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Fast Company
15-07-2025
- Science
- Fast Company
Why 1995 was the year the internet grew up
The internet wasn't born whole—it came together from parts. Most know of ARPANET, the internet's most famous precursor, but it was always limited strictly to government use. It was NSFNET that brought many networks together, and the internet that we use today is almost NSFNET itself. Almost, but not quite: in 1995, the government that had raised the internet from its infancy gave it a firm shove out the door. Call it a graduation, or a coming of age. I think of it as the internet getting its first real job. In the early 1980s, the National Science Foundation sought to establish the United States as a leader in scientific computing. The plan required a fleet of supercomputers that researchers could readily use, a difficult feat when the computers routinely cost more than the buildings that housed them. Business computing had solved similar problems with time-sharing and remote terminals, and ARPANET had demonstrated that terminals could be connected to computers across the country using a packet-switching network. The Computer Science Network, or CSNET, was the NSF's first foray into wide area networking. It connected universities that didn't have defense contracts and, as a result, had been left out of ARPANET. With dozens of sites, CSNET was much smaller than ARPANET but proved that a group of universities could share computing resources. When the NSF funded five cutting-edge supercomputing centers in 1985, it planned to make them available to users over a similar network. The problem was that big computers invited big data: CSNET just wasn't fast enough for interactive work with large data sets, and it was falling further behind as traffic doubled about every two weeks. After a sluggish 56 Kbps pilot effort (about a thousand times slower than today's common broadband connections), the NSF contracted the University of Michigan to develop an all-new replacement based on MERIT—a Michigan inter-university network that had already started to expand its high-speed digital telephone and geostationary satellite links into other states. In 1987, the MERIT team brought on IBM and upstart long-distance carrier MCI, freshly invigorated by the antitrust breakup of their principal competitor and truly feeling their oats. They worked at a breakneck pace. In under a year, NSFNET connected the supercomputing centers and a half dozen regional networks at blistering T1 speeds: 1.5 Mbps—an almost 28-fold increase. Just after 8 p.m. on June 30, 1988, Hans-Werner Braun, the project's co-principal investigator, sent an email to the NSFNET mailing list to announce these new high-capacity links—among the fastest long-distance computer connections ever deployed—with typical scientific understatement: 'The NSFnet Backbone has reached a state where we would like to more officially let operational traffic on.' Braun's email 'received little notice at the time,' the NSF wrote in a 2008 announcement. But 'those simple words announced the birth of the modern Internet.' NSFNET was a runaway success. Besides its massive capacity, the network maintained an open door for interconnection. Overseas academic computer networks established peer connections with NSFNET, and in 1989 the federal government opened two Federal Internet Exchanges that routed traffic between NSFNET, ARPANET, and other government networks. The superior speed of NSFNET meant that these exchanges served mostly to bring NSFNET to federal users, and ARPANET's fate was sealed. The military network, birthplace of many internet technologies, was deemed obsolete and decommissioned the next year. At the turn of the 1990s, NSFNET had become the Internet: the unified backbone by which regional and institutional networks came together. NSFNET never stopped growing. It was a remarkable problem: at every stage, NSFNET traffic grew faster than anticipated. During 1989 alone, traffic increased by five times. The state of the art T1 links were overwhelmed, demanding a 1991 upgrade to 45 Mbps T3 connections. To manage the rapidly expanding infrastructure, the original NSFNET partners formed Advanced Network and Services (ANS). ANS was an independent nonprofit that could be called the first backbone ISP, the service provider that service providers themselves connected to. The popularity of this new communications system was not limited to government and academia. Private industry took note as well. During the 1980s, 'online services' had sprouted: companies like CompuServe, PlayNet, and AOL that are often considered early ISPs but were, in fact, something else. Online services, for both businesses and consumers, were walled gardens. They descended from time-sharing systems that connected users to a single computer, providing only a 'curated' experience of software provided by the online service itself. The internet, in the tradition of ARPANET and especially NSFNET, was very different. It was a collection of truly independent networks, autonomous systems, with the freedom to communicate across geographical and organizational boundaries. It could feel like chaos, but it also fostered innovation. The internet offered possibilities that the online services never could. Douglas Van Houweling, director of the MERIT office, called NSFNET's university origin 'the only community that understands that great