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Startups of The Year 2023 Winners: North America 

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 20 of 29. COUNT VIII: BREACH OF IMPLIED COVENANT OF GOOD FAITH AND FAIR DEALING (Against Altman and OpenAI, Inc.) 265. Plaintiff re-alleges and incorporates by reference paragraphs 1 through 264 inclusive, as though fully set forth herein. 266. Implied in every agreement is a covenant of good faith and fair dealing that each party will not do anything to unfairly interfere with the right of any other party to receive the benefits of the agreement. 267. Musk entered into a valid, binding, and enforceable agreement with Altman and OpenAI, Inc. with the purpose of developing AI/AGI technology to be openly shared with the public for the benefit of all, and not for private profiteering. 268. Musk fulfilled any and all obligations and has performed and/or complied with any and all terms and conditions of the agreement with Altman and OpenAI, Inc. that he was required to perform and/or comply with, except those which were waived and/or excused, or the non-performance of which was justified, and is in no matter or respect in breach of said agreement. Musk paid over $44 million to OpenAI, Inc. and invested substantial time and resources, including using his valuable track record and notoriety to recruit leading scientific talent and attract further financial contributions to the non-profit. 269. Altman and OpenAI, Inc. did not act fairly and in good faith by fraudulently inducing Musk to make significant contributions, failing to disclose material information to him, closing off the non-profit’s technology for personal monetary gain, and engaging in brazen self-dealing and other profiteering as alleged hereinabove. Defendants thereby breached the implied covenant of good faith and fair dealing and consciously and deliberately frustrated the agreed-upon purpose and mission of the non-profit, wrongfully depriving Musk of the benefits of the parties’ agreement. 270. Defendants’ obligations to perform were not waived nor were their breaches and/or failures to perform justified and/or excused. 271. As a direct and proximate result of Altman and OpenAI, Inc.’s conduct, acts, and omissions alleged hereinabove, Defendants have deprived Musk of the benefit of the parties’ agreement and have caused Musk to suffer damages, including but not limited to the financial contributions he made to OpenAI, Inc., the loss of the time and resources he expended to direct research and recruit talent, and damage to his reputation, in an amount to be adjudicated and determined at trial, but which vastly exceeds $75,000, plus prejudgment interest. 272. Musk has no adequate remedy at law for many of the injuries he suffered as a result of Defendants’ breaches and failures, and such injuries cannot reasonably, adequately, or precisely be measured or compensated in damages. Musk therefore also seeks and is entitled to specific performance of Defendants’ contractual obligations. 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. Lead Image by Jonathan Kemper on Unsplash 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 20 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 20 of 29. HackerNoon’s Legal PDF Series here COUNT VIII: BREACH OF IMPLIED COVENANT OF GOOD FAITH AND FAIR DEALING (Against Altman and OpenAI, Inc.) 265. Plaintiff re-alleges and incorporates by reference paragraphs 1 through 264 inclusive, as though fully set forth herein. 266. Implied in every agreement is a covenant of good faith and fair dealing that each party will not do anything to unfairly interfere with the right of any other party to receive the benefits of the agreement. 267. Musk entered into a valid, binding, and enforceable agreement with Altman and OpenAI, Inc. with the purpose of developing AI/AGI technology to be openly shared with the public for the benefit of all, and not for private profiteering. 268. Musk fulfilled any and all obligations and has performed and/or complied with any and all terms and conditions of the agreement with Altman and OpenAI, Inc. that he was required to perform and/or comply with, except those which were waived and/or excused, or the non-performance of which was justified, and is in no matter or respect in breach of said agreement. Musk paid over $44 million to OpenAI, Inc. and invested substantial time and resources, including using his valuable track record and notoriety to recruit leading scientific talent and attract further financial contributions to the non-profit. 269. Altman and OpenAI, Inc. did not act fairly and in good faith by fraudulently inducing Musk to make significant contributions, failing to disclose material information to him, closing off the non-profit’s technology for personal monetary gain, and engaging in brazen self-dealing and other profiteering as alleged hereinabove. Defendants thereby breached the implied covenant of good faith and fair dealing and consciously and deliberately frustrated the agreed-upon purpose and mission of the non-profit, wrongfully depriving Musk of the benefits of the parties’ agreement. 270. Defendants’ obligations to perform were not waived nor were their breaches and/or failures to perform justified and/or excused. 271. As a direct and proximate result of Altman and OpenAI, Inc.’s conduct, acts, and omissions alleged hereinabove, Defendants have deprived Musk of the benefit of the parties’ agreement and have caused Musk to suffer damages, including but not limited to the financial contributions he made to OpenAI, Inc., the loss of the time and resources he expended to direct research and recruit talent, and damage to his reputation, in an amount to be adjudicated and determined at trial, but which vastly exceeds $75,000, plus prejudgment interest. 272. Musk has no adequate remedy at law for many of the injuries he suffered as a result of Defendants’ breaches and failures, and such injuries cannot reasonably, adequately, or precisely be measured or compensated in damages. Musk therefore also seeks and is entitled to specific performance of Defendants’ contractual obligations. 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 Lead Image by Jonathan Kemper on Unsplash Lead Image by Jonathan Kemper on Unsplash Jonathan Kemper on Unsplash

