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feature image: studio Ghibli style recreation of the famous photo of Mira Murati (left), Sam Altman (standing), Greg Brockman, and Ilya Sutskever (right), by ChatGPT (duh) feature image: studio Ghibli style recreation of the famous photo of Mira Murati (left), Sam Altman (standing), Greg Brockman, and Ilya Sutskever (right), by ChatGPT (duh) Remember that weird weekend in November 2023 when Sam Altman got fired from OpenAI and then… sort of hired by Microsoft and then… reinstated at OpenAI in less than a week? I wrote three posts about that drama in between Thanksgiving madness and oh my goodness, SO MUCH has happened since. If I were to catch you up on all things OpenAI and AI related, it would take a book. (For one, I bet if you are reading this story, your relationship with ChatGPT or similar chatbot has significantly changed since 2023. You are probably too distracted to read my article in full and will go to said chatbot to ask for a summary LOL) Sam Altman got fired from OpenAI sort of hired by Microsoft reinstated at OpenAI I wrote three posts about that drama In any case….. In any case….. Now, over a year later, we finally have SOME clarity—thanks to new reporting by Wall Street Journal reporter Keach Hagey, who hunted down all the major sources in the story for over a year, including Sam Altman himself, and conducted over 250 interviews! Shout out to the good folks at the Hard Fork Podcast whose interview finally gave me some much needed closure (ish) on this topic! https://x.com/sama/status/1908163013192069460?embedable=true https://x.com/sama/status/1908163013192069460?embedable=true Tweet above: Sam Altman confirming the authenticity of the book Tweet above: Sam Altman confirming the authenticity of the book So without further ado, here’s what actually happened, what’s changed since then, and how everyone’s kind of regretting everything... but also getting richer? Part 1: What Happened Since (And Why It Matters Even More Now) Since the week after Sam got fired, then unfired in November of 2023, here are a non-exhaustive list of what has happened in that world. The board got reshuffled. Bret Taylor, Larry Summers, and Adam D’Angelo became the new grown-ups in the room. Everyone promised to work on "governance" (lol).
Investigation confirmed the vibes. A law firm (WilmerHale - not to brag but a lot of UWC graduates work here. UWC of course is the education system that trained Mira Murati, and yours truly lol) hired by OpenAI concluded the board's firing of Altman was due to a "loss of trust," not a smoking gun about AI safety or some secret doomsday project. New Board agreed with the findings. Essentially, both old Board and new Board had the same facts; yet the latter had much more favorable views of Sam than the former.
Key people left. Ilya Sutskever (co-founder and chief scientist) and Mira Murati (CTO) both left OpenAI for their own ventures in May and September of 2024, respectively. Ilya now runs a new startup called Safe Superintelligence Inc., which already hit a $30B valuation, while Mira founded Thinking Machines Lab, which aimed at a $9B valuation with $1B raised.
Altman rejoined the board. Because of course he did.
OpenAI went full steam ahead. The tender offer (where employees get to cash out some equity) went through. Sam’s reinstatement as CEO somehow “coincided” with a valuation jump from around $30B to $90B post-chaos. OpenAI is still the belle of the AI ball, and Sam Altman remains its unshakable poster child.
OpenAI is trying to ditch its nonprofit origins. The company is lobbying to loosen the governance shackles imposed by its nonprofit parent—because, you know, it’s really hard to scale capitalism when you technically still report to a nonprofit mission. It’s not like anyone else at that point was still fooled about OpenAI profit mode.
Elon Musk is trolling from the sidelines. Musk (one of OpenAI cofounders to refresh your memory) reportedly offered to buy OpenAI for $97.4 billion, which of course Altman rejected, citing how OpenAI is "not for sale," but the point was made. Musk is of course too busy dealing with “reshaping" the entire US government to close the deal.
AI is going mainstream at breakneck speed. ChatGPT is basically the new Google for a huge chunk of the population, and the recent rollout of GPT-4 Turbo (with native image generation capabilities baked right in, like the 2 images you could see above) only poured rocket fuel on the adoption curve. Everyone from students to startups is using it, often daily, like it’s no big deal. The board got reshuffled. Bret Taylor, Larry Summers, and Adam D’Angelo became the new grown-ups in the room. Everyone promised to work on "governance" (lol). The board got reshuffled. Investigation confirmed the vibes. A law firm (WilmerHale - not to brag but a lot of UWC graduates work here. UWC of course is the education system that trained Mira Murati, and yours truly lol) hired by OpenAI concluded the board's firing of Altman was due to a "loss of trust," not a smoking gun about AI safety or some secret doomsday project. New Board agreed with the findings. Essentially, both old Board and new Board had the same facts; yet the latter had much more favorable views of Sam than the former. Investigation confirmed the vibes. Key people left. Ilya Sutskever (co-founder and chief scientist) and Mira Murati (CTO) both left OpenAI for their own ventures in May and September of 2024, respectively. Ilya now runs a new startup called Safe Superintelligence Inc., which already hit a $30B valuation, while Mira founded Thinking Machines Lab, which aimed at a $9B valuation with $1B raised. Key people left. Altman rejoined the board. Because of course he did. Altman rejoined the board. OpenAI went full steam ahead. The tender offer (where employees get to cash out some equity) went through. Sam’s reinstatement as CEO somehow “coincided” with a valuation jump from around $30B to $90B post-chaos. OpenAI is still the belle of the AI ball, and Sam Altman remains its unshakable poster child. OpenAI went full steam ahead. OpenAI is trying to ditch its nonprofit origins. The company is lobbying to loosen the governance shackles imposed by its nonprofit parent—because, you know, it’s really hard to scale capitalism when you technically still report to a nonprofit mission. It’s not like anyone else at that point was still fooled about OpenAI profit mode. OpenAI is trying to ditch its nonprofit origins. Elon Musk is trolling from the sidelines. Musk (one of OpenAI cofounders to refresh your memory) reportedly offered to buy OpenAI for $97.4 billion, which of course Altman rejected, citing how OpenAI is "not for sale," but the point was made. Musk is of course too busy dealing with “reshaping" the entire US government to close the deal. Elon Musk is trolling from the sidelines. AI is going mainstream at breakneck speed. ChatGPT is basically the new Google for a huge chunk of the population, and the recent rollout of GPT-4 Turbo (with native image generation capabilities baked right in, like the 2 images you could see above) only poured rocket fuel on the adoption curve. Everyone from students to startups is using it, often daily, like it’s no big deal. AI is going mainstream at breakneck speed. Part 2: So what actually caused the firing of Sam Altman? Thanks to Keach Hagey’s new book, coming out in a few weeks, we finally got a sneak peek at the full(ish) story—on the record, from the key players. Contrary to popular belief at the time (AGI is getting close!!!!), it really was about miscommunication, personalities, and interpersonal dynamics in a fast moving industry. The through-line though, was money. was Ilya and Mira were instrumental in flagging Sam’s behavior to the board multiple times prior to the firing. Ilya had major issues with how Sam approached safety/super-alignment research, for years at that point. Independent of Ilya, Mira had also come to the Board with screenshots from her Slack of what she saw as patterns of toxic behaviors (which she apparently had brought up to Sam himself already), for example telling whatever he thinks one wants to hear then promptly going back on his words behind their backs; or siding with Greg Brockman who was supposed to report to Mira on some of his disruptive tendency in teamwork. Both Ilya and Mira were top dogs within OpenAI, one being the cofounder/main researcher, the other Chief Technology Officer, so the Board trusted their judgment over Sam’s. One Board member also personally experienced this pattern of toxic behavior herself (presumably Helen Toner, who published a research paper with more favorable view on Anthropic than OpenAI, its competitor)
They regretted it almost immediately. Both Mira and Ilya expressed their regret almost overnight. In Mira’s telling, she thought the Board would simply get Sam an executive coach as opposed to replacing him with HER. Definitely a bad look if you value organizational harmony. Ilya also did not understand the extent to which the next thing would happen, which was…
Employees almost fully sided with Sam. Why? Not just because he’s charismatic and powerful, but because their own paydays would be jeopardized. At the time, a tender offer valuing company at $90B with low strike price ($30B for existing employees) was on the line. It was a no-brainer to everyone that if Sam was fired, the offer was gonna go away. So essentially, they had to choose between dealing with toxic leadership vs. losing millions. Stuck in between this rock and a hard place - guess which one people chose 😉
The rest is history. Sam got reinstated. Ilya and Mira promptly left. And many, many other OpenAI influential people have also departed since. Ilya and Mira were instrumental in flagging Sam’s behavior to the board multiple times prior to the firing. Ilya had major issues with how Sam approached safety/super-alignment research, for years at that point. Independent of Ilya, Mira had also come to the Board with screenshots from her Slack of what she saw as patterns of toxic behaviors (which she apparently had brought up to Sam himself already), for example telling whatever he thinks one wants to hear then promptly going back on his words behind their backs; or siding with Greg Brockman who was supposed to report to Mira on some of his disruptive tendency in teamwork. Both Ilya and Mira were top dogs within OpenAI, one being the cofounder/main researcher, the other Chief Technology Officer, so the Board trusted their judgment over Sam’s. One Board member also personally experienced this pattern of toxic behavior herself (presumably Helen Toner, who published a research paper with more favorable view on Anthropic than OpenAI, its competitor) Ilya and Mira were instrumental They regretted it almost immediately. Both Mira and Ilya expressed their regret almost overnight. In Mira’s telling, she thought the Board would simply get Sam an executive coach as opposed to replacing him with HER. Definitely a bad look if you value organizational harmony. Ilya also did not understand the extent to which the next thing would happen, which was… They regretted it almost immediately. Employees almost fully sided with Sam. Why? Not just because he’s charismatic and powerful, but because their own paydays would be jeopardized. At the time, a tender offer valuing company at $90B with low strike price ($30B for existing employees) was on the line. It was a no-brainer to everyone that if Sam was fired, the offer was gonna go away. So essentially, they had to choose between dealing with toxic leadership vs. losing millions. Stuck in between this rock and a hard place - guess which one people chose 😉 Employees almost fully sided with Sam. At the time, a tender offer valuing company at $90B with low strike price ($30B for existing employees) was on the line. The rest is history. Sam got reinstated. Ilya and Mira promptly left. And many, many other OpenAI influential people have also departed since. Part 3: Why come clean now? The book also revealed some major details about the big question in probably everyone’s mind, which is: Why now? Why not then? It was such a secretive and confusing firing. Even after WilmerHale and the new Board came out with the new findings, and after many reportings about subsequent departures of key OpenAI people, nobody still really knows WHY he was fired in the first place. And we finally have some answers about why now. Sam initially didn’t want to talk for the book, but eventually did. Why? Presumably because he must think he might have lost a tiny battle, but won the fight. If you remember, back in March of 2023, a letter signed by over 1000 influential people within the AI community calling for the slowdown of AI was signed, notably by some of his competitors like Elon Musk and Steve Wozniak. It’s safe to say that not only AI did not slow down one bit, but its development has been one of the most, if not the most, important tech stories of the past 2-3 years. The industry as a whole had raised over 150 billions since, and 20 billions alone in the first 2 months of 2025. Elon Musk of course has gone on to found X-AI around the same time as signing the letter (lol), and acquired X (formerly Twitter) a year later, in March of 2025, presumably to help a dying company with his new lucrative and definitely overvalued company. So - it’s 1-0 for the accelerationists against the safety first people in AI.


Sam wants to be remembered as one of the greats At the time of his firing, not many people know about Sam, except for a niche community within tech. And now, everybody does. According to Keach Hagey herself, Sam wants to matter. He thinks of himself as one of the great men of history, right alongside Edison and Oppenheimer. In the book, she described a meeting in which Sam mentioned to her that it’s too early to tell the story of OpenAI. So in a way, he thinks that his involvement with OpenAI or even AI as a whole is not all of his story. His ambition goes far beyond AI - he’s thinking at the scale of national identity and economic systems, as hinted in his America Equity essay here. Sam initially didn’t want to talk for the book, but eventually did. Why? Presumably because he must think he might have lost a tiny battle, but won the fight. If you remember, back in March of 2023, a letter signed by over 1000 influential people within the AI community calling for the slowdown of AI was signed, notably by some of his competitors like Elon Musk and Steve Wozniak. It’s safe to say that not only AI did not slow down one bit, but its development has been one of the most, if not the most, important tech stories of the past 2-3 years. The industry as a whole had raised over 150 billions since, and 20 billions alone in the first 2 months of 2025. Elon Musk of course has gone on to found X-AI around the same time as signing the letter (lol), and acquired X (formerly Twitter) a year later, in March of 2025, presumably to help a dying company with his new lucrative and definitely overvalued company. So - it’s 1-0 for the accelerationists against the safety first people in AI. Sam initially didn’t want to talk for the book, but eventually did. Why? Presumably because he must think he might have lost a tiny battle, but won the fight. If you remember, back in March of 2023, a letter signed by over 1000 influential people within the AI community calling for the slowdown of AI was signed, notably by some of his competitors like Elon Musk and Steve Wozniak. It’s safe to say that not only AI did not slow down one bit, but its development has been one of the most, if not the most, important tech stories of the past 2-3 years. The industry as a whole had raised over 150 billions since, and 20 billions alone in the first 2 months of 2025. Elon Musk of course has gone on to found X-AI around the same time as signing the letter (lol), and acquired X (formerly Twitter) a year later, in March of 2025, presumably to help a dying company with his new lucrative and definitely overvalued company. So - it’s 1-0 for the accelerationists against the safety first people in AI. Sam initially didn’t want to talk he must think he might have lost a tiny battle, but won the fight. Sam wants to be remembered as one of the greats At the time of his firing, not many people know about Sam, except for a niche community within tech. And now, everybody does. According to Keach Hagey herself, Sam wants to matter. He thinks of himself as one of the great men of history, right alongside Edison and Oppenheimer. In the book, she described a meeting in which Sam mentioned to her that it’s too early to tell the story of OpenAI. So in a way, he thinks that his involvement with OpenAI or even AI as a whole is not all of his story. His ambition goes far beyond AI - he’s thinking at the scale of national identity and economic systems, as hinted in his America Equity essay here. Sam wants to be remembered as one of the greats At the time of his firing, not many people know about Sam, except for a niche community within tech. And now, everybody does. According to Keach Hagey herself, Sam wants to matter. He thinks of himself as one of the great men of history, right alongside Edison and Oppenheimer. In the book, she described a meeting in which Sam mentioned to her that it’s too early to tell the story of OpenAI. So in a way, he thinks that his involvement with OpenAI or even AI as a whole is not all of his story. His ambition goes far beyond AI - he’s thinking at the scale of national identity and economic systems, as hinted in his America Equity essay here. Sam wants to be remembered as one of the greats America Equity here Part 4: So what now? I feel like I finally understand everything. It looks like the same traits that make Sam so effective at raising money and pushing product are the ones that make him really hard to work with or trust. It’s no coincidence that literally every major player in the AI community has had some kind of messy breakup with the guy, including his own sister (not a AI player, but I just thought it’s noteworthy to point out that his own sister is also suing him). really hard Yet, at the end of the day, everyone involved kinda moved on quickly from the whole incident. The board regrets how they fired him. Ilya and Mira regret triggering the chaos. Employees felt trapped between values and financial upside. Meanwhile, Sam just... wins again. The governance experiment failed. The board was supposed to act as a counterweight to Sam’s unchecked power. But when push came to shove, the company sided with the founder. Like they always do. The nonprofit structure was supposed to safeguard humanity from corporate overreach. Instead, it caved under the same pressures it was built to resist. he nonprofit structure was supposed to safeguard humanity from corporate overreach. Instead, it caved under the same pressures it was built to resist. So what now? We’re back where we started—Sam Altman at the helm, OpenAI chasing AGI, the board trying to stay relevant, and the rest of us watching from the sidelines. Only now we know more about what actually went down—and how fragile the whole setup really was. OpenAI was supposed to be structured differently. Instead, it played out like every other Silicon Valley drama. Power won. Money won. And the guy who believes he’s building the future didn’t just survive—he became inevitable. Boom. Done.

