Latest news with #GPT-5
Miami Herald
5 hours ago
- Business
- Miami Herald
Michael Hiltzik: Say farewell to the AI bubble, and get ready for the crash
Most people not deeply involved in the artificial intelligence frenzy may not have noticed, but perceptions of AI's relentless march toward becoming more intelligent than humans, even becoming a threat to humanity, came to a screeching halt Aug. 7. That was the day when the most widely followed AI company, OpenAI, released GPT-5, an advanced product that the firm had long promised would put competitors to shame and launch a new revolution in this purportedly revolutionary technology. As it happened, GPT-5 was a bust. It turned out to be less user-friendly and in many ways less capable than its predecessors in OpenAI's arsenal. It made the same sort of risible errors in answering users' prompts, was no better in math (or even worse), and not at all the advance that OpenAI and its chief executive, Sam Altman, had been talking up. "The thought was that this growth would be exponential," says Alex Hanna, a technology critic and co-author (with Emily M. Bender of the University of Washington) of the indispensable new book "The AI Con: How to Fight Big Tech's Hype and Create the Future We Want." "Instead, Hanna says, "We're hitting a wall." The consequences go beyond how so many business leaders and ordinary Americans have been led to expect, even fear, the penetration of AI into our lives. Hundreds of billions of dollars have been invested by venture capitalists and major corporations such as Google, Amazon and Microsoft in OpenAI and its multitude of fellow AI labs, even though none of the AI labs has turned a profit. Public companies have scurried to announce AI investments or claim AI capabilities for their products in the hope of turbocharging their share prices, much as an earlier generation of businesses promoted themselves as "dot-coms" in the 1990s to look more glittery in investors' eyes. Nvidia, the maker of a high-powered chip powering AI research, plays almost the same role as a stock market leader that Intel Corp., another chip-maker, played in the 1990s - helping to prop up the bull market in equities. If the promise of AI turns out to be as much of a mirage as dot-coms did, stock investors may face a painful reckoning. The cheerless rollout of GPT-5 could bring the day of reckoning closer. "AI companies are really buoying the American economy right now, and it's looking very bubble-shaped," Hanna told me. The rollout was so disappointing that it shined a spotlight on the degree that the whole AI industry has been dependent on hype. Here's Altman, speaking just before the unveiling of GPT-5, comparing it with its immediate predecessor, GPT-4o: "GPT-4o maybe it was like talking to a college student," he said. "With GPT-5 now it's like talking to an expert - a legitimate PhD-level expert in anything any area you need on demand ... whatever your goals are." Well, not so much. When one user asked it to produce a map of the U.S. with all the states labeled, GPT-5 extruded a fantasyland, including states such as Tonnessee, Mississipo and West Wigina. Another prompted the model for a list of the first 12 presidents, with names and pictures. It only came up with nine, including presidents Gearge Washington, John Quincy Adama and Thomason Jefferson. Experienced users of the new version's predecessor models were appalled, not least by OpenAI's decision to shut down access to its older versions and force users to rely on the new one. "GPT5 is horrible," wrote a user on Reddit. "Short replies that are insufficient, more obnoxious ai stylized talking, less 'personality' … and we don't have the option to just use other models." (OpenAI quickly relented, reopening access to the older versions.) The tech media was also unimpressed. "A bit of a dud," judged the website Futurism and Ars Technica termed the rollout "a big mess." I asked OpenAI to comment on the dismal public reaction to GPT-5, but didn't hear back. None of this means that the hype machine underpinning most public expectations of AI has taken a breather. Rather, it remains in overdrive. A projection of AI's development over the coming years published by something called the AI Futures Project under the title "AI 2027" states: "We predict that the impact of superhuman AI over the next decade will be enormous, exceeding that of the