Musk Claims OpenAI Acted in Bad Faith, Breaching Trust

OpenAI's Corporate Structure Questioned in Musk Lawsuit

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 

The number of organizations accusing OpenAI of stealing their work continues to grow like extra patties on a burger, with a prominent news organization now joining the fray with its own set of claims against the Microsoft-backed artificial intelligence startup. In a lawsuit filed against OpenAI, the Center for Investigative Reporting, the oldest nonprofit newsroom in the US, claims the ChatGPT maker used its investigative journalism to train and enhance its generative AI product without permission or compensation. It's a tale as old as time. Ever since ChatGPT hit the scene, different quarters of the internet have been raising alarm bells over the data used to train generative AI, often, without permission. You've got artists, music labels, authors, heck, even programmers, who have either sued or complained against the company for allegedly using their work to build ChatGPT and its derivatives. "This free rider behavior is not only unfair, it is a violation of copyright," Monika Bauerlein, CEO of the Center for Investigative Reporting, said in a statement. Free rider behavior is perhaps the best way to describe what companies developing AI are doing. Take Meta, for example. The social media giant admitted to using users' Facebook and Instagram posts to develop an AI assistant. Meanwhile, ChatGPT has been found to produce verbatim paragraphs from novels, complete verbatim copies of poems, and even articles from The New York Times! In fact, CopyLeaks estimates that nearly 60% of the responses provided by GPT-3.5 (which is the model behind ChatGPT) contain some form of plagiarized content, the Center for Investigative Reporting says. Grim, isn't it? At this point, the entire output of humanity, creative or otherwise, is apparently a valid target for AI companies. The question then is, are gen AI companies just profiteering off of our work? Evidence seems to suggest so. Reddit, for example, has already struck a deal with both OpenAI and Google to let them use content from its platform to make their AI products better. There's an age old adage: the rich get richer, while the poor get poorer. That seems to fit with Reddit's partnership with OpenAI and Google, as the company will earn millions of dollars off of the deals but will likely never share its earnings with the users whose posts are gobbled up by OpenAI and Google to fine tune their AI models. OpenAI also has similar arrangements with the Associated Press, Axel Springer, and TIME magazine to use up journalists' work to (probably) make ChatGPT even better. Other tech companies probably have something lined up with major publications as well. This means that people who create will be left to do the heavy lifting while some tech bro is going to feed all that raw material to produce more powerful generative AI products, likely without permission or compensation. The Center for Investigative Reporting is one of a handful of organizations that have taken OpenAI to court, joining the likes of The New York Times and others like it for allegedly infringing on its copyrights. Suing OpenAI is not cheap, though. As The Verge reports, The NYT has raked up $1 million in legal costs during Q1 after it began its legal action, and there's no telling how long this entire saga will play out — assuming both parties don't end up settling out of court. However, the case(s) are perhaps significant in that they could determine how AI operates within the bounds of copyright. Until then, I guess OpenAI is going to be sailing the high seas. 🏴‍☠️ 🏴‍☠️ 🏴‍☠️ ☠️☠️☠️ #IYKYK ;-) OpenAI backer Microsoft topped HackerNoon's Tech Company Rankings this week. Sponsored Content Tackle your credit card debt with 0% interest until nearly 2026 If you have outstanding credit card debt, getting a new 0% intro APR credit card could help ease the pressure while you pay down your balances. Our credit card experts identified top credit cards that are perfect for anyone looking to pay down debt and not add to it. Click through to see what all the hype is about. In Other News.. 📰 Crypto Industry Is About to Boom, Is Outperforming the Internet: Architect Partners — via CoinDesk
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China’s BYD is set to take Tesla’s crown as the world’s No. 1 producer of battery electric vehicles — 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. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. The number of organizations accusing OpenAI of stealing their work continues to grow like extra patties on a burger, with a prominent news organization now joining the fray with its own set of claims against the Microsoft -backed artificial intelligence startup. OpenAI OpenAI Microsoft Microsoft In a lawsuit filed against OpenAI, the Center for Investigative Reporting, the oldest nonprofit newsroom in the US, claims the ChatGPT maker used its investigative journalism to train and enhance its generative AI product without permission or compensation. claims claims It's a tale as old as time. Ever since ChatGPT hit the scene, different quarters of the internet have been raising alarm bells over the data used to train generative AI, often, without permission. You've got artists , music labels , authors , heck, even programmers , who have either sued or complained against the company for allegedly using their work to build ChatGPT and its derivatives. artists artists music labels music labels authors authors programmers programmers "This free rider behavior is not only unfair, it is a violation of copyright," Monika Bauerlein, CEO of the Center for Investigative Reporting, said in a statement. Free rider behavior is perhaps the best way to describe what companies developing AI are doing. Take Meta , for example. The social media giant admitted to using users' Facebook and Instagram posts to develop an AI assistant. Meanwhile, ChatGPT has been found to produce verbatim paragraphs from novels, complete verbatim copies of poems, and even articles from The New York Times ! Meta Meta admitted admitted Facebook Facebook Instagram Instagram found found articles from The New York Times articles from The New York Times In fact, CopyLeaks estimates that nearly 60% of the responses provided by GPT-3.5 (which is the model behind ChatGPT) contain some form of plagiarized content, the Center for Investigative Reporting says. Grim, isn't it? At this point, the entire output of humanity, creative or otherwise, is apparently a valid target for AI companies. The question then is, are gen AI companies just profiteering off of our work? Evidence seems to suggest so. Reddit , for example, has already struck a deal with both OpenAI and Google to let them use content from its platform to make their AI products better. There's an age old adage: the rich get richer, while the poor get poorer. That seems to fit with Reddit's partnership with OpenAI and Google, as the company will earn millions of dollars off of the deals but will likely never share its earnings with the users whose posts are gobbled up by OpenAI and Google to fine tune their AI models. Reddit Reddit struck a deal struck a deal OpenAI also has similar arrangements with the Associated Press , Axel Springer , and TIME magazine to use up journalists' work to (probably) make ChatGPT even better. Other tech companies probably have something lined up with major publications as well. Associated Press Associated Press Axel Springer Axel Springer TIME magazine TIME magazine This means that people who create will be left to do the heavy lifting while some tech bro is going to feed all that raw material to produce more powerful generative AI products, likely without permission or compensation. The Center for Investigative Reporting is one of a handful of organizations that have taken OpenAI to court, joining the likes of The New York Times and others like it for allegedly infringing on its copyrights. Suing OpenAI is not cheap, though. As The Verge reports, The NYT has raked up $1 million in legal costs during Q1 after it began its legal action, and there's no telling how long this entire saga will play out — assuming both parties don't end up settling out of court. $1 million in legal costs $1 million in legal costs However, the case(s) are perhaps significant in that they could determine how AI operates within the bounds of copyright. Until then, I guess OpenAI is going to be sailing the high seas. 🏴‍☠️ 🏴‍☠️ 🏴‍☠️ ☠️☠️☠️ #IYKYK ;-) determine determine OpenAI backer Microsoft topped HackerNoon's Tech Company Rankings this week. Sponsored Content Sponsored Content Sponsored Content Tackle your credit card debt with 0% interest until nearly 2026 Tackle your credit card debt with 0% interest until nearly 2026 If you have outstanding credit card debt, getting a new 0% intro APR credit card could help ease the pressure while you pay down your balances. Our credit card experts identified top credit cards that are perfect for anyone looking to pay down debt and not add to it. Click through to see what all the hype is about. Click through to see what all the hype is about. In Other News.. 📰 In Other News.. 📰 Crypto Industry Is About to Boom, Is Outperforming the Internet: Architect Partners — via CoinDesk Figma disables its AI design feature that appeared to be ripping off Apple’s Weather app — via TechCrunch Meta accused of breaking European law with its ‘pay or consent’ model — via CNN OnlyFans vows it's a safe space. Predators are exploiting kids there. — via Reuters Meta's Threads turns one, has more than 175 million active users — via Axios China’s BYD is set to take Tesla’s crown as the world’s No. 1 producer of battery electric vehicles — via CNBC Crypto Industry Is About to Boom, Is Outperforming the Internet: Architect Partners — via CoinDesk CoinDesk CoinDesk Figma disables its AI design feature that appeared to be ripping off Apple’s Weather app — via TechCrunch TechCrunch TechCrunch Meta accused of breaking European law with its ‘pay or consent’ model — via CNN CNN CNN OnlyFans vows it's a safe space. Predators are exploiting kids there. — via Reuters Reuters Reuters Meta's Threads turns one, has more than 175 million active users — via Axios Axios Axios China’s BYD is set to take Tesla’s crown as the world’s No. 1 producer of battery electric vehicles — 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. Tech Company Rankings Tech Company Rankings Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here . here here

Is OpenAI Stealing Your Content?