OpenAI Saga Part 4: The firing & unfiring of CEO Sam Altman FINALLY explained

OpenAI Saga Část 4: Vyhození a odvolání generálního ředitele Sama Altmana KONEČNĚ vysvětleno

OpenAI Saga Parte 4: El despido y el despido del CEO Sam Altman finalmente explicado

Saga OpenAI Partie 4 : Le licenciement et le retour du PDG Sam Altman ENFIN expliqués

OpenAI Saga Part 4: Pembongkaran dan pembongkaran CEO Sam Altman Akhirnya dijelaskan

OpenAI Saga Part 4: CEO サム・アルトマン(Sam Altman)の解雇と解雇

OpenAI Saga ნაწილი 4: აღმასრულებელი დირექტორის სემ ალტმანის გათავისუფლება და გათავისუფლება საბოლოოდ განმარტა

OpenAI Saga Part 4: Ny famotsorana sy ny famotsorana ny CEO Sam Altman farany mazava

OpenAI Saga Część 4: W KOŃCU wyjaśniono zwolnienie i odwołanie CEO Sama Altmana

OpenAI Saga Part 4: The firing & unfiring of CEO Sam Altman இறுதியாக விளக்கப்பட்டது

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OpenAI Saga Bölüm 4: CEO Sam Altman'ın kovulması ve kovulmasının SONUNDA açıklanması

OpenAI Saga Phần 4: Sự sa thải và không sa thải của CEO Sam Altman cuối cùng đã giải thích

From Eisenhower to Trump and Stargate; from roads and highways to AI, data, and sovereign control…. In 1956, the Federal-Aid Highway Act (FAHA) gave rise to the interstate highway system, a revolutionary network of roads designed for the rapid movement of troops and supplies during wartime. Its significance, however, extended far beyond military strategy—it transformed the American economy and way of life. By connecting communities across vast landscapes, it unlocked mobility, catalyzed new industries, and fueled unprecedented economic growth. Today, in 2025, the U.S. faces a similar transformative moment, but this time the highways are digital. The proposed $500 billion Stargate initiative aims to create the infrastructure needed for a sovereign AI and data economy, setting the stage for the next industrial revolution. AI and data are the new information highways—and huge economic drivers. By next year, more data will be generated globally than in the entirety of human history prior. But the real competitive edge—or roadblock—for how companies leverage AI isn’t sheer data volume; it’s the quality, accessibility, and infrastructure of that data. AI has already infused itself into nearly every aspect of our lives, from virtual medical assistants to predictive analytics in manufacturing and financial services. The Office of Management and Budget (OMB) estimates that 5% of the U.S. economy relies on AI today. With AI adoption growing at nearly 30% annually, its contribution could reach 14% GDP—rivaling the economic impact of the healthcare industry—by the time we have our next presidential election in four years. Research by McKinsey underscores the power of AI-driven personalization, which can boost company margins by up to 15%. In healthcare, AI has proven to be faster and more accurate than human doctors in initial diagnostic consultations, according to a study by the British Medical Association. These examples highlight the immense promise of AI—provided the right infrastructure is in place. Infrastructure is the road for AI and data. It must be vast and connected, with the right level of investment. By 2030, 9% of U.S. electricity consumption will be dedicated to data and AI, according to energy researcher EPRI. That’s a jump from nearly negligible levels just a decade ago. To put this in perspective, one in every 11 dollars spent on energy will power AI and data systems. “This AI revolution puts the preceding waves of digital transformation into what will be considered a very small context in five years’ time,” says Michael Gale, Wall Street Journal bestselling author on digital transformation. An enormous infrastructure is required to support the volumes of data needed to create, orchestrate, and adjust AI models. A single query on ChatGPT consumes 10–25 times the energy of a Google search, according to various reports. Now multiply that by all the ways AI is going to touch our lives every day, and we see that the processing power needs and energy consumption will grow exponentially. As noted by Kevin Dallas, CEO of EDB, a leader in Postgres® data and AI, “Investing in sovereign data infrastructure ensures that this growth benefits the U.S. economy while safeguarding data security and privacy.” Enterprises have seen the power and need for AI and data sovereignty. Enterprises are already seeing sovereignty of their data and new AI models as a vital component for differentiation. A 2024 study by EDB found that 67% of large U.S. enterprises adopt hybrid data configurations—combining on-premises and cloud systems—to maximize flexibility and mitigate risks. Meanwhile, 81% of enterprises have embraced open-source databases such as PostgreSQL to accelerate innovation and deliver value at speed. “Enterprises need to take the same Stargate mentality around their AI and data infrastructure. They need engineered solutions that get them on this new AI and data highway. AI needs data right next to it; it requires an integrated, high-performance architecture. That’s why EDB is building this solution with Supermicro and EDB Postgres AI, ensuring that enterprises can deploy AI-plus data infrastructure anywhere, anytime, any way,” says Dallas. Data sovereignty is about being certain that critical assets remain under your control, enabling businesses to innovate without constraints. Open source is the foundation of an agile and secure data economy. Postgres is both scalable and flexible, making it the ideal platform for enterprises to build sovereign AI systems. What history tells us about the Stargate vision Much as the FAHA transformed America’s physical landscape, Stargate has the potential to revolutionize its digital economy. According to the Henry Ford Museum, the original interstate highway system cost $114 billion to build (equivalent to more than $400 billion today), yet Fortune Financial estimates that every dollar invested returned $1.80 in economic benefits. It created new industries, fueled mobility, and even reshaped American culture. The $500 billion Stargate initiative may seem ambitious, but its potential ROI could be monumental. While few—aside from giants like Elon Musk and Nvidia—can afford to build their own AI and data “highway,” everyone stands to benefit from this access and innovation. Stargate offers the whole of the country an opportunity to deliver competitive value, create jobs, and safeguard a critical slice of the AI and data economy. “Building this vision on open source foundations, rather than proprietary systems, ensures value for everyone. For Stargate to succeed, it must serve as an open highway for innovation—a platform for sovereign AI and data that drives both national and enterprise success,” says Dallas. From Eisenhower to Trump and Stargate; from roads and highways to AI, data, and sovereign control…. From Eisenhower to Trump and Stargate; from roads and highways to AI, data, and sovereign control…. In 1956, the Federal-Aid Highway Act (FAHA) gave rise to the interstate highway system, a revolutionary network of roads designed for the rapid movement of troops and supplies during wartime. Its significance, however, extended far beyond military strategy—it transformed the American economy and way of life. By connecting communities across vast landscapes, it unlocked mobility, catalyzed new industries, and fueled unprecedented economic growth. Today, in 2025, the U.S. faces a similar transformative moment, but this time the highways are digital. The proposed $500 billion Stargate initiative aims to create the infrastructure needed for a sovereign AI and data economy, setting the stage for the next industrial revolution. AI and data are the new information highways—and huge economic drivers. AI and data are the new information highways—and huge economic drivers. By next year, more data will be generated globally than in the entirety of human history prior. But the real competitive edge—or roadblock—for how companies leverage AI isn’t sheer data volume; it’s the quality, accessibility, and infrastructure of that data. AI has already infused itself into nearly every aspect of our lives, from virtual medical assistants to predictive analytics in manufacturing and financial services. The Office of Management and Budget (OMB) estimates that 5% of the U.S. economy relies on AI today. With AI adoption growing at nearly 30% annually, its contribution could reach 14% GDP—rivaling the economic impact of the healthcare industry—by the time we have our next presidential election in four years. Research by McKinsey underscores the power of AI-driven personalization, which can boost company margins by up to 15%. In healthcare, AI has proven to be faster and more accurate than human doctors in initial diagnostic consultations, according to a study by the British Medical Association . These examples highlight the immense promise of AI—provided the right infrastructure is in place. McKinsey McKinsey British Medical Association British Medical Association Infrastructure is the road for AI and data. It must be vast and connected, with the right level of investment. Infrastructure is the road for AI and data. It must be vast and connected, with the right level of investment. By 2030, 9% of U.S. electricity consumption will be dedicated to data and AI, according to energy researcher EPRI . That’s a jump from nearly negligible levels just a decade ago. To put this in perspective, one in every 11 dollars spent on energy will power AI and data systems. energy researcher EPRI energy researcher EPRI “This AI revolution puts the preceding waves of digital transformation into what will be considered a very small context in five years’ time,” says Michael Gale, Wall Street Journal b estselling author on digital transformation. Wall Street Journal b An enormous infrastructure is required to support the volumes of data needed to create, orchestrate, and adjust AI models. A single query on ChatGPT consumes 10–25 times the energy of a Google search, according to various reports. Now multiply that by all the ways AI is going to touch our lives every day, and we see that the processing power needs and energy consumption will grow exponentially. As noted by Kevin Dallas, CEO of EDB , a leader in Postgres® data and AI, “Investing in sovereign data infrastructure ensures that this growth benefits the U.S. economy while safeguarding data security and privacy.” EDB EDB Enterprises have seen the power and need for AI and data sovereignty. Enterprises are already seeing sovereignty of their data and new AI models as a vital component for differentiation. A 2024 study by EDB found that 67% of large U.S. enterprises adopt hybrid data configurations—combining on-premises and cloud systems—to maximize flexibility and mitigate risks. Meanwhile, 81% of enterprises have embraced open-source databases such as PostgreSQL to accelerate innovation and deliver value at speed. databases such as PostgreSQL databases such as PostgreSQL “Enterprises need to take the same Stargate mentality around their AI and data infrastructure. They need engineered solutions that get them on this new AI and data highway. AI needs data right next to it; it requires an integrated, high-performance architecture. That’s why EDB is building this solution with Supermicro and EDB Postgres AI, ensuring that enterprises can deploy AI-plus data infrastructure anywhere, anytime, any way,” says Dallas. Data sovereignty is about being certain that critical assets remain under your control, enabling businesses to innovate without constraints. Open source is the foundation of an agile and secure data economy. Postgres is both scalable and flexible, making it the ideal platform for enterprises to build sovereign AI systems. What history tells us about the Stargate vision What history tells us about the Stargate vision Much as the FAHA transformed America’s physical landscape, Stargate has the potential to revolutionize its digital economy. According to the Henry Ford Museum, the original interstate highway system cost $114 billion to build (equivalent to more than $400 billion today), yet Fortune Financial estimates that every dollar invested returned $1.80 in economic benefits. It created new industries, fueled mobility, and even reshaped American culture. $114 billion $114 billion $1.80 $1.80 The $500 billion Stargate initiative may seem ambitious, but its potential ROI could be monumental. While few—aside from giants like Elon Musk and Nvidia—can afford to build their own AI and data “highway,” everyone stands to benefit from this access and innovation. Stargate offers the whole of the country an opportunity to deliver competitive value, create jobs, and safeguard a critical slice of the AI and data economy. “Building this vision on open source foundations, rather than proprietary systems, ensures value for everyone. For Stargate to succeed, it must serve as an open highway for innovation—a platform for sovereign AI and data that drives both national and enterprise success,” says Dallas.

From Route 66 to Route AI: Charting the New Highways of Sovereign Data and Artificial Intelligence

Ruta 66 ukhat Ruta AI ukar puriñkama: Machaq jach’a thakhinak Datos Soberanos ukat Inteligencia Artificial ukanakan uñacht’ayaña

Ад Route 66 да Route AI: пабудова новых магістраляў суверэнных даных і штучнага інтэлекту

De la Ruta 66 a la Ruta AI: trazando las nuevas autopistas de los datos soberanos y la inteligencia artificial

از مسیر 66 تا مسیر هوش مصنوعی: ترسیم بزرگراه های جدید داده های مستقل و هوش مصنوعی

Od ceste 66 do ceste AI: iscrtavanje novih autocesta suverenih podataka i umjetne inteligencije

Dalla Route 66 alla Route AI: tracciare le nuove autostrade dei dati sovrani e dell'intelligenza artificiale

ルート 66 からルート AI へ: 主権データと人工知能の新しいハイウェイを描く

მარშრუტი 66-დან მარშრუტ AI-მდე: სუვერენული მონაცემებისა და ხელოვნური ინტელექტის ახალი მაგისტრალების დიაგრამა

Van Route 66 naar Route AI: de nieuwe snelwegen van soevereine data en kunstmatige intelligentie in kaart brengen

Go tloga go Tsela ya 66 go ya go Tsela ya AI: Go Chata Ditsela tše Mpsha tša Datha ye e Ipušago le Bohlale bja Maitirelo

66-yo'nalishdan AI marshrutigacha: suveren ma'lumotlar va sun'iy intellektning yangi magistrallarini chizish

