• Aspora gets $50M from Sequioa to build remittance and banking solutions for Indian diaspora

    India has been one of the top recipients of remittances in the world for more than a decade. Inward remittances jumped from billion in 2010-11 to billion in 2023-24, according to data from the country’s central bank. The bank projects that figure will reach billion in 2029.
    This means there is an increasing market for digitalized banking experiences for non-resident Indians, ranging from remittances to investing in different assets back home.
    Asporais trying to build a verticalized financial experience for the Indian diaspora by keeping convenience at the center. While a lot of financial products are in its future roadmap, the company currently focuses largely on remittances.
    “While multiple financial products for non-resident Indians exist, they don’t know about them because there is no digital journey for them. They possibly use the same banking app as residents, which makes it harder for them to discover products catered towards them,” Garg said.
    In the last year, the company has grown the volume of remittances by 6x — from million to billion in yearly volume processed.
    With this growth, the company has attracted a lot of investor interest. It raised million in Series A funding last December — which was previously unreported — led by Sequoia with participation from Greylock, Y Combinator, Hummingbird Ventures, and Global Founders Capital. The round pegged the company’s valuation at million. In the four months following, the company tripled its transaction volume, prompting investors to put in more money.
    The company announced today it has raised million in Series B funding, co-led by Sequoia and Greylock, with Hummingbird, Quantum Light Ventures, and Y Combinator also contributing to the round. The startup said this round values the company at million. The startup has raised over million in funding to date.

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    After pivoting from being Pipe.com for India, the company started by offering remittance for NRIs in the U.K. in 2023 and has expanded its presence in other markets, including Europe and the United Arab Emirates. It charges a flat fee for money transfer and offers a competitive rate. Now it also allows customers to invest in mutual funds in India. The startup markets its exchange rates as “Google rate” as customers often search for currency conversion rates, even though they may not reflect live rates.
    The startup is also set to launch in the U.S., one of the biggest remittance corridors to India, next month. Plus, it plans to open up shop in Canada, Singapore, and Australia by the fourth quarter of this year.
    Garg, who grew up in the UAE, said that remittances are just the start, and the company wants to build out more financial tools for NRIs.
    “We want to use remittances as a wedge and build all the financial solutions that the diaspora needs, including banking, investing, insurance, lending in the home country, and products that help them take care of their parents,” he told TechCrunch.
    He added that a large chunk of money that NRIs send home is for wealth creation rather than family sustenance. The startup said that 80% of its users are sending money to their own accounts back home.
    In the next few months, the company is launching a few products to offer more services. This month, it plans to launch a bill payment platform to let users pay for services like rent and utilities. Next month, it plans to launch fixed deposit accounts for non-resident Indians that allow them to park money in foreign currency. By the end of the year, it plans to launch a full-stack banking account for NRIs that typically takes days for users to open. While these accounts can help the diaspora maintain their tax status in India, a lot of people use a family member’s account because of the cumbersome process, and Aspora wants to simplify this.
    Apart from banking, the company also plans to launch a product that would help NRIs take care of their parents back home by offering regular medical checkups, emergency care coverage, and concierge services for other assistance.
    Besides global competitors like Remittly and Wise, the company also has India-based rivals like Abound, which was spun off from Times Internet.
    Sequoia’s Luciana Lixandru is confident that Aspora’s execution speed and verticalized solution will give it an edge.
    “Speed of execution, for me, is one of the main indicators in the early days of the future success of a company,” she told TechCrunch over a call. “Aspora moves fast, but it is also very deliberate in building corridor by corridor, which is very important in financial services.”
    #aspora #gets #50m #sequioa #build
    Aspora gets $50M from Sequioa to build remittance and banking solutions for Indian diaspora
    India has been one of the top recipients of remittances in the world for more than a decade. Inward remittances jumped from billion in 2010-11 to billion in 2023-24, according to data from the country’s central bank. The bank projects that figure will reach billion in 2029. This means there is an increasing market for digitalized banking experiences for non-resident Indians, ranging from remittances to investing in different assets back home. Asporais trying to build a verticalized financial experience for the Indian diaspora by keeping convenience at the center. While a lot of financial products are in its future roadmap, the company currently focuses largely on remittances. “While multiple financial products for non-resident Indians exist, they don’t know about them because there is no digital journey for them. They possibly use the same banking app as residents, which makes it harder for them to discover products catered towards them,” Garg said. In the last year, the company has grown the volume of remittances by 6x — from million to billion in yearly volume processed. With this growth, the company has attracted a lot of investor interest. It raised million in Series A funding last December — which was previously unreported — led by Sequoia with participation from Greylock, Y Combinator, Hummingbird Ventures, and Global Founders Capital. The round pegged the company’s valuation at million. In the four months following, the company tripled its transaction volume, prompting investors to put in more money. The company announced today it has raised million in Series B funding, co-led by Sequoia and Greylock, with Hummingbird, Quantum Light Ventures, and Y Combinator also contributing to the round. The startup said this round values the company at million. The startup has raised over million in funding to date. Techcrunch event + on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. + on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Boston, MA | July 15 REGISTER NOW After pivoting from being Pipe.com for India, the company started by offering remittance for NRIs in the U.K. in 2023 and has expanded its presence in other markets, including Europe and the United Arab Emirates. It charges a flat fee for money transfer and offers a competitive rate. Now it also allows customers to invest in mutual funds in India. The startup markets its exchange rates as “Google rate” as customers often search for currency conversion rates, even though they may not reflect live rates. The startup is also set to launch in the U.S., one of the biggest remittance corridors to India, next month. Plus, it plans to open up shop in Canada, Singapore, and Australia by the fourth quarter of this year. Garg, who grew up in the UAE, said that remittances are just the start, and the company wants to build out more financial tools for NRIs. “We want to use remittances as a wedge and build all the financial solutions that the diaspora needs, including banking, investing, insurance, lending in the home country, and products that help them take care of their parents,” he told TechCrunch. He added that a large chunk of money that NRIs send home is for wealth creation rather than family sustenance. The startup said that 80% of its users are sending money to their own accounts back home. In the next few months, the company is launching a few products to offer more services. This month, it plans to launch a bill payment platform to let users pay for services like rent and utilities. Next month, it plans to launch fixed deposit accounts for non-resident Indians that allow them to park money in foreign currency. By the end of the year, it plans to launch a full-stack banking account for NRIs that typically takes days for users to open. While these accounts can help the diaspora maintain their tax status in India, a lot of people use a family member’s account because of the cumbersome process, and Aspora wants to simplify this. Apart from banking, the company also plans to launch a product that would help NRIs take care of their parents back home by offering regular medical checkups, emergency care coverage, and concierge services for other assistance. Besides global competitors like Remittly and Wise, the company also has India-based rivals like Abound, which was spun off from Times Internet. Sequoia’s Luciana Lixandru is confident that Aspora’s execution speed and verticalized solution will give it an edge. “Speed of execution, for me, is one of the main indicators in the early days of the future success of a company,” she told TechCrunch over a call. “Aspora moves fast, but it is also very deliberate in building corridor by corridor, which is very important in financial services.” #aspora #gets #50m #sequioa #build
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    Aspora gets $50M from Sequioa to build remittance and banking solutions for Indian diaspora
    India has been one of the top recipients of remittances in the world for more than a decade. Inward remittances jumped from $55.6 billion in 2010-11 to $118.7 billion in 2023-24, according to data from the country’s central bank. The bank projects that figure will reach $160 billion in 2029. This means there is an increasing market for digitalized banking experiences for non-resident Indians(NRIs), ranging from remittances to investing in different assets back home. Aspora (formerly Vance) is trying to build a verticalized financial experience for the Indian diaspora by keeping convenience at the center. While a lot of financial products are in its future roadmap, the company currently focuses largely on remittances. “While multiple financial products for non-resident Indians exist, they don’t know about them because there is no digital journey for them. They possibly use the same banking app as residents, which makes it harder for them to discover products catered towards them,” Garg said. In the last year, the company has grown the volume of remittances by 6x — from $400 million to $2 billion in yearly volume processed. With this growth, the company has attracted a lot of investor interest. It raised $35 million in Series A funding last December — which was previously unreported — led by Sequoia with participation from Greylock, Y Combinator, Hummingbird Ventures, and Global Founders Capital. The round pegged the company’s valuation at $150 million. In the four months following, the company tripled its transaction volume, prompting investors to put in more money. The company announced today it has raised $50 million in Series B funding, co-led by Sequoia and Greylock, with Hummingbird, Quantum Light Ventures, and Y Combinator also contributing to the round. The startup said this round values the company at $500 million. The startup has raised over $99 million in funding to date. Techcrunch event Save $200+ on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Save $200+ on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Boston, MA | July 15 REGISTER NOW After pivoting from being Pipe.com for India, the company started by offering remittance for NRIs in the U.K. in 2023 and has expanded its presence in other markets, including Europe and the United Arab Emirates. It charges a flat fee for money transfer and offers a competitive rate. Now it also allows customers to invest in mutual funds in India. The startup markets its exchange rates as “Google rate” as customers often search for currency conversion rates, even though they may not reflect live rates. The startup is also set to launch in the U.S., one of the biggest remittance corridors to India, next month. Plus, it plans to open up shop in Canada, Singapore, and Australia by the fourth quarter of this year. Garg, who grew up in the UAE, said that remittances are just the start, and the company wants to build out more financial tools for NRIs. “We want to use remittances as a wedge and build all the financial solutions that the diaspora needs, including banking, investing, insurance, lending in the home country, and products that help them take care of their parents,” he told TechCrunch. He added that a large chunk of money that NRIs send home is for wealth creation rather than family sustenance. The startup said that 80% of its users are sending money to their own accounts back home. In the next few months, the company is launching a few products to offer more services. This month, it plans to launch a bill payment platform to let users pay for services like rent and utilities. Next month, it plans to launch fixed deposit accounts for non-resident Indians that allow them to park money in foreign currency. By the end of the year, it plans to launch a full-stack banking account for NRIs that typically takes days for users to open. While these accounts can help the diaspora maintain their tax status in India, a lot of people use a family member’s account because of the cumbersome process, and Aspora wants to simplify this. Apart from banking, the company also plans to launch a product that would help NRIs take care of their parents back home by offering regular medical checkups, emergency care coverage, and concierge services for other assistance. Besides global competitors like Remittly and Wise, the company also has India-based rivals like Abound, which was spun off from Times Internet. Sequoia’s Luciana Lixandru is confident that Aspora’s execution speed and verticalized solution will give it an edge. “Speed of execution, for me, is one of the main indicators in the early days of the future success of a company,” she told TechCrunch over a call. “Aspora moves fast, but it is also very deliberate in building corridor by corridor, which is very important in financial services.”
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  • Here's how big business leaders are reacting to the Trump-Musk breakup

    Business leaders are weighing in on the Elon Musk and Donald Trump breakup.

    Kevin Dietsch/Getty Images

    2025-06-06T05:49:58Z

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    The friendship between Elon Musk and Donald Trump publicly unravelled on Thursday.
    It all started when Musk criticized Trump's "Big Beautiful Bill."
    Here's what business leaders like Mark Cuban and Bill Ackman have to say about the breakup.

    Amid a dramatic falling out between Donald Trump and his "first buddy," Elon Musk, some of the business world's most influential voices are weighing in.The relationship between the president and his once-close ally imploded on Thursday as they clashed publicly over Trump's "Big Beautiful Bill."Musk, who stepped down from his role at DOGE in May, took to X to criticize the bill, calling it the "Debt Slavery Bill" and the "Big Ugly Spending Bill."In response, Trump fired back at Musk during a White House event. He also defended the bill on Truth Social, while threatening to cancel Musk's government contracts.Musk saw his net worth fall by billion on Thursday, per the Bloomberg Billionaires Index. Tesla shares were also down by more than 14%.Here's what several business leaders have to say about the row.Mark Cuban

    Mark Cuban appeared to support Elon Musk's suggestion to start a new political party.

    Richard Rodriguez/Getty Images

    Amid his feud with Trump, Musk proposed creating a new political party for "the middle" in a poll on X.Mark Cuban appeared to endorse the idea, quoting Musk's post and replying with three check marks.
    The former "Shark Tank" star previously said he's "not a fan of either party," but would run as a Republican if he wanted to join politics.Bill Ackman

    Bill Ackman called on Musk and Trump to reconcile.

    Brian Snyder/Reuters

    Hedge fund billionaire Bill Ackman voiced his support for both Trump and Musk on X, calling on the two to put aside their differences and "make peace for the benefit of our country."Ackman, who had endorsed Trump for his 2024 presidential bid, wrote: "We are much stronger together than apart." "You're not wrong," Musk responded.Paul Graham

    Paul Graham also took to X to share his thoughts on the feud.

    Joe Corrigan/Getty Images for AOL

    Paul Graham, cofounder of the startup accelerator Y Combinator, also weighed in on the public feud between the president and the Tesla CEO.
    "A lot of people seem to be treating this as if it were just a beef. But the underlying allegation is a very serious one. If it's true, Trump is surely going to have to resign," he wrote in a post on X.Graham did not specify what allegation he was referring to.Hours before Graham made his post, Musk went on X and accused Trump of withholding information about Jeffrey Epstein."Time to drop the really big bomb: @realDonaldTrump is in the Epstein files. That is the real reason they have not been made public. Have a nice day, DJT!" Musk wrote on X.Graham told Musk in February that he should work with the government "carefully" because it's not "just a company."A representative for Graham did not immediately respond to a request for comment from Business Insider.
    #here039s #how #big #business #leaders
    Here's how big business leaders are reacting to the Trump-Musk breakup
    Business leaders are weighing in on the Elon Musk and Donald Trump breakup. Kevin Dietsch/Getty Images 2025-06-06T05:49:58Z d Read in app This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now. Have an account? The friendship between Elon Musk and Donald Trump publicly unravelled on Thursday. It all started when Musk criticized Trump's "Big Beautiful Bill." Here's what business leaders like Mark Cuban and Bill Ackman have to say about the breakup. Amid a dramatic falling out between Donald Trump and his "first buddy," Elon Musk, some of the business world's most influential voices are weighing in.The relationship between the president and his once-close ally imploded on Thursday as they clashed publicly over Trump's "Big Beautiful Bill."Musk, who stepped down from his role at DOGE in May, took to X to criticize the bill, calling it the "Debt Slavery Bill" and the "Big Ugly Spending Bill."In response, Trump fired back at Musk during a White House event. He also defended the bill on Truth Social, while threatening to cancel Musk's government contracts.Musk saw his net worth fall by billion on Thursday, per the Bloomberg Billionaires Index. Tesla shares were also down by more than 14%.Here's what several business leaders have to say about the row.Mark Cuban Mark Cuban appeared to support Elon Musk's suggestion to start a new political party. Richard Rodriguez/Getty Images Amid his feud with Trump, Musk proposed creating a new political party for "the middle" in a poll on X.Mark Cuban appeared to endorse the idea, quoting Musk's post and replying with three check marks. The former "Shark Tank" star previously said he's "not a fan of either party," but would run as a Republican if he wanted to join politics.Bill Ackman Bill Ackman called on Musk and Trump to reconcile. Brian Snyder/Reuters Hedge fund billionaire Bill Ackman voiced his support for both Trump and Musk on X, calling on the two to put aside their differences and "make peace for the benefit of our country."Ackman, who had endorsed Trump for his 2024 presidential bid, wrote: "We are much stronger together than apart." "You're not wrong," Musk responded.Paul Graham Paul Graham also took to X to share his thoughts on the feud. Joe Corrigan/Getty Images for AOL Paul Graham, cofounder of the startup accelerator Y Combinator, also weighed in on the public feud between the president and the Tesla CEO. "A lot of people seem to be treating this as if it were just a beef. But the underlying allegation is a very serious one. If it's true, Trump is surely going to have to resign," he wrote in a post on X.Graham did not specify what allegation he was referring to.Hours before Graham made his post, Musk went on X and accused Trump of withholding information about Jeffrey Epstein."Time to drop the really big bomb: @realDonaldTrump is in the Epstein files. That is the real reason they have not been made public. Have a nice day, DJT!" Musk wrote on X.Graham told Musk in February that he should work with the government "carefully" because it's not "just a company."A representative for Graham did not immediately respond to a request for comment from Business Insider. #here039s #how #big #business #leaders
    WWW.BUSINESSINSIDER.COM
    Here's how big business leaders are reacting to the Trump-Musk breakup
    Business leaders are weighing in on the Elon Musk and Donald Trump breakup. Kevin Dietsch/Getty Images 2025-06-06T05:49:58Z Save Saved Read in app This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now. Have an account? The friendship between Elon Musk and Donald Trump publicly unravelled on Thursday. It all started when Musk criticized Trump's "Big Beautiful Bill." Here's what business leaders like Mark Cuban and Bill Ackman have to say about the breakup. Amid a dramatic falling out between Donald Trump and his "first buddy," Elon Musk, some of the business world's most influential voices are weighing in.The relationship between the president and his once-close ally imploded on Thursday as they clashed publicly over Trump's "Big Beautiful Bill."Musk, who stepped down from his role at DOGE in May, took to X to criticize the bill, calling it the "Debt Slavery Bill" and the "Big Ugly Spending Bill."In response, Trump fired back at Musk during a White House event. He also defended the bill on Truth Social, while threatening to cancel Musk's government contracts.Musk saw his net worth fall by $34 billion on Thursday, per the Bloomberg Billionaires Index. Tesla shares were also down by more than 14%.Here's what several business leaders have to say about the row.Mark Cuban Mark Cuban appeared to support Elon Musk's suggestion to start a new political party. Richard Rodriguez/Getty Images Amid his feud with Trump, Musk proposed creating a new political party for "the middle" in a poll on X.Mark Cuban appeared to endorse the idea, quoting Musk's post and replying with three check marks. The former "Shark Tank" star previously said he's "not a fan of either party," but would run as a Republican if he wanted to join politics.Bill Ackman Bill Ackman called on Musk and Trump to reconcile. Brian Snyder/Reuters Hedge fund billionaire Bill Ackman voiced his support for both Trump and Musk on X, calling on the two to put aside their differences and "make peace for the benefit of our country."Ackman, who had endorsed Trump for his 2024 presidential bid, wrote: "We are much stronger together than apart." "You're not wrong," Musk responded.Paul Graham Paul Graham also took to X to share his thoughts on the feud. Joe Corrigan/Getty Images for AOL Paul Graham, cofounder of the startup accelerator Y Combinator, also weighed in on the public feud between the president and the Tesla CEO. "A lot of people seem to be treating this as if it were just a beef. But the underlying allegation is a very serious one. If it's true, Trump is surely going to have to resign," he wrote in a post on X.Graham did not specify what allegation he was referring to.Hours before Graham made his post, Musk went on X and accused Trump of withholding information about Jeffrey Epstein."Time to drop the really big bomb: @realDonaldTrump is in the Epstein files. That is the real reason they have not been made public. Have a nice day, DJT!" Musk wrote on X.Graham told Musk in February that he should work with the government "carefully" because it's not "just a company."A representative for Graham did not immediately respond to a request for comment from Business Insider.
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  • Sam Altman biographer Keach Hagey explains why the OpenAI CEO was ‘born for this moment’

    In “The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future,” Wall Street Journal reporter Keach Hagey examines our AI-obsessed moment through one of its key figures — Sam Altman, co-founder and CEO of OpenAI.
    Hagey begins with Altman’s Midwest childhood, then takes readers through his career at startup Loopt, accelerator Y Combinator, and now at OpenAI. She also sheds new light on the dramatic few days when Altman was fired, then quickly reinstated, as OpenAI’s CEO.
    Looking back at what OpenAI employees now call “the Blip,” Hagey said the failed attempt to oust Altman revealed that OpenAI’s complex structure — with a for-profit company controlled by a nonprofit board — is “not stable.” And with OpenAI largely backing down from plans to let the for-profit side take control, Hagey predicted that this “fundamentally unstable arrangement” will “continue to give investors pause.”
    Does that mean OpenAI could struggle to raise the funds it needs to keep going? Hagey replied that it could “absolutely” be an issue.
    “My research into Sam suggests that he might well be up to that challenge,” she said. “But success is not guaranteed.”
    In addition, Hagey’s biographyexamines Altman’s politics, which she described as “pretty traditionally progressive” — making it a bit surprising that he’s struck massive infrastructure deals with the backing of the Trump administration.
    “But this is one area where, in some ways, I feel like Sam Altman has been born for this moment, because he is a deal maker and Trump is a deal maker,” Hagey said. “Trump respects nothing so much as a big deal with a big price tag on it, and that is what Sam Altman is really great at.”

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    In an interview with TechCrunch, Hagey also discussed Altman’s response to the book, his trustworthiness, and the AI “hype universe.”
    This interview has been edited for length and clarity. 
    You open the book by acknowledging some of the reservations that Sam Altman had about the project —  this idea that we tend to focus too much on individuals rather than organizations or broad movements, and also that it’s way too early to assess the impact of OpenAI. Did you share those concerns?
    Well, I don’t really share them, because this was a biography. This project was to look at a person, not an organization. And I also think that Sam Altman has set himself up in a way where it does matter what kind of moral choices he has made and what his moral formation has been, because the broad project of AI is really a moral project. That is the basis of OpenAI’s existence. So I think these are fair questions to ask about a person, not just an organization.
    As far as whether it’s too soon, I mean, sure, it’s definitelyassess the entire impact of AI. But it’s been an extraordinary story for OpenAI — just so far, it’s already changed the stock market, it has changed the entire narrative of business. I’m a business journalist. We do nothing but talk about AI, all day long, every day. So in that way, I don’t think it’s too early.
    And despite those reservations, Altman did cooperate with you. Can you say more about what your relationship with him was like during the process of researching the book?
    Well, he was definitely not happy when he was informed about the book’s existence. And there was a long period of negotiation, frankly. In the beginning, I figured I was going to write this book without his help — what we call, in the business, a write-around profile. I’ve done plenty of those over my career, and I figured this would just be one more.
    Over time, as I made more and more calls, he opened up a little bit. Andhe was generous to sit down with me several times for long interviews and share his thoughts with me.
    Has he responded to the finished book at all?
    No. He did tweet about the project, about his decision to participate with it, but he was very clear that he was never going to read it. It’s the same way that I don’t like to watch my TV appearances or podcasts that I’m on.
    In the book, he’s described as this emblematic Silicon Valley figure. What do you think are the key characteristics that make him representative of the Valley and the tech industry?
    In the beginning, I think it was that he was young. The Valley really glorifies youth, and he was 19 years old when he started his first startup. You see him going into these meetings with people twice his age, doing deals with telecom operators for his first startup, and no one could get over that this kid was so smart.
    The other is that he is a once-in-a-generation fundraising talent, and that’s really about being a storyteller. I don’t think it’s an accident that you have essentially a salesman and a fundraiser at the top of the most important AI company today,
    That ties into one of the questions that runs through the book — this question about Altman’s trustworthiness. Can you say more about the concerns people seem to have about that? To what extent is he a trustworthy figure? 
    Well, he’s a salesman, so he’s really excellent at getting in a room and convincing people that he can see the future and that he has something in common with them. He gets people to share his vision, which is a rare talent.
    There are people who’ve watched that happen a bunch of times, who think, “Okay, what he says does not always map to reality,” and have, over time, lost trust in him. This happened both at his first startup and very famously at OpenAI, as well as at Y Combinator. So it is a pattern, but I think it’s a typical critique of people who have the salesman skill set.
    So it’s not necessarily that he’s particularly untrustworthy, but it’s part-and-parcel of being a salesman leading these important companies.
    I mean, there also are management issues that are detailed in the book, where he is not great at dealing with conflict, so he’ll basically tell people what they want to hear. That causes a lot of sturm-und-drang in the management ranks, and it’s a pattern. Something like that happened at Loopt, where the executives asked the board to replace him as CEO. And you saw it happen at OpenAI as well.
    You’ve touched on Altman’s firing, which was also covered in a book excerpt that was published in the Wall Street Journal. One of the striking things to me, looking back at it, was just how complicated everything was — all the different factions within the company, all the people who seemed pro-Altman one day and then anti-Altman the next. When you pull back from the details, what do you think is the bigger significance of that incident?
    The very big picture is that the nonprofit governance structure is not stable. You can’t really take investment from the likes of Microsoft and a bunch of other investors and then give them absolutely no say whatsoever in the governance of the company.
    That’s what they have tried to do, but I think what we saw in that firing is how power actually works in the world. When you have stakeholders, even if there’s a piece of paper that says they have no rights, they still have power. And when it became clear that everyone in the company was going to go to Microsoft if they didn’t reinstate Sam Altman, they reinstated Sam Altman.
    In the book, you take the story up to maybe the end of 2024. There have been all these developments since then, which you’ve continued to report on, including this announcement that actually, they’re not fully converting to a for-profit. How do you think that’s going to affect OpenAI going forward? 
    It’s going to make it harder for them to raise money, because they basically had to do an about-face. I know that the new structure going forward of the public benefit corporation is not exactly the same as the current structure of the for-profit — it is a little bit more investor friendly, it does clarify some of those things.
    But overall, what you have is a nonprofit board that controls a for-profit company, and that fundamentally unstable arrangement is what led to the so-called Blip. And I think you would continue to give investors pause, going forward, if they are going to have so little control over their investment.
    Obviously, OpenAI is still such a capital intensive business. If they have challenges raising more money, is that an existential question for the company?
    It absolutely could be. My research into Sam suggests that he might well be up to that challenge. But success is not guaranteed.
    Like you said, there’s a dual perspective in the book that’s partly about who Sam is, and partly about what that says about where AI is going from here. How did that research into his particular story shape the way you now look at these broader debates about AI and society?
    I went down a rabbit hole in the beginning of the book,into Sam’s father, Jerry Altman, in part because I thought it was striking how he’d been written out of basically every other thing that had ever been written about Sam Altman. What I found in this research was a very idealistic man who was, from youth, very interested in these public-private partnerships and the power of the government to set policy. He ended up having an impact on the way that affordable housing is still financed to this day.
    And when I traced Sam’s development, I saw that he has long believed that the government should really be the one that is funding and guiding AI research. In the early days of OpenAI, they went and tried to get the government to invest, as he’s publicly said, and it didn’t work out. But he looks back to these great mid-20th century labs like Xerox PARC and Bell Labs, which are private, but there was a ton of government money running through and supporting that ecosystem. And he says, “That’s the right way to do it.”
    Now I am watching daily as it seems like the United States is summoning the forces of state capitalism to get behind Sam Altman’s project to build these data centers, both in the United States and now there was just one last week announced in Abu Dhabi. This is a vision he has had for a very, very long time.
    My sense of the vision, as he presented it earlier, was one where, on the one hand, the government is funding these things and building this infrastructure, and on the other hand, the government is also regulating and guiding AI development for safety purposes. And it now seems like the path being pursued is one where they’re backing away from the safety side and doubling down on the government investment side.
    Absolutely. Isn’t it fascinating? 
    You talk about Sam as a political figure, as someone who’s had political ambitions at different times, but also somebody who has what are in many ways traditionally liberal political views while being friends with folks like — at least early on — Elon Musk and Peter Thiel. And he’s done a very good job of navigating the Trump administration. What do you think his politics are right now?
    I’m not sure his actual politics have changed, they are pretty traditionally progressive politics. Not completely — he’s been critical about things like cancel culture, but in general, he thinks the government is there to take tax revenue and solve problems.
    His success in the Trump administration has been fascinating because he has been able to find their one area of overlap, which is the desire to build a lot of data centers, and just double down on that and not talk about any other stuff. But this is one area where, in some ways, I feel like Sam Altman has been born for this moment, because he is a deal maker and Trump is a deal maker. Trump respects nothing so much as a big deal with a big price tag on it, and that is what Sam Altman is really great at.
    You open and close the book not just with Sam’s father, but with his family as a whole. What else is worth highlighting in terms of how his upbringing and family shapes who he is now?
    Well, you see both the idealism from his father and also the incredible ambition from his mother, who was a doctor, and had four kids and worked as a dermatologist. I think both of these things work together to shape him. They also had a more troubled marriage than I realized going into the book. So I do think that there’s some anxiety there that Sam himself is very upfront about, that he was a pretty anxious person for much of his life, until he did some meditation and had some experiences.
    And there’s his current family — he just had a baby and got married not too long ago. As a young gay man, growing up in the Midwest, he had to overcome some challenges, and I think those challenges both forged him in high school as a brave person who could stand up and take on a room as a public speaker, but also shaped his optimistic view of the world. Because, on that issue, I paint the scene of his wedding: That’s an unimaginable thing from the early ‘90s, or from the ‘80s when he was born. He’s watched society develop and progress in very tangible ways, and I do think that that has helped solidify his faith in progress.
    Something that I’ve found writing about AI is that the different visions being presented by people in the field can be so diametrically opposed. You have these wildly utopian visions, but also these warnings that AI could end the world. It gets so hyperbolic that it feels like people are not living in the same reality. Was that a challenge for you in writing the book?
    Well, I see those two visions — which feel very far apart — actually being part of the same vision, which is that AI is super important, and it’s going to completely transform everything. No one ever talks about the true opposite of that, which is, “Maybe this is going to be a cool enterprise tool, another way to waste time on the internet, and not quite change everything as much as everyone thinks.” So I see the doomers and the boomers feeding off each other and being part of the same sort of hype universe.
    As a journalist and as a biographer, you don’t necessarily come down on one side or the other — but actually, can you say where you come down on that?
    Well, I will say that I find myself using it a lot more recently, because it’s gotten a lot better. In the early stages, when I was researching the book, I was definitely a lot more skeptical of its transformative economic power. I’m less skeptical now, because I just use it a lot more.
    #sam #altman #biographer #keach #hagey
    Sam Altman biographer Keach Hagey explains why the OpenAI CEO was ‘born for this moment’
    In “The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future,” Wall Street Journal reporter Keach Hagey examines our AI-obsessed moment through one of its key figures — Sam Altman, co-founder and CEO of OpenAI. Hagey begins with Altman’s Midwest childhood, then takes readers through his career at startup Loopt, accelerator Y Combinator, and now at OpenAI. She also sheds new light on the dramatic few days when Altman was fired, then quickly reinstated, as OpenAI’s CEO. Looking back at what OpenAI employees now call “the Blip,” Hagey said the failed attempt to oust Altman revealed that OpenAI’s complex structure — with a for-profit company controlled by a nonprofit board — is “not stable.” And with OpenAI largely backing down from plans to let the for-profit side take control, Hagey predicted that this “fundamentally unstable arrangement” will “continue to give investors pause.” Does that mean OpenAI could struggle to raise the funds it needs to keep going? Hagey replied that it could “absolutely” be an issue. “My research into Sam suggests that he might well be up to that challenge,” she said. “But success is not guaranteed.” In addition, Hagey’s biographyexamines Altman’s politics, which she described as “pretty traditionally progressive” — making it a bit surprising that he’s struck massive infrastructure deals with the backing of the Trump administration. “But this is one area where, in some ways, I feel like Sam Altman has been born for this moment, because he is a deal maker and Trump is a deal maker,” Hagey said. “Trump respects nothing so much as a big deal with a big price tag on it, and that is what Sam Altman is really great at.” Techcrunch event now through June 4 for TechCrunch Sessions: AI on your ticket to TC Sessions: AI—and get 50% off a second. Hear from leaders at OpenAI, Anthropic, Khosla Ventures, and more during a full day of expert insights, hands-on workshops, and high-impact networking. These low-rate deals disappear when the doors open on June 5. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you’ve built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | June 5 REGISTER NOW In an interview with TechCrunch, Hagey also discussed Altman’s response to the book, his trustworthiness, and the AI “hype universe.” This interview has been edited for length and clarity.  You open the book by acknowledging some of the reservations that Sam Altman had about the project —  this idea that we tend to focus too much on individuals rather than organizations or broad movements, and also that it’s way too early to assess the impact of OpenAI. Did you share those concerns? Well, I don’t really share them, because this was a biography. This project was to look at a person, not an organization. And I also think that Sam Altman has set himself up in a way where it does matter what kind of moral choices he has made and what his moral formation has been, because the broad project of AI is really a moral project. That is the basis of OpenAI’s existence. So I think these are fair questions to ask about a person, not just an organization. As far as whether it’s too soon, I mean, sure, it’s definitelyassess the entire impact of AI. But it’s been an extraordinary story for OpenAI — just so far, it’s already changed the stock market, it has changed the entire narrative of business. I’m a business journalist. We do nothing but talk about AI, all day long, every day. So in that way, I don’t think it’s too early. And despite those reservations, Altman did cooperate with you. Can you say more about what your relationship with him was like during the process of researching the book? Well, he was definitely not happy when he was informed about the book’s existence. And there was a long period of negotiation, frankly. In the beginning, I figured I was going to write this book without his help — what we call, in the business, a write-around profile. I’ve done plenty of those over my career, and I figured this would just be one more. Over time, as I made more and more calls, he opened up a little bit. Andhe was generous to sit down with me several times for long interviews and share his thoughts with me. Has he responded to the finished book at all? No. He did tweet about the project, about his decision to participate with it, but he was very clear that he was never going to read it. It’s the same way that I don’t like to watch my TV appearances or podcasts that I’m on. In the book, he’s described as this emblematic Silicon Valley figure. What do you think are the key characteristics that make him representative of the Valley and the tech industry? In the beginning, I think it was that he was young. The Valley really glorifies youth, and he was 19 years old when he started his first startup. You see him going into these meetings with people twice his age, doing deals with telecom operators for his first startup, and no one could get over that this kid was so smart. The other is that he is a once-in-a-generation fundraising talent, and that’s really about being a storyteller. I don’t think it’s an accident that you have essentially a salesman and a fundraiser at the top of the most important AI company today, That ties into one of the questions that runs through the book — this question about Altman’s trustworthiness. Can you say more about the concerns people seem to have about that? To what extent is he a trustworthy figure?  Well, he’s a salesman, so he’s really excellent at getting in a room and convincing people that he can see the future and that he has something in common with them. He gets people to share his vision, which is a rare talent. There are people who’ve watched that happen a bunch of times, who think, “Okay, what he says does not always map to reality,” and have, over time, lost trust in him. This happened both at his first startup and very famously at OpenAI, as well as at Y Combinator. So it is a pattern, but I think it’s a typical critique of people who have the salesman skill set. So it’s not necessarily that he’s particularly untrustworthy, but it’s part-and-parcel of being a salesman leading these important companies. I mean, there also are management issues that are detailed in the book, where he is not great at dealing with conflict, so he’ll basically tell people what they want to hear. That causes a lot of sturm-und-drang in the management ranks, and it’s a pattern. Something like that happened at Loopt, where the executives asked the board to replace him as CEO. And you saw it happen at OpenAI as well. You’ve touched on Altman’s firing, which was also covered in a book excerpt that was published in the Wall Street Journal. One of the striking things to me, looking back at it, was just how complicated everything was — all the different factions within the company, all the people who seemed pro-Altman one day and then anti-Altman the next. When you pull back from the details, what do you think is the bigger significance of that incident? The very big picture is that the nonprofit governance structure is not stable. You can’t really take investment from the likes of Microsoft and a bunch of other investors and then give them absolutely no say whatsoever in the governance of the company. That’s what they have tried to do, but I think what we saw in that firing is how power actually works in the world. When you have stakeholders, even if there’s a piece of paper that says they have no rights, they still have power. And when it became clear that everyone in the company was going to go to Microsoft if they didn’t reinstate Sam Altman, they reinstated Sam Altman. In the book, you take the story up to maybe the end of 2024. There have been all these developments since then, which you’ve continued to report on, including this announcement that actually, they’re not fully converting to a for-profit. How do you think that’s going to affect OpenAI going forward?  It’s going to make it harder for them to raise money, because they basically had to do an about-face. I know that the new structure going forward of the public benefit corporation is not exactly the same as the current structure of the for-profit — it is a little bit more investor friendly, it does clarify some of those things. But overall, what you have is a nonprofit board that controls a for-profit company, and that fundamentally unstable arrangement is what led to the so-called Blip. And I think you would continue to give investors pause, going forward, if they are going to have so little control over their investment. Obviously, OpenAI is still such a capital intensive business. If they have challenges raising more money, is that an existential question for the company? It absolutely could be. My research into Sam suggests that he might well be up to that challenge. But success is not guaranteed. Like you said, there’s a dual perspective in the book that’s partly about who Sam is, and partly about what that says about where AI is going from here. How did that research into his particular story shape the way you now look at these broader debates about AI and society? I went down a rabbit hole in the beginning of the book,into Sam’s father, Jerry Altman, in part because I thought it was striking how he’d been written out of basically every other thing that had ever been written about Sam Altman. What I found in this research was a very idealistic man who was, from youth, very interested in these public-private partnerships and the power of the government to set policy. He ended up having an impact on the way that affordable housing is still financed to this day. And when I traced Sam’s development, I saw that he has long believed that the government should really be the one that is funding and guiding AI research. In the early days of OpenAI, they went and tried to get the government to invest, as he’s publicly said, and it didn’t work out. But he looks back to these great mid-20th century labs like Xerox PARC and Bell Labs, which are private, but there was a ton of government money running through and supporting that ecosystem. And he says, “That’s the right way to do it.” Now I am watching daily as it seems like the United States is summoning the forces of state capitalism to get behind Sam Altman’s project to build these data centers, both in the United States and now there was just one last week announced in Abu Dhabi. This is a vision he has had for a very, very long time. My sense of the vision, as he presented it earlier, was one where, on the one hand, the government is funding these things and building this infrastructure, and on the other hand, the government is also regulating and guiding AI development for safety purposes. And it now seems like the path being pursued is one where they’re backing away from the safety side and doubling down on the government investment side. Absolutely. Isn’t it fascinating?  You talk about Sam as a political figure, as someone who’s had political ambitions at different times, but also somebody who has what are in many ways traditionally liberal political views while being friends with folks like — at least early on — Elon Musk and Peter Thiel. And he’s done a very good job of navigating the Trump administration. What do you think his politics are right now? I’m not sure his actual politics have changed, they are pretty traditionally progressive politics. Not completely — he’s been critical about things like cancel culture, but in general, he thinks the government is there to take tax revenue and solve problems. His success in the Trump administration has been fascinating because he has been able to find their one area of overlap, which is the desire to build a lot of data centers, and just double down on that and not talk about any other stuff. But this is one area where, in some ways, I feel like Sam Altman has been born for this moment, because he is a deal maker and Trump is a deal maker. Trump respects nothing so much as a big deal with a big price tag on it, and that is what Sam Altman is really great at. You open and close the book not just with Sam’s father, but with his family as a whole. What else is worth highlighting in terms of how his upbringing and family shapes who he is now? Well, you see both the idealism from his father and also the incredible ambition from his mother, who was a doctor, and had four kids and worked as a dermatologist. I think both of these things work together to shape him. They also had a more troubled marriage than I realized going into the book. So I do think that there’s some anxiety there that Sam himself is very upfront about, that he was a pretty anxious person for much of his life, until he did some meditation and had some experiences. And there’s his current family — he just had a baby and got married not too long ago. As a young gay man, growing up in the Midwest, he had to overcome some challenges, and I think those challenges both forged him in high school as a brave person who could stand up and take on a room as a public speaker, but also shaped his optimistic view of the world. Because, on that issue, I paint the scene of his wedding: That’s an unimaginable thing from the early ‘90s, or from the ‘80s when he was born. He’s watched society develop and progress in very tangible ways, and I do think that that has helped solidify his faith in progress. Something that I’ve found writing about AI is that the different visions being presented by people in the field can be so diametrically opposed. You have these wildly utopian visions, but also these warnings that AI could end the world. It gets so hyperbolic that it feels like people are not living in the same reality. Was that a challenge for you in writing the book? Well, I see those two visions — which feel very far apart — actually being part of the same vision, which is that AI is super important, and it’s going to completely transform everything. No one ever talks about the true opposite of that, which is, “Maybe this is going to be a cool enterprise tool, another way to waste time on the internet, and not quite change everything as much as everyone thinks.” So I see the doomers and the boomers feeding off each other and being part of the same sort of hype universe. As a journalist and as a biographer, you don’t necessarily come down on one side or the other — but actually, can you say where you come down on that? Well, I will say that I find myself using it a lot more recently, because it’s gotten a lot better. In the early stages, when I was researching the book, I was definitely a lot more skeptical of its transformative economic power. I’m less skeptical now, because I just use it a lot more. #sam #altman #biographer #keach #hagey
    TECHCRUNCH.COM
    Sam Altman biographer Keach Hagey explains why the OpenAI CEO was ‘born for this moment’
    In “The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future,” Wall Street Journal reporter Keach Hagey examines our AI-obsessed moment through one of its key figures — Sam Altman, co-founder and CEO of OpenAI. Hagey begins with Altman’s Midwest childhood, then takes readers through his career at startup Loopt, accelerator Y Combinator, and now at OpenAI. She also sheds new light on the dramatic few days when Altman was fired, then quickly reinstated, as OpenAI’s CEO. Looking back at what OpenAI employees now call “the Blip,” Hagey said the failed attempt to oust Altman revealed that OpenAI’s complex structure — with a for-profit company controlled by a nonprofit board — is “not stable.” And with OpenAI largely backing down from plans to let the for-profit side take control, Hagey predicted that this “fundamentally unstable arrangement” will “continue to give investors pause.” Does that mean OpenAI could struggle to raise the funds it needs to keep going? Hagey replied that it could “absolutely” be an issue. “My research into Sam suggests that he might well be up to that challenge,” she said. “But success is not guaranteed.” In addition, Hagey’s biography (also available as an audiobook on Spotify) examines Altman’s politics, which she described as “pretty traditionally progressive” — making it a bit surprising that he’s struck massive infrastructure deals with the backing of the Trump administration. “But this is one area where, in some ways, I feel like Sam Altman has been born for this moment, because he is a deal maker and Trump is a deal maker,” Hagey said. “Trump respects nothing so much as a big deal with a big price tag on it, and that is what Sam Altman is really great at.” Techcrunch event Save now through June 4 for TechCrunch Sessions: AI Save $300 on your ticket to TC Sessions: AI—and get 50% off a second. Hear from leaders at OpenAI, Anthropic, Khosla Ventures, and more during a full day of expert insights, hands-on workshops, and high-impact networking. These low-rate deals disappear when the doors open on June 5. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you’ve built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | June 5 REGISTER NOW In an interview with TechCrunch, Hagey also discussed Altman’s response to the book, his trustworthiness, and the AI “hype universe.” This interview has been edited for length and clarity.  You open the book by acknowledging some of the reservations that Sam Altman had about the project —  this idea that we tend to focus too much on individuals rather than organizations or broad movements, and also that it’s way too early to assess the impact of OpenAI. Did you share those concerns? Well, I don’t really share them, because this was a biography. This project was to look at a person, not an organization. And I also think that Sam Altman has set himself up in a way where it does matter what kind of moral choices he has made and what his moral formation has been, because the broad project of AI is really a moral project. That is the basis of OpenAI’s existence. So I think these are fair questions to ask about a person, not just an organization. As far as whether it’s too soon, I mean, sure, it’s definitely [early to] assess the entire impact of AI. But it’s been an extraordinary story for OpenAI — just so far, it’s already changed the stock market, it has changed the entire narrative of business. I’m a business journalist. We do nothing but talk about AI, all day long, every day. So in that way, I don’t think it’s too early. And despite those reservations, Altman did cooperate with you. Can you say more about what your relationship with him was like during the process of researching the book? Well, he was definitely not happy when he was informed about the book’s existence. And there was a long period of negotiation, frankly. In the beginning, I figured I was going to write this book without his help — what we call, in the business, a write-around profile. I’ve done plenty of those over my career, and I figured this would just be one more. Over time, as I made more and more calls, he opened up a little bit. And [eventually,] he was generous to sit down with me several times for long interviews and share his thoughts with me. Has he responded to the finished book at all? No. He did tweet about the project, about his decision to participate with it, but he was very clear that he was never going to read it. It’s the same way that I don’t like to watch my TV appearances or podcasts that I’m on. In the book, he’s described as this emblematic Silicon Valley figure. What do you think are the key characteristics that make him representative of the Valley and the tech industry? In the beginning, I think it was that he was young. The Valley really glorifies youth, and he was 19 years old when he started his first startup. You see him going into these meetings with people twice his age, doing deals with telecom operators for his first startup, and no one could get over that this kid was so smart. The other is that he is a once-in-a-generation fundraising talent, and that’s really about being a storyteller. I don’t think it’s an accident that you have essentially a salesman and a fundraiser at the top of the most important AI company today, That ties into one of the questions that runs through the book — this question about Altman’s trustworthiness. Can you say more about the concerns people seem to have about that? To what extent is he a trustworthy figure?  Well, he’s a salesman, so he’s really excellent at getting in a room and convincing people that he can see the future and that he has something in common with them. He gets people to share his vision, which is a rare talent. There are people who’ve watched that happen a bunch of times, who think, “Okay, what he says does not always map to reality,” and have, over time, lost trust in him. This happened both at his first startup and very famously at OpenAI, as well as at Y Combinator. So it is a pattern, but I think it’s a typical critique of people who have the salesman skill set. So it’s not necessarily that he’s particularly untrustworthy, but it’s part-and-parcel of being a salesman leading these important companies. I mean, there also are management issues that are detailed in the book, where he is not great at dealing with conflict, so he’ll basically tell people what they want to hear. That causes a lot of sturm-und-drang in the management ranks, and it’s a pattern. Something like that happened at Loopt, where the executives asked the board to replace him as CEO. And you saw it happen at OpenAI as well. You’ve touched on Altman’s firing, which was also covered in a book excerpt that was published in the Wall Street Journal. One of the striking things to me, looking back at it, was just how complicated everything was — all the different factions within the company, all the people who seemed pro-Altman one day and then anti-Altman the next. When you pull back from the details, what do you think is the bigger significance of that incident? The very big picture is that the nonprofit governance structure is not stable. You can’t really take investment from the likes of Microsoft and a bunch of other investors and then give them absolutely no say whatsoever in the governance of the company. That’s what they have tried to do, but I think what we saw in that firing is how power actually works in the world. When you have stakeholders, even if there’s a piece of paper that says they have no rights, they still have power. And when it became clear that everyone in the company was going to go to Microsoft if they didn’t reinstate Sam Altman, they reinstated Sam Altman. In the book, you take the story up to maybe the end of 2024. There have been all these developments since then, which you’ve continued to report on, including this announcement that actually, they’re not fully converting to a for-profit. How do you think that’s going to affect OpenAI going forward?  It’s going to make it harder for them to raise money, because they basically had to do an about-face. I know that the new structure going forward of the public benefit corporation is not exactly the same as the current structure of the for-profit — it is a little bit more investor friendly, it does clarify some of those things. But overall, what you have is a nonprofit board that controls a for-profit company, and that fundamentally unstable arrangement is what led to the so-called Blip. And I think you would continue to give investors pause, going forward, if they are going to have so little control over their investment. Obviously, OpenAI is still such a capital intensive business. If they have challenges raising more money, is that an existential question for the company? It absolutely could be. My research into Sam suggests that he might well be up to that challenge. But success is not guaranteed. Like you said, there’s a dual perspective in the book that’s partly about who Sam is, and partly about what that says about where AI is going from here. How did that research into his particular story shape the way you now look at these broader debates about AI and society? I went down a rabbit hole in the beginning of the book, [looking] into Sam’s father, Jerry Altman, in part because I thought it was striking how he’d been written out of basically every other thing that had ever been written about Sam Altman. What I found in this research was a very idealistic man who was, from youth, very interested in these public-private partnerships and the power of the government to set policy. He ended up having an impact on the way that affordable housing is still financed to this day. And when I traced Sam’s development, I saw that he has long believed that the government should really be the one that is funding and guiding AI research. In the early days of OpenAI, they went and tried to get the government to invest, as he’s publicly said, and it didn’t work out. But he looks back to these great mid-20th century labs like Xerox PARC and Bell Labs, which are private, but there was a ton of government money running through and supporting that ecosystem. And he says, “That’s the right way to do it.” Now I am watching daily as it seems like the United States is summoning the forces of state capitalism to get behind Sam Altman’s project to build these data centers, both in the United States and now there was just one last week announced in Abu Dhabi. This is a vision he has had for a very, very long time. My sense of the vision, as he presented it earlier, was one where, on the one hand, the government is funding these things and building this infrastructure, and on the other hand, the government is also regulating and guiding AI development for safety purposes. And it now seems like the path being pursued is one where they’re backing away from the safety side and doubling down on the government investment side. Absolutely. Isn’t it fascinating?  You talk about Sam as a political figure, as someone who’s had political ambitions at different times, but also somebody who has what are in many ways traditionally liberal political views while being friends with folks like — at least early on — Elon Musk and Peter Thiel. And he’s done a very good job of navigating the Trump administration. What do you think his politics are right now? I’m not sure his actual politics have changed, they are pretty traditionally progressive politics. Not completely — he’s been critical about things like cancel culture, but in general, he thinks the government is there to take tax revenue and solve problems. His success in the Trump administration has been fascinating because he has been able to find their one area of overlap, which is the desire to build a lot of data centers, and just double down on that and not talk about any other stuff. But this is one area where, in some ways, I feel like Sam Altman has been born for this moment, because he is a deal maker and Trump is a deal maker. Trump respects nothing so much as a big deal with a big price tag on it, and that is what Sam Altman is really great at. You open and close the book not just with Sam’s father, but with his family as a whole. What else is worth highlighting in terms of how his upbringing and family shapes who he is now? Well, you see both the idealism from his father and also the incredible ambition from his mother, who was a doctor, and had four kids and worked as a dermatologist. I think both of these things work together to shape him. They also had a more troubled marriage than I realized going into the book. So I do think that there’s some anxiety there that Sam himself is very upfront about, that he was a pretty anxious person for much of his life, until he did some meditation and had some experiences. And there’s his current family — he just had a baby and got married not too long ago. As a young gay man, growing up in the Midwest, he had to overcome some challenges, and I think those challenges both forged him in high school as a brave person who could stand up and take on a room as a public speaker, but also shaped his optimistic view of the world. Because, on that issue, I paint the scene of his wedding: That’s an unimaginable thing from the early ‘90s, or from the ‘80s when he was born. He’s watched society develop and progress in very tangible ways, and I do think that that has helped solidify his faith in progress. Something that I’ve found writing about AI is that the different visions being presented by people in the field can be so diametrically opposed. You have these wildly utopian visions, but also these warnings that AI could end the world. It gets so hyperbolic that it feels like people are not living in the same reality. Was that a challenge for you in writing the book? Well, I see those two visions — which feel very far apart — actually being part of the same vision, which is that AI is super important, and it’s going to completely transform everything. No one ever talks about the true opposite of that, which is, “Maybe this is going to be a cool enterprise tool, another way to waste time on the internet, and not quite change everything as much as everyone thinks.” So I see the doomers and the boomers feeding off each other and being part of the same sort of hype universe. As a journalist and as a biographer, you don’t necessarily come down on one side or the other — but actually, can you say where you come down on that? Well, I will say that I find myself using it a lot more recently, because it’s gotten a lot better. In the early stages, when I was researching the book, I was definitely a lot more skeptical of its transformative economic power. I’m less skeptical now, because I just use it a lot more.
    0 التعليقات 0 المشاركات
  • OpenAI: The power and the pride

    In April, Paul Graham, the founder of the tech startup accelerator Y Combinator, sent a tweet in response to former YC president and current OpenAI CEO Sam Altman. Altman had just bid a public goodbye to GPT-4 on X, and Graham had a follow-up question. 

    “If you hadetched on a piece of metal in the most compressed form,” Graham wrote, referring to the values that determine the model’s behavior, “how big would the piece of metal have to be? This is a mostly serious question. These models are history, and by default digital data evaporates.” 

    There is no question that OpenAI pulled off something historic with its release of ChatGPT 3.5 in 2022. It set in motion an AI arms race that has already changed the world in a number of ways and seems poised to have an even greater long-term effect than the short-term disruptions to things like education and employment that we are already beginning to see. How that turns out for humanity is something we are still reckoning with and may be for quite some time. But a pair of recent books both attempt to get their arms around it with accounts of what two leading technology journalists saw at the OpenAI revolution. 

    In Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI, Karen Hao tells the story of the company’s rise to power and its far-reaching impact all over the world. Meanwhile, The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future, by the Wall Street Journal’s Keach Hagey, homes in more on Altman’s personal life, from his childhood through the present day, in order to tell the story of OpenAI. Both paint complex pictures and show Altman in particular as a brilliantly effective yet deeply flawed creature of Silicon Valley—someone capable of always getting what he wants, but often by manipulating others. 

    Hao, who was formerly a reporter with MIT Technology Review, began reporting on OpenAI while at this publication and remains an occasional contributor. One chapter of her book grew directly out of that reporting. And in fact, as Hao says in the acknowledgments of Empire of AI, some of her reporting for MIT Technology Review, a series on AI colonialism, “laid the groundwork for the thesis and, ultimately, the title of this book.” So you can take this as a kind of disclaimer that we are predisposed to look favorably on Hao’s work. 

    With that said, Empire of AI is a powerful work, bristling not only with great reporting but also with big ideas. This comes across in service to two main themes. 

    The first is simple: It is the story of ambition overriding ethics. The history of OpenAI as Hao tells itis very much a tale of a company that was founded on the idealistic desire to create a safety-focused artificial general intelligence but instead became more interested in winning. This is a story we’ve seen many times before in Big Tech. See Theranos, which was going to make diagnostics easier, or Uber, which was founded to break the cartel of “Big Taxi.” But the closest analogue might be Google, which went from “Don’t be evil” toillegal monopolist. For that matter, consider how Google went from holding off on releasing its language model as a consumer product out of an abundance of caution to rushing a chatbot out the door to catch up with and beat OpenAI. In Silicon Valley, no matter what one’s original intent, it always comes back to winning.  

    The second theme is more complex and forms the book’s thesis about what Hao calls AI colonialism. The idea is that the large AI companies act like traditional empires, siphoning wealth from the bottom rungs of society in the forms of labor, creative works, raw materials, and the like to fuel their ambition and enrich those at the top of the ladder. “I’ve found only one metaphor that encapsulates the nature of what these AI power players are: empires,” she writes.

    “During the long era of European colonialism, empires seized and extracted resources that were not their own and exploited the labor of the people they subjugated to mine, cultivate, and refine those resources for the empires’ enrichment.” She goes on to chronicle her own growing disillusionment with the industry. “With increasing clarity,” she writes, “I realized that the very revolution promising to bring a better future was instead, for people on the margins of society, reviving the darkest remnants of the past.” 

    To document this, Hao steps away from her desk and goes out into the world to see the effects of this empire as it sprawls across the planet. She travels to Colombia to meet with data labelers tasked with teaching AI what various images show, one of whom she describes sprinting back to her apartment for the chance to make a few dollars. She documents how workers in Kenya who performed data-labeling content moderation for OpenAI came away traumatized by seeing so much disturbing material. In Chile she documents how the industry extracts precious resources—water, power, copper, lithium—to build out data centers. 

    She lands on the ways people are pushing back against the empire of AI across the world. Hao draws lessons from New Zealand, where Maori people are attempting to save their language using a small language model of their own making. Trained on volunteers’ voice recordings and running on just two graphics processing units, or GPUs, rather than the thousands employed by the likes of OpenAI, it’s meant to benefit the community, not exploit it. 

    Hao writes that she is not against AI. Rather: “What I reject is the dangerous notion that broad benefit from AI can only be derived from—indeed will ever emerge from—a vision of the technology that requires the complete capitulation of our privacy, our agency, and our worth, including the value of our labor and art, toward an ultimately imperial centralization project …shows us another way. It imagines how AI could be exactly the opposite. Models can be small and task-specific, their training data contained and knowable, ridding the incentives for widespread exploitative and psychologically harmful labor practices and the all-consuming extractivism of producing and running massive supercomputers.” 

    Hagey’s book is more squarely focused on Altman’s ambition, which she traces back to his childhood. Yet interestingly, she also  zeroes in on the OpenAI CEO’s attempt to create an empire. Indeed, “Altman’s departure from YC had not slowed his civilization-building ambitions,” Hagey writes. She goes on to chronicle how Altman, who had previously mulled a run for governor of California, set up experiments with income distribution via Tools for Humanity, the parent company of Worldcoin. She quotes Altman saying of it, “I thought it would be interesting to see … just how far technology could accomplish some of the goals that used to be done by nation-states.” 

    Overall, The Optimist is the more straightforward business biography of the two. Hagey has packed it full with scoops and insights and behind-the-scenes intrigue. It is immensely readable as a result, especially in the second half, when OpenAI really takes over the story. Hagey also seems to have been given far more access to Altman and his inner circles, personal and professional, than Hao did, and that allows for a fuller telling of the CEO’s story in places. For example, both writers cover the tragic story of Altman’s sister Annie, her estrangement from the family, and her accusations in particular about suffering sexual abuse at the hands of Sam. Hagey’s telling provides a more nuanced picture of the situation, with more insight into family dynamics. 

    Hagey concludes by describing Altman’s reckoning with his role in the long arc of human history and what it will mean to create a “superintelligence.” His place in that sweep is something that clearly has consumed the CEO’s thoughts. When Paul Graham asked about preserving GPT-4, for example, Altman had a response at the ready. He replied that the company had already considered this, and that the sheet of metal would need to be 100 meters square.
    #openai #power #pride
    OpenAI: The power and the pride
    In April, Paul Graham, the founder of the tech startup accelerator Y Combinator, sent a tweet in response to former YC president and current OpenAI CEO Sam Altman. Altman had just bid a public goodbye to GPT-4 on X, and Graham had a follow-up question.  “If you hadetched on a piece of metal in the most compressed form,” Graham wrote, referring to the values that determine the model’s behavior, “how big would the piece of metal have to be? This is a mostly serious question. These models are history, and by default digital data evaporates.”  There is no question that OpenAI pulled off something historic with its release of ChatGPT 3.5 in 2022. It set in motion an AI arms race that has already changed the world in a number of ways and seems poised to have an even greater long-term effect than the short-term disruptions to things like education and employment that we are already beginning to see. How that turns out for humanity is something we are still reckoning with and may be for quite some time. But a pair of recent books both attempt to get their arms around it with accounts of what two leading technology journalists saw at the OpenAI revolution.  In Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI, Karen Hao tells the story of the company’s rise to power and its far-reaching impact all over the world. Meanwhile, The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future, by the Wall Street Journal’s Keach Hagey, homes in more on Altman’s personal life, from his childhood through the present day, in order to tell the story of OpenAI. Both paint complex pictures and show Altman in particular as a brilliantly effective yet deeply flawed creature of Silicon Valley—someone capable of always getting what he wants, but often by manipulating others.  Hao, who was formerly a reporter with MIT Technology Review, began reporting on OpenAI while at this publication and remains an occasional contributor. One chapter of her book grew directly out of that reporting. And in fact, as Hao says in the acknowledgments of Empire of AI, some of her reporting for MIT Technology Review, a series on AI colonialism, “laid the groundwork for the thesis and, ultimately, the title of this book.” So you can take this as a kind of disclaimer that we are predisposed to look favorably on Hao’s work.  With that said, Empire of AI is a powerful work, bristling not only with great reporting but also with big ideas. This comes across in service to two main themes.  The first is simple: It is the story of ambition overriding ethics. The history of OpenAI as Hao tells itis very much a tale of a company that was founded on the idealistic desire to create a safety-focused artificial general intelligence but instead became more interested in winning. This is a story we’ve seen many times before in Big Tech. See Theranos, which was going to make diagnostics easier, or Uber, which was founded to break the cartel of “Big Taxi.” But the closest analogue might be Google, which went from “Don’t be evil” toillegal monopolist. For that matter, consider how Google went from holding off on releasing its language model as a consumer product out of an abundance of caution to rushing a chatbot out the door to catch up with and beat OpenAI. In Silicon Valley, no matter what one’s original intent, it always comes back to winning.   The second theme is more complex and forms the book’s thesis about what Hao calls AI colonialism. The idea is that the large AI companies act like traditional empires, siphoning wealth from the bottom rungs of society in the forms of labor, creative works, raw materials, and the like to fuel their ambition and enrich those at the top of the ladder. “I’ve found only one metaphor that encapsulates the nature of what these AI power players are: empires,” she writes. “During the long era of European colonialism, empires seized and extracted resources that were not their own and exploited the labor of the people they subjugated to mine, cultivate, and refine those resources for the empires’ enrichment.” She goes on to chronicle her own growing disillusionment with the industry. “With increasing clarity,” she writes, “I realized that the very revolution promising to bring a better future was instead, for people on the margins of society, reviving the darkest remnants of the past.”  To document this, Hao steps away from her desk and goes out into the world to see the effects of this empire as it sprawls across the planet. She travels to Colombia to meet with data labelers tasked with teaching AI what various images show, one of whom she describes sprinting back to her apartment for the chance to make a few dollars. She documents how workers in Kenya who performed data-labeling content moderation for OpenAI came away traumatized by seeing so much disturbing material. In Chile she documents how the industry extracts precious resources—water, power, copper, lithium—to build out data centers.  She lands on the ways people are pushing back against the empire of AI across the world. Hao draws lessons from New Zealand, where Maori people are attempting to save their language using a small language model of their own making. Trained on volunteers’ voice recordings and running on just two graphics processing units, or GPUs, rather than the thousands employed by the likes of OpenAI, it’s meant to benefit the community, not exploit it.  Hao writes that she is not against AI. Rather: “What I reject is the dangerous notion that broad benefit from AI can only be derived from—indeed will ever emerge from—a vision of the technology that requires the complete capitulation of our privacy, our agency, and our worth, including the value of our labor and art, toward an ultimately imperial centralization project …shows us another way. It imagines how AI could be exactly the opposite. Models can be small and task-specific, their training data contained and knowable, ridding the incentives for widespread exploitative and psychologically harmful labor practices and the all-consuming extractivism of producing and running massive supercomputers.”  Hagey’s book is more squarely focused on Altman’s ambition, which she traces back to his childhood. Yet interestingly, she also  zeroes in on the OpenAI CEO’s attempt to create an empire. Indeed, “Altman’s departure from YC had not slowed his civilization-building ambitions,” Hagey writes. She goes on to chronicle how Altman, who had previously mulled a run for governor of California, set up experiments with income distribution via Tools for Humanity, the parent company of Worldcoin. She quotes Altman saying of it, “I thought it would be interesting to see … just how far technology could accomplish some of the goals that used to be done by nation-states.”  Overall, The Optimist is the more straightforward business biography of the two. Hagey has packed it full with scoops and insights and behind-the-scenes intrigue. It is immensely readable as a result, especially in the second half, when OpenAI really takes over the story. Hagey also seems to have been given far more access to Altman and his inner circles, personal and professional, than Hao did, and that allows for a fuller telling of the CEO’s story in places. For example, both writers cover the tragic story of Altman’s sister Annie, her estrangement from the family, and her accusations in particular about suffering sexual abuse at the hands of Sam. Hagey’s telling provides a more nuanced picture of the situation, with more insight into family dynamics.  Hagey concludes by describing Altman’s reckoning with his role in the long arc of human history and what it will mean to create a “superintelligence.” His place in that sweep is something that clearly has consumed the CEO’s thoughts. When Paul Graham asked about preserving GPT-4, for example, Altman had a response at the ready. He replied that the company had already considered this, and that the sheet of metal would need to be 100 meters square. #openai #power #pride
    WWW.TECHNOLOGYREVIEW.COM
    OpenAI: The power and the pride
    In April, Paul Graham, the founder of the tech startup accelerator Y Combinator, sent a tweet in response to former YC president and current OpenAI CEO Sam Altman. Altman had just bid a public goodbye to GPT-4 on X, and Graham had a follow-up question.  “If you had [GPT-4’s model weights] etched on a piece of metal in the most compressed form,” Graham wrote, referring to the values that determine the model’s behavior, “how big would the piece of metal have to be? This is a mostly serious question. These models are history, and by default digital data evaporates.”  There is no question that OpenAI pulled off something historic with its release of ChatGPT 3.5 in 2022. It set in motion an AI arms race that has already changed the world in a number of ways and seems poised to have an even greater long-term effect than the short-term disruptions to things like education and employment that we are already beginning to see. How that turns out for humanity is something we are still reckoning with and may be for quite some time. But a pair of recent books both attempt to get their arms around it with accounts of what two leading technology journalists saw at the OpenAI revolution.  In Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI, Karen Hao tells the story of the company’s rise to power and its far-reaching impact all over the world. Meanwhile, The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future, by the Wall Street Journal’s Keach Hagey, homes in more on Altman’s personal life, from his childhood through the present day, in order to tell the story of OpenAI. Both paint complex pictures and show Altman in particular as a brilliantly effective yet deeply flawed creature of Silicon Valley—someone capable of always getting what he wants, but often by manipulating others.  Hao, who was formerly a reporter with MIT Technology Review, began reporting on OpenAI while at this publication and remains an occasional contributor. One chapter of her book grew directly out of that reporting. And in fact, as Hao says in the acknowledgments of Empire of AI, some of her reporting for MIT Technology Review, a series on AI colonialism, “laid the groundwork for the thesis and, ultimately, the title of this book.” So you can take this as a kind of disclaimer that we are predisposed to look favorably on Hao’s work.  With that said, Empire of AI is a powerful work, bristling not only with great reporting but also with big ideas. This comes across in service to two main themes.  The first is simple: It is the story of ambition overriding ethics. The history of OpenAI as Hao tells it (and as Hagey does too) is very much a tale of a company that was founded on the idealistic desire to create a safety-focused artificial general intelligence but instead became more interested in winning. This is a story we’ve seen many times before in Big Tech. See Theranos, which was going to make diagnostics easier, or Uber, which was founded to break the cartel of “Big Taxi.” But the closest analogue might be Google, which went from “Don’t be evil” to (at least in the eyes of the courts) illegal monopolist. For that matter, consider how Google went from holding off on releasing its language model as a consumer product out of an abundance of caution to rushing a chatbot out the door to catch up with and beat OpenAI. In Silicon Valley, no matter what one’s original intent, it always comes back to winning.   The second theme is more complex and forms the book’s thesis about what Hao calls AI colonialism. The idea is that the large AI companies act like traditional empires, siphoning wealth from the bottom rungs of society in the forms of labor, creative works, raw materials, and the like to fuel their ambition and enrich those at the top of the ladder. “I’ve found only one metaphor that encapsulates the nature of what these AI power players are: empires,” she writes. “During the long era of European colonialism, empires seized and extracted resources that were not their own and exploited the labor of the people they subjugated to mine, cultivate, and refine those resources for the empires’ enrichment.” She goes on to chronicle her own growing disillusionment with the industry. “With increasing clarity,” she writes, “I realized that the very revolution promising to bring a better future was instead, for people on the margins of society, reviving the darkest remnants of the past.”  To document this, Hao steps away from her desk and goes out into the world to see the effects of this empire as it sprawls across the planet. She travels to Colombia to meet with data labelers tasked with teaching AI what various images show, one of whom she describes sprinting back to her apartment for the chance to make a few dollars. She documents how workers in Kenya who performed data-labeling content moderation for OpenAI came away traumatized by seeing so much disturbing material. In Chile she documents how the industry extracts precious resources—water, power, copper, lithium—to build out data centers.  She lands on the ways people are pushing back against the empire of AI across the world. Hao draws lessons from New Zealand, where Maori people are attempting to save their language using a small language model of their own making. Trained on volunteers’ voice recordings and running on just two graphics processing units, or GPUs, rather than the thousands employed by the likes of OpenAI, it’s meant to benefit the community, not exploit it.  Hao writes that she is not against AI. Rather: “What I reject is the dangerous notion that broad benefit from AI can only be derived from—indeed will ever emerge from—a vision of the technology that requires the complete capitulation of our privacy, our agency, and our worth, including the value of our labor and art, toward an ultimately imperial centralization project … [The New Zealand model] shows us another way. It imagines how AI could be exactly the opposite. Models can be small and task-specific, their training data contained and knowable, ridding the incentives for widespread exploitative and psychologically harmful labor practices and the all-consuming extractivism of producing and running massive supercomputers.”  Hagey’s book is more squarely focused on Altman’s ambition, which she traces back to his childhood. Yet interestingly, she also  zeroes in on the OpenAI CEO’s attempt to create an empire. Indeed, “Altman’s departure from YC had not slowed his civilization-building ambitions,” Hagey writes. She goes on to chronicle how Altman, who had previously mulled a run for governor of California, set up experiments with income distribution via Tools for Humanity, the parent company of Worldcoin. She quotes Altman saying of it, “I thought it would be interesting to see … just how far technology could accomplish some of the goals that used to be done by nation-states.”  Overall, The Optimist is the more straightforward business biography of the two. Hagey has packed it full with scoops and insights and behind-the-scenes intrigue. It is immensely readable as a result, especially in the second half, when OpenAI really takes over the story. Hagey also seems to have been given far more access to Altman and his inner circles, personal and professional, than Hao did, and that allows for a fuller telling of the CEO’s story in places. For example, both writers cover the tragic story of Altman’s sister Annie, her estrangement from the family, and her accusations in particular about suffering sexual abuse at the hands of Sam (something he and the rest of the Altman family vehemently deny). Hagey’s telling provides a more nuanced picture of the situation, with more insight into family dynamics.  Hagey concludes by describing Altman’s reckoning with his role in the long arc of human history and what it will mean to create a “superintelligence.” His place in that sweep is something that clearly has consumed the CEO’s thoughts. When Paul Graham asked about preserving GPT-4, for example, Altman had a response at the ready. He replied that the company had already considered this, and that the sheet of metal would need to be 100 meters square.
    0 التعليقات 0 المشاركات
  • Meet NovelSeek: A Unified Multi-Agent Framework for Autonomous Scientific Research from Hypothesis Generation to Experimental Validation

    Scientific research across fields like chemistry, biology, and artificial intelligence has long relied on human experts to explore knowledge, generate ideas, design experiments, and refine results. Yet, as problems grow more complex and data-intensive, discovery slows. While AI tools, such as language models and robotics, can handle specific tasks, like literature searches or code analysis, they rarely encompass the entire research cycle. Bridging the gap between idea generation and experimental validation remains a key challenge. For AI to autonomously advance science, it must propose hypotheses, design and execute experiments, analyze outcomes, and refine approaches in an iterative loop. Without this integration, AI risks producing disconnected ideas that depend on human supervision for validation.
    Before the introduction of a unified system, researchers relied on separate tools for each stage of the process. Large language models could help find relevant scientific papers, but they didn’t directly feed into experiment design or result analysis. Robotics can assist in automating physical experiments, and coding libraries like PyTorch can help build models; however, these tools operate independently of each other. There was no single system capable of handling the entire process, from forming ideas to verifying them through experiments. This led to bottlenecks, where researchers had to connect the dots manually, slowing progress and leaving room for errors or missed opportunities. The need for an integrated system that could handle the entire research cycle became clear.
    Researchers from the NovelSeek Team at the Shanghai Artificial Intelligence Laboratory developed NovelSeek, an AI system designed to run the entire scientific discovery process autonomously. NovelSeek comprises four main modules that work in tandem: a system that generates and refines research ideas, a feedback loop where human experts can interact with and refine these ideas, a method for translating ideas into code and experiment plans, and a process for conducting multiple rounds of experiments. What makes NovelSeek stand out is its versatility; it works across 12 scientific research tasks, including predicting chemical reaction yields, understanding molecular dynamics, forecasting time-series data, and handling functions like 2D semantic segmentation and 3D object classification. The team designed NovelSeek to minimize human involvement, expedite discoveries, and deliver consistent, high-quality results.

    The system behind NovelSeek involves multiple specialized agents, each focused on a specific part of the research workflow. The “Survey Agent” helps the system understand the problem by searching scientific papers and identifying relevant information based on keywords and task definitions. It adapts its search strategy by first doing a broad survey of papers, then going deeper by analyzing full-text documents for detailed insights. This ensures that the system captures both general trends and specific technical knowledge. The “Code Review Agent” examines existing codebases, whether user-uploaded or sourced from public repositories like GitHub, to understand how current methods work and identify areas for improvement. It checks how code is structured, looks for errors, and creates summaries that help the system build on past work. The “Idea Innovation Agent” generates creative research ideas, pushing the system to explore different approaches and refine them by comparing them to related studies and previous results. The system even includes a “Planning and Execution Agent” that turns ideas into detailed experiments, handles errors during the testing process, and ensures smooth execution of multi-step research plans.

    NovelSeek delivered impressive results across various tasks. In chemical reaction yield prediction, NovelSeek improved performance from a baseline of 24.2%to 34.8%in just 12 hours, progress that human researchers typically need months to achieve. In enhancer activity prediction, a key task in biology, NovelSeek raised the Pearson correlation coefficient from 0.65 to 0.79 within 4 hours. For 2D semantic segmentation, a task used in computer vision, precision improved from 78.8% to 81.0% in just 30 hours. These performance boosts, achieved in a fraction of the time typically needed, highlight the system’s efficiency. NovelSeek also successfully managed large, complex codebases with multiple files, demonstrating its ability to handle research tasks at a project level, not just in small, isolated tests. The team has made the code open-source, allowing others to use, test, and contribute to its improvement.

    Several Key Takeaways from the Research on NovelSeek include:

    NovelSeek supports 12 research tasks, including chemical reaction prediction, molecular dynamics, and 3D object classification.
    Reaction yield prediction accuracy improved from 24.2% to 34.8% in 12 hours.
    Enhancer activity prediction performance increased from 0.65 to 0.79 in 4 hours.
    2D semantic segmentation precision improved from 78.8% to 81.0% in 30 hours.
    NovelSeek includes agents for literature search, code analysis, idea generation, and experiment execution.
    The system is open-source, enabling reproducibility and collaboration across scientific fields.

    In conclusion, NovelSeek demonstrates how combining AI tools into a single system can accelerate scientific discovery and reduce its dependence on human effort. It ties together the key steps, generating ideas, turning them into methods, and testing them through experiments, into one streamlined process. What once took researchers months or years can now be done in days or even hours. By linking every stage of research into a continuous loop, NovelSeek helps teams move from rough ideas to real-world results more quickly. This system highlights the power of AI not just to assist, but to drive scientific research in a way that could reshape how discoveries are made across many fields.

    Check out the Paper and GitHub Page . All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter.
    NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces ARM and Ada-GRPO: Adaptive Reasoning Models for Efficient and Scalable Problem-SolvingNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces WEB-SHEPHERD: A Process Reward Model for Web Agents with 40K Dataset and 10× Cost EfficiencyNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces MMaDA: A Unified Multimodal Diffusion Model for Textual Reasoning, Visual Understanding, and Image GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural Networks
    #meet #novelseek #unified #multiagent #framework
    Meet NovelSeek: A Unified Multi-Agent Framework for Autonomous Scientific Research from Hypothesis Generation to Experimental Validation
    Scientific research across fields like chemistry, biology, and artificial intelligence has long relied on human experts to explore knowledge, generate ideas, design experiments, and refine results. Yet, as problems grow more complex and data-intensive, discovery slows. While AI tools, such as language models and robotics, can handle specific tasks, like literature searches or code analysis, they rarely encompass the entire research cycle. Bridging the gap between idea generation and experimental validation remains a key challenge. For AI to autonomously advance science, it must propose hypotheses, design and execute experiments, analyze outcomes, and refine approaches in an iterative loop. Without this integration, AI risks producing disconnected ideas that depend on human supervision for validation. Before the introduction of a unified system, researchers relied on separate tools for each stage of the process. Large language models could help find relevant scientific papers, but they didn’t directly feed into experiment design or result analysis. Robotics can assist in automating physical experiments, and coding libraries like PyTorch can help build models; however, these tools operate independently of each other. There was no single system capable of handling the entire process, from forming ideas to verifying them through experiments. This led to bottlenecks, where researchers had to connect the dots manually, slowing progress and leaving room for errors or missed opportunities. The need for an integrated system that could handle the entire research cycle became clear. Researchers from the NovelSeek Team at the Shanghai Artificial Intelligence Laboratory developed NovelSeek, an AI system designed to run the entire scientific discovery process autonomously. NovelSeek comprises four main modules that work in tandem: a system that generates and refines research ideas, a feedback loop where human experts can interact with and refine these ideas, a method for translating ideas into code and experiment plans, and a process for conducting multiple rounds of experiments. What makes NovelSeek stand out is its versatility; it works across 12 scientific research tasks, including predicting chemical reaction yields, understanding molecular dynamics, forecasting time-series data, and handling functions like 2D semantic segmentation and 3D object classification. The team designed NovelSeek to minimize human involvement, expedite discoveries, and deliver consistent, high-quality results. The system behind NovelSeek involves multiple specialized agents, each focused on a specific part of the research workflow. The “Survey Agent” helps the system understand the problem by searching scientific papers and identifying relevant information based on keywords and task definitions. It adapts its search strategy by first doing a broad survey of papers, then going deeper by analyzing full-text documents for detailed insights. This ensures that the system captures both general trends and specific technical knowledge. The “Code Review Agent” examines existing codebases, whether user-uploaded or sourced from public repositories like GitHub, to understand how current methods work and identify areas for improvement. It checks how code is structured, looks for errors, and creates summaries that help the system build on past work. The “Idea Innovation Agent” generates creative research ideas, pushing the system to explore different approaches and refine them by comparing them to related studies and previous results. The system even includes a “Planning and Execution Agent” that turns ideas into detailed experiments, handles errors during the testing process, and ensures smooth execution of multi-step research plans. NovelSeek delivered impressive results across various tasks. In chemical reaction yield prediction, NovelSeek improved performance from a baseline of 24.2%to 34.8%in just 12 hours, progress that human researchers typically need months to achieve. In enhancer activity prediction, a key task in biology, NovelSeek raised the Pearson correlation coefficient from 0.65 to 0.79 within 4 hours. For 2D semantic segmentation, a task used in computer vision, precision improved from 78.8% to 81.0% in just 30 hours. These performance boosts, achieved in a fraction of the time typically needed, highlight the system’s efficiency. NovelSeek also successfully managed large, complex codebases with multiple files, demonstrating its ability to handle research tasks at a project level, not just in small, isolated tests. The team has made the code open-source, allowing others to use, test, and contribute to its improvement. Several Key Takeaways from the Research on NovelSeek include: NovelSeek supports 12 research tasks, including chemical reaction prediction, molecular dynamics, and 3D object classification. Reaction yield prediction accuracy improved from 24.2% to 34.8% in 12 hours. Enhancer activity prediction performance increased from 0.65 to 0.79 in 4 hours. 2D semantic segmentation precision improved from 78.8% to 81.0% in 30 hours. NovelSeek includes agents for literature search, code analysis, idea generation, and experiment execution. The system is open-source, enabling reproducibility and collaboration across scientific fields. In conclusion, NovelSeek demonstrates how combining AI tools into a single system can accelerate scientific discovery and reduce its dependence on human effort. It ties together the key steps, generating ideas, turning them into methods, and testing them through experiments, into one streamlined process. What once took researchers months or years can now be done in days or even hours. By linking every stage of research into a continuous loop, NovelSeek helps teams move from rough ideas to real-world results more quickly. This system highlights the power of AI not just to assist, but to drive scientific research in a way that could reshape how discoveries are made across many fields. Check out the Paper and GitHub Page . All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces ARM and Ada-GRPO: Adaptive Reasoning Models for Efficient and Scalable Problem-SolvingNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces WEB-SHEPHERD: A Process Reward Model for Web Agents with 40K Dataset and 10× Cost EfficiencyNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces MMaDA: A Unified Multimodal Diffusion Model for Textual Reasoning, Visual Understanding, and Image GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural Networks #meet #novelseek #unified #multiagent #framework
    WWW.MARKTECHPOST.COM
    Meet NovelSeek: A Unified Multi-Agent Framework for Autonomous Scientific Research from Hypothesis Generation to Experimental Validation
    Scientific research across fields like chemistry, biology, and artificial intelligence has long relied on human experts to explore knowledge, generate ideas, design experiments, and refine results. Yet, as problems grow more complex and data-intensive, discovery slows. While AI tools, such as language models and robotics, can handle specific tasks, like literature searches or code analysis, they rarely encompass the entire research cycle. Bridging the gap between idea generation and experimental validation remains a key challenge. For AI to autonomously advance science, it must propose hypotheses, design and execute experiments, analyze outcomes, and refine approaches in an iterative loop. Without this integration, AI risks producing disconnected ideas that depend on human supervision for validation. Before the introduction of a unified system, researchers relied on separate tools for each stage of the process. Large language models could help find relevant scientific papers, but they didn’t directly feed into experiment design or result analysis. Robotics can assist in automating physical experiments, and coding libraries like PyTorch can help build models; however, these tools operate independently of each other. There was no single system capable of handling the entire process, from forming ideas to verifying them through experiments. This led to bottlenecks, where researchers had to connect the dots manually, slowing progress and leaving room for errors or missed opportunities. The need for an integrated system that could handle the entire research cycle became clear. Researchers from the NovelSeek Team at the Shanghai Artificial Intelligence Laboratory developed NovelSeek, an AI system designed to run the entire scientific discovery process autonomously. NovelSeek comprises four main modules that work in tandem: a system that generates and refines research ideas, a feedback loop where human experts can interact with and refine these ideas, a method for translating ideas into code and experiment plans, and a process for conducting multiple rounds of experiments. What makes NovelSeek stand out is its versatility; it works across 12 scientific research tasks, including predicting chemical reaction yields, understanding molecular dynamics, forecasting time-series data, and handling functions like 2D semantic segmentation and 3D object classification. The team designed NovelSeek to minimize human involvement, expedite discoveries, and deliver consistent, high-quality results. The system behind NovelSeek involves multiple specialized agents, each focused on a specific part of the research workflow. The “Survey Agent” helps the system understand the problem by searching scientific papers and identifying relevant information based on keywords and task definitions. It adapts its search strategy by first doing a broad survey of papers, then going deeper by analyzing full-text documents for detailed insights. This ensures that the system captures both general trends and specific technical knowledge. The “Code Review Agent” examines existing codebases, whether user-uploaded or sourced from public repositories like GitHub, to understand how current methods work and identify areas for improvement. It checks how code is structured, looks for errors, and creates summaries that help the system build on past work. The “Idea Innovation Agent” generates creative research ideas, pushing the system to explore different approaches and refine them by comparing them to related studies and previous results. The system even includes a “Planning and Execution Agent” that turns ideas into detailed experiments, handles errors during the testing process, and ensures smooth execution of multi-step research plans. NovelSeek delivered impressive results across various tasks. In chemical reaction yield prediction, NovelSeek improved performance from a baseline of 24.2% (with a variation of ±4.2) to 34.8% (with a much smaller variation of ±1.1) in just 12 hours, progress that human researchers typically need months to achieve. In enhancer activity prediction, a key task in biology, NovelSeek raised the Pearson correlation coefficient from 0.65 to 0.79 within 4 hours. For 2D semantic segmentation, a task used in computer vision, precision improved from 78.8% to 81.0% in just 30 hours. These performance boosts, achieved in a fraction of the time typically needed, highlight the system’s efficiency. NovelSeek also successfully managed large, complex codebases with multiple files, demonstrating its ability to handle research tasks at a project level, not just in small, isolated tests. The team has made the code open-source, allowing others to use, test, and contribute to its improvement. Several Key Takeaways from the Research on NovelSeek include: NovelSeek supports 12 research tasks, including chemical reaction prediction, molecular dynamics, and 3D object classification. Reaction yield prediction accuracy improved from 24.2% to 34.8% in 12 hours. Enhancer activity prediction performance increased from 0.65 to 0.79 in 4 hours. 2D semantic segmentation precision improved from 78.8% to 81.0% in 30 hours. NovelSeek includes agents for literature search, code analysis, idea generation, and experiment execution. The system is open-source, enabling reproducibility and collaboration across scientific fields. In conclusion, NovelSeek demonstrates how combining AI tools into a single system can accelerate scientific discovery and reduce its dependence on human effort. It ties together the key steps, generating ideas, turning them into methods, and testing them through experiments, into one streamlined process. What once took researchers months or years can now be done in days or even hours. By linking every stage of research into a continuous loop, NovelSeek helps teams move from rough ideas to real-world results more quickly. This system highlights the power of AI not just to assist, but to drive scientific research in a way that could reshape how discoveries are made across many fields. Check out the Paper and GitHub Page . All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces ARM and Ada-GRPO: Adaptive Reasoning Models for Efficient and Scalable Problem-SolvingNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces WEB-SHEPHERD: A Process Reward Model for Web Agents with 40K Dataset and 10× Cost EfficiencyNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces MMaDA: A Unified Multimodal Diffusion Model for Textual Reasoning, Visual Understanding, and Image GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural Networks
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  • Top 10 Startup Funding Sources for New Entrepreneurs

    Posted on : May 28, 2025

    By

    Tech World Times

    Business 

    Rate this post

    Starting a new business is exciting. But money is often the first big hurdle. Without funds, it’s hard to build products, hire staff, or run ads. That’s where Startup Funding becomes important. It helps entrepreneurs turn ideas into real businesses. In this article, we share the top 10 startup funding sources for new entrepreneurs in 2025.
    1. Personal Savings
    Most founders start with their own money. It’s simple and quick. You don’t owe anyone else. Using savings shows you believe in your idea. But only use what you can afford to lose. Avoid risking your rent or emergency funds.
    2. Friends and Family
    This is a common early funding source. People who know and trust you may help. They might give you money or offer loans. But always keep it professional. Write down terms and make repayment plans clear. It avoids confusion and protects relationships.
    3. Angel Investors
    Angel investors are wealthy individuals who support startups. They often invest in early stages of business. They bring both money and advice. They usually take equity in return. Search platforms like AngelList or attend startup events to meet them.
    4. Venture CapitalVC firms invest big money in fast-growing startups. They look for high returns and innovation. VC funding is best for tech or scalable startups. They take equity and sometimes want a say. It’s competitive, so prepare strong pitch decks.
    5. Business Incubators and Accelerators
    These programs help startups grow fast. They offer funding, training, and mentorship. Some popular examples are Y Combinator and Techstars. They often end with demo days to attract investors. You may give up a small equity share.
    6. Crowdfunding
    Crowdfunding is when many people fund your idea online. Sites like Kickstarter and Indiegogo make this easy. You offer early access or rewards instead of equity. It works well for products people can see. Be sure to promote your campaign heavily.
    7. Bank Loans
    Banks offer loans for startups and small businesses. They usually require a credit check and a business plan. Interest rates vary. Some banks need collateral. If approved, loans give quick access to funds. This option doesn’t dilute your ownership.
    8. Government Grants
    Many governments support small businesses. They offer grants for innovation, research, or job creation. Grants don’t require repayment or equity. But they involve paperwork and clear guidelines. Search local government or small business websites.
    9. Business Competitions
    Pitch competitions can offer funding and exposure. Entrepreneurs present their ideas to a panel of judges. Winners receive cash prizes or investment offers. Even if you don’t win, you gain feedback. Search for startup contests in your city or industry.
    10. Corporate Venture Funds
    Big companies often invest in small startups. They want access to new ideas and technologies. These corporate funds work like VCs. But they may also offer partnerships or clients. Look for companies related to your industry.
    Tips for Choosing the Right Source
    Choosing the right Startup Funding source is important. Think about your business stage and goals. Do you need quick cash or long-term help? Can you give up equity or not? Always read the terms and plan your pitch.
    How to Get Ready for Funding
    Before applying for funding, get prepared. Make a clear business plan and pitch deck. Know your numbers: costs, sales, profits, and growth plans. Show why your idea is different or better. Practice your pitch until you feel confident.
    Pros and Cons of Funding Options
    Each Startup Funding source has pros and cons. Here’s a quick comparison for easy understanding:
    SourceProsConsSavingsFull controlRisk of personal lossFriends/FamilyEasy accessCan hurt relationshipsAngelsSmart moneyGive up equityVCsLarge fundsHigh pressure to growIncubatorsSupportiveEquity shareCrowdfundingNo equity neededNeeds marketingBank LoansKeep full ownershipInterest, credit checksGrantsFree moneySlow and competitiveCompetitionsWin fundingNo guaranteeCorporate FundsBig supportMight want exclusivity
    Choose what fits your situation best.
    Final Thoughts
    Finding Startup Funding takes effort, but it’s possible. Start small, and build trust with each step. Many successful companies began with tiny investments. You don’t need millions to get started. Pick the right funding source and stay focused. With patience and planning, your startup can grow. Keep learning, keep networking, and never stop pitching. Your big break might be one meeting away.
    Tech World TimesTech World Times, a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com
    #top #startup #funding #sources #new
    Top 10 Startup Funding Sources for New Entrepreneurs
    Posted on : May 28, 2025 By Tech World Times Business  Rate this post Starting a new business is exciting. But money is often the first big hurdle. Without funds, it’s hard to build products, hire staff, or run ads. That’s where Startup Funding becomes important. It helps entrepreneurs turn ideas into real businesses. In this article, we share the top 10 startup funding sources for new entrepreneurs in 2025. 1. Personal Savings Most founders start with their own money. It’s simple and quick. You don’t owe anyone else. Using savings shows you believe in your idea. But only use what you can afford to lose. Avoid risking your rent or emergency funds. 2. Friends and Family This is a common early funding source. People who know and trust you may help. They might give you money or offer loans. But always keep it professional. Write down terms and make repayment plans clear. It avoids confusion and protects relationships. 3. Angel Investors Angel investors are wealthy individuals who support startups. They often invest in early stages of business. They bring both money and advice. They usually take equity in return. Search platforms like AngelList or attend startup events to meet them. 4. Venture CapitalVC firms invest big money in fast-growing startups. They look for high returns and innovation. VC funding is best for tech or scalable startups. They take equity and sometimes want a say. It’s competitive, so prepare strong pitch decks. 5. Business Incubators and Accelerators These programs help startups grow fast. They offer funding, training, and mentorship. Some popular examples are Y Combinator and Techstars. They often end with demo days to attract investors. You may give up a small equity share. 6. Crowdfunding Crowdfunding is when many people fund your idea online. Sites like Kickstarter and Indiegogo make this easy. You offer early access or rewards instead of equity. It works well for products people can see. Be sure to promote your campaign heavily. 7. Bank Loans Banks offer loans for startups and small businesses. They usually require a credit check and a business plan. Interest rates vary. Some banks need collateral. If approved, loans give quick access to funds. This option doesn’t dilute your ownership. 8. Government Grants Many governments support small businesses. They offer grants for innovation, research, or job creation. Grants don’t require repayment or equity. But they involve paperwork and clear guidelines. Search local government or small business websites. 9. Business Competitions Pitch competitions can offer funding and exposure. Entrepreneurs present their ideas to a panel of judges. Winners receive cash prizes or investment offers. Even if you don’t win, you gain feedback. Search for startup contests in your city or industry. 10. Corporate Venture Funds Big companies often invest in small startups. They want access to new ideas and technologies. These corporate funds work like VCs. But they may also offer partnerships or clients. Look for companies related to your industry. Tips for Choosing the Right Source Choosing the right Startup Funding source is important. Think about your business stage and goals. Do you need quick cash or long-term help? Can you give up equity or not? Always read the terms and plan your pitch. How to Get Ready for Funding Before applying for funding, get prepared. Make a clear business plan and pitch deck. Know your numbers: costs, sales, profits, and growth plans. Show why your idea is different or better. Practice your pitch until you feel confident. Pros and Cons of Funding Options Each Startup Funding source has pros and cons. Here’s a quick comparison for easy understanding: SourceProsConsSavingsFull controlRisk of personal lossFriends/FamilyEasy accessCan hurt relationshipsAngelsSmart moneyGive up equityVCsLarge fundsHigh pressure to growIncubatorsSupportiveEquity shareCrowdfundingNo equity neededNeeds marketingBank LoansKeep full ownershipInterest, credit checksGrantsFree moneySlow and competitiveCompetitionsWin fundingNo guaranteeCorporate FundsBig supportMight want exclusivity Choose what fits your situation best. Final Thoughts Finding Startup Funding takes effort, but it’s possible. Start small, and build trust with each step. Many successful companies began with tiny investments. You don’t need millions to get started. Pick the right funding source and stay focused. With patience and planning, your startup can grow. Keep learning, keep networking, and never stop pitching. Your big break might be one meeting away. Tech World TimesTech World Times, a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com #top #startup #funding #sources #new
    TECHWORLDTIMES.COM
    Top 10 Startup Funding Sources for New Entrepreneurs
    Posted on : May 28, 2025 By Tech World Times Business  Rate this post Starting a new business is exciting. But money is often the first big hurdle. Without funds, it’s hard to build products, hire staff, or run ads. That’s where Startup Funding becomes important. It helps entrepreneurs turn ideas into real businesses. In this article, we share the top 10 startup funding sources for new entrepreneurs in 2025. 1. Personal Savings Most founders start with their own money. It’s simple and quick. You don’t owe anyone else. Using savings shows you believe in your idea. But only use what you can afford to lose. Avoid risking your rent or emergency funds. 2. Friends and Family This is a common early funding source. People who know and trust you may help. They might give you money or offer loans. But always keep it professional. Write down terms and make repayment plans clear. It avoids confusion and protects relationships. 3. Angel Investors Angel investors are wealthy individuals who support startups. They often invest in early stages of business. They bring both money and advice. They usually take equity in return. Search platforms like AngelList or attend startup events to meet them. 4. Venture Capital (VC) VC firms invest big money in fast-growing startups. They look for high returns and innovation. VC funding is best for tech or scalable startups. They take equity and sometimes want a say. It’s competitive, so prepare strong pitch decks. 5. Business Incubators and Accelerators These programs help startups grow fast. They offer funding, training, and mentorship. Some popular examples are Y Combinator and Techstars. They often end with demo days to attract investors. You may give up a small equity share. 6. Crowdfunding Crowdfunding is when many people fund your idea online. Sites like Kickstarter and Indiegogo make this easy. You offer early access or rewards instead of equity. It works well for products people can see. Be sure to promote your campaign heavily. 7. Bank Loans Banks offer loans for startups and small businesses. They usually require a credit check and a business plan. Interest rates vary. Some banks need collateral. If approved, loans give quick access to funds. This option doesn’t dilute your ownership. 8. Government Grants Many governments support small businesses. They offer grants for innovation, research, or job creation. Grants don’t require repayment or equity. But they involve paperwork and clear guidelines. Search local government or small business websites. 9. Business Competitions Pitch competitions can offer funding and exposure. Entrepreneurs present their ideas to a panel of judges. Winners receive cash prizes or investment offers. Even if you don’t win, you gain feedback. Search for startup contests in your city or industry. 10. Corporate Venture Funds Big companies often invest in small startups. They want access to new ideas and technologies. These corporate funds work like VCs. But they may also offer partnerships or clients. Look for companies related to your industry. Tips for Choosing the Right Source Choosing the right Startup Funding source is important. Think about your business stage and goals. Do you need quick cash or long-term help? Can you give up equity or not? Always read the terms and plan your pitch. How to Get Ready for Funding Before applying for funding, get prepared. Make a clear business plan and pitch deck. Know your numbers: costs, sales, profits, and growth plans. Show why your idea is different or better. Practice your pitch until you feel confident. Pros and Cons of Funding Options Each Startup Funding source has pros and cons. Here’s a quick comparison for easy understanding: SourceProsConsSavingsFull controlRisk of personal lossFriends/FamilyEasy accessCan hurt relationshipsAngelsSmart moneyGive up equityVCsLarge fundsHigh pressure to growIncubatorsSupportiveEquity shareCrowdfundingNo equity neededNeeds marketingBank LoansKeep full ownershipInterest, credit checksGrantsFree moneySlow and competitiveCompetitionsWin fundingNo guaranteeCorporate FundsBig supportMight want exclusivity Choose what fits your situation best. Final Thoughts Finding Startup Funding takes effort, but it’s possible. Start small, and build trust with each step. Many successful companies began with tiny investments. You don’t need millions to get started. Pick the right funding source and stay focused. With patience and planning, your startup can grow. Keep learning, keep networking, and never stop pitching. Your big break might be one meeting away. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com
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  • This AI Paper Introduces ARM and Ada-GRPO: Adaptive Reasoning Models for Efficient and Scalable Problem-Solving

    Reasoning tasks are a fundamental aspect of artificial intelligence, encompassing areas like commonsense understanding, mathematical problem-solving, and symbolic reasoning. These tasks often involve multiple steps of logical inference, which large language modelsattempt to mimic through structured approaches such as chain-of-thoughtprompting. However, as LLMs grow in size and complexity, they tend to produce longer outputs across all tasks, regardless of difficulty, leading to significant inefficiencies. The field has been striving to balance the depth of reasoning with computational cost while also ensuring that models can adapt their reasoning strategies to meet the unique needs of each problem.
    A key issue with current reasoning models is the inability to tailor the reasoning process to different task complexities. Most models, including well-known ones like OpenAI’s o1 and DeepSeek-R1, apply a uniform strategy—typically relying on Long CoT across all tasks. This causes the “overthinking” problem, where models generate unnecessarily verbose explanations for simpler tasks. Not only does this waste resources, but it also degrades accuracy, as excessive reasoning can introduce irrelevant information. Approaches such as prompt-guided generation or token budget estimation have attempted to mitigate this issue. Still, these methods are limited by their dependence on predefined assumptions, which are not always reliable for diverse tasks.