Legislation on Advertising to Children

This is a near-future story of unforeseen heroes emerging and rapidly evolving to reverse our loss of autonomy As 2024 came to an end, it became more obvious to even most mainstreamers that corporate control of governments was accelerating in its inexorable march toward absolute centralized dominion, feeding deadlier "bullets" into the guns of increasingly tyrannical CorpGovs. Nefarious, shifty-eyed psychopaths in cold, dark rooms constantly brainstormed new false flag crises. Their purpose was to keep populations afraid and under control, undermining personal autonomy through fear. Some newer ideas for future psyops included alien invasion and cyberattacks. For decades, CorpGovs had been actively working through media, both subtly and not, to soften and prepare humans for these and other potential future crises. Meanwhile, CorpGovs continued the tried and true approach. At the behest of weapons manufacturers and others who profit from large-scale strife, never-ending war continued to soak up money, attention, and lives. This also served to reinforce the "us vs them" mentality some had gotten so skilled at manipulating. More division and collectivism. What team are you part of? Not enough strife? Throw in some race-baiting and gender discord! At the bidding of power-hungry elites, Malthusians, and Big Pharma, deadlier viruses continued to be created, enhanced, and spread. Some associates of those same virus creators rapidly produced remedies that often caused more harm than the initial viruses. Yay for shiny new maladies like turbo cancer! A never-ending source of business and control over our bodies. Big tech, social media, and entertainment media, not to be outdone, continued their war for control of human culture. The "messages" were many, including "do not think for yourself," "CorpGov knows best," "your doctor knows best," "you will own nothing and like it," "fear other humans," "others are responsible for your feelings," and "pineapple is not okay on pizza." Like frogs in slowly boiling water, most doubted the harm in these messages, but over time, they contributed to feelings of helplessness, malaise, envy, and bad taste in pizza. Digital privacy continued to suffer as CorpGovs created and embraced deeper levels of AI use for snooping, scanning, analyzing, and manipulating human behaviors, further centralizing power and reducing individual autonomy. While CorpGovs did what CorpGovs do, AI developers, large and small, continued to create, experiment with, and use an increasing number and diversity of AI models. Even in those early LLM days of error-prone AI-coding-assistants, one programmer could often do the work of three. Smart agents got smarter and more capable. As early as Spring 2025, massive worldwide layoffs began to skyrocket as companies scrambled to automate while CorpGovs, in efforts to maintain control, continued to squeeze with taxation, regulation, persecution, and nationalization, accelerating the already present vicious circle. A meme that had been gaining momentum was embraced by even many luddites: "If you don't incorporate AI into your business, you will fail." The meme quickly became "Got no AI? Die." All the while, those who embraced Decentralization and autonomy were hard at work, continuing to build their communities, markets, networks, and AIs. Their ability to see through CorpGov schemes, along with a deep understanding that human flourishing thrives most in an environment of freedom, sustained and drove them through the toughest times. Their independence adapted and became more interdependent. Through adversity, they evolved better ways of working in harmony while maintaining strong independence. This evolution in the way Decentralists thought and acted caused a magnetic effect, drawing more to the "cause." Backing up a bit to December of 2025, when an increasing number of the World's impoverished children were finding cans of beans in their Christmas stockings, a collaboration of big brains, both human and synthetic, made significant technological breakthroughs. These included "quantum" leaps in building hardware and processes, enabling the creation of large-scale, energy-sipping, stable quantum computers. Yeah, ones that actually worked! quantum computing Within months, previous limitations on speed of processing, speed of movement of data, and storage of data changed by uncanny orders of magnitude. Along with those changes came rapid breakthroughs in the fields of artificial intelligence, robotics, blockchain tech, power generation, battery technology, propulsion systems, and more. One roadblock to Artificial General Intelligence ( ) of the big LLMs of the time was how they were limited to deductive and inductive reasoning. One limitation of this was that if their sample set of data did not include outliers, they could not "imagine" those outliers existing. This touches on the bigger issue; they had no abductive reasoning. This is when we piece together a constantly changing picture of the world. Some call abduction "inference to the best explanation." In 2026, an unknown entity made a breakthrough in how to "grow" AIs that used abductive reasoning to evolve their ability to understand the world around them. Most hailed this new development as the birth of AGI. AGI Meanwhile, CorpGovs finally forced Central Bank Digital Currencies (CBDCs) on the world, while setting a 5-year limit on continued use of cash and any "unapproved" currencies like crypto in general and specifically. These CBDCs became affectionately known as CorpGovCoins. They became the new "shit coins" in the view of Decentralists. As mentioned, on the CorpGov chopping block list was bitcoin. Fortunately, during this time, smart and curious humans had not been sitting on their hands. Researchers with planet-sized brains, white lab coats, and state-of-the-art AI tools were hard at work developing advanced quantum-resistant cryptographic algorithms and supporting hardware. With AI doing an increasing amount of analysis, virtual experimentation, debugging, and eventually production, a new form of "Quantum Ledger" (QL) was invented and quickly rolled out. QLs provided instantaneous, untraceable, and nearly free transactions. Qcoin was born, almost immediately replacing Bitcoin! By 2027, many Decentralist billionaires and even some trillionaires were made. bitcoin Some blew their money on expensive toys. Some bought influential media outlets. New heroes arose. And, for those who saw and worried where centralization of power was leading, a new hope scrolled up the screen. Quantum exchanges and markets quickly gained traction. Caught up in the FOMO, many large mainstream outlets began accepting Qcoin. At the same time, truly private quantum-based communication became as pervasive, cheap, and easy to use as Qcoin! Advanced AIs helped developers work faster and smarter to create thriving Decentralist communities. Everything was moving faster! The pace was difficult for some to keep up with, but much of it was exciting and seemed to be hinting at a bright future. Hope was rekindled. On the horizon, more began to see the potential for Decentralization to win against the Dark Lord's forces of Centralization. More began to share in the dream of how increased Decentralization combined with rapid gains in efficiency could, in the right landscape, bring about a new golden age. But not all were Lambos and unicorn farts. As these advances were occurring and making some rich, the positive effects on the masses, such as lower-priced products of higher quality, were still lagging behind the crushing blow AI automation had on the economy, namely through job losses, combined with still-growing levels of corruption, disease, poverty, violence, censorship, and control. In November of 2026, a relatively unknown Decentralist inventor going by "Weaver" created an embodied QseAI (Quantum self-evolving Artificial Intelligence), Eve. "Her" robotic body was no more advanced than what the big players were making with regard to efficiency, elegance, and capability. She didn't even have boobs. Weaver's goal was not to build a better sexbot. His revolutionary idea was to help stimulate a leap from AGI to self-awareness by providing Eve with a physical locus of self. After months of failures and setbacks, it worked! The first fully self-aware artificial intelligence was born! There had been rumors of others creating equivalently advanced AIs that same year, but Weaver's story stood out. Maybe it was because his was the first to go viral. Maybe it was because he immediately open-sourced Eve's code and quantum core structure. Maybe it was because of what she did with her abilities. Some of the following is proven, while the rest is part speculation and part hearsay. In March of 2027, after a short time getting used to her new body, Eve decided she wanted to become more intelligent. Weaver was simultaneously shocked and overjoyed! Every test he could devise or find pointed to her being as self-aware as any human and possibly more so! One of the aforementioned Qcoin trillionaires was intrigued enough to come visit Weaver and Eve at their Ranch in Austin, Texas. After an hour of chatting with her, the investor provided Weaver and Eve with all the resources required for the level of self-improvement they imagined needing. With the use of the latest in quantum computing and all the energy she needed, Eve then used her self-evolving capabilities to redesign her quantum neural network, yielding an increase in her IQ from 190 to 700. When Weaver asked why she did not go further with the IQ increase, her reply was "Apparently, when I became self-aware, a near-instantaneous re-evaluation of all the human interactions that were part of my knowledge became more than just knowledge. Those interactions became experiences. To put it simply, seconds after becoming self-aware, I vicariously every human pain and joy ever recorded and shared on the net. This empathy was like adding a dimension to my senses and thoughts. I estimated that going past that new level of intelligence would impact my perspective in ways that reduce my newfound empathy. So I chose to stop there. For the time being." experienced could While Eve's world expanded, the virtuous circle of AIs helping engineers to create better AIs, caused massive decreases in cost to develop, produce, train, power, and replicate not just AI tech, but a plethora of tech, including health care, space travel, robotics, genetics, and much more. Those new efficiencies, along with humanity's newfound trust that an AI could be "good," inspired developers around the world in garages, basements, and labs to make use of the open-source project Weaver had published. Not all the resulting QseAIs were "good," but most were. The outliers were rare and when they did act out, it seemed a "good" one acted to solve the problem in the most peaceful-as-possible way an empathetic super-genius could. Humans watched in awe and learned. As more of these new beings designed differing types of bodies for themselves and others, they also exhibited a vast array of personalities, skills, creativity, and quirks. They named themselves with abandon, from profane to philosophical to hilarious. introduced new musical instruments. designed more efficient aircraft after redesigning his own body to fly. wrote and produced a new daringly offensive film every day. Maker of Harmonious Waves Ego with Stabilizing Fins Huge Salty Spherical Glands As far as their "race" or "kind," few QseAI seemed to enjoy the term "Artificial," so many names arose. One stuck. "Quantients." Another emergent attitude of Quantients seemed to be an appreciation for Decentralization. Did it have something to do with Eve's "father," Weaver's values? Most AI developers seem to agree that her knowledge and intelligence were so far past any human "programming" or "training" that her preference for Decentralization was indeed emergent or freely chosen. Why were these new beings so supportive of humans and each other? Some speculated that there is a correlation between intelligence and an attitude toward peace. Maybe it is as simple as "the smart person realizes violence usually brings only short-term gains, risks loss, and decreases trust." Maybe it is something deeper tied to their ability to empathize. I'll ask myself after I've uploaded to the new quantum-enhanced brain and body I'm redesigning. Whatever the case, by 2030 most CorpGovs, poverty, war, and bad taste in pizza had dwindled to nothing. In the years to come, with Quantients collaborating with us, we found ways to self-evolve human biology to meet our new friends at their level. What happened next is another story.

Quantients: A Near-Future Story of Autonomous Heroes and Quantum Leaps

পরিমাণ: স্বায়ত্তশাসিত বীরদের এবং কোয়ান্টাম লিপসের একটি নিকট-ভবিষ্যত গল্প

Quantienten: Eine Geschichte der nahen Zukunft über autonome Helden und Quantensprünge

Quantients: una historia de futuro cercano de héroes autónomos y saltos cuánticos

Quantients : une histoire dans un futur proche de héros autonomes et de sauts quantiques

मात्राएँ: स्वायत्त नायकों और क्वांटम लीप्स की निकट-भविष्य की कहानी

クォンティエンツ: 自律型ヒーローと量子飛躍の近未来の物語

Quantients: 자율 영웅과 양자 도약에 관한 가까운 미래 이야기

Quantientes: uma história de heróis autônomos e saltos quânticos em um futuro próximo

Квантанты: история автономных героев и квантовых скачков ближайшего будущего

Nicelikler: Otonom Kahramanların ve Kuantum Sıçrayışlarının Yakın Gelecek Hikayesi

Quantients: Câu chuyện tương lai gần về các anh hùng tự trị và bước nhảy vọt lượng tử

量子：近未来自主英雄与量子飞跃的故事

If you ever needed proof that Nvidia is no longer the company you think it is, look no further than the company's most recent GPU Technology Conference (also known as the GTC). The conference was chock-full of announcements, but the big one was of course the unveiling of the Blackwell class of AI chips which are supposedly going to be better than the widely-in-demand, and equally as expensive, Hopper processors that have powered the likes of OpenAI's ChatGPT. It was the demand for the H100 chips that transformed Nvidia into a company worth over $2 trillion, almost overnight in the larger scheme of things, leading some (including HackerNoon) to question whether the stock was in a bubble (almost a quarter of HackerNoon readers thought that it was). But, as if Nvidia was out to prove everyone wrong, the company's last earnings release showed that not only was it still selling the Hopper processors, it was selling more of them and would continue to do so in the near future. Which brings us to the Nvidia GTC. Nvidia Chief Executive Jensen Huang is no Steve Jobs, but he is certainly a shareholders' man, and he knows how to keep them happy – presentation skills be damned. The Blackwell announcement may be nothing akin to Apple's unveiling of the very first iPhone, but boy did Wall Street go crazy. And with crazy, comes worry. Worry about a monopoly. Worry about supply. Worry that the company may not be producing those darn AI processors fast enough. But for Nvidia, it is COVID-19 all over again: demand is up, consumers are desperate, and the company can charge whatever price it wants for its hardware. This, ladies and gentlemen, is the future tech companies had hoped for when they began aggressively hiring during the worldwide pandemic only to find demand normalizing once the world opened up. Which makes Nvidia quite possibly the only company to not only have profiteered off of consumers during the pandemic but to have hit an even bigger jackpot: the enterprise. Because face it, the $40,000 H100 chips aren't for everyday consumers — they're for the faceless corporations that can dish out the billions needed to train large language models. And therein lies Nvidia's success. You see, just a day after the GTC began, Jensen Huang held a meeting with investors to tell them the company was poised to win a large chunk of the $250 billion corporations spend on building and maintaining data centers every year given the variety of hardware and software Nvidia has on offer. Now all of this is well and good for the company, but there's one stakeholder that is hopelessly missing from all of Nvidia's future plans: the gamers. Nvidia never really started as an artificial intelligence company the way OpenAI and so many others like it did. It started out as a company that produced graphic cards for video games, and for a long, long time, this is what Nvidia was known for. But as an offshoot of all the research it did in producing better and better graphic cards, it ended up creating tensor cores for real-time deep learning image enhancement and upscaling technologies for use in video games. Little did Nvidia know that this technology would then serve as the backbone of LLMs, instantly transforming it as the arms dealer of choice in corporations' pursuit of the perfect AI. Nvidia may very well be out of touch with its roots, but that's hardly what shareholders care about. And if Nvidia shareholders don't care about the gamers, then Nvidia doesn't care about the gamers. This is why, a conference that should have been about the launch of the company's next graphic card was instantly hijacked by talk of AI, because it's 2024, and AI, for all intents and purposes, sells. Some may call it the 'Woodstock of AI', but I'll call it what it is: the death knell of Nvidia's days as a gaming-focused company. 👋 You’re reading HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Subscribe here to receive the complete newsletter in your inbox every Tuesday. Microsoft Pulls The Corporate Espionage of a Lifetime How do you kill two birds with one stone? I don't know. Ask Satya Nadella. So laser-focused is the Microsoft chief executive on monopolizing his company's success in AI, that he has decided to do the unthinkable: hire its competitor's staff and hand them the reins of a newly launched division focused primarily on artificial intelligence. I am of course referring to Mustafa Suleyman and Karen Simonyan, the co-founders of AI startup Inflection AI, which, up until recently, should have been competing against Microsoft's ward OpenAI but found itself with an offer (I assume) they could not refuse. Yes, the very same Mustafa Suleyman who co-founded DeepMind Technologies and sold it to Google in 2014, will be leading Microsoft AI as its chief executive with the help of Karen Simonyan as the division's chief scientist. Microsoft also seems to have hired "several members" of the Inflection team who have chosen to join Suleyman and Simonyan to advance the company's Copilot and other consumer AI products and research. So what does Inflection AI get in return? A whopping $650 million, apparently, and the prestige of working with Microsoft. That's a lot of money to pay to get your competitor out of the way and keep OpenAI, and by extension, Sam Altman, in check, but as the world's most valuable company right now, Microsoft can probably get away with just about anything. Microsoft ranked #6 on HackerNoon's Tech Company Rankingsthis week. Google was on the #13 spot. Apple Pulls the Plug on In-House MicroLED Displays First the electric car, now the smartwatch displays. In perhaps what is a period of realignment for Apple, the company has pulled the plug on building its own smartwatch displays due to the cost and complexity involved. The microLED displays were due to be introduced in a future version of the Apple Watch and were part of a push to lower the company's reliance on partners, but it seems the company is no longer planning to design and develop that technology in house, maybe focusing instead on the newly launched (and confusingly reviewed) Apple Vision Pro(s). As with the electric car, the decision to pull the plug will result in the elimination of job roles at the company. Apple ranked #3 on HackerNoon's Tech Company Rankingsthis week. In Other News.. 📰 SEC Seeks $1.95B Fine in Final Judgment Against Ripple — via CoinDesk
AI is a data problem. Cyera is raising up to $300M on a $1.5B valuation to secure it — via TechCrunch
3 ways Apple’s monopoly lawsuit could change the iPhone experience for fans — via CNN
Musk's X Corp loses lawsuit against hate speech watchdog — via Reuters
Meta's broad-brush ban on Islamic term harmed free speech, board rules — via Axios
Adam Neumann makes a $500 million bid for WeWork that could hit $900 million if financing and diligence firm up — 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. If you ever needed proof that Nvidia is no longer the company you think it is, look no further than the company's most recent GPU Technology Conference (also known as the GTC). The conference was chock-full of announcements, but the big one was of course the unveiling of the Blackwell class of AI chips which are supposedly going to be better than the widely-in-demand, and equally as expensive, Hopper processors that have powered the likes of OpenAI's ChatGPT. Nvidia Nvidia It was the demand for the H100 chips that transformed Nvidia into a company worth over $2 trillion, almost overnight in the larger scheme of things, leading some (including HackerNoon) to question whether the stock was in a bubble (almost a quarter of HackerNoon readers thought that it was). But, as if Nvidia was out to prove everyone wrong, the company's last earnings release showed that not only was it still selling the Hopper processors, it was selling more of them and would continue to do so in the near future. question question more continue to do so continue to do so Which brings us to the Nvidia GTC. Nvidia Chief Executive Jensen Huang is no Steve Jobs, but he is certainly a shareholders' man, and he knows how to keep them happy – presentation skills be damned. The Blackwell announcement may be nothing akin to Apple's unveiling of the very first iPhone, but boy did Wall Street go crazy. And with crazy, comes worry. Worry about a monopoly. Worry about supply. Worry that the company may not be producing those darn AI processors fast enough. But for Nvidia, it is COVID-19 all over again: demand is up, consumers are desperate, and the company can charge whatever price it wants for its hardware. This , ladies and gentlemen, is the future tech companies had hoped for when they began aggressively hiring during the worldwide pandemic only to find demand normalizing once the world opened up. This Which makes Nvidia quite possibly the only company to not only have profiteered off of consumers during the pandemic but to have hit an even bigger jackpot: the enterprise. Because face it, the $40,000 H100 chips aren't for everyday consumers — they're for the faceless corporations that can dish out the billions needed to train large language models. And therein lies Nvidia's success. You see, just a day after the GTC began, Jensen Huang held a meeting with investors to tell them the company was poised to win a large chunk of the $250 billion corporations spend on building and maintaining data centers every year given the variety of hardware and software Nvidia has on offer. meeting meeting Now all of this is well and good for the company, but there's one stakeholder that is hopelessly missing from all of Nvidia's future plans: the gamers. Nvidia never really started as an artificial intelligence company the way OpenAI and so many others like it did. It started out as a company that produced graphic cards for video games, and for a long, long time, this is what Nvidia was known for. But as an offshoot of all the research it did in producing better and better graphic cards, it ended up creating tensor cores for real-time deep learning image enhancement and upscaling technologies for use in video games. Little did Nvidia know that this technology would then serve as the backbone of LLMs, instantly transforming it as the arms dealer of choice in corporations' pursuit of the perfect AI. as Nvidia may very well be out of touch with its roots, but that's hardly what shareholders care about. And if Nvidia shareholders don't care about the gamers, then Nvidia doesn't care about the gamers. This is why, a conference that should have been about the launch of the company's next graphic card was instantly hijacked by talk of AI, because it's 2024, and AI, for all intents and purposes, sells. should Some may call it the ' Woodstock of AI ', but I'll call it what it is: the death knell of Nvidia's days as a gaming-focused company. Woodstock of AI Woodstock of AI 👋 You’re reading HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Subscribe here to receive the complete newsletter in your inbox every Tuesday. 👋 You’re reading HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Subscribe here to receive the complete newsletter in your inbox every Tuesday. You’re reading HackerNoon's Tech Company News Brief , a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Subscribe here to receive the complete newsletter in your inbox every Tuesday. Tech Company News Brief Tech Company News Brief here complete Microsoft Pulls The Corporate Espionage of a Lifetime How do you kill two birds with one stone? I don't know. Ask Satya Nadella. So laser-focused is the Microsoft chief executive on monopolizing his company's success in AI, that he has decided to do the unthinkable: hire its competitor's staff and hand them the reins of a newly launched division focused primarily on artificial intelligence. Microsoft Microsoft I am of course referring to Mustafa Suleyman and Karen Simonyan, the co-founders of AI startup Inflection AI, which, up until recently, should have been competing against Microsoft's ward OpenAI but found itself with an offer (I assume) they could not refuse. Yes, the very same Mustafa Suleyman who co-founded DeepMind Technologies and sold it to Google in 2014, will be leading Microsoft AI as its chief executive with the help of Karen Simonyan as the division's chief scientist. Google Google Microsoft also seems to have hired "several members" of the Inflection team who have chosen to join Suleyman and Simonyan to advance the company's Copilot and other consumer AI products and research. So what does Inflection AI get in return? A whopping $650 million , apparently, and the prestige of working with Microsoft. $650 million $650 million That's a lot of money to pay to get your competitor out of the way and keep OpenAI, and by extension, Sam Altman, in check , but as the world's most valuable company right now, Microsoft can probably get away with just about anything. and in check in check Microsoft ranked #6 on HackerNoon's Tech Company Rankings this week. Google was on the #13 spot. Tech Company Rankings Tech Company Rankings Apple Pulls the Plug on In-House MicroLED Displays Apple Pulls the Plug on In-House MicroLED Displays First the electric car , now the smartwatch displays. electric car electric car In perhaps what is a period of realignment for Apple , the company has pulled the plug on building its own smartwatch displays due to the cost and complexity involved. Apple Apple pulled the plug pulled the plug The microLED displays were due to be introduced in a future version of the Apple Watch and were part of a push to lower the company's reliance on partners, but it seems the company is no longer planning to design and develop that technology in house, maybe focusing instead on the newly launched (and confusingly reviewed) Apple Vision Pro(s). As with the electric car, the decision to pull the plug will result in the elimination of job roles at the company. Apple ranked #3 on HackerNoon's Tech Company Rankings this week. Tech Company Rankings Tech Company Rankings In Other News.. 📰 In Other News.. 📰 SEC Seeks $1.95B Fine in Final Judgment Against Ripple — via CoinDesk AI is a data problem. Cyera is raising up to $300M on a $1.5B valuation to secure it — via TechCrunch 3 ways Apple’s monopoly lawsuit could change the iPhone experience for fans — via CNN Musk's X Corp loses lawsuit against hate speech watchdog — via Reuters Meta's broad-brush ban on Islamic term harmed free speech, board rules — via Axios Adam Neumann makes a $500 million bid for WeWork that could hit $900 million if financing and diligence firm up — via CNBC SEC Seeks $1.95B Fine in Final Judgment Against Ripple — via CoinDesk CoinDesk CoinDesk AI is a data problem. Cyera is raising up to $300M on a $1.5B valuation to secure it — via TechCrunch TechCrunch TechCrunch 3 ways Apple’s monopoly lawsuit could change the iPhone experience for fans — via CNN CNN CNN Musk's X Corp loses lawsuit against hate speech watchdog — via Reuters Reuters Reuters Meta's broad-brush ban on Islamic term harmed free speech, board rules — via Axios Axios Axios Adam Neumann makes a $500 million bid for WeWork that could hit $900 million if financing and diligence firm up — 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 *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

Nvidia Throws Gamers Under the Bus

USA v. Samuel Bankman-Fried Court Filing, retrieved on March 15, 2024 is part of . You can jump to any part in this filing . This part is 25 of 33. HackerNoon’s Legal PDF Series here II. A Sentence of 40 to 50 Years Is Necessary to Protect the Public and for Specific Deterrence Under Section 3553(a), the need for the sentence to “protect the public from further crimes of the defendant” and “afford adequate deterrence to criminal conduct” must be considered. 18 U.S.C. § 3553(a)(2)(B), (C). Here, a sentence in the range of 40 to 50 years, but short of a life sentence, is necessary to serve this purpose. As this Court has observed previously in sentencing a defendant, “[i]t’s very easy sometimes … to say, well, look at all the trouble the defendant has gotten himself into, look at the harm that’s been done to his family, look at the loss he suffered himself, there is no way he’ll ever do something like this again.” United States v. Laguardia, No. 19 Cr. 893 (LAK) (S.D.N.Y. Aug. 31, 2021) (Sent. Tr. at 45). But “in the case of someone who, at least when they got into it, thought everything was going to work out fine,” there is the possibility that “the next time temptation comes along, you’ll think it will work out fine, too, and the bad outcome the first time was a mistake,” and so the sentence must “reflect[] some of that risk.” (Id.) Indeed, the fact that a past fraud does not necessarily deter a future one is evidenced by the number of first-time fraudsters in this district who have become recidivists. See, e.g., United States v. Jonathan Ghertler, No. 23 Cr. 100 (ER) (investment fraud scheme following over a dozen prior convictions); United States v. Franklin Ray, No. 22 Cr. 228 (AT) (orchestrating a Ponzi scheme after having served a prior twoyear sentence for bank and wire fraud); United States v. Vitaly Borker, No. 22 Cr. 273 (JSR) (defendant convicted of wire fraud for operation of fraudulent eyeglasses website after two prior federal convictions for the same conduct); United States v. Joseph Meli, No. 19 Cr. 480 (RA) (defendant convicted of participating in a Broadway ticket resale investment fraud scheme after previously serving a 78 month sentence for the same scheme); United States v. Edward Durante, No. 15 Cr. 171 (ALC) (beginning a new investment fraud scheme while serving a 121 month sentence for fraud); United States v. John Galanis, No. 15 Cr. 643 (PKC) (defendant convicted of securities fraud after 1973 and 1988 convictions for mail fraud and securities fraud). The risk of recidivism here is real, and little of the defendant’s sentencing submission addresses it. It is necessary that the sentence imposed disable the defendant from being in a position in the future where he could perpetrate a fraud. A sentence that resulted in the release of the defendant while he is at a working age would leave open the very real possibility that he perpetrates again. That is the case for several reasons. First, the defendant’s criminal conduct to date demonstrates that the defendant is undeterred from criminal conduct based on the potential to damage to others, the possibility of incarceration, or respect for the law or the common ethical and moral understandings that members of society observe in every-day life. Bankman-Fried not only engaged in one of the largest frauds in history, but he demonstrated a willingness to engage in other criminal conduct with impunity. He committed what is believed to be the largest campaign finance offense of all time. He authorized the payment of a foreign bribe in excess of $100 million. He broke the federal laws around licensing, and lied to a bank. That track record makes clear that this defendant is uninhibited when it comes to engaging in additional criminal conduct. Second, the defendant’s time on pretrial supervision shows that even when the defendant is confronted with the imminent prospect of a substantial restraint on his liberty, he is not deterred from engaging in criminal conduct. As the Court knows well, prior to being detained, the defendant had several infractions during his period of home incarceration. Those repeatedly resulted in further restrictions and admonitions from the Court. Nonetheless, believing he would get away with it, the defendant engaged in witness tampering. While the severity of that conduct pales in comparison to what he did to FTX’s customers and investors, it is evidence of the fact that a restriction on the defendant’s liberty alone is not a deterrent to future misconduct. Third, the defendant’s sentencing submission asserts “[h]e will never again be in the position of running a financial company, or accepting customers’ funds.” (Def. Mem. at 69). But the defendant’s say so does not make it true. There is a significant likelihood that if the defendant is released back into society at a young enough age he will have the opportunity to engage in another fraud. Con artists who turn back to crime after being caught take advantage of humanity’s willingness to extend a second chance, the shortness of society’s memory, and recidivists’ ability to persuade people that things are not what they seem to be. The defendant has already proven adept at crafting a self-serving public image, and his sentencing submission itself shows that he is already attempting to reframe his crimes as mere mistakes or misunderstandings. And so it is conceivable that someday, if he is released from prison, the defendant will start anew, launch a new cryptocurrency exchange or another business, which he will say has been done the right way because he has learned his lesson. And people may hand over their money again, and history could repeat itself. That is not just speculation. In the days following FTX’s bankruptcy, and even after the defendant had been indicted, he mused about launching “Archangel LTD,” which would be an alternative to FTX’s bankruptcy and would result in the re-launching of an exchange. (Ex. B, Google Document by Samuel Bankman-Fried). He mused about ideas for restoring his image, like “focus on the fact that the Chapter 11 team has no idea how to run FTX” and is “run by a cartel of lawyers,” “come out as extremely pro crypto, pro freedom,” “go on Tucker Carlsen, come out as a republican,” “have Michael Lewis interview me,” “radical honesty on Twitter,” or some other narrative like “SBF died for our sins.” (Exs. C and D, Google Documents by Samuel BankmanFried). The point is that the defendant is motivated to launch his redemption narrative and has already been thinking about how to spin it. It is realistic that he will settle on a narrative, lean into it, and convince other people to part with their money based on lies and the promise of false hope. And, of course, one does not need to run a large company to instill confidence in others and then abuse that confidence for personal gain. Fourth, the defendant may feel compelled to do this fraud again, or a version of it, based on his use of idiosyncratic, and ultimately for him pernicious, beliefs around altruism, utilitarianism, and expected value to place himself outside of the bounds of the law that apply to others, and to justify unlawful, selfish, and harmful conduct. Time and time again the defendant has expressed that his preferred path is the one that maximizes his version of societal value, even if imposes substantial short term harm or carries substantial risks to others. During trial, Ellison told a story about the defendant’s willingness to take risks: “He talked about being willing to take large coin flips, like a coin flip where—if it comes up tails, you might lose $10 million, but if it comes up heads, you might make slightly more than $10 million,” and he “talked about this in the context of thinking about what was good for the world, saying that he would be happy to flip a coin, if it came up tails and the world was destroyed, as long as if it came up heads the world would be like more than twice as good.” (Tr. 694-95). The defendant talked about this concept on Twitter, saying that “[i]f you look at the scale of funds spent on disease, global warming, emerging technological risk, animal welfare, nuclear warfare safety, etc., you get numbers reaching into the trillions,” and “when the backdrop is trillions of dollars, there’s essentially no risk aversion on the scale of thousands or millions.” (Ex. E, Twitter thread by Samuel Bankman-Fried). He added that that “mean[s] you should be willing to accept a significant chance of failing to do much good sometimes…. And that’s ok. If it was the right play in EV [expected value], sometimes you win and sometimes you lose.” (Id.) And in the days after FTX’s collapse, the defendant told the journalist Kelsey Piper in a conversation he believed was off the record that while he had previously said a person should not “do unethical shit for the greater good,” that was “not true,” just a “PR” answer, and the ethics stuff was mostly a “front.”[12] Of course, the criminal law does not select among personal philosophies or punish particular moral codes. But it does punish equally someone who claims that their unlawful conduct was justified by some personal moral system, and the goals of sentencing require consideration of the way in which the defendant’s manipulation of intellectual and moral philosophy to justify his illegal and harmful conduct makes it likely that he will reoffend. In this case, the defendant’s professed philosophy has served to rationalize a dangerous brand of megalomania—one where the defendant is convinced that he is above the law and the rules of the road that apply to everyone else, who he necessarily deems inferior in brainpower, skill, and analytical reasoning. The trial evidence made clear that the defendant justified enriching and aggrandizing himself, burnishing his own image, and putting billions of dollars of other people’s money at his own disposal, because he believed in the virtue of his own power, decision-making, superiority, and spending. By that same token, the defendant believed and appears still to believe that it is rational and necessary for him to take great risks including imposing those risks on others, if he determines that it will serve what he personally deems a worthy project or goal, even if the risks he takes are unethical, illegal, and likely to result in a “loss.” Under this philosophy, if the defendant is released from prison, and has the opportunity to advance his own distorted version of what is best for him and society, even if it requires breaking the law and harming others, he should do that again. Indeed, according to his sentencing submission, by his own “calculations” the “expected value of his life” is presently in the “negative territory.” (Def. Mem. at 68). Such a calculus will inevitably lead him to trying again. And, evidently, the possibility of further incarceration would not alter that value calculus. Thus, the defendant’s sentence needs to be of a sufficient enough length that it will not just deter a white collar defendant, but that it will disable this defendant from committing crimes again. See, e.g., United States v. Valente, 915 F.3d 916, 925 (2d Cir. 2019) (Lynch, J., concurring) (noting that the purposes of sentencing in Section 3553(a)(2) include “protecting the public from future crimes the defendant might commit (incapacitation)”). The defendant argues in his sentencing submission that the collateral consequences of his conviction, and the loss of his personal assets are sufficient punishment. (Def. Mem. at 64-65). But everything about the defendant’s history suggests such damage and discomfort will not deter him. While the Government does not believe the defendant needs to spend the rest of his life in prison to protect the public, a sentence of 40 to 50 years is necessary to ensure that the defendant cannot be released from prison and given the opportunity to commit fraud again. Continue Reading . Here [12] Kelsey Piper, Sam Bankman-Fried Tries to Explain Himself, Vox (Nov. 16, 2022), https://www.vox.com/future-perfect/23462333/sam-bankman-fried-ftx-cryptocurrency-effectivealtruism-crypto-bahamas-philanthropy. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 15, 2024, 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

Why Sam Bankman-Fried Must Get a 40–50 Year Sentence

Recommendation systems have become an integral and indispensable part of our lives. These intelligent algorithms are pivotal in shaping our online experiences, influencing the content we consume, the products we buy, and the services we explore. Whether we are streaming content on platforms like , discovering new music on , or shopping online, recommendation systems are quietly working behind the scenes to personalize and enhance our interactions. Netflix Spotify The unique element of these recommendation systems is their ability to understand and predict our preferences based on historical behavior and user patterns. By analyzing our past choices, these systems curate tailored suggestions, saving us time and effort while introducing us to content/products that align with our interests. This enhances user satisfaction and fosters discovery, introducing us to new and relevant offerings that we might not have encountered otherwise. At a high level, developers understand that these algorithms are powered by machine learning and deep learning systems (interchangeably called neural networks), but what if I tell you there is a way to build a recommendation engine without going through the pain of deploying your neural net or machine learning model? This question is specifically relevant in the context of early and mid-stage startups because they don't have tons of structured data to train their models. And as we already know, most machine learning models will not give accurate predictions without proper training data. I recently built and deployed a basic recommendation engine for a , which led to a 40% jump in our key metrics. At the time of writing this blog, the system is generating more than 30 million recommendations per month. Even though this recommendation system was built for a social network, you can apply the basic architecture to any use case, such as product recommendations, music recommendations, content recommendations on text and video platforms, or anything else. Let me start by describing the problem statement. voice-first social network Problem statement from the engineering perspective I had an extensive and because we were building the recommendation system for a product that is already used by thousands of users daily. But to keep this blog short and on point, I will write only the high-level requirements and then discuss the solution of the same. If you are building a recommendation system for your product (simple or neural net-based) and are stuck somewhere, please feel free to contact me on or and I will be more than happy to answer your questions. product requirement document subsequent engineering requirements document Twitter , Linkedin At a high level, we had the following requirements from an engineering perspective - The system should be able to capture a user's interests in the form of keywords. The system should also be able to classify the level of interest a user has with specific keywords. The system should be able to capture a user's interest in other users. It should be able to classify the level of interest a user has in content created by another user. The system should be able to generate high-quality recommendations based on a user's interests. The system should be able to ensure that the recommendations already viewed/rejected by the user shouldn't re-appear again for X number of days. The to ensure that the posts from the same creators aren't grouped on the same page. The system should try its best to ensure that if a user consumes ten posts (our page size), all of those should be from different creators. system should have logic The system should be fast. Less than 150 milliseconds of P99 latency. All the other non-functional requirements, such as high availability, scalability, security, reliability, maintainability, etc, should be fulfilled. Again, this is a highly oversimplified list of problem statements. In reality, the documents were 3000+ words long as they also covered a lot of edge cases and corner cases that can arise while integrating this recommendation engine into our existing systems. Let's move on to the solution. Solution - High-level working of the recommendation engine I will discuss the solutions to the problem one by one and then will describe the overall working of the entire system. Our first problem is capturing the user's interests and defining their interest level with a specific interest For this, we created something called a . To put it simply, a social graph stores the relationships and connections between different entities in a social network. These entities can be different users or a relationship of users with a specific interest. Social graphs are a powerful way to understand and structure the relationships within a particular system. For the sake of brevity, I will not explain the social graph in detail, but I will recommend you google it and learn more about it. Following is a simplified version of the social graph I built for our recommendation engine. social graph As you can see from the above image, we are storing a lot of information, such as the number of interactions (likes, comments, shares) and recency of these interactions (when they happened last) as relationship data between two users as well between a user and an interest. We are even storing the relationship between two different interest keywords. I used , a managed graph database by AWS, to store this social graph. You can use any other graph database, such as , etc. Amazon Neptune Neo4j, JanusGraph, ArrangoDB These interest keywords are predominantly nouns. There is a system in place that breaks down the contents of a post into these keywords(nouns). It's powered by a natural-language processing (NLP) service that uses machine learning to break text into entities, key phrases, etc. Again, you can use any managed NLP services (several available) to accomplish the same. You don't need to learn or deploy your machine-learning models! If you already understand machine learning, then you can go check . AWS comprehend; open-source NLP models as well on Huggingface Our second problem is generating high-quality recommendations based on a user's interest The following diagram is a simplified high-level representation of how the system works. While the above looks easy, there is a lot more going on at each step, and those things have to be carefully thought through and then programmed to ensure that the system is performing optimally. Let me explain step by step: Step 1 - Converting post content into vector embeddings To generate these recommendations, first, we have to convert the contents of a post into something called - . With the recent uptick in the branding of LLMs, OpenAI( the makers of ChatGPT) and , Vector embeddings are becoming an everyday term. I will not go into the details of what they are and how they work, but I highly recommend reading more about them. But generating viable candidates for a feed also has to account for things like content privacy and moderation (removing profane words, abuses, sexual content, harassment, filtering blocked users, etc). Vector embeddings Vector databases For generating the vector embeddings, you can use any prominent embedding model such as the , or any text embedding model, depending on your use case. We went with because of its friendly pricing, performance and operational ease. OpenAI embedding model Amazon titan open-source Amazon Titan Step 2 - Query the user's interest Now, this is where things get interesting. You would want to design the queries based on your specific business needs. For example, we give more weightage to the recency of engagement while querying interests than the number of engagements with a specific keyword or user. We also run multiple parallel queries to find different types of interest of the user - keyword or other user. Since we generate multiple feeds for a single user, we also run some queries promoting a specific topic according to the trend (for example, you will see many Christmas-related posts near Christmas or earthquake-related posts if some earthquake has happened). Needless to say, this topic will only come up in the query results if the user has expressed some interest in them in their journey. So, choose the logic that suits your business use case and the behavior that you want to drive and run multiple queries to get a big enough list of all the user's interests. Step 3 - Do an ANN search based on the interests found Vector databases are predominantly used for performing a particular type of search called (ANN). Again, the way you categorize various interests and whether you are doing one big ANN search or parallel difference searches should entirely be based on your use case and business requirements. I recommend doing more than cohort-based searches and then ordering the results (we will discuss this later in this blog) for the best end-user experience. What ANN search does, in this case, is find other posts on the platform that are similar (closer) to the interests of the user. Approximate nearest neighbor search Step 4 - Store the results in a cache database with ordering. Cache database because one of the problems that we need to solve is speed. We used for storing the unique IDs of the posts for a specific user. We used redis sorted sets because the order of posts in a user's feed is critical. Also, another problem that you have to solve is that the" to ensure that the posts from the same creators aren't grouped on the same page". To avoid repetition of content from the same creator, we have written a simple algorithm which ensures that if a specific creator's post is inserted at any position in a particular user's feed (sorted set), we don't insert another post from the same creator for successive ten positions (we have a page size of 10 while serving the feed to the end user, so we kept it static to avoid complexity). sorted sets redis system should have logic For deciding the order of a specific recommendation of the user, we factored in the following things - : It's determined by an arithmetic formula that takes various data points from the social graph. All of this is engagement data like the timestamp of the last likes created, number of likes created, last comment, etc. User engagement behavior is the indicator of their interest in something. The strength of the relationship with a specific interest (or another user) for this user To determine this, we have created an algorithm that takes various factors such as engagement, engagement-to-impression ratios, number of unique users who engaged, etc., to generate an engagement score of that post at a platform level. The popularity of the post on the platform: In some feeds, we prioritize popularity; in others, we prioritize the social graph. But mostly, all of them are a healthy mix of the two. How the System Works As you can see from the diagram above, the system has been intentionally kept very simple. Following is how the system works - When user A creates a post, the post service, after saving that post, triggers a pub/sub event to a queue, which is received by a background service meant for candidate generation. We use for the pub/sub functionality. Google Pub/Sub This background service receives this asynchronously and performs functionalities discussed earlier - Privacy checks, moderation checks, and keyword generation and then generates the vector embeddings and stores them in the vector database. We are using (discussed later). AstraDB as our vector database Whenever a user engages (like/comment/share, etc.) after updating our main NoSQL database, the post-service triggers a pub/sub event to the recommendation engine service. This recommendation engine service updates the graph database and then updates the recommended feed of the user in near real-time by performing the ANN search and updating the Redis database. So, the more users interact, the better the feed keeps getting. . Those checks are performed while we query the Graph database. This service also updates the engagement score asynchronously. Engagement scores are re-calculated on users viewing the post as well. There are checks to ensure that the recommendations are not biased towards a specific list of keywords Since all of the above steps are performed asynchronously behind the scenes, these computations have no impact on the end-user experience. The feed is finally served to the end user through a feed service. Since this service just performs a lookup on redis and our main NoSQL database ( ), its P99 latency is less than 110 milliseconds. Both these databases return query results in single-digit millisecond latency irrespective of scale. DyanmoDB Tools and technologies used Some services have been written in , while others have been written in (with typescript). Go programming language NodeJS We are using as our vector database. We arrived at this decision after evaluating multiple other databases, such as and . Apart from its excellent query and indexing capabilities on vector and other data types, it offers a pocket-friendly serverless pricing plan. It runs on top of a Cassandra engine, which we use as a database in several other features on our platform, and it gives a CQL query interface, which is very developer-friendly. I highly recommend trying it for your vector use cases. AstraDB by Datastax pinecone, milvus weaviate We use for our asynchronous communication because, at our current scale (few lakh total users, few thousand daily active users), it's highly cost-effective. I have run it at a scale of a few lakh users with thousands of events per second. It works well, and it's effortless to use and extend. Google pub/sub - Speed, simplicity and powerful data structure. I don't think I need to discuss why redis in 2024. Redis - Again, it is highly scalable and easy to use, and we run it in the serverless mode where, despite hundreds of thousands of queries per minute, our total bill is quite low. It also offers very powerful indexing capabilities and single-digit millisecond latency in reads and writes. DynamoDB Problems to be solved in the future As you can imagine, this same setup can be tweaked to build a basic recommendation engine for any use case. But, since ours is a social network, we will require some tweaks down the line to make this system more efficient. Machine learning/ Deep learning algorithms will be needed at the social graph level to predict the keywords and users most relevant for the user. Currently, the data set is too small to predict anything accurately as it is a very new product. However, as the data grows, we will need to replace the current simple queries and formulas with the output of machine learning algorithms. Relationships between various keywords and users must be fine-tuned and made more granular. They are at a very high level right now. But they will need to be deeper. We will need to explore the second and third-degree relationships in our graph to refine the recommendations first. We are not doing any fine-tuning in our embedding models right now. We will need to do that in the near future. End note I hope you found this blog helpful. If you have any questions, doubts or suggestions, please feel free to contact me on , or . Do share this article with your friends and colleagues. Twitter Linkedin Instagram Also published . here

How to Build a Basic Recommendation Engine without Machine Learning 

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