从 66 号公路到 AI 路线：绘制主权数据和人工智能的新高速公路

1. What We Owe the Future The book was written in 2022 by the Scottish philosopher and ethicist William MacAskill, an associate professor in philosophy at the University of Oxford. New York Times Bestseller. More info: https://whatweowethefuture.com 2. Why Greatness Cannot Be Planned Stanley and Lehman begin with a surprising scientific discovery in artificial intelligence that leads ultimately to the conclusion that the objective obsession has gone too far. More info: https://www.amazon.com/Why-Greatness-Cannot-Planned-Objective/dp/3319155237 3. Zero to One Notes on Startups, or How to Build the Future is a 2014 book by the American entrepreneur and investor Peter Thiel co-written with Blake Masters. It is a condensed and updated version of a highly popular set of online notes taken by Masters for the CS183 class on startups, as taught by Thiel at Stanford University in Spring 2012. More info: https://www.amazon.com/Zero-One-Notes-Startups-Future/dp/0804139296 4. The Foundation Trilogy The Foundation series is a science fiction book series written by American author Isaac Asimov. First published as a series of short stories and novellas in 1942–50, and subsequently in three books in 1951–53, for nearly thirty years the series was widely known as The Foundation Trilogy More info: https://www.amazon.com/Foundation-Trilogy-Isaac-Asimov/dp/0307292061 5. Superintelligence Superintelligence: Paths, Dangers, Strategies is a 2014 book by the philosopher Nick Bostrom. It explores how superintelligence could be created and what its features and motivations might be. More info: https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111 6. Isaacson’s biographies on Einstein and Franklin New York Times bestselling author Walter Isaacson features his definitive biographies: Steve Jobs, Einstein, Benjamin Franklin, and Leonardo da Vinci. More info: https://parents.simonandschuster.com/9781982130428 7. Poor Charlie’s Almanack Poor Charlie’s Almanack is a collection of speeches and best advice given over 30 years talks by Charlie Munger, compiled by Peter D. Kaufman. First published in 2005, it was released in an expanded edition three years later. It was republished in 2023 by Stripe Press, shortly before Munger's death. More info: https://press.stripe.com/poor-charlies-almanack 8. The Comfort Crisis Discover the evolutionary mind and body benefits of living at the edges of your comfort zone and reconnecting with the wild. The Comfort Crisis has become a bestseller and been adopted by Major League Baseball teams, top-ranked NCAA D1 football programs, top-tier universities and law programs, major corporations, tier-one military units, and more. More info: https://eastermichael.com/book/ 9. A Mathematician’s Apology Is a 1940 essay by British mathematician G. H. Hardy which defends the pursuit of mathematics for its own sake. More info: https://www.amazon.com/Mathematicians-Apology-Canto-Classics/dp/110760463X 10. The Anxious Generation How the Great Rewiring of Childhood Is Causing an Epidemic of Mental Illness is a 2024 book by Jonathan Haidt which argues that the spread of smartphones, social media and overprotective parenting have led to a "rewiring" of childhood and a rise in mental illness. Bill Gates review: https://www.gatesnotes.com/the-anxious-generation More info: https://www.anxiousgeneration.com/book 11. Bad Therapy From the author of Irreversible Damage Abigail Shrier, an investigation into a mental health industry that is harming, not healing, American children. New York Times Bestseller More info: https://www.amazon.com/Bad-Therapy-Kids-Arent-Growing-ebook/dp/B0CBYHTV2D 12. The Creative Act From the legendary music producer Rick Rubin, a master at helping people connect with the wellsprings of their creativity, comes a beautifully crafted book many years in the making that offers that same deep wisdom to all of us. New York Times Bestseller More info: https://www.amazon.com/Creative-Act-Way-Being/dp/0593652886 13. Build Tony Fadell led the teams that created the iPod, iPhone and Nest Learning Thermostat and learned enough in 30+ years in Silicon Valley about leadership, design, startups, Apple, Google, decision-making, mentorship, devastating failure and unbelievable success to fill an encyclopedia. New York Times, Wall Street Journal, and USAToday Bestseller for May 2022 More info: https://www.buildc.com/the-book 14. Good Energy Written by Casey Means and Calley Means. A bold new vision for optimizing our health now and in the future. Good Energy is a unifying framework for understanding what is causing symptoms and diseases, and how to feel amazing now and in the future. New York Times Bestseller More info: https://www.caseymeans.com/goodenergy 15. Read Write Own A potent exploration of the power of blockchains to reshape the future of the internet—and how that affects us all—from influential technology entrepreneur and startup investor Chris Dixon. New York Times Bestseller More info: https://readwriteown.com 16. The Life Impossible The remarkable new novel from Matt Haig, author of the #1 New York Times bestseller The Midnight Library, which has sold more than nine million copies worldwide. New York Times Bestseller More info: https://www.goodreads.com/book/show/198281740-the-life-impossible 1. What We Owe the Future 1. What We Owe the Future The book was written in 2022 by the Scottish philosopher and ethicist William MacAskill , an associate professor in philosophy at the University of Oxford. New York Times Bestseller. William MacAskill New York Times Bestseller. More info: https://whatweowethefuture.com More info: https://whatweowethefuture.com 2. Why Greatness Cannot Be Planned 2. Why Greatness Cannot Be Planned Stanley and Lehman begin with a surprising scientific discovery in artificial intelligence that leads ultimately to the conclusion that the objective obsession has gone too far. More info: https://www.amazon.com/Why-Greatness-Cannot-Planned-Objective/dp/3319155237 More info: https://www.amazon.com/Why-Greatness-Cannot-Planned-Objective/dp/3319155237 3. Zero to One 3. Zero to One Notes on Startups, or How to Build the Future is a 2014 book by the American entrepreneur and investor Peter Thiel co-written with Blake Masters . It is a condensed and updated version of a highly popular set of online notes taken by Masters for the CS183 class on startups, as taught by Thiel at Stanford University in Spring 2012. Peter Thiel Blake Masters More info: https://www.amazon.com/Zero-One-Notes-Startups-Future/dp/0804139296 More info: https://www.amazon.com/Zero-One-Notes-Startups-Future/dp/0804139296 4. The Foundation Trilogy 4. The Foundation Trilogy The Foundation series is a science fiction book series written by American author Isaac Asimov . First published as a series of short stories and novellas in 1942–50, and subsequently in three books in 1951–53, for nearly thirty years the series was widely known as The Foundation Trilogy Foundation science fiction Isaac Asimov The Foundation Trilogy The Foundation Trilogy More info: https://www.amazon.com/Foundation-Trilogy-Isaac-Asimov/dp/0307292061 More info: https://www.amazon.com/Foundation-Trilogy-Isaac-Asimov/dp/0307292061 5. Superintelligence 5. Superintelligence Superintelligence: Paths, Dangers, Strategies is a 2014 book by the philosopher Nick Bostrom . It explores how superintelligence could be created and what its features and motivations might be. Nick Bostrom More info: https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111 More info: https://www.amazon.com/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111 6. Isaacson’s biographies on Einstein and Franklin 6. Isaacson’s biographies on Einstein and Franklin New York Times bestselling author Walter Isaacson features his definitive biographies: Steve Jobs, Einstein, Benjamin Franklin, and Leonardo da Vinci. More info: https://parents.simonandschuster.com/9781982130428 More info: https://parents.simonandschuster.com/9781982130428 7. Poor Charlie’s Almanack 7. Poor Charlie’s Almanack Poor Charlie’s Almanack is a collection of speeches and best advice given over 30 years talks by Charlie Munger , compiled by Peter D. Kaufman. First published in 2005, it was released in an expanded edition three years later. It was republished in 2023 by Stripe Press, shortly before Munger's death. Charlie Munger More info: https://press.stripe.com/poor-charlies-almanack More info: https://press.stripe.com/poor-charlies-almanack 8. The Comfort Crisis 8. The Comfort Crisis Discover the evolutionary mind and body benefits of living at the edges of your comfort zone and reconnecting with the wild. The Comfort Crisis has become a bestseller and been adopted by Major League Baseball teams, top-ranked NCAA D1 football programs, top-tier universities and law programs, major corporations, tier-one military units, and more. More info: https://eastermichael.com/book/ More info: https://eastermichael.com/book/ 9. A Mathematician’s Apology 9. A Mathematician’s Apology Is a 1940 essay by British mathematician G. H. Hardy which defends the pursuit of mathematics for its own sake. More info: https://www.amazon.com/Mathematicians-Apology-Canto-Classics/dp/110760463X More info: https://www.amazon.com/Mathematicians-Apology-Canto-Classics/dp/110760463X 10. The Anxious Generation How the Great Rewiring of Childhood Is Causing an Epidemic of Mental Illness is a 2024 book by Jonathan Haidt which argues that the spread of smartphones, social media and overprotective parenting have led to a "rewiring" of childhood and a rise in mental illness. Jonathan Haidt Bill Gates review: https://www.gatesnotes.com/the-anxious-generation Bill Gates review: https://www.gatesnotes.com/the-anxious-generation More info: https://www.anxiousgeneration.com/book More info: https://www.anxiousgeneration.com/book 11. Bad Therapy From the author of Irreversible Damage Abigail Shrier, an investigation into a mental health industry that is harming, not healing, American children. New York Times Bestseller Irreversible Damage New York Times Bestseller More info: https://www.amazon.com/Bad-Therapy-Kids-Arent-Growing-ebook/dp/B0CBYHTV2D More info: https://www.amazon.com/Bad-Therapy-Kids-Arent-Growing-ebook/dp/B0CBYHTV2D 12. The Creative Act 12. The Creative Act From the legendary music producer Rick Rubin, a master at helping people connect with the wellsprings of their creativity, comes a beautifully crafted book many years in the making that offers that same deep wisdom to all of us. New York Times Bestseller New York Times Bestseller More info: https://www.amazon.com/Creative-Act-Way-Being/dp/0593652886 More info: https://www.amazon.com/Creative-Act-Way-Being/dp/0593652886 13. Build 13. Build Tony Fadell led the teams that created the iPod, iPhone and Nest Learning Thermostat and learned enough in 30+ years in Silicon Valley about leadership, design, startups, Apple, Google, decision-making, mentorship, devastating failure and unbelievable success to fill an encyclopedia. New York Times, Wall Street Journal, and USAToday Bestseller for May 2022 New York Times, Wall Street Journal, and USAToday Bestseller for May 2022 More info: https://www.buildc.com/the-book More info: https://www.buildc.com/the-book 14. Good Energy 14. Good Energy Written by Casey Means and Calley Means. A bold new vision for optimizing our health now and in the future. Good Energy is a unifying framework for understanding what is causing symptoms and diseases, and how to feel amazing now and in the future. Good Energy New York Times Bestseller New York Times Bestseller More info: https://www.caseymeans.com/goodenergy More info: https://www.caseymeans.com/goodenergy 15. Read Write Own 15. Read Write Own A potent exploration of the power of blockchains to reshape the future of the internet—and how that affects us all—from influential technology entrepreneur and startup investor Chris Dixon. New York Times Bestseller New York Times Bestseller More info: https://readwriteown.com More info: https://readwriteown.com 16. The Life Impossible 16. The Life Impossible The remarkable new novel from Matt Haig, author of the #1 New York Times bestseller The Midnight Library, which has sold more than nine million copies worldwide. New York Times Bestseller New York Times Bestseller More info: https://www.goodreads.com/book/show/198281740-the-life-impossible More info: https://www.goodreads.com/book/show/198281740-the-life-impossible

Successful Entrepreneurs Recommend These Books to Those Looking to Change Their Mindset

ስኬታማ ስራ ፈጣሪዎች አስተሳሰባቸውን ለመለወጥ ለሚፈልጉ እነዚህን መጽሃፎች ይመክራሉ

Els empresaris d'èxit recomanen aquests llibres a aquells que volen canviar la seva mentalitat

Los emprendedores exitosos recomiendan estos libros a quienes buscan cambiar su mentalidad

יזמים מצליחים ממליצים על ספרים אלה לאלה המעוניינים לשנות את הלך הרוח שלהם

Uspješni poduzetnici preporučuju ove knjige onima koji žele promijeniti svoj način razmišljanja

Antreprenè ki gen siksè yo rekòmande liv sa yo bay moun k ap chèche chanje mantalite yo

成功した起業家が、考え方を変えたい人にこれらの本を勧める

Borakgwebo bao ba Atlegilego ba Kgothaletša Bao ba Lebelelago go Fetoša Kgopolo ya Bona Dipuku Tše

Empreendedores de sucesso recomendam estes livros para aqueles que buscam mudar sua mentalidade

Vanamuzvinabhizinesi Vanobudirira Vanokurudzira Aya Mabhuku kune Avo Vanoda Kuchinja Mafungiro Avo

Framgångsrika entreprenörer rekommenderar dessa böcker till dem som vill ändra sitt tänkesätt

வெற்றிகரமான தொழில்முனைவோர் தங்கள் மனநிலையை மாற்ற விரும்புவோருக்கு இந்த புத்தகங்களை பரிந்துரைக்கின்றனர்

Amazon's $8.45 Billion Gamble Clashes with Broccoli Family's Iron Grip on 007

Amazon ukax 8,45 waranqa waranqa waranqa dolaranakan qullqit anatt’awipax Broccoli Familia ukan Iron Grip ukampiw 007 maran ch’axwawayxi

Хазартът на Amazon за 8,45 милиарда долара се сблъсква с желязната хватка на семейство Броколи върху 007

Amazons 8,45 milliarder dollars hasardspil støder sammen med Broccoli-familiens jerngreb på 007

La apuesta de 8.450 millones de dólares de Amazon choca con el férreo control de la familia Broccoli sobre 007

تقابل قمار 8.45 میلیارد دلاری آمازون با دستگیره آهنین خانواده بروکلی در 007

अमेज़न का 8.45 बिलियन डॉलर का जुआ, ब्रोकली परिवार की 007 पर मजबूत पकड़ से टकराता है

アマゾンの84億5000万ドルの賭けがブロッコリー家の007に対する強固な支配と衝突

Amazon компаниясының 8,45 миллиард долларлық Gamble ойыны 007-де Брокколи отбасының темір тұтқышымен қақтығысты

ການພະນັນຂອງ Amazon $8.45 ຕື້ໂດລາ ມີການປະທະກັນກັບ ຄອບຄອງເຫຼັກຂອງ Broccoli ໃນ 007

د امازون 8.45 ملیارد ډالرو قمار په 007 کې د بروکولي کورنۍ د اوسپنې گرفت سره ټکر وکړ

Gamble Amazon v hodnote 8,45 miliardy dolárov sa v roku 007 zrazil s železnou rukoväťou Broccoli Family

การพนันมูลค่า 8.45 พันล้านเหรียญของ Amazon ขัดแย้งกับการควบคุม 007 ของครอบครัว Broccoli

Table of Links Abstract and 1. Introduction 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 4 The Future of GPT4All Limitations and References Limitations By enabling access to large language models, the GPT4All project also inherits many of the ethical concerns associated with generative models. Principal among these is the concern that unfiltered language models like GPT4All enable malicious users to generate content that could be harmful and dangerous (e.g., instructions on building bioweapons). While we recognize this risk, we also acknowledge the risk of concentrating this technology in the hands of a limited number of increasingly secretive research groups. We believe that the risk of focusing on the benefits of language model technology significantly outweighs the risk of misuse, and hence we prefer to make the technology as widely available as possible. Finally, we realize the challenge in assigning credit for large-scale open source initiatives. We make a first attempt at fair credit assignment by explicitly including the GPT4All open source developers as authors on this work, but recognize that this is insufficient fully characterize everyone involved in the GPT4All effort. Furthermore, we acknowledge the difficulty in citing open source works that do not necessarily have standardized citations, and do our best in this paper to provide URLs to projects whenever possible. We encourage further research in the area of open source credit assignment, and hope to be able to support some of this research ourselves in the future. References Nomic AI. 2023. Atlas. https://atlas.nomic.ai/. Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Merouane Debbah, Etienne Goffinet, Daniel Heslow, Julien Launay, Quentin Malartic, Badreddine Noune, Baptiste Pannier, and Guilherme Penedo. 2023. Falcon-40B: an open large language model with state-of-the-art performance. Yuvanesh Anand, Zach Nussbaum, Brandon Duderstadt, Benjamin Schmidt, and Andriy Mulyar. 2023. Gpt4all: Training an assistant-style chatbot with large scale data distillation from gpt-3.5-turbo. https://github.com/nomic-ai/gpt4all. BBC News. 2023. Chatgpt banned in italy over privacy concerns. BBC News. Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. 2023. Pythia: A suite for analyzing large language models across training and scaling. Harrison Chase. 2022. langchain. https://github. com/langchain-ai/langchain. Mike Conover, Matt Hayes, Ankit Mathur, Xiangrui Meng, Jianwei Xie, Jun Wan, Ali Ghodsi, Patrick Wendell, and Matei Zaharia. 2023a. Hello dolly: Democratizing the magic of chatgpt with open models. Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin. 2023b. Free dolly: Introducing the world’s first truly open instructiontuned llm. Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wallace, Pieter Abbeel, Sergey Levine, and Dawn Song. 2023. Koala: A dialogue model for academic research. Blog post. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. imartinez. 2023. privategpt. https://github.com/ imartinez/privateGPT. Oscar Leo. 2023. GitHub: The Fastest Growing Repositories of All Time. Robert McMillan. 2023. A meta platforms leak put powerful ai in the hands of everyone. The Wall Street Journal. MindsDB. 2023. Mindsdb. https://github.com/ mindsdb/mindsdb. GitHub repository. MosaicML-Team. 2023. Introducing mpt-7b: A new standard for open-source, commercially usable llms. Accessed: 2023-08-07. Nous-Research. 2023a. gpt4-x-vicuna-13b. https://huggingface.co/NousResearch/ gpt4-x-vicuna-13b. Model on Hugging Face. Nous-Research. 2023b. Nous-hermes-13b. https://huggingface.co/NousResearch/ Nous-Hermes-13b. Model on Hugging Face. Nous-Research. 2023c. Nous-hermes-llama-2-7b. https://huggingface.co/NousResearch/ Nous-Hermes-llama-2-7b. Model on Hugging Face. Nous-Research. 2023d. Redmond-puffin-13b. https://huggingface.co/NousResearch/ Redmond-Puffin-13B. Model on Hugging Face. OpenAI. 2023. Gpt-4 technical report. Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush. 2021. Multitask prompted training enables zero-shot task generalization. Stability-AI. 2023. Stablelm. https://github.com/ Stability-AI/StableLM. GitHub repository. StanGirard. 2023. quivr. https://github.com/ StanGirard/quivr. GitHub repository. Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford alpaca: An instruction-following llama model. https:// github.com/tatsu-lab/stanford_alpaca. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. The Verge. 2023. Meta’s powerful ai language model has leaked online — what happens now? The Verge. James Vincent. 2023. As an ai generated language model: The phrase that shows how ai is polluting the web. The Verge. Ben Wang and Aran Komatsuzaki. 2021. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/kingoflolz/ mesh-transformer-jax. Eric J. Wang. 2023. alpaca-lora. https://github. com/tloen/alpaca-lora. GitHub repository. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. Self-instruct: Aligning language models with self-generated instructions. Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. 2023. Wizardlm: Empowering large language models to follow complex instructions. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai;
(2) Zach Nussbaum, Nomic AI, zach@nomic.ai;
(3) Adam Treat, Nomic AI, adam@nomic.ai;
(4) Aaron Miller, Nomic AI, aaron@nomic.ai;
(5) Richard Guo, Nomic AI, richard@nomic.ai;
(6) Ben Schmidt, Nomic AI, ben@nomic.ai;
(7) GPT4All Community, Planet Earth;
(8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship;
(9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 The Original GPT4All Model 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 3.3 The Current State of GPT4All 4 The Future of GPT4All 4 The Future of GPT4All Limitations and References Limitations and References Limitations By enabling access to large language models, the GPT4All project also inherits many of the ethical concerns associated with generative models. Principal among these is the concern that unfiltered language models like GPT4All enable malicious users to generate content that could be harmful and dangerous (e.g., instructions on building bioweapons). While we recognize this risk, we also acknowledge the risk of concentrating this technology in the hands of a limited number of increasingly secretive research groups. We believe that the risk of focusing on the benefits of language model technology significantly outweighs the risk of misuse, and hence we prefer to make the technology as widely available as possible. Finally, we realize the challenge in assigning credit for large-scale open source initiatives. We make a first attempt at fair credit assignment by explicitly including the GPT4All open source developers as authors on this work, but recognize that this is insufficient fully characterize everyone involved in the GPT4All effort. Furthermore, we acknowledge the difficulty in citing open source works that do not necessarily have standardized citations, and do our best in this paper to provide URLs to projects whenever possible. We encourage further research in the area of open source credit assignment, and hope to be able to support some of this research ourselves in the future. References Nomic AI. 2023. Atlas. https://atlas.nomic.ai/. Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Merouane Debbah, Etienne Goffinet, Daniel Heslow, Julien Launay, Quentin Malartic, Badreddine Noune, Baptiste Pannier, and Guilherme Penedo. 2023. Falcon-40B: an open large language model with state-of-the-art performance. Yuvanesh Anand, Zach Nussbaum, Brandon Duderstadt, Benjamin Schmidt, and Andriy Mulyar. 2023. Gpt4all: Training an assistant-style chatbot with large scale data distillation from gpt-3.5-turbo. https://github.com/nomic-ai/gpt4all. BBC News. 2023. Chatgpt banned in italy over privacy concerns. BBC News. Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. 2023. Pythia: A suite for analyzing large language models across training and scaling. Harrison Chase. 2022. langchain. https://github. com/langchain-ai/langchain. Mike Conover, Matt Hayes, Ankit Mathur, Xiangrui Meng, Jianwei Xie, Jun Wan, Ali Ghodsi, Patrick Wendell, and Matei Zaharia. 2023a. Hello dolly: Democratizing the magic of chatgpt with open models. Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin. 2023b. Free dolly: Introducing the world’s first truly open instructiontuned llm. Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wallace, Pieter Abbeel, Sergey Levine, and Dawn Song. 2023. Koala: A dialogue model for academic research. Blog post. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. imartinez. 2023. privategpt. https://github.com/ imartinez/privateGPT. Oscar Leo. 2023. GitHub: The Fastest Growing Repositories of All Time. Robert McMillan. 2023. A meta platforms leak put powerful ai in the hands of everyone. The Wall Street Journal. MindsDB. 2023. Mindsdb. https://github.com/ mindsdb/mindsdb. GitHub repository. MosaicML-Team. 2023. Introducing mpt-7b: A new standard for open-source, commercially usable llms. Accessed: 2023-08-07. Nous-Research. 2023a. gpt4-x-vicuna-13b. https://huggingface.co/NousResearch/ gpt4-x-vicuna-13b. Model on Hugging Face. Nous-Research. 2023b. Nous-hermes-13b. https://huggingface.co/NousResearch/ Nous-Hermes-13b. Model on Hugging Face. Nous-Research. 2023c. Nous-hermes-llama-2-7b. https://huggingface.co/NousResearch/ Nous-Hermes-llama-2-7b. Model on Hugging Face. Nous-Research. 2023d. Redmond-puffin-13b. https://huggingface.co/NousResearch/ Redmond-Puffin-13B. Model on Hugging Face. OpenAI. 2023. Gpt-4 technical report. Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush. 2021. Multitask prompted training enables zero-shot task generalization. Stability-AI. 2023. Stablelm. https://github.com/ Stability-AI/StableLM. GitHub repository. StanGirard. 2023. quivr. https://github.com/ StanGirard/quivr. GitHub repository. Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford alpaca: An instruction-following llama model. https:// github.com/tatsu-lab/stanford_alpaca. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. The Verge. 2023. Meta’s powerful ai language model has leaked online — what happens now? The Verge. James Vincent. 2023. As an ai generated language model: The phrase that shows how ai is polluting the web. The Verge. Ben Wang and Aran Komatsuzaki. 2021. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/kingoflolz/ mesh-transformer-jax. Eric J. Wang. 2023. alpaca-lora. https://github. com/tloen/alpaca-lora. GitHub repository. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. Self-instruct: Aligning language models with self-generated instructions. Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. 2023. Wizardlm: Empowering large language models to follow complex instructions. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Authors: Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship.

GPT4All: Limitations and References

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

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

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

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

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

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

USDTドラマ：テザー、WSJの米国調査報道を「無責任な報道」と非難

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

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

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

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

ดราม่า USDT: Tether กล่าวหา WSJ ว่าการสอบสวนของสหรัฐฯ เป็นการ “รายงานข่าวที่ไม่รับผิดชอบ”

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

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

How to Get Around Paywalls on Major Websites

প্রধান ওয়েবসাইটগুলিতে পেওয়ালের চারপাশে কীভাবে পাবেন

So umgehen Sie Paywalls auf großen Websites

Cómo evitar los muros de pago en los principales sitios web

Comment contourner les paywalls sur les principaux sites Web

प्रमुख वेबसाइटों पर पेवॉल से कैसे बचें

主要ウェブサイトのペイウォールを回避する方法

주요 웹사이트의 페이월을 우회하는 방법

Como contornar os Paywalls nos principais sites

Как обойти платный доступ на крупных веб-сайтах

Büyük Web Sitelerindeki Ödeme Duvarlarını Nasıl Aşarsınız?

Cách vượt qua Paywall trên các trang web lớn

如何绕过主要网站的付费墙

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

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

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

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