT also added that it had “extensive dealings with law enforcement to crack down on bad actors.” deny deny WSJ articles often have a major impact on financial market dynamics, so USDT faced a serious test after the article was released. In the short term, the USDT found itself under pressure and temporarily lost its peg to the U.S. dollar. However, it should be noted that the sell-off was not strong and USDT has quickly recovered, highlighting its ability to overcome FUD. Previously, the usual FUD on Tether was mostly focused on the company’s reserves. Typically, analysts or other talking heads claimed that Tether did not have enough reserves to support the stablecoin and was basically printing money out of thin air. These claims were issued every year and none of them was confirmed. Now that the capitalization of USDT exceeds $120 billion, few people take claims about Tether’s “insufficient reserves” seriously. Tether’s business has reached a huge scale and the company’s executives have no reason to risk their freedom as they are already making tons of money. Apparently, the time has come for a new type of FUD on Tether. The stablecoin is accused of being used by illicit actors all over the world. How serious is the potential threat? It’s not the first time that WSJ has issued FUD on Tether. In recent years, WSJ accused Tether of insufficient reserves , weak management, and even the falsification of documents . Meanwhile, the capitalization of USDT continued to grow together with its role in the crypto world. None of the allegations has been confirmed. It is impossible to confirm or deny WSJ allegations without access to insider information, but history tells us that WSJ reporting on Tether should be taken with a grain of salt. insufficient reserves insufficient reserves weak management , weak management falsification of documents falsification of documents What happens if WSJ reporting is accurate and the investigation against Tether is underway? At this point, it looks like the most probable outcome in this scenario is the tightening of compliance in USDT and a series of actions against illicit actors. U.S. sanctions on Tether do not look like the most likely outcome of the potential investigation against the company. Tether has grown into a significant player in the U.S. debt market. Sanctions against the company would force it to sell its holdings, triggering a strong sell-off in the U.S. debt markets and leading to rising interest rates. The economic damage made by such a move would be too big to ignore. At this point, the markets do not believe in such a scenario. Bitcoin and other cryptos (which will certainly suffer sell-offs if the U.S. imposes sanctions on Tether) have been steadily moving higher in recent weeks, so traders bet that WSJ issued just another FUD on Tether.

USDT Drama: Tether Labels WSJ U.S. Probe Claims as ‘Irresponsible Reporting’

Драма при USDT: Tether етикетира твърденията на WSJ U.S. Probe като „безотговорно докладване“

Drama sobre el USDT: Tether califica de “informes irresponsables” las afirmaciones del WSJ sobre la investigación estadounidense

USDT drama: Tether označava tvrdnje američke istrage WSJ-a kao 'neodgovorno izvještavanje'

Dram USDT: Tether make WSJ US Probe reklamasyon kòm 'Rapò irèsponsab'

USDTドラマ：テザー、WSJの米国調査報道を「無責任な報道」と非難

USDT драмасы: Тетер WSJ АКШнын иликтөөсү "жоопкерсиз отчеттуулук" деп ырастады

USDT драма: Тетер WSJ АНУ-ын мөрдөн байцаалтыг "хариуцлагагүй мэдээлсэн" гэж мэдэгдэв

USDT Riwaayadda: Tether waxay ku calaamadisay WSJ Baaritaanka Mareykanka wuxuu ku andacoonayaa inay tahay 'warbixin Mas'uuliyad darro ah'

USDT Drama: Tether etiketterar WSJ U.S. Probe-påståenden som "oansvarig rapportering"

ดราม่า USDT: Tether กล่าวหา WSJ ว่าการสอบสวนของสหรัฐฯ เป็นการ “รายงานข่าวที่ไม่รับผิดชอบ”

USDT Draması: Tether, WSJ ABD Soruşturma İddialarını 'Sorumsuz Habercilik' Olarak Etiketledi

IDrama ye-USDT: I-Tether Labels WSJ U.S. ABango loPhando njenge 'Ngxelo eNgakhathaliyo'