things can happen when no one's in charge.' At first, it was contractors who took their business to the internet. ARPANET had always been strictly for government business, but still, companies with the privilege of ARPANET connections found it hard not to use them for other work. Despite prohibitions, ARPANET users exchanged personal messages, coordinated visits, and even distributed the first spam. NSFNET's much wider scope, welcoming anyone with a nexus to research or education, naturally invited users to push the limits further. Besides, the commercial internet was starting to form. CERN engineer Tim Berners-Lee had invented HTML and, along with it, the World Wide Web. In 1993, NCSA—one of the same NSF supercomputing centers that NSFNET was built to connect—released Mosaic, the first popular web browser. Early private ISPs, companies like PSINet and Cerfnet, started out as regional academic networks (New York and California's). There was obvious business interest, and for cash-strapped academic networks paying customers were hard to turn down. NSFNET went into business on its own, with ANS establishing its own for-profit commercial subsidiary called ANS CO+RE. The term 'internet backbone' still finds use today, but in a less literal sense. NSFNET truly was the spine of the early 1990s internet, the only interconnection between otherwise disparate networks. It facilitated the internet's growth, but it also became a gatekeeper: NSF funding came with the condition that it be used for research and education. NSFNET had always kept a somewhat liberal attitude towards its users' online activities, but the growth of outright for-profit networks made the conflict between academia and commerce impossible to ignore. Several commercial ISPs established their own exchange, an option for business traffic to bypass NSFNET, but it couldn't provide the level of connectivity that NSFNET did. Besides, ANS itself opposed fragmentation of the internet and refused to support direct interconnection between other ISPs. In 1992, a series of NSFNET policy changes and an act of Congress opened the door to business traffic on a more formal basis, but the damage was done. A divide had formed between the internet as an academic venture and the internet as a business, a divide that was only deepened by mistrust between upstart internet businesses and incumbent providers ANS, IBM, and MCI. The network was not the only place that cracks formed. Dating back to ARPANET, a database called the Domain Name System maintained a mapping between numeric addresses and more human-friendly names. While DNS was somewhat distributed, it required a central organization to maintain the top level of the hierarchy. There had been different databases for different networks, but consolidation onto NSFNET required unifying the name system as well. By 1993, all of the former name registries had contracted the work to a single company called Network Solutions. At first, Network Solutions benefited from the same federal largesse as NSFNET. Registry services were funded by government contracts and free to users. Requests came faster and faster, though, and the database grew larger and larger. In 1995, Network Solutions joined the ranks of the defense industrial complex with an acquisition by SAIC. Along with the new owner came new terms: SAIC negotiated an amendment to the NSF contracts that, for the first time, introduced a fee to register a domain name. Claiming a name on the internet would run $100 per two years. By then, commercial ISPs had proliferated. Despite policy changes, NSFNET remained less enthusiastic about commercial users than academic ones. Besides, traffic hadn't stopped growing, and improved routing technologies meant the network could scale across multiple routes. The internet became competitive. MCI, benefiting from their experience operating NSFNET links, had built its own backbone network. Sprint, never far behind MCI, had one too. ANS reorganized their assets, placing much of their backbone infrastructure under their commercial operations. Government support of the increasingly profit-driven internet seemed unwise and, ultimately, unnecessary. In April of 1995, the internet changed: NSF shut down the NSFNET backbone. The government funded, academically motivated core of the internet was replaced by a haphazard but thriving interconnection of commercial ventures. ANS, now somewhat lost for purpose, stepped out into the new world of internet industry and sold its infrastructure to AOL. Network Solutions became embroiled in a monopoly controversy that saw DNS reorganized into a system of competitive private registrars. Modems became standard equipment on newly popular personal computers, and millions of Americans dialed into a commercial ISP. We built communities, businesses, and the shape of the 21st century over infrastructure that had been, just years before, a collection of universities with an NSF grant. The internet, born in the 1960s, spent its young adult years in the university. It learned a lot: the policies, the protocols, the basic shape of the internet, all solidified under the tutelage of research institutions and the NSF. And then, the internet graduated. It went out, got a job, and found its own way. Just where that way leads, we're still finding out. The super-early-rate deadline for Fast Company's Most Innovative Companies Awards is Friday, July 25, at 11:59 p.m. PT. Apply today.
The Star
24-06-2025
- Science
- The Star
Can AI quicken the pace of math discovery?
Artificial intelligence can write a poem in the style of Walt Whitman, provide dating advice and suggest the best way to cook an artichoke. But when it comes to mathematics, large language models like OpenAI's immensely popular ChatGPT have sometimes stumbled over basic problems. Some see this as an inherent limitation of the technology, especially when it comes to complex reasoning. A new initiative from the Defense Advanced Research Projects Agency seeks to account for that shortfall by enlisting researchers in finding ways to conduct high-level mathematics research with an AI 'co-author.' The goal of the new grant-making program, Exponentiating Mathematics, is to speed up the pace of progress in pure (as opposed to applied) math – and, in doing so, to turn AI into a superlative mathematician. 'Mathematics is this great test bed for what is right now the key pain point for AI systems,' said Patrick Shafto, a Rutgers University mathematician and computer scientist who now serves as a program manager in DARPA's information innovation office, known as I20. 'So if we overcome that, potentially, it would unleash much more powerful AI.' He added, 'There's huge potential benefit to the community of mathematicians and to society at large.' Shafto spoke from his office at DARPA's headquarters, an anonymous building in northern Virginia whose facade of bluish glass gives little indication that it houses one of the most unusual agencies in the federal government. Inside the building's airy lobby, visitors surrender their cellphones. Near a bank of chairs, a glass display shows a prosthetic arm that can be controlled by the wearer's brain signals. 'By improving mathematics, we're also understanding how AI works better,' said Alondra Nelson, who served as a top science adviser in President Joe Biden's administration and is a faculty member at the Institute for Advanced Study in Princeton, New Jersey. 'So I think it's kind of a virtuous cycle of understanding.' She suggested that, down the road, math-adept AI could enhance cryptography and aid in space exploration. Started after World War II to compete with the Soviet Union in the space race, DARPA is most famous for fostering the research that led to the creation of ARPANET, the precursor to the internet we use today. At the agency's small gift store, which is not accessible to the public, one can buy replicas of a cocktail napkin on which someone sketched out the rudimentary state of computer networks in 1969. DARPA later funded the research that gave rise to drones and Apple's digital assistant, Siri. But it is also responsible for the development of Agent Orange, the potent defoliant used to devastating effect during the Vietnam War. 'I'm sure this isn't 100% innocent,' Andrew Granville, a mathematician at the University of Montreal, said of DARPA's math initiative, although he emphasised that he was only speculating about eventual outcomes. DARPA is, after all, part of the Pentagon, even if it has traditionally operated with enviable independence. The US military is rapidly incorporating AI into its operations, with the aim of not losing out to China and its People's Liberation Army or to Russia, which has been testing out new technologies on the battlefield in Ukraine. At the same time, Granville praised the endeavour, which comes as the Trump administration is cutting funding for scientific research. 'We are in disastrous times for US science,' Granville said. 'I'm very pleased that DARPA is able to funnel money to academia.' A surfer and skateboarder in his free time, Shafto, 49, sat in a sparse conference room one recent afternoon, imagining a future when AI would be as good at solving multistep problems as it is at trying to glean meaning from huge troves of texts, which it does through the use of probability theory. Despite the unseasonably raw weather, Shafto seemed dressed for the beach in a blue-and-white Hawaiian-style shirt, white flannel trousers and sandals, with a trilby hat on the table before him. His vibe was, on the whole, decidedly closer to that of Santa Cruz than of Capitol Hill, largely in keeping with DARPA's traditional disregard for the capital's slow, bureaucratic pace. (The agency sets priorities and funds outside scientists but does not do research on its own; academics like Shafto spend an average of four years as program managers.) 'There are great mathematicians who work on age-old problems,' Shafto said. 'That's not the kind of thing that I'm particularly interested in.' Instead, he wanted the discipline to move more quickly by using AI to save time. 'Problems in mathematics take decades or centuries, sometimes, to solve,' he said in a recent presentation at DARPA's headquarters on the Exponentiating Mathematics project, which is accepting applications through mid-July. He then shared a slide showing that, in terms of the number of papers published, math had stagnated during the last century while life and technical sciences had exploded. In case the point wasn't clear, the slide's heading drove it home: 'Math is sloooowwww. …' The kind of pure math Shafto wants to accelerate tends to be 'sloooowwww' because it is not seeking numerical solutions to concrete problems, the way applied mathematics does. Instead, pure math is the heady domain of visionary theoreticians who make audacious observations about how the world works, which are promptly scrutinised (and sometimes torn apart) by their peers. 'Proof is king,' Granville said. Math proofs consist of multiple building blocks called lemmas, minor theorems employed to prove bigger ones. Whether each Jenga tower of lemmas can maintain integrity in the face of intense scrutiny is precisely what makes pure math such a 'long and laborious process,' acknowledged Bryna R. Kra, a mathematician at Northwestern University. 'All of math builds on previous math, so you can't really prove new things if you don't understand how to prove the old things,' she said. 'To be a research mathematician, the current practice is that you go through every step, you prove every single detail.' Lean, a software-based proof assistant, can speed up the process, but Granville said it was 'annoying, because it has its own protocols and language,' requiring programming expertise. 'We need to have a much better way of communication,' he added. Could artificial intelligence save the day? That's the hope, according to Shafto. An AI model that could reliably check proofs would save enormous amounts of time, freeing mathematicians to be more creative. 'The constancy of math coincides with the fact that we practice math more or less the same: still people standing at a chalkboard,' Shafto said. 'It's hard not to draw the correlation and say, 'Well, you know, maybe if we had better tools, that would change progress.'' AI would benefit, too, Shafto and others believe. Large language models like ChatGPT can scour the digitised storehouses of human knowledge to produce a half-convincing college essay on the Russian Revolution. But thinking through the many intricate steps of a mathematical problem remains elusive. 'I think we'll learn a lot about what the capabilities of various AI protocols are from how well we can get them to generate material that's of interest,' said Jordan S. Ellenberg, a mathematician at the University of Wisconsin-Madison who is part of a team applying for an Exponentiating Mathematics grant. 'We have no intuition yet about which problems are going to be hard and which problems are easy. We need to learn that.' One of the more disconcerting truths about artificial intelligence is that we do not entirely understand how it works. 'This lack of understanding is essentially unprecedented in the history of technology,' Dario Amodei, CEO of the artificial intelligence company Anthropic, wrote in a recent essay. Ellenberg somewhat downplayed that assertion, pointing out that electricity was widely used before its properties were fully understood. Then again, with some AI experts worrying that artificial intelligence could destroy the world, any clarity into its operations tends to be welcome. Nelson, the former White House adviser, acknowledged 'legitimate' concerns about the rapid pace at which artificial intelligence is being integrated into seemingly every sector of society. All the more reason, she argued, to have DARPA on the case. 'There's a much higher benchmark that needs to be reached than whether or not your chatbot is hallucinating if you ask it a question about Shakespeare,' she said. 'The stakes are much higher.' – ©2025 The New York Times Company This article originally appeared in The New York Times.
Indian Express
22-06-2025
- Science
- Indian Express
Can AI quicken the pace of math discovery?
Artificial intelligence can write a poem in the style of Walt Whitman, provide dating advice and suggest the best way to cook an artichoke. But when it comes to mathematics, large language models like OpenAI's immensely popular ChatGPT have sometimes stumbled over basic problems. Some see this as an inherent limitation of the technology, especially when it comes to complex reasoning. A new initiative from the Defense Advanced Research Projects Agency seeks to account for that shortfall by enlisting researchers in finding ways to conduct high-level mathematics research with an AI 'co-author.' The goal of the new grant-making program, Exponentiating Mathematics, is to speed up the pace of progress in pure (as opposed to applied) math — and, in doing so, to turn AI into a superlative mathematician. 'Mathematics is this great test bed for what is right now the key pain point for AI systems,' said Patrick Shafto, a Rutgers University mathematician and computer scientist who now serves as a program manager in DARPA's information innovation office, known as I20. 'So if we overcome that, potentially, it would unleash much more powerful AI.' He added, 'There's huge potential benefit to the community of mathematicians and to society at large.' Shafto spoke from his office at DARPA's headquarters, an anonymous building in northern Virginia whose facade of bluish glass gives little indication that it houses one of the most unusual agencies in the federal government. Inside the building's airy lobby, visitors surrender their cellphones. Near a bank of chairs, a glass display shows a prosthetic arm that can be controlled by the wearer's brain signals. 'By improving mathematics, we're also understanding how AI works better,' said Alondra Nelson, who served as a top science adviser in President Joe Biden's administration and is a faculty member at the Institute for Advanced Study in Princeton, New Jersey. 'So I think it's kind of a virtuous cycle of understanding.' She suggested that, down the road, math-adept AI could enhance cryptography and aid in space exploration. Started after World War II to compete with the Soviet Union in the space race, DARPA is most famous for fostering the research that led to the creation of ARPANET, the precursor to the internet we use today. At the agency's small gift store, which is not accessible to the public, one can buy replicas of a cocktail napkin on which someone sketched out the rudimentary state of computer networks in 1969. DARPA later funded the research that gave rise to drones and Apple's digital assistant, Siri. But it is also responsible for the development of Agent Orange, the potent defoliant used to devastating effect during the Vietnam War. 'I'm sure this isn't 100% innocent,' Andrew Granville, a mathematician at the University of Montreal, said of DARPA's math initiative, although he emphasized that he was only speculating about eventual outcomes. DARPA is, after all, part of the Pentagon, even if it has traditionally operated with enviable independence. The U.S. military is rapidly incorporating AI into its operations, with the aim of not losing out to China and its People's Liberation Army or to Russia, which has been testing out new technologies on the battlefield in Ukraine. At the same time, Granville praised the endeavor, which comes as the Trump administration is cutting funding for scientific research. 'We are in disastrous times for U.S. science,' Granville said. 'I'm very pleased that DARPA is able to funnel money to academia.' A surfer and skateboarder in his free time, Shafto, 49, sat in a sparse conference room one recent afternoon, imagining a future when AI would be as good at solving multistep problems as it is at trying to glean meaning from huge troves of texts, which it does through the use of probability theory. Despite the unseasonably raw weather, Shafto seemed dressed for the beach in a blue-and-white Hawaiian-style shirt, white flannel trousers and sandals, with a trilby hat on the table before him. His vibe was, on the whole, decidedly closer to that of Santa Cruz than of Capitol Hill, largely in keeping with DARPA's traditional disregard for the capital's slow, bureaucratic pace. (The agency sets priorities and funds outside scientists but does not do research on its own; academics like Shafto spend an average of four years as program managers.) 'There are great mathematicians who work on age-old problems,' Shafto said. 'That's not the kind of thing that I'm particularly interested in.' Instead, he wanted the discipline to move more quickly by using AI to save time. 'Problems in mathematics take decades or centuries, sometimes, to solve,' he said in a recent presentation at DARPA's headquarters on the Exponentiating Mathematics project, which is accepting applications through mid-July. He then shared a slide showing that, in terms of the number of papers published, math had stagnated during the last century while life and technical sciences had exploded. In case the point wasn't clear, the slide's heading drove it home: 'Math is sloooowwww. …' The kind of pure math Shafto wants to accelerate tends to be 'sloooowwww' because it is not seeking numerical solutions to concrete problems, the way applied mathematics does. Instead, pure math is the heady domain of visionary theoreticians who make audacious observations about how the world works, which are promptly scrutinized (and sometimes torn apart) by their peers. 'Proof is king,' Granville said. Math proofs consist of multiple building blocks called lemmas, minor theorems employed to prove bigger ones. Whether each Jenga tower of lemmas can maintain integrity in the face of intense scrutiny is precisely what makes pure math such a 'long and laborious process,' acknowledged Bryna R. Kra, a mathematician at Northwestern University. 'All of math builds on previous math, so you can't really prove new things if you don't understand how to prove the old things,' she said. 'To be a research mathematician, the current practice is that you go through every step, you prove every single detail.' Lean, a software-based proof assistant, can speed up the process, but Granville said it was 'annoying, because it has its own protocols and language,' requiring programming expertise. 'We need to have a much better way of communication,' he added. Could artificial intelligence save the day? That's the hope, according to Shafto. An AI model that could reliably check proofs would save enormous amounts of time, freeing mathematicians to be more creative. 'The constancy of math coincides with the fact that we practice math more or less the same: still people standing at a chalkboard,' Shafto said. 'It's hard not to draw the correlation and say, 'Well, you know, maybe if we had better tools, that would change progress.'' AI would benefit, too, Shafto and others believe. Large language models like ChatGPT can scour the digitized storehouses of human knowledge to produce a half-convincing college essay on the Russian Revolution. But thinking through the many intricate steps of a mathematical problem remains elusive. 'I think we'll learn a lot about what the capabilities of various AI protocols are from how well we can get them to generate material that's of interest,' said Jordan S. Ellenberg, a mathematician at the University of Wisconsin-Madison who is part of a team applying for an Exponentiating Mathematics grant. 'We have no intuition yet about which problems are going to be hard and which problems are easy. We need to learn that.' One of the more disconcerting truths about artificial intelligence is that we do not entirely understand how it works. 'This lack of understanding is essentially unprecedented in the history of technology,' Dario Amodei, CEO of the artificial intelligence company Anthropic, wrote in a recent essay. Ellenberg somewhat downplayed that assertion, pointing out that electricity was widely used before its properties were fully understood. Then again, with some AI experts worrying that artificial intelligence could destroy the world, any clarity into its operations tends to be welcome. Nelson, the former White House adviser, acknowledged 'legitimate' concerns about the rapid pace at which artificial intelligence is being integrated into seemingly every sector of society. All the more reason, she argued, to have DARPA on the case. 'There's a much higher benchmark that needs to be reached than whether or not your chatbot is hallucinating if you ask it a question about Shakespeare,' she said. 'The stakes are much higher.'
Forbes
20-05-2025
- Business
- Forbes
Silicon Valley's Shifting Politics—and The One Thing Uniting Them
Sillicon Valley - road sign information When I started covering Silicon Valley as an industry analyst in 1981, most top tech execs were politically conservative. They were entrepreneurs who believed in a free market and wanted the least government intervention as they began inventing our digital future. However, there was a significant tech milestone in the mid-1990s when tech executives' political positions began shifting slightly towards the middle, and they needed government support to further their information age agenda. It came when Netscape introduced the first internet browser, and even die-hard conservatives began interacting with Democratic President Bill Clinton and Vice President Al Gore. Venture capitalists and tech executives realized that the internet was set to become a revolutionary technology that would need more bandwidth, new telecom regulations, and government backing to help grow it for use in broader government, business, education, and eventually consumer markets. Tech execs also knew that they might have a significant ally in the then current administration as Vice President Gore, as a senator, was instrumental in the development of major legislation that made the internet possible. According to Wikipedia, "On June 24, 1986, Gore introduced S-2594, Supercomputer Network Study Act of 1986. As a senator, Gore began to craft the High Performance Computing and Communication Act of 1991 (commonly referred to as 'The Gore Bill ') after hearing the 1988 report Toward a National Research Network submitted to Congress by a group chaired by the University of California, Los Angeles professor of computer science, Leonard Kleinrock, one of the central creators of the ARPANET (the ARPANET, first deployed by Kleinrock and others in 1969, is the predecessor of the Internet). Then, as vice president, Gore promoted the development of the Information Superhighway. This was discussed in detail a few days after winning the election in November 1992 in The New York Times article "Clinton to Promote High Technology, With Gore in Charge." They planned to finance research 'that will flood the economy with innovative goods and services, lifting the general level of prosperity and strengthening American industry.' However, today's tech leaders' political positions are a mixed bag. Last week, Vice President JD Vance came to Silicon Valley for a fundraiser, and the local NBC station asked me how he was viewed in Silicon Valley. Although the station used only a small portion of my answer on the broadcast, here is what I told them about the current political climate with tech execs in Silicon Valley today: A faction of Silicon Valley's tech elite, particularly those aligned with conservative or libertarian ideologies, has strongly supported Vance. Figures such as Elon Musk, David Sacks, and Peter Thiel have publicly endorsed him, viewing his selection as a bridge between technological innovation and national leadership. Musk described the Trump-Vance ticket as an "excellent decision," while Sacks referred to Vance as an "American patriot." This group appreciates Vance's venture capital background and his advocacy for deregulation, which they believe could spur innovation and economic growth. Some moderates in the tech industry acknowledge Vance's understanding of technology and entrepreneurship as potential assets. For instance, venture capitalist Matt Murphy noted that having a tech-savvy individual in leadership is beneficial. However, this optimism is tempered by concerns over Vance's regulatory positions and his alignment with certain conservative policies. Progressive voices and major tech companies express apprehension regarding Vance's stance on issues like Section 230, a statute that shields tech platforms from liability for third-party content posted on their sites, and antitrust enforcement. His support for reducing liability protections for social media platforms and his praise for antitrust actions against big tech firms signal a potential for increased regulatory scrutiny. These positions raise concerns about the future relationship between the federal government and major technology companies. Vance's advocacy for "American Dynamism," which emphasizes aligning technology with national interests, resonates with certain segments of the tech community seeking a renewed sense of purpose in innovation. However, his conservative cultural views, including critiques of multiculturalism and support for restrictive immigration policies, have drawn criticism from others in the industry who value diversity and global engagement. Vice President JD Vance is a polarizing figure in Silicon Valley. While he garners support from conservative and libertarian tech leaders who appreciate his industry background and deregulatory stance, he faces skepticism from progressives and major tech firms concerned about potential regulatory challenges and cultural conservatism. This divide underscores the evolving political dynamics within the tech industry. Although Silicon Valley tech leaders' politics are mixed, they do seem to agree on one thing—that AI is a powerful technology that can be used for good and bad and needs some form of regulation. Current Budget Reconciliation Bill legislation would prohibit federal and state governments from regulating artificial intelligence (AI) for the next ten years. This development, if enacted, would fundamentally reshape the landscape of AI governance in the U.S., with profound implications for innovation, public safety, civil rights, and the global competitive balance. The latest move to use the Budget Reconciliation Bill as a vehicle for AI policy has sparked intrigue and alarm. By leveraging this procedural tool—which allows budgetary measures to pass with a simple majority—the proposed language effectively places a ten-year moratorium on any federal or state regulation of artificial intelligence technologies. Proponents frame this as a bold step toward ensuring America remains at the forefront of AI innovation, removing bureaucratic friction and giving developers a clear runway for growth. However, critics see a glaring blind spot. With no regulatory guardrails, the door opens to unchecked algorithmic bias, data misuse, and the accelerated spread of AI-driven misinformation. The intent to spark innovation is admirable, but the absence of oversight during such rapid technological evolution is deeply concerning. AI is not just a tool of productivity—it's a force shaping economies, societies, and even democratic processes. A decade without regulation could hardwire unintended consequences into the very foundations of our digital future.
Forbes
01-05-2025
- Forbes
How The Internet Became Essential—And Dangerous
Despite its benefits, the Internet has many downsides that people might not realize. getty The Internet has been in use by the general public for decades, and with it have come a wealth of benefits. Originally developed as a way for researchers from the United States, the United Kingdom, and France to collaborate more easily with each other. It started in earnest with the development of ARPANET in 1969 under contracts awarded by the US Department of Defense. Eventually, public dial-in Internet networks became available starting in 1979 with CompuServe, with other services like America Online and Prodigy soon to follow. The rise of multimedia computers and cell phones in the 1990s gave way to even more homes having online connectivity, and it's only skyrocketed since then. Online shopping, once considered a novelty (remember, Amazon started out only selling books!), became a necessity in people's lives. The Internet wasn't just for nerds anymore! In December 2022, the Bureau of Economic Analysis estimated the United States' 'digital GDP' coming in at a whopping $2.57 trillion; if ranked by itself alongside the world, the US digital economy would be the world's eighth biggest. In addition, the Internet has made global communication easier and faster than ever before. The Digital 2025 Global Overview Report estimates that as of early 2026, 67.9% of the world's population (5.56 billion people) use the Internet. Simply put, the Internet has been a boon for businesses, especially those with international clients. As the saying goes, what goes up must come down. Despite its benefits, the Internet has many downsides that people might not realize. Lack of regulation by governments and companies alike has led to a myriad of ways bad guys try to take advantage of people's families and finances. In the 1990s, the Internet was a bit of the Wild West, but people were getting their feet wet as to what it was really capable of. It's a different world now, to say the least! The Census Bureau of the Department of Commerce announced that e-commerce sales for Q4 2024 accounted for $352.9 billion, making e-commerce a ripe target for fraudsters. While spam emails of the past may have been riddled with spelling mistakes, the rise of AI has allowed spam asking for someone to log into their e-commerce site (Amazon, eBay, Etsy) to look more authentic than ever. A recent article from Crowdfund Insider notes how this allows bad actors to create 'high-quality attacks with greater frequency.' An online scam on the rise is pig-butchering, a long con often involving scammers working out of compounds that spend months building trust with the target (the pig) only to get them and their friends to invest large amounts of money (butchering) before they move on to their next target. The Economist ran a cover story detailing how these scammers appeal to their targets from an emotional angle by preying on 'fear, loneliness, greed, grief, and boredom.' Things have come a long way from the simpler Nigerian Prince scams of yore. It's not just the wealthy that are targeted; scammers are targeting teens as well through financial sextortion. Criminals trick teens into sending compromising photos of themselves, then demand money or else they'll send the pictures to their friends, family, or post them in a public forum. In 2024, the UK's National Crime Agency's Child Exploitation and Online Protection platform received over 380 sextortion reports. Whether it's for you or your family, knowledge is the best defense against modern, sophisticated methods of Internet fraud. By delving deeper into these topics, we can emerge more aware and wiser than ever before in this rapidly changing world of scams that the modern Internet has not thoughtfully provided.