    Attempts to address these issues include methods like GRPO, length-penalty mechanisms, and rule-based prompt controls. While GRPO enables models to learn different reasoning strategies by rewarding correct answers, it leads to a “format collapse,” where models increasingly rely on Long CoT, crowding out more efficient formats, such as Short CoT or Direct Answer. Length-penalty techniques, such as those applied in methods like THINKPRUNE, control output length during training or inference, but often at the cost of reduced accuracy, especially in complex problem-solving tasks. These solutions struggle to achieve a consistent trade-off between reasoning effectiveness and efficiency, highlighting the need for an adaptive approach.
    A team of researchers from Fudan University and Ohio State University introduced the Adaptive Reasoning Model, which dynamically adjusts reasoning formats based on task difficulty. ARM supports four distinct reasoning styles: Direct Answer for simple tasks, Short CoT for concise reasoning, Code for structured problem-solving, and Long CoT for deep multi-step reasoning. It operates in an Adaptive Mode by default, automatically selecting the appropriate format, and also provides Instruction-Guided and Consensus-Guided Modes for explicit control or aggregation across formats. The key innovation lies in its training process, which utilizes Ada-GRPO, an extension of GRPO that introduces a format diversity reward mechanism. This prevents the dominance of Long CoT and ensures that ARM continues to explore and use simpler reasoning formats when appropriate.

    The ARM methodology is built on a two-stage framework. First, the model undergoes Supervised Fine-Tuningwith 10.8K questions, each annotated across four reasoning formats, sourced from datasets like AQuA-Rat and generated with tools such as GPT-4o and DeepSeek-R1. This stage teaches the model the structure of each reasoning format but does not instill adaptiveness. The second stage applies Ada-GRPO, where the model receives scaled rewards for using less frequent formats, such as Direct Answer or Short CoT. A decaying factor ensures that this reward gradually shifts back to accuracy as training progresses, preventing long-term bias toward inefficient exploration. This structure enables ARM to avoid format collapse and dynamically match reasoning strategies to task difficulty, achieving a balance of efficiency and performance.

    ARM demonstrated impressive results across various benchmarks, including commonsense, mathematical, and symbolic reasoning tasks. It reduced token usage by an average of 30%, with reductions as high as 70% for simpler tasks, compared to models relying solely on Long CoT. ARM achieved a 2x training speedup over GRPO-based models, accelerating model development without sacrificing accuracy. For example, ARM-7B achieved 75.9% accuracy on the challenging AIME’25 task while using 32.5% fewer tokens. ARM-14B achieved 85.6% accuracy on OpenBookQA and 86.4% accuracy on the MATH dataset, with a token usage reduction of over 30% compared to Qwen2.5SFT+GRPO models. These numbers demonstrate ARM’s ability to maintain competitive performance while delivering significant efficiency gains.
    Overall, the Adaptive Reasoning Model addresses the persistent inefficiency of reasoning models by enabling the adaptive selection of reasoning formats based on task difficulty. The introduction of Ada-GRPO and the multi-format training framework ensures that models no longer waste resources on overthinking. Instead, ARM provides a flexible and practical solution for balancing accuracy and computational cost in reasoning tasks, making it a promising approach for scalable and efficient large language models.

    Check out the Paper, Models on Hugging Face and Project Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter.
    NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces WEB-SHEPHERD: A Process Reward Model for Web Agents with 40K Dataset and 10× Cost EfficiencyNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces MMaDA: A Unified Multimodal Diffusion Model for Textual Reasoning, Visual Understanding, and Image GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural NetworksNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces GRIT: A Method for Teaching MLLMs to Reason with Images by Interleaving Text and Visual Grounding
    #this #paper #introduces #arm #adagrpo
    This AI Paper Introduces ARM and Ada-GRPO: Adaptive Reasoning Models for Efficient and Scalable Problem-Solving
    Reasoning tasks are a fundamental aspect of artificial intelligence, encompassing areas like commonsense understanding, mathematical problem-solving, and symbolic reasoning. These tasks often involve multiple steps of logical inference, which large language modelsattempt to mimic through structured approaches such as chain-of-thoughtprompting. However, as LLMs grow in size and complexity, they tend to produce longer outputs across all tasks, regardless of difficulty, leading to significant inefficiencies. The field has been striving to balance the depth of reasoning with computational cost while also ensuring that models can adapt their reasoning strategies to meet the unique needs of each problem. A key issue with current reasoning models is the inability to tailor the reasoning process to different task complexities. Most models, including well-known ones like OpenAI’s o1 and DeepSeek-R1, apply a uniform strategy—typically relying on Long CoT across all tasks. This causes the “overthinking” problem, where models generate unnecessarily verbose explanations for simpler tasks. Not only does this waste resources, but it also degrades accuracy, as excessive reasoning can introduce irrelevant information. Approaches such as prompt-guided generation or token budget estimation have attempted to mitigate this issue. Still, these methods are limited by their dependence on predefined assumptions, which are not always reliable for diverse tasks. Attempts to address these issues include methods like GRPO, length-penalty mechanisms, and rule-based prompt controls. While GRPO enables models to learn different reasoning strategies by rewarding correct answers, it leads to a “format collapse,” where models increasingly rely on Long CoT, crowding out more efficient formats, such as Short CoT or Direct Answer. Length-penalty techniques, such as those applied in methods like THINKPRUNE, control output length during training or inference, but often at the cost of reduced accuracy, especially in complex problem-solving tasks. These solutions struggle to achieve a consistent trade-off between reasoning effectiveness and efficiency, highlighting the need for an adaptive approach. A team of researchers from Fudan University and Ohio State University introduced the Adaptive Reasoning Model, which dynamically adjusts reasoning formats based on task difficulty. ARM supports four distinct reasoning styles: Direct Answer for simple tasks, Short CoT for concise reasoning, Code for structured problem-solving, and Long CoT for deep multi-step reasoning. It operates in an Adaptive Mode by default, automatically selecting the appropriate format, and also provides Instruction-Guided and Consensus-Guided Modes for explicit control or aggregation across formats. The key innovation lies in its training process, which utilizes Ada-GRPO, an extension of GRPO that introduces a format diversity reward mechanism. This prevents the dominance of Long CoT and ensures that ARM continues to explore and use simpler reasoning formats when appropriate. The ARM methodology is built on a two-stage framework. First, the model undergoes Supervised Fine-Tuningwith 10.8K questions, each annotated across four reasoning formats, sourced from datasets like AQuA-Rat and generated with tools such as GPT-4o and DeepSeek-R1. This stage teaches the model the structure of each reasoning format but does not instill adaptiveness. The second stage applies Ada-GRPO, where the model receives scaled rewards for using less frequent formats, such as Direct Answer or Short CoT. A decaying factor ensures that this reward gradually shifts back to accuracy as training progresses, preventing long-term bias toward inefficient exploration. This structure enables ARM to avoid format collapse and dynamically match reasoning strategies to task difficulty, achieving a balance of efficiency and performance. ARM demonstrated impressive results across various benchmarks, including commonsense, mathematical, and symbolic reasoning tasks. It reduced token usage by an average of 30%, with reductions as high as 70% for simpler tasks, compared to models relying solely on Long CoT. ARM achieved a 2x training speedup over GRPO-based models, accelerating model development without sacrificing accuracy. For example, ARM-7B achieved 75.9% accuracy on the challenging AIME’25 task while using 32.5% fewer tokens. ARM-14B achieved 85.6% accuracy on OpenBookQA and 86.4% accuracy on the MATH dataset, with a token usage reduction of over 30% compared to Qwen2.5SFT+GRPO models. These numbers demonstrate ARM’s ability to maintain competitive performance while delivering significant efficiency gains. Overall, the Adaptive Reasoning Model addresses the persistent inefficiency of reasoning models by enabling the adaptive selection of reasoning formats based on task difficulty. The introduction of Ada-GRPO and the multi-format training framework ensures that models no longer waste resources on overthinking. Instead, ARM provides a flexible and practical solution for balancing accuracy and computational cost in reasoning tasks, making it a promising approach for scalable and efficient large language models. Check out the Paper, Models on Hugging Face and Project Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces WEB-SHEPHERD: A Process Reward Model for Web Agents with 40K Dataset and 10× Cost EfficiencyNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces MMaDA: A Unified Multimodal Diffusion Model for Textual Reasoning, Visual Understanding, and Image GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural NetworksNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces GRIT: A Method for Teaching MLLMs to Reason with Images by Interleaving Text and Visual Grounding #this #paper #introduces #arm #adagrpo
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    This AI Paper Introduces ARM and Ada-GRPO: Adaptive Reasoning Models for Efficient and Scalable Problem-Solving
    Reasoning tasks are a fundamental aspect of artificial intelligence, encompassing areas like commonsense understanding, mathematical problem-solving, and symbolic reasoning. These tasks often involve multiple steps of logical inference, which large language models (LLMs) attempt to mimic through structured approaches such as chain-of-thought (CoT) prompting. However, as LLMs grow in size and complexity, they tend to produce longer outputs across all tasks, regardless of difficulty, leading to significant inefficiencies. The field has been striving to balance the depth of reasoning with computational cost while also ensuring that models can adapt their reasoning strategies to meet the unique needs of each problem. A key issue with current reasoning models is the inability to tailor the reasoning process to different task complexities. Most models, including well-known ones like OpenAI’s o1 and DeepSeek-R1, apply a uniform strategy—typically relying on Long CoT across all tasks. This causes the “overthinking” problem, where models generate unnecessarily verbose explanations for simpler tasks. Not only does this waste resources, but it also degrades accuracy, as excessive reasoning can introduce irrelevant information. Approaches such as prompt-guided generation or token budget estimation have attempted to mitigate this issue. Still, these methods are limited by their dependence on predefined assumptions, which are not always reliable for diverse tasks. Attempts to address these issues include methods like GRPO (Group Relative Policy Optimization), length-penalty mechanisms, and rule-based prompt controls. While GRPO enables models to learn different reasoning strategies by rewarding correct answers, it leads to a “format collapse,” where models increasingly rely on Long CoT, crowding out more efficient formats, such as Short CoT or Direct Answer. Length-penalty techniques, such as those applied in methods like THINKPRUNE, control output length during training or inference, but often at the cost of reduced accuracy, especially in complex problem-solving tasks. These solutions struggle to achieve a consistent trade-off between reasoning effectiveness and efficiency, highlighting the need for an adaptive approach. A team of researchers from Fudan University and Ohio State University introduced the Adaptive Reasoning Model (ARM), which dynamically adjusts reasoning formats based on task difficulty. ARM supports four distinct reasoning styles: Direct Answer for simple tasks, Short CoT for concise reasoning, Code for structured problem-solving, and Long CoT for deep multi-step reasoning. It operates in an Adaptive Mode by default, automatically selecting the appropriate format, and also provides Instruction-Guided and Consensus-Guided Modes for explicit control or aggregation across formats. The key innovation lies in its training process, which utilizes Ada-GRPO, an extension of GRPO that introduces a format diversity reward mechanism. This prevents the dominance of Long CoT and ensures that ARM continues to explore and use simpler reasoning formats when appropriate. The ARM methodology is built on a two-stage framework. First, the model undergoes Supervised Fine-Tuning (SFT) with 10.8K questions, each annotated across four reasoning formats, sourced from datasets like AQuA-Rat and generated with tools such as GPT-4o and DeepSeek-R1. This stage teaches the model the structure of each reasoning format but does not instill adaptiveness. The second stage applies Ada-GRPO, where the model receives scaled rewards for using less frequent formats, such as Direct Answer or Short CoT. A decaying factor ensures that this reward gradually shifts back to accuracy as training progresses, preventing long-term bias toward inefficient exploration. This structure enables ARM to avoid format collapse and dynamically match reasoning strategies to task difficulty, achieving a balance of efficiency and performance. ARM demonstrated impressive results across various benchmarks, including commonsense, mathematical, and symbolic reasoning tasks. It reduced token usage by an average of 30%, with reductions as high as 70% for simpler tasks, compared to models relying solely on Long CoT. ARM achieved a 2x training speedup over GRPO-based models, accelerating model development without sacrificing accuracy. For example, ARM-7B achieved 75.9% accuracy on the challenging AIME’25 task while using 32.5% fewer tokens. ARM-14B achieved 85.6% accuracy on OpenBookQA and 86.4% accuracy on the MATH dataset, with a token usage reduction of over 30% compared to Qwen2.5SFT+GRPO models. These numbers demonstrate ARM’s ability to maintain competitive performance while delivering significant efficiency gains. Overall, the Adaptive Reasoning Model addresses the persistent inefficiency of reasoning models by enabling the adaptive selection of reasoning formats based on task difficulty. The introduction of Ada-GRPO and the multi-format training framework ensures that models no longer waste resources on overthinking. Instead, ARM provides a flexible and practical solution for balancing accuracy and computational cost in reasoning tasks, making it a promising approach for scalable and efficient large language models. Check out the Paper, Models on Hugging Face and Project Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces WEB-SHEPHERD: A Process Reward Model for Web Agents with 40K Dataset and 10× Cost EfficiencyNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces MMaDA: A Unified Multimodal Diffusion Model for Textual Reasoning, Visual Understanding, and Image GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural NetworksNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces GRIT: A Method for Teaching MLLMs to Reason with Images by Interleaving Text and Visual Grounding
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