How to Get Around Paywalls on Major Websites

High-quality data is crucial for evaluating the financial risks of climate change. How can AI support financial institutions and investors in making well-informed decisions about incorporating climate risks into their portfolios? Climate risk and sustainable finance are becoming critical points in global economic discussions as the world faces the intensifying effects of climate change. Recent news highlights that extreme weather events, rising sea levels, and environmental degradation are not only humanitarian concerns but also financial risks that could destabilize economies. The Network for Greening the Financial System (NGFS) has emphasized that climate-related risks could reduce global GDP by up to 18% by 2050 if left unaddressed. Sustainable finance plays a crucial role in mitigating these risks by channeling investments into environmentally friendly initiatives. It goes beyond simply accounting for climate risk; it integrates other elements such as social responsibility and governance. S&P Global Ratings __projects__that the issuance of green, social, sustainability, and sustainability-linked bonds (GSSSB) will rise to approximately $1 trillion in 2024. Channeling green investment Sustainable finance expands beyond climate risks by incorporating broader environmental, social, and governance (ESG) factors. Financial institutions are increasingly recognizing the need to integrate these risks into their models to support long-term economic stability. The importance of sustainable finance lies in channeling investment toward initiatives that are environmentally responsible while mitigating the negative effects of climate change on the global economy. A failure to consider these risks may result in significant financial losses, job disruption, and inflation pressures. Incorporating AI and data science into sustainable finance could be a game-changer. These technologies can enhance decision-making by providing better risk assessments and predictive models, helping investors identify truly sustainable companies. AI can also reduce climate risk threats by analyzing complex climate data and generating insights that can inform more resilient financial strategies, thus mitigating future impacts. How can AI and data science assist? AI and data science are essential in assessing climate risks and enabling well-informed decisions in sustainable finance. They help analyze large datasets on weather patterns, carbon emissions, and corporate sustainability efforts to provide accurate risk assessments. This allows investors and lenders to allocate credit more responsibly, minimizing exposure to high-risk ventures. Additionally, AI can help reduce climate risk by forecasting environmental changes, improving supply chain efficiency, and identifying opportunities for innovation in low-carbon technologies. By integrating AI tools, the finance sector can make smarter investments that support the transition to a sustainable future. At the "Building Solid Data Foundations for Sustainable Investing and Reporting" event organized by Markus Evans, data science and credit risk models expert Varun Nakra emphasized the importance of AI in sustainable finance. During his talk, he captivated the audience with his in-depth exploration of AI's potential in enhancing predictive ESG risk models and managing climate-related risks. Nakra highlighted the transformative role of machine learning (ML) in ESG ratings, citing the work of Svanberg et al. (2022). This study demonstrated how ML models can predict corporate governance controversies by analyzing ESG metrics, showcasing a prime example of how AI can improve the accuracy and objectivity of ESG assessments. Varun Nakra stressed that many ESG rating methodologies suffer from inconsistencies, but non-linear models have the potential to standardize and strengthen these ratings. "This is a great use case for predictive AI to make an impact," Nakra noted. The talk was widely praised for providing actionable insights on AI’s application to real-world ESG issues. Nakra also addressed innovative approaches, such as using climate change news as a proxy for hedging against climate risk. He referenced how tools like text analytics and term frequency-inverse document frequency can sift through vast data, such as Wall Street Journal articles, to build climate risk factors. This technique significantly enhances the predictive capabilities of sustainable finance models, revealing new ways to quantify climate risks. One of the key areas Nakra focused on was carbon footprint analysis, where AI plays a pivotal role. “AI helps companies track their carbon footprint by analyzing data from various sources, including energy consumption, supply chain, and transportation,” he said. Nakra highlighted AgriTech companies, such as MistEO, which utilize IoT sensors and weather data to provide farmers with real-time climate and crop analytics, offering a deeper understanding of agricultural carbon footprints. In terms of climate risk management, Nakra emphasized that AI tools can help financial institutions estimate risks posed by climate change, such as extreme weather events. Companies like Intensel, which leverage AI, satellite imagery, and cloud platforms, provide predictive insights on infrastructure vulnerability, helping institutions mitigate climate risks in their investments. Finally, Nakra noted AI’s growing influence in investment management, with machine learning algorithms analyzing market trends and corporate performance data to identify sustainable investment opportunities that align with investors’ values. Creating a more resilient financial ecosystem Nakra's insights bridged the gap between cutting-edge technology and its applications in creating a more resilient, sustainable financial ecosystem. Over the years, Nakra’s research has garnered widespread recognition, with over 300 citations highlighting the depth and influence of his work in AI and machine learning across various domains. Nakra has delved into complex research problems, including predictive ESG risk modeling, climate risk assessment, and carbon footprint analysis, all of which are crucial for the future of sustainable finance. His research often intersects with emerging technologies, leveraging machine learning algorithms, satellite imagery, and text analytics to offer solutions that support financial institutions and investors in incorporating climate risk into their portfolios. Nakra's work, which includes high impact and complex data science modeling projects across major financial institutions such as Standard Chartered bank in Singapore, National Australia Bank in Australia and Deutsche bank in the US focuses on the application of AI and machine learning on credit risk. “The same concepts and technologies can be applied to managing climate risk and solving sustainable finance problems”, Nakra explains. By developing interpretable machine learning models, he has contributed significantly to improving the transparency and accuracy of credit risk ratings; and he is focused on applying his knowledge to ESG ratings, a key factor in climate risk management. His research work on climate risk factors—using data-driven techniques such as text mining from news articles—has provided innovative pathways for identifying and mitigating financial risks associated with climate change. These innovative approaches not only allow investors to make data-driven decisions but also help them identify sustainable investment opportunities, mitigate climate-related risks, and align their portfolios with ESG principles. Nakra’s contributions have proven invaluable in bridging the gap between advanced AI methodologies and practical applications in finance. High-quality data is crucial for evaluating the