Industrial Revolution." The rest of the document, mapping a course to late 2027 when an AI agent "finally understands its own cognition," is so loopily over the top that I wondered whether it wasn't meant as a parody of excessive AI hype. I asked its creators if that was so, but haven't received a reply. One problem underscored by GPT-5's underwhelming rollout is that it exploded one of the most cherished principles of the AI world, which is that "scaling up" - endowing the technology with more computing power and more data - would bring the grail of artificial general intelligence, or AGI, ever closer to reality. That's the principle undergirding the AI industry's vast expenditures on data centers and high-performance chips. The demand for more data and more data-crunching capabilities will require about $3 trillion in capital just by 2028, in the estimation of Morgan Stanley. That would outstrip the capacity of the global credit and derivative securities markets. But if AI won't scale up, most if not all that money will be wasted. As Bender and Hanna point out in their book, AI promoters have kept investors and followers enthralled by relying on a vague public understanding of the term "intelligence." AI bots seem intelligent, because they've achieved the ability to seem coherent in their use of language. But that's different from cognition. "So we're imagining a mind behind the words," Hanna says, "and that becomes associated with consciousness or intelligence. But the notion of general intelligence is not really well-defined." Indeed, as long ago as the 1960s, that phenomenon was noticed by Joseph Weizenbaum, the designer of the pioneering chatbot ELIZA, which replicated the responses of a psychotherapist so convincingly that even test subjects who knew they were conversing with a machine thought it displayed emotions and empathy. "What I had not realized," Weizenbaum wrote in 1976, "is that extremely short exposures to a relatively simple computer program could induce powerful delusional thinking in quite normal people." Weizenbaum warned that the "reckless anthropomorphization of the computer" - that is, treating it as some sort of thinking companion - produced a "simpleminded view of intelligence." That tendency has been exploited by today's AI promoters. They label the frequent mistakes and fabrications produced by AI bots as "hallucinations," which suggests that the bots have perceptions that may have gone slightly awry. But the bots "don't have perceptions," Bender and Hanna write, "and suggesting that they do is yet more unhelpful anthropomorphization." The general public may finally be cottoning on to the failed promise of AI more generally. Predictions that AI will lead to large-scale job losses in creative and STEM fields (science, technology, engineering and math) might inspire feelings that the whole enterprise was a tech-industry scam from the outset. Predictions that AI would yield a burst of increased worker productivity haven't been fulfilled; in many fields, productivity declines, in part because workers have to be deployed to double-check AI outputs, lest their mistakes or fabrications find their way into mission-critical applications - legal briefs incorporating nonexistent precedents, medical prescriptions with life-threatening ramifications and so on. Some economists are dashing cold water on predictions of economic gains more generally. MIT economist Daron Acemoglu, for example, forecast last year that AI would produce an increase of only about 0.5% in U.S. productivity and an increase of about 1% in gross domestic product over the next 10 years, mere fractions of the AI camp's projections. The value of Bender's and Hanna's book, and the lesson of GPT-5, is that they remind us that "artificial intelligence" isn't a scientific term or an engineering term. It's a marketing term. And that's true of all the chatter about AI eventually taking over the world. "Claims around consciousness and sentience are a tactic to sell you on AI," Bender and Hanna write. So, too, is the talk about the billions, or trillions, to be made in AI. As with any technology, the profits will go to a small cadre, while the rest of us pay the price ... unless we gain a much clearer perception of what AI is, and more importantly, what it isn't. Copyright (C) 2025, Tribune Content Agency, LLC. Portions copyrighted by the respective providers.