financial risks of climate change. How can AI support financial institutions and investors in making well-informed decisions about incorporating climate risks into their portfolios? Climate risk and sustainable finance are becoming critical points in global economic discussions as the world faces the intensifying effects of climate change. Recent news highlights that extreme weather events, rising sea levels, and environmental degradation are not only humanitarian concerns but also financial risks that could destabilize economies. The Network for Greening the Financial System (NGFS) has emphasized that climate-related risks could reduce global GDP by up to 18% by 2050 if left unaddressed. Sustainable finance plays a crucial role in mitigating these risks by channeling investments into environmentally friendly initiatives. It goes beyond simply accounting for climate risk; it integrates other elements such as social responsibility and governance. has emphasized has emphasized S&P Global Ratings __ projects __that the issuance of green, social, sustainability, and sustainability-linked bonds (GSSSB) will rise to approximately $1 trillion in 2024. projects Channeling green investment Channeling green investment Sustainable finance expands beyond climate risks by incorporating broader environmental, social, and governance (ESG) factors. Financial institutions are increasingly recognizing the need to integrate these risks into their models to support long-term economic stability. The importance of sustainable finance lies in channeling investment toward initiatives that are environmentally responsible while mitigating the negative effects of climate change on the global economy. A failure to consider these risks may result in significant financial losses, job disruption, and inflation pressures. Incorporating AI and data science into sustainable finance could be a game-changer. These technologies can enhance decision-making by providing better risk assessments and predictive models, helping investors identify truly sustainable companies. AI can also reduce climate risk threats by analyzing complex climate data and generating insights that can inform more resilient financial strategies, thus mitigating future impacts. How can AI and data science assist? How can AI and data science assist? AI and data science are essential in assessing climate risks and enabling well-informed decisions in sustainable finance. They help analyze large datasets on weather patterns, carbon emissions, and corporate sustainability efforts to provide accurate risk assessments. This allows investors and lenders to allocate credit more responsibly, minimizing exposure to high-risk ventures. Additionally, AI can help reduce climate risk by forecasting environmental changes, improving supply chain efficiency, and identifying opportunities for innovation in low-carbon technologies. By integrating AI tools, the finance sector can make smarter investments that support the transition to a sustainable future. At the "Building Solid Data Foundations for Sustainable Investing and Reporting" event organized by Markus Evans, data science and credit risk models expert Varun Nakra emphasized the importance of AI in sustainable finance. During his talk, he captivated the audience with his in-depth exploration of AI's potential in enhancing predictive ESG risk models and managing climate-related risks. Nakra highlighted the transformative role of machine learning (ML) in ESG ratings, citing the work of Svanberg et al. (2022). This study demonstrated how ML models can predict corporate governance controversies by analyzing ESG metrics, showcasing a prime example of how AI can improve the accuracy and objectivity of ESG assessments. Varun Nakra stressed that many ESG rating methodologies suffer from inconsistencies, but non-linear models have the potential to standardize and strengthen these ratings. "This is a great use case for predictive AI to make an impact," Nakra noted. The talk was widely praised for providing actionable insights on AI’s application to real-world ESG issues. Nakra also addressed innovative approaches, such as using climate change news as a proxy for hedging against climate risk. He referenced how tools like text analytics and term frequency-inverse document frequency can sift through vast data, such as Wall Street Journal articles, to build climate risk factors. This technique significantly enhances the predictive capabilities of sustainable finance models, revealing new ways to quantify climate risks. One of the key areas Nakra focused on was carbon footprint analysis, where AI plays a pivotal role. “AI helps companies track their carbon footprint by analyzing data from various sources, including energy consumption, supply chain, and transportation,” he said. Nakra highlighted AgriTech companies, such as MistEO, which utilize IoT sensors and weather data to provide farmers with real-time climate and crop analytics, offering a deeper understanding of agricultural carbon footprints. In terms of climate risk management, Nakra emphasized that AI tools can help financial institutions estimate risks posed by climate change, such as extreme weather events. Companies like Intensel, which leverage AI, satellite imagery, and cloud platforms, provide predictive insights on infrastructure vulnerability, helping institutions mitigate climate risks in their investments. Finally, Nakra noted AI’s growing influence in investment management, with machine learning algorithms analyzing market trends and corporate performance data to identify sustainable investment opportunities that align with investors’ values. Creating a more resilient financial ecosystem Creating a more resilient financial ecosystem Nakra's insights bridged the gap between cutting-edge technology and its applications in creating a more resilient, sustainable financial ecosystem. Over the years, Nakra’s research has garnered widespread recognition, with over 300 citations highlighting the depth and influence of his work in AI and machine learning across various domains. Nakra has delved into complex research problems, including predictive ESG risk modeling, climate risk assessment, and carbon footprint analysis, all of which are crucial for the future of sustainable finance. His research often intersects with emerging technologies, leveraging machine learning algorithms, satellite imagery, and text analytics to offer solutions that support financial institutions and investors in incorporating climate risk into their portfolios. Nakra's work, which includes high impact and complex data science modeling projects across major financial institutions such as Standard Chartered bank in Singapore, National Australia Bank in Australia and Deutsche bank in the US focuses on the application of AI and machine learning on credit risk. “The same concepts and technologies can be applied to managing climate risk and solving sustainable finance problems”, Nakra explains. By developing interpretable machine learning models, he has contributed significantly to improving the transparency and accuracy of credit risk ratings; and he is focused on applying his knowledge to ESG ratings, a key factor in climate risk management. His research work on climate risk factors—using data-driven techniques such as text mining from news articles—has provided innovative pathways for identifying and mitigating financial risks associated with climate change. These innovative approaches not only allow investors to make data-driven decisions but also help them identify sustainable investment opportunities, mitigate climate-related risks, and align their portfolios with ESG principles. Nakra’s contributions have proven invaluable in bridging the gap between advanced AI methodologies and practical applications in finance.

AI in Sustainable Finance: The New Frontier in Tackling Climate Risk

Mr. Altman's Imaginarium: Is the Future Far Away?

Elon Musk v OpenAI, Court Filing, retrieved on April 30, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 9 of 29. F. Defendants Craft Their Path to Profit 91. After Musk rebuffed Altman’s proposal to transform OpenAI, Inc. into a for-profit venture in 2017, Defendants continued to pursue their ambitions, but pivoted to a shrewder and methodical plan. 92. The first step was to quietly craft a profitmaking apparatus that would allow Defendants to do indirectly what Musk had expressly denied them. Altman was appointed the non-profit’s CEO in 2019. At his urging and with his assistance, on information and belief, Defendants began forming numerous for profit entities, in which Altman held generous stakes, and weaving them into an increasingly labyrinthian OpenAI corporate web for the purpose of profiting from OpenAI, Inc.’s assets. 93. On information and belief, Defendants’ OpenAI web proceeded as follows: On March 11, 2019, Altman announced a new for-profit entity, OpenAI, L.P., which was established as, what Defendants call a “capped-profit company,” where investors can make a profit capped at a certain multiple of their investment (e.g., 100x investment). On January 23, 2023, OpenAI, L.P. was converted to OpenAI OpCo, LLC. 94. On September 17, 2020, OpenAI, L.L.C. was formed in Delaware. OpenAI, L.L.C.’s sole member is OpenAI OpCo, LLC. 95. On December 28, 2022, OpenAI Global, LLC was formed in Delaware. On information and belief, OpenAI Global, LLC, like OpenAI, L.P., is a “capped” for-profit entity. OpenAI Global, LLC has two members: Microsoft and OAI Corporation, LLC. 96. On March 17, 2023, OAI Corporation, LLC, was formed in Delaware as a limited liability company. The sole owner of OAI Corporation, LLC is OpenAI Holdings, LLC. 97. On March 17, 2023, OpenAI Holdings, LLC was formed in Delaware, and has multiple members including Aestas, LLC and various individuals. 98. On information and belief, OpenAI OpCo, LLC and OpenAI Global, LLC are managed by OpenAI GP, L.L.C., which on September 19, 2018 was registered as a limited liability company in Delaware. 99. On February 10, 2023, Aestas Management Company, LLC was formed as a limited liability company in Delaware and is managed by OpenAI GP, L.L.C. 100. On information and belief, the other entities—OpenAI Investment LLC, OpenAI Startup Fund Management, LLC, OpenAI Startup Fund GP I, L.L.C., OpenAI Startup Fund I, L.P., OpenAI Startup Fund SPV GP I, L.L.C., OpenAI Startup Fund SPV GP II, L.L.C., OpenAI Startup Fund SPV GP III, L.L.C., OpenAI Startup Fund SPV GP IV, L.L.C., OpenAI Startup Fund SPV I, L.P., OpenAI Startup Fund SPV II, L.P., OpenAI Startup Fund SPV III, L.P., OpenAI Startup Fund SPV IV, L.P.—are also interwoven into Defendants’ corporate web for the purpose of profiting from the non-profit’s assets.[3] Many of these entities were only recently registered and indeed, more OpenAI entities are popping up every month as part of Defendants’ shell game. 101. As with many things, the issue here is one of degree. While there is little concern caused by using a for-profit entity to help fundraise for a nonprofit, it is quite another thing to launch a dense fleet of dozens of for-profit entities to facilitate veiled and unchecked profiteering, rife with conflicts including those of Altman, the non-profit’s CEO and Board member, and Brockman, its CTO, as Defendants have done. 102. The complex profiteering arm of OpenAI—in which, on information and belief, Microsoft and Altman are significant shareholders, and Musk is not—while publicly cloaked as a mere fundraising apparatus, is in reality, the foundation for Defendants’ scheme to control and cash in on OpenAI, Inc.’s technology. 103. When Defendants launched OpenAI, L.P. (now OpenAI OpCo, LLC), they drained the non-profit OpenAI, Inc. of most of its staff and transferred them over to the new company, which also now houses much of OpenAI’s research and development. Altman and Brockman too are now employees of the private, for-profit OpenAI OpCo, LLC, which conveniently shields them from the public oversight and financial disclosures non-profits like OpenAI, Inc. must make. 104. With their web in place, Defendants’ next step was to lock down the non-profit’s technology. Obviously, the public would not pay for something that was open and free, so as detailed below, Defendants began withholding OpenAI, Inc.’s scientific research and hoarding its technology. 105. From there, on information and belief, they hollowed out the nonprofit and fed its assets to the OpenAI For-Profit Entities, from which they stood to make a bundle. Just follow the money. The non-profit’s 2022 IRS tax return[4] showed just $44,485.00 in revenue, but one year later, OpenAI overall reportedly generated hundreds of millions of dollars. 106. In addition, Altman, with the assistance and/or cooperation of Brockman and the OpenAI For-Profit Entities, began to self-deal with impunity. 107. It has been reported that Altman deliberately withheld key information and lied about his personal holdings and investments both in and outside of OpenAI in an effort to keep the then-independent Board from discovering his glaring conflicts of interest. 108. For example, a June 3, 2024 Wall Street Journal article[5] reported that Altman induced OpenAI to partner with Reddit in a deal wherein OpenAI would pay to bring Reddit’s content to OpenAI, Inc.’s ChatGPT. On information and belief, Altman and/or entities he controls own a whopping 7.6% of Reddit, making him one of the company’s largest outside shareholders. After the deal was announced, Reddit’s stock shot up 10%, boosting Altman’s stake by $69 million. 109. On information and belief, Altman, in seeking further business with the non-profit, claimed to be an independent board member of the venture fund, OpenAI Startup Fund, while intentionally withholding from the non-profit’s Board that he, in fact, owned the OpenAI Startup Fund and stood to personally profit from it. Further still, on information and belief, Altman caused OpenAI to sign a $51 million AI chip deal with Rain AI, a company in which he also held a significant interest. On information and belief, Altman and former Apple chief design officer, Jony Ive, have also reportedly launched their own AI device company, which plans to exploit OpenAI’s technology to compete with Apple and build the “iPhone of AI.” And currently, on information and belief, OpenAI is hammering out a deal with Helion Energy (in which Altman owns a massive stake) for OpenAI to buy vast quantities of electricity to power its data centers. 110. On information and belief, Altman, Brockman, and the OpenAI For-Profit Entities have been and will continue to be enriched by their respective stake in OpenAI’s for-profit machine and Altman alone stands to make billions from the humble non-profit Musk co-founded. 111. Defendants’ scheme has now become clear: lure Musk with phony philanthropy; exploit his money, stature, and contacts to secure world-class AI scientists to develop leading technology; then feed the non-profit’s lucrative assets into an opaque profit engine and proceed to cash in. 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 retrieved on August 05, 2024, deadline.com 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. [3] OpenAI, L.P., OpenAI, L.L.C., OpenAI GP, L.L.C., OpenAI OpCo, LLC, OpenAI Global, LLC, OAI Corporation, LLC, OpenAI Holdings, LLC, OpenAI Investment LLC, OpenAI Startup Fund Management, LLC, OpenAI Startup Fund GP I, L.L.C., OpenAI Startup Fund I, L.P., OpenAI Startup Fund SPV GP I, L.L.C., OpenAI Startup Fund SPV GP II, L.L.C., OpenAI Startup Fund SPV GP III, L.L.C., OpenAI Startup Fund SPV GP IV, L.L.C., OpenAI Startup Fund SPV I, L.P., OpenAI Startup Fund SPV II, L.P., OpenAI Startup Fund SPV III, L.P., OpenAI Startup Fund SPV IV, L.P., Aestas Management Company, LLC, and Aestas, LLC are collectively referred to hereinafter as the “OpenAI For-Profit Entities.” [4] 2022 was the last year Defendants made such documents readily available to the public. [5] Jin, Dotan & Hagey, The Opaque Investment Empire Making OpenAI’s Sam Altman Rich, Wall Street Journal (June 3, 2024), https://www.wsj.com/tech/ai/openai-sam-altmaninvestments-004fc785?mod=hp_lead_pos1. Elon Musk v OpenAI, Court Filing, retrieved on April 30, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 9 of 29. Elon Musk v OpenAI, Court Filing, retrieved on April 30, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 9 of 29. HackerNoon’s Legal PDF Series here F. Defendants Craft Their Path to Profit 91. After Musk rebuffed Altman’s proposal to transform OpenAI, Inc. into a for-profit venture in 2017, Defendants continued to pursue their ambitions, but pivoted to a shrewder and methodical plan. 92. The first step was to quietly craft a profitmaking apparatus that would allow Defendants to do indirectly what Musk had expressly denied them. Altman was appointed the non-profit’s CEO in 2019. At his urging and with his assistance, on information and belief, Defendants began forming numerous for profit entities, in which Altman held generous stakes, and weaving them into an increasingly labyrinthian OpenAI corporate web for the purpose of profiting from OpenAI, Inc.’s assets. 93. On information and belief, Defendants’ OpenAI web proceeded as follows: On March 11, 2019, Altman announced a new for-profit entity, OpenAI, L.P., which was established as, what Defendants call a “capped-profit company,” where investors can make a profit capped at a certain multiple of their investment (e.g., 100x investment). On January 23, 2023, OpenAI, L.P. was converted to OpenAI OpCo, LLC. 94. On September 17, 2020, OpenAI, L.L.C. was formed in Delaware. OpenAI, L.L.C.’s sole member is OpenAI OpCo, LLC. 95. On December 28, 2022, OpenAI Global, LLC was formed in Delaware. On information and belief, OpenAI Global, LLC, like OpenAI, L.P., is a “capped” for-profit entity. OpenAI Global, LLC has two members: Microsoft and OAI Corporation, LLC. 96. On March 17, 2023, OAI Corporation, LLC, was formed in Delaware as a limited liability company. The sole owner of OAI Corporation, LLC is OpenAI Holdings, LLC. 97. On March 17, 2023, OpenAI Holdings, LLC was formed in Delaware, and has multiple members including Aestas, LLC and various individuals. 98. On information and belief, OpenAI OpCo, LLC and OpenAI Global, LLC are managed by OpenAI GP, L.L.C., which on September 19, 2018 was registered as a limited liability company in Delaware. 99. On February 10, 2023, Aestas Management Company, LLC was formed as a limited liability company in Delaware and is managed by OpenAI GP, L.L.C. 100. On information and belief, the other entities—OpenAI Investment LLC, OpenAI Startup Fund Management, LLC, OpenAI Startup Fund GP I, L.L.C., OpenAI Startup Fund I, L.P., OpenAI Startup Fund SPV GP I, L.L.C., OpenAI Startup Fund SPV GP II, L.L.C., OpenAI Startup Fund SPV GP III, L.L.C., OpenAI Startup Fund SPV GP IV, L.L.C., OpenAI Startup Fund SPV I, L.P., OpenAI Startup Fund SPV II, L.P., OpenAI Startup Fund SPV III, L.P., OpenAI Startup Fund SPV IV, L.P.—are also interwoven into Defendants’ corporate web for the purpose of profiting from the non-profit’s assets.[3] Many of these entities were only recently registered and indeed, more OpenAI entities are popping up every month as part of Defendants’ shell game. 101. As with many things, the issue here is one of degree. While there is little concern caused by using a for-profit entity to help fundraise for a nonprofit, it is quite another thing to launch a dense fleet of dozens of for-profit entities to facilitate veiled and unchecked profiteering, rife with conflicts including those of Altman, the non-profit’s CEO and Board member, and Brockman, its CTO, as Defendants have done. 102. The complex profiteering arm of OpenAI—in which, on information and belief, Microsoft and Altman are significant shareholders, and Musk is not—while publicly cloaked as a mere fundraising apparatus, is in reality, the foundation for Defendants’ scheme to control and cash in on OpenAI, Inc.’s technology. 103. When Defendants launched OpenAI, L.P. (now OpenAI OpCo, LLC), they drained the non-profit OpenAI, Inc. of most of its staff and transferred them over to the new company, which also now houses much of OpenAI’s research and development. Altman and Brockman too are now employees of the private, for-profit OpenAI OpCo, LLC, which conveniently shields them from the public oversight and financial disclosures non-profits like OpenAI, Inc. must make. 104. With their web in place, Defendants’ next step was to lock down the non-profit’s technology. Obviously, the public would not pay for something that was open and free, so as detailed below, Defendants began withholding OpenAI, Inc.’s scientific research and hoarding its technology. 105. From there, on information and belief, they hollowed out the nonprofit and fed its assets to the OpenAI For-Profit Entities, from which they stood to make a bundle. Just follow the money. The non-profit’s 2022 IRS tax return[4] showed just $44,485.00 in revenue, but one year later, OpenAI overall reportedly generated hundreds of millions of dollars. 106. In addition, Altman, with the assistance and/or cooperation of Brockman and the OpenAI For-Profit Entities, began to self-deal with impunity. 107. It has been reported that Altman deliberately withheld key information and lied about his personal holdings and investments both in and outside of OpenAI in an effort to keep the then-independent Board from discovering his glaring conflicts of interest. 108. For example, a June 3, 2024 Wall Street Journal article[5] reported that Altman induced OpenAI to partner with Reddit in a deal wherein OpenAI would pay to bring Reddit’s content to OpenAI, Inc.’s ChatGPT. On information and belief, Altman and/or entities he controls own a whopping 7.6% of Reddit, making him one of the company’s largest outside shareholders. After the deal was announced, Reddit’s stock shot up 10%, boosting Altman’s stake by $69 million. 109. On information and belief, Altman, in seeking further business with the non-profit, claimed to be an independent board member of the venture fund, OpenAI Startup Fund, while intentionally withholding from the non-profit’s Board that he, in fact, owned the OpenAI Startup Fund and stood to personally profit from it. Further still, on information and belief, Altman caused OpenAI to sign a $51 million AI chip deal with Rain AI, a company in which he also held a significant interest. On information and belief, Altman and former Apple chief design officer, Jony Ive, have also reportedly launched their own AI device company, which plans to exploit OpenAI’s technology to compete with Apple and build the “iPhone of AI.” And currently, on information and belief, OpenAI is hammering out a deal with Helion Energy (in which Altman owns a massive stake) for OpenAI to buy vast quantities of electricity to power its data centers. 110. On information and belief, Altman, Brockman, and the OpenAI For-Profit Entities have been and will continue to be enriched by their respective stake in OpenAI’s for-profit machine and Altman alone stands to make billions from the humble non-profit Musk co-founded. 111. Defendants’ scheme has now become clear: lure Musk with phony philanthropy; exploit his money, stature, and contacts to secure world-class AI scientists to develop leading technology; then feed the non-profit’s lucrative assets into an opaque profit engine and proceed to cash in. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on August 05, 2024, deadline.com 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. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on August 05, 2024, deadline.com 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. deadline.com [3] OpenAI, L.P., OpenAI, L.L.C., OpenAI GP, L.L.C., OpenAI OpCo, LLC, OpenAI Global, LLC, OAI Corporation, LLC, OpenAI Holdings, LLC, OpenAI Investment LLC, OpenAI Startup Fund Management, LLC, OpenAI Startup Fund GP I, L.L.C., OpenAI Startup Fund I, L.P., OpenAI Startup Fund SPV GP I, L.L.C., OpenAI Startup Fund SPV GP II, L.L.C., OpenAI Startup Fund SPV GP III, L.L.C., OpenAI Startup Fund SPV GP IV, L.L.C., OpenAI Startup Fund SPV I, L.P., OpenAI Startup Fund SPV II, L.P., OpenAI Startup Fund SPV III, L.P., OpenAI Startup Fund SPV IV, L.P., Aestas Management Company, LLC, and Aestas, LLC are collectively referred to hereinafter as the “OpenAI For-Profit Entities.” [4] 2022 was the last year Defendants made such documents readily available to the public. [5] Jin, Dotan & Hagey, The Opaque Investment Empire Making OpenAI’s Sam Altman Rich, Wall Street Journal (June 3, 2024), https://www.wsj.com/tech/ai/openai-sam-altmaninvestments-004fc785?mod=hp_lead_pos1.

Musk Accuses Sam Altman of Self-Dealing and Mismanagement 

Intel caused quite a stir this past week after it announced that it would be letting go of thousands of employees as part of a broader cost-cutting plan to save billions in dollars. Joining the staff members on the chopping block: office fruit. In a town hall announcing the layoffs, the company said it would "cancel complimentary fruit and beverage" and "right-size dining programs," according to a screengrab shared with Gamers Nexus. The announcement came as Intel posted a Q2 loss of $1.6 billion, which compared to a profit of $1.5 billion a year ago, even though the company's revenue remained largely flat year-over-year at $12.8 billion. Things don't look too bright for the future either as Intel said it expects to post a loss again this year during Q3 and will stop paying out a dividend altogether starting in Q4, presumably to save money. Investors weren't too happy with the announcements and pummeled the stock so hard that it lost a quarter of its value in a single day. Intel employees weren't happy either as the company announced a 15% headcount reduction as part of a broader cost saving plan to conserve $10 billion. According to The Verge, the layoffs could impact as much as 19,000 people. "Our revenues have not grown as expected – and we’ve yet to fully benefit from powerful trends, like AI. Our costs are too high, our margins are too low. We need bolder actions to address both," Intel CEO Pat Gelsinger said in a memo to employees after announcing the Q2 results. As Gamers Nexus put it, Intel is perhaps one of the only big tech companies that, despite following the AI fad, is losing out.  No wonder then why the Wall Street Journal put out a news piece saying Gelsinger’s dream job had taken on a nightmarish turn. "This is the hardest thing I’ve done in my career," Gelsinger said, which perhaps explains why he tweeted scripture from the Bible after announcing the layoffs. In some ways, things have gone full circle. Intel dominated the chip market in the 80s and the 90s, and was a titan of the industry at the time Nvidia and AMD were finding their footing in the industry. Now, Intel appears to be a pittance in comparison to both companies. "Intel has been one of the forgotten horsemen of technology the last couple decades - never overtaking its year 2000 highs and struggling to get earnings back to where they were before the AI revolution," Michael Schulman, chief investment officer of Running Point Capital, was quoted as saying by Reuters. 👋 Hey! Welcome to HackerNoon. If you want to read more of my work, subscribe to the ‘Tech, What the Heck!?’ newsletter to receive a recap of some of the trending events that are shaping the world of tech. Also, did you know, that you can get published on HackerNoon? Start writing today and engage with millions of technologists just like yourself! Intel's issues seem to stem from the company playing second fiddle to its competitors on two fronts: losing a battle against Taiwan's TSMC on the foundry front and struggling to keep up with Nvidia and AMD on AI chips. In fact, for a long time, Intel's engineers found the video gaming chip architecture (which was far superior in AI intensive loads) as "ugly," and the company's management believed that a CPU could more effectively handle processing tasks needed to build and run AI models. Both sides were wrong. Ironically, Intel was this close to hitting the jackpot that Nvidia eventually ended up striking. In 2017 and 2018, the company had the opportunity to invest in OpenAI but decided against it because then-CEO Bob Swan did not believe generative AI would make it to the market soon enough for it to be worthwhile the $1 billion Intel would have had to pay for a 15% stake. OpenAI also offered a further 15% stake if Intel agreed to make hardware for it at cost price — but the company refused, leaving OpenAI to rely on Nvidia's chips instead. Imagine how much Intel could have gained by building hardware for AI. Instead, the company was caught with its pants down when the AI boom hit: without a chip that could meet businesses' demand. The company says it is working on launching an AI chip in 2025 that would contend with what's on the market right now, but who knows how that would compare to Intel's competitors, and how much more the company would have lost by then. As somebody from Hacker News recently put it: there's a real possibility that Intel may fade out of existence just like all the juggernauts of the 70s and the 80s. This was Part 2 of HackerNoon's round up of quarterly earnings. In Other News.. 📰 Bitcoin's Impending 'Death Cross' May Trap Bears as Bank of Japan Eases Rate Concerns — via CoinDesk
Automattic launches AI writing tool that aims to make WordPress blogs more readable and succinct — via TechCrunch
Meta must do more to address non-consensual, deepfake porn, Oversight Board says — via CNN
Amazon Prime Video plots Hollywood expansion — via Reuters
Hackers to back Harris at Vegas fundraiser — via Axios
Google’s antitrust ruling has experts looking to 25-year-old Microsoft case for answers — via CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Intel caused quite a stir this past week after it announced that it would be letting go of thousands of employees as part of a broader cost-cutting plan to save billions in dollars. Joining the staff members on the chopping block: office fruit. Intel Intel In a town hall announcing the layoffs, the company said it would "cancel complimentary fruit and beverage" and "right-size dining programs," according to a screengrab shared with Gamers Nexus. screengrab screengrab The announcement came as Intel posted a Q2 loss of $1.6 billion, which compared to a profit of $1.5 billion a year ago, even though the company's revenue remained largely flat year-over-year at $12.8 billion. Q2 loss Q2 loss Things don't look too bright for the future either as Intel said it expects to post a loss again this year during Q3 and will stop paying out a dividend altogether starting in Q4, presumably to save money. again Investors weren't too happy with the announcements and pummeled the stock so hard that it lost a quarter of its value in a single day . lost lost single day Intel employees weren't happy either as the company announced a 15% headcount reduction as part of a broader cost saving plan to conserve $10 billion. According to The Verge , the layoffs could impact as much as 19,000 people. The Verge The Verge "Our revenues have not grown as expected – and we’ve yet to fully benefit from powerful trends, like AI. Our costs are too high, our margins are too low. We need bolder actions to address both," Intel CEO Pat Gelsinger said in a memo to employees after announcing the Q2 results. As Gamers Nexus put it, Intel is perhaps one of the only big tech companies that, despite following the AI fad, is losing out.  No wonder then why the Wall Street Journal put out a news piece saying Gelsinger’s dream job had taken on a nightmarish turn. taken taken "This is the hardest thing I’ve done in my career," Gelsinger said, which perhaps explains why he tweeted scripture from the Bible after announcing the layoffs. tweeted tweeted In some ways, things have gone full circle. Intel dominated the chip market in the 80s and the 90s, and was a titan of the industry at the time Nvidia and AMD were finding their footing in the industry. Now, Intel appears to be a pittance in comparison to both companies. Nvidia Nvidia AMD AMD "Intel has been one of the forgotten horsemen of technology the last couple decades - never overtaking its year 2000 highs and struggling to get earnings back to where they were before the AI revolution," Michael Schulman, chief investment officer of Running Point Capital, was quoted as saying by Reuters . Reuters Reuters 👋 Hey! Welcome to HackerNoon. If you want to read more of my work, subscribe to the ‘Tech, What the Heck!?’ newsletter to receive a recap of some of the trending events that are shaping the world of tech. Also, did you know, that you can get published on HackerNoon? Start writing today and engage with millions of technologists just like yourself! 👋 Hey! Welcome to HackerNoon. If you want to read more of my work, subscribe to the ‘Tech, What the Heck!?’ newsletter to receive a recap of some of the trending events that are shaping the world of tech. Also, did you know, that you can get published on HackerNoon? Start writing today and engage with millions of technologists just like yourself! Hey! Welcome to HackerNoon. If you want to read more of my work, subscribe to the ‘Tech, What the Heck!?’ newsletter to receive a recap of some of the trending events that are shaping the world of tech. Also, did you know, that you can get published on HackerNoon? Start writing today and engage with millions of technologists just like yourself! Hey! Welcome to HackerNoon. If you want to read more of my work, subscribe to the ‘Tech, What the Heck!?’ newsletter to receive a recap of some of the trending events that are shaping the world of tech. Also, did you know, that you can get published on HackerNoon? Start writing today and engage with millions of technologists just like yourself! subscribe Start writing today Intel's issues seem to stem from the company playing second fiddle to its competitors on two fronts: losing a battle against Taiwan's TSMC on the foundry front and struggling to keep up with Nvidia and AMD on AI chips. issues issues In fact, for a long time, Intel's engineers found the video gaming chip architecture (which was far superior in AI intensive loads) as "ugly," and the company's management believed that a CPU could more effectively handle processing tasks needed to build and run AI models. Both sides were wrong . wrong wrong Ironically, Intel was this close to hitting the jackpot that Nvidia eventually ended up striking. In 2017 and 2018, the company had the opportunity to invest in OpenAI but decided against it because then-CEO Bob Swan did not believe generative AI would make it to the market soon enough for it to be worthwhile the $1 billion Intel would have had to pay for a 15% stake. this hitting the jackpot hitting the jackpot OpenAI also offered a further 15% stake if Intel agreed to make hardware for it at cost price — but the company refused, leaving OpenAI to rely on Nvidia's chips instead. Imagine how much Intel could have gained by building hardware for AI. OpenAI OpenAI Instead, the company was caught with its pants down when the AI boom hit: without a chip that could meet businesses' demand. The company says it is working on launching an AI chip in 2025 that would contend with what's on the market right now, but who knows how that would compare to Intel's competitors, and how much more the company would have lost by then. As somebody from Hacker News recently put it : there's a real possibility that Intel may fade out of existence just like all the juggernauts of the 70s and the 80s. recently put it recently put it This was Part 2 of HackerNoon's round up of quarterly earnings. This was Part 2 of HackerNoon's round up of quarterly earnings. This was Part 2 of HackerNoon's round up of quarterly earnings. In Other News.. 📰 In Other News.. 📰 Bitcoin's Impending 'Death Cross' May Trap Bears as Bank of Japan Eases Rate Concerns — via CoinDesk Automattic launches AI writing tool that aims to make WordPress blogs more readable and succinct — via TechCrunch Meta must do more to address non-consensual, deepfake porn, Oversight Board says — via CNN Amazon Prime Video plots Hollywood expansion — via Reuters Hackers to back Harris at Vegas fundraiser — via Axios Google’s antitrust ruling has experts looking to 25-year-old Microsoft case for answers — via CNBC Bitcoin's Impending 'Death Cross' May Trap Bears as Bank of Japan Eases Rate Concerns — via CoinDesk CoinDesk CoinDesk Automattic launches AI writing tool that aims to make WordPress blogs more readable and succinct — via TechCrunch TechCrunch TechCrunch Meta must do more to address non-consensual, deepfake porn, Oversight Board says — via CNN CNN CNN Amazon Prime Video plots Hollywood expansion — via Reuters Reuters Reuters Hackers to back Harris at Vegas fundraiser — via Axios Axios Axios Google’s antitrust ruling has experts looking to 25-year-old Microsoft case for answers — via CNBC CNBC CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan , Editor, Business Tech @ HackerNoon Sheharyar Khan Sheharyar Khan *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech Company Rankings Tech Company Rankings

Intel Stuns Employees By Cutting Free Office Fruit (And Staff)

The Bitcoin Hypocrisy Repository - Keeping Track Of Mainstream Lies & Fake Narratives

Ukraine's Rapid Push to Deploy AI-Enabled Drones for Battlefield Supremacy

Authors:
(1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk);
(2) Constantin Petrov, Fidelity Investments, London, UK. Table of Links Abstract and Intro Data Framework Results Conclusions, Acknowledgements, and References 5 Conclusions Starting from a curated selection of economic articles sourced from The Wall Street Journal, our research introduces an innovative and dynamic approach to dissecting news content. We leverage on GPT3.5 to sift out the most salient entities within each article, which become the building blocks of a proposed series of graphs. The graphs track indeed the co-occurrence of such entities among news on a weekly basis, and allow investigations on the inter-relations of topics discussed over time. Network analysis techniques and fuzzy community detection are then used to design a comprehensive framework, which systematically unveils interpretable topics and surrounding narratives within news. The importance of the proposed investigations is highlighted by the results of the logistic regression models. Indeed, we test whether there is a statistically significant connection between the features and structure of news, and moments of dislocation within financial markets. As expected (and desired), lower sentiment within news is more likely to be associated with weeks of market dislocation. However, multiple features computed from our graph construct are found to be also significant, especially from the entropy of discussions and consequent likelihood of contagion of sentiment, both in the contemporaneous and predictive scenarios. This suggests that the interconnectedness of news’ topics and structure therein are meaningful aspect to further analyse within financial research, for which our proposed study desires to serve as a first baseline. Improving entity recognition, extending the corpus of news, and designing generalisation studies are examples of possible advances to pursue in this research branch. As a final remark, we desire to point to the problem of network alignment, which is especially important in network biology [26]. Many related studies try to find a measure of protein similarity between proteins in different species, since similar protein structures often imply the same biological results. With a parallel approach, one could investigate more deeply whether equivalent structures among news (but that do not account for the actual “label” of the topic) result indeed in similar market reactions. Acknowledgements Deborah Miori’s research was supported by the EPSRC CDT in Mathematics of Random Systems (EPSRC Grant EP/S023925/1). References [1] Nadine Strauß, Rens Vliegenthart, and Piet Verhoeven. Intraday News Trading: The Reciprocal Relationships Between the Stock Market and Economic News. Communication Research, 45(7):1054–1077, 2018. PMID: 30443092. [2] Fabrizio Lillo, Salvatore Miccichè, Michele Tumminello, Jyrki Piilo, and Rosario N. Mantegna. How news affects the trading behaviour of different categories of investors in a financial market. Quantitative Finance, 15(2):213–229, 2015. [3] Eric C. So and Sean Wang. News-driven return reversals: Liquidity provision ahead of earnings announcements. Journal of Financial Economics, 114(1):20–35, 2014. [4] Haizhou Qu and Dimitar Kazakov. Quantifying correlation between financial news and stocks. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–6, 2016. [5] Ryohei Hisano, Didier Sornette, Takayuki Mizuno, Takaaki Ohnishi, and Tsutomu Watanabe. High quality topic extraction from business news explains abnormal financial market volatility. CARF F-Series CARF-F-299, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, October 2012. [6] Shaheen Syed and Marco Spruit. Full-text or abstract? examining topic coherence scores using latent dirichlet allocation. In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pages 165–174, 2017. [7] Weisi Chen, Fethi Rabhi, Wenqi Liao, and Islam Al-Qudah. Leveraging state-of-the-art topic modeling for news impact analysis on financial markets: A comparative study. Electronics, 12(12), 2023. [8] Dimo Angelov. Top2vec: Distributed representations of topics, 2020. [9] Maarten Grootendorst. BERTopic: Neural topic modeling with a class-based TF-IDF procedure, 2022. [10] Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, and Yanbin Lu. Temporal data meets llm – explainable financial time series forecasting, 2023. [11] Udit Gupta. Gpt-investar: Enhancing stock investment strategies through annual report analysis with large language models, 2023. [12] Rick Steinert and Saskia Altmann. Linking microblogging sentiments to stock price movement: An application of gpt-4, 2023. [13] Paolo Pasquariello. Financial market dislocations. The Review of Financial Studies, 27(6):1868–1914, 2014. [14] OpenAI. Gpt-4 technical report, 2023. [15] Partha Pratim Ray. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3:121–154, 2023. [16] C.J. Hutto and Eric Gilbert. Vader: A parsimonious rule-based model for sentiment analysis of social media text. 01 2015. [17] Mark E. J. Newman. Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23):8577–8582, 2006. [18] Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, Oct 2008. [19] Shihua Zhang, Rui-Sheng Wang, and Xiang-Sun Zhang. Uncovering fuzzy community structure in complex networks. Physical Review E, 76(4):046103, 2007. [20] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space, 2013. [21] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks, 2016. [22] Eghbal Rahimikia, Stefan Zohren, and Ser-Huang Poon. Realised Volatility Forecasting: Machine Learning via Financial Word Embedding, 08 2021. [23] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems, volume 26. Curran Associates, Inc., 2013. [24] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321–357, jun 2002. [25] Yi-Jiao Zhang, Kai-Cheng Yang, and Filippo Radicchi. Systematic comparison of graph embedding methods in practical tasks. Phys. Rev. E, 104:044315, Oct 2021. [26] Walter Nelson, Marinka Zitnik, Bo Wang, Jure Leskovec, Anna Goldenberg, and Roded Sharan. To Embed or Not: Network Embedding as a Paradigm in Computational Biology. Frontiers in Genetics, 10, 2019. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk); (2) Constantin Petrov, Fidelity Investments, London, UK. Authors: Authors: (1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk); (2) Constantin Petrov, Fidelity Investments, London, UK. Table of Links Abstract and Intro Abstract and Intro Data Data Framework Framework Results Results Conclusions, Acknowledgements, and References Conclusions, Acknowledgements, and References 5 Conclusions Starting from a curated selection of economic articles sourced from The Wall Street Journal, our research introduces an innovative and dynamic approach to dissecting news content. We leverage on GPT3.5 to sift out the most salient entities within each article, which become the building blocks of a proposed series of graphs. The graphs track indeed the co-occurrence of such entities among news on a weekly basis, and allow investigations on the inter-relations of topics discussed over time. Network analysis techniques and fuzzy community detection are then used to design a comprehensive framework, which systematically unveils interpretable topics and surrounding narratives within news. The importance of the proposed investigations is highlighted by the results of the logistic regression models. Indeed, we test whether there is a statistically significant connection between the features and structure of news, and moments of dislocation within financial markets. As expected (and desired), lower sentiment within news is more likely to be associated with weeks of market dislocation. However, multiple features computed from our graph construct are found to be also significant, especially from the entropy of discussions and consequent likelihood of contagion of sentiment, both in the contemporaneous and predictive scenarios. This suggests that the interconnectedness of news’ topics and structure therein are meaningful aspect to further analyse within financial research, for which our proposed study desires to serve as a first baseline. Improving entity recognition, extending the corpus of news, and designing generalisation studies are examples of possible advances to pursue in this research branch. As a final remark, we desire to point to the problem of network alignment , which is especially important in network biology [26]. Many related studies try to find a measure of protein similarity between proteins in different species, since similar protein structures often imply the same biological results. With a parallel approach, one could investigate more deeply whether equivalent structures among news (but that do not account for the actual “label” of the topic) result indeed in similar market reactions. network alignment Acknowledgements Deborah Miori’s research was supported by the EPSRC CDT in Mathematics of Random Systems (EPSRC Grant EP/S023925/1). EPSRC CDT in Mathematics of Random Systems References [1] Nadine Strauß, Rens Vliegenthart, and Piet Verhoeven. Intraday News Trading: The Reciprocal Relationships Between the Stock Market and Economic News. Communication Research, 45(7):1054–1077, 2018. PMID: 30443092. [2] Fabrizio Lillo, Salvatore Miccichè, Michele Tumminello, Jyrki Piilo, and Rosario N. Mantegna. How news affects the trading behaviour of different categories of investors in a financial market. Quantitative Finance, 15(2):213–229, 2015. [3] Eric C. So and Sean Wang. News-driven return reversals: Liquidity provision ahead of earnings announcements. Journal of Financial Economics, 114(1):20–35, 2014. [4] Haizhou Qu and Dimitar Kazakov. Quantifying correlation between financial news and stocks. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–6, 2016. [5] Ryohei Hisano, Didier Sornette, Takayuki Mizuno, Takaaki Ohnishi, and Tsutomu Watanabe. High quality topic extraction from business news explains abnormal financial market volatility. CARF F-Series CARF-F-299, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, October 2012. [6] Shaheen Syed and Marco Spruit. Full-text or abstract? examining topic coherence scores using latent dirichlet allocation. In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pages 165–174, 2017. [7] Weisi Chen, Fethi Rabhi, Wenqi Liao, and Islam Al-Qudah. Leveraging state-of-the-art topic modeling for news impact analysis on financial markets: A comparative study. Electronics, 12(12), 2023. [8] Dimo Angelov. Top2vec: Distributed representations of topics, 2020. [9] Maarten Grootendorst. BERTopic: Neural topic modeling with a class-based TF-IDF procedure, 2022. [10] Xinli Yu, Zheng Chen, Yuan Ling, Shujing Dong, Zongyi Liu, and Yanbin Lu. Temporal data meets llm – explainable financial time series forecasting, 2023. [11] Udit Gupta. 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