Forbes
10 hours ago
- Business
- Forbes
The Billion-Dollar Company Of One Is Coming Faster Than You Think
OpenAI CEO Sam Altman recently revealed that he believes the first $1 billion solo startup will be built by one person with a laptop, an internet connection, and an army of AI agents. I have experienced GPT-5, and I now believe that this milestone will arrive significantly earlier than most people project, likely by 2028. The image of the billionaire founder is about to be rewritten. No sprawling teams. No Ivy League pedigrees. No nine-figure venture war chests. The next mega empire will be operated behind a kitchen table and directed by an individual strong builder with an army of AI agents, no-code automation and the capacity to disseminate concepts around the world in real-time. The silos that required separate teams dedicated to research, marketing, sales, and customer support are being replaced by programs that operate 24/7 in the cloud. Five converging forces Five converging forces are making this not just possible, but inevitable. The first is the emergence of agentic AI: autonomous and focused programs that have the ability to guide research, produce content, reach out to customers, conduct analytics, and even sell, all without downtime, burnout, or red tape. The second is the fusion of no-code tools with API orchestration, enabling entrepreneurs to assemble fully functional, end-to-end business systems in days rather than months. The third is the reach of global digital distribution. Platforms, such as X, LinkedIn, and YouTube, are no longer content hosts, but services that offer all users direct access to in-depth knowledge of the dynamics of attention, reaching tens of millions to hundreds of millions of people. The fourth is the arrival of mass-market AI-powered micro-monetization, subscription-based pricing, and AI-only-delivered services that can scale without the overhead of human labor costs. And finally, there is the falling cost of intelligence itself. 'Thinking work' is becoming as cheap as cloud storage—an economic inversion that changes the rules of productivity entirely. Rapid Change will Continue The trend is already visible. Sam Altman has openly predicted that the first one-person billion-dollar company is on the horizon, and after the recent GPT-5 reveal, that prediction feels less like futurism and more like a weather forecast. Tech leaders and investors are quietly adjusting their expectations. Others, such as Dario Amodei of Anthropic, think that such a reality may come as soon as 2026. According to Carta data, less than a third of newly formed startups are led by solo founders, nearly double the rate from 2015, due to the availability and effectiveness of AI tools. The irony is that the most challenging part of building this kind of company will not be the building itself. As AI agents become increasingly capable, spinning up an operational stack will be trivial. The actual choke point will be sales: making yourself heard, building confidence and turning interest into dollars. A New Inevitable Trajectory for Founders This new breed of founder will follow a trajectory that feels almost inevitable. They will begin by creating an audience and introducing an AI-powered service that appeals to a specific community. That service will be refined into a product, supported by a growing network of AI agents, until they dominate a niche. From there, expansion into new verticals will follow, along with API integrations and enterprise deals. Over time, their operation will evolve from a service to a platform and, ultimately, into an ecosystem that others build upon. That journey may sound ambitious, but it is increasingly within reach of anyone willing to work with both urgency and focus. The building blocks, such as automation, global distribution, and monetization models, are already in place. What's missing in most cases is not the means to execute, but the willingness and work ethic to move with the speed and precision necessary. The founder who can master both will not only create a successful business, but they will also reset the playbook for being an entrepreneur. The Era of Solo-preneuership Indeed, in practice, we are already getting early balloons of this shift. Replit's CEO calls it 'vibe-coding,' where applications are built in hours through natural-language prompts rather than weeks of manual coding. Hobbyists are becoming product creators overnight. Meanwhile, researchers are outlining the 'Solo Revolution' theory, which shows how AI lowers the barriers to entrepreneurship through skill augmentation and radically reduces resource requirements. The most important insight to keep in mind is that we are not replacing human judgment with AI; we are augmenting it. Taste, vision and the ability to motivate others are purely human attributes, as automation eliminates the mechanical component of these qualities. The solo founder who wields AI agents with discernment will have the leverage once reserved for Fortune 500 companies. This is why the first $1 B solo startup is no longer a thought experiment. It is the logical endpoint of the trends we are currently observing. Code is abundant. Intelligence is cheap. Distribution is limitless. The deciding factor will be the nerve to act before the rest of the world catches on. And when that moment comes, it will not be won by the person with the 'best code.' It will belong to the founder who knows what matters, who can see clearly where others hesitate, and who has the audacity to press 'launch' before the ink on their idea is dry. Grit, optimism, and a laptop are all they'll need. Everything else will be handled by an army of agents. Not only is solo-preneurship not a fringe model in the age of AI, it is the ideal model. By 2028, it will not be a surprise when a one-person billion-dollar company emerges. The real surprise will come when the rest of the world tries to follow suit and how fast it will be.
India Today
11 hours ago
- Business
- India Today
AI hype over? Just months after spending millions of dollars, Meta is suddenly firing AI engineers
Meta's AI division is reportedly going through a big shake-up. According to a Bloomberg report, the company is splitting its AI unit into four different groups, each with a separate focus. On paper, this looks like a way to redefine objectives and avoid confusion. But behind the scenes, AI engineers and researchers seem worried. There are already talks of job cuts and reassignments, even though Meta has spent the last year aggressively hiring AI talent with high other words, this sudden shift in tone from expansion to possible downsizing has left staff unsettled. It has also, as the move comes just days after a rather muted GPT-5 launch from OpenAI, sparked speculation that the AI hype is already slowing down, and suddenly various tech companies might be curtailing their AI ambitions. Restructuring under new leadershipAt Meta, the new structure of the AI division is being led by the company's recently appointed chief AI officer and former CEO of Scale AI, Alexandr Wang. In an internal note, Wang wrote that Meta needs to be better organised if it wants to reach its long-term goal of building superintelligence. 'Superintelligence is coming, and in order to take it seriously, we need to organise around the key areas that will be critical to reach it — research, product and infra,' he wrote. As part of this plan, the AI division — now renamed Meta Superintelligence Labs (MSL) — will run as four teams. One team will handle large language models such as Llama. Another, under the FAIR banner, will continue long-term AI research. A third, led by former GitHub CEO Nat Friedman, will work on consumer-facing AI products. The fourth will focus on infrastructure, including data centres and computing while this may look tidy in theory, not everyone is convinced. As per a New York Times report, Meta is considering firing some AI engineers or moving staff around within its AI workforce, which now numbers several thousand. Of course, nothing is confirmed yet, but the uncertainty has definitely rattled employees. Some executives have already left, adding to the sense of instability. For a division once described as Meta's biggest growth driver, even the possibility of layoffs feels like a sharp the AI hype over?All said, the sudden change raises a bigger question — is the AI hype beginning to cool off? This is particularly a relevant question given that Meta's move comes just days after OpenAI launched a muted ChatGPT-5, which didn't impress users as much as the company's earlier AI models did. advertisementOver the last two years, AI has dominated headlines, and companies have rushed to call themselves AI-first. But just like previously hyped technologies — blockchain or the metaverse — is AI also going the same way, or is it real?Take DeepSeek, for example. It grabbed attention almost overnight with a free, powerful model that could rival OpenAI's ChatGPT and Google's Gemini. But since then, it has gone quiet, and the excitement has a day ago, a report highlighted that many organisations that tried to make AI part of their workflow have not been very successful. The Study by MIT — titled State of AI in Business 2025 — found that only about 5 per cent of AI pilot projects actually lead to rapid revenue growth, while the vast majority stall or fail to deliver impact. 'Almost everywhere we went, enterprises were trying to build their own tool,' said Aditya Challapally, the lead author of the OpenAI CEO Sam Altman is now admitting that the industry is in a kind of bubble, although he qualifies it in terms of short run and long run. According to a Verge report, Altman said, 'When bubbles happen, smart people get overexcited about a kernel of truth. Are we in a phase where investors as a whole are overexcited about AI? My opinion is yes. Is AI the most important thing to happen in a very long time? My opinion is also yes.'advertisementBut is it really an AI slowdown at Meta?While there are some signs of the AI train slowing down, the events at Meta AI could be something else entirely — that is just a regular, good old restructuring. It is possible that the shake-up is indeed more about efficiency. With Meta splitting its AI division into four clear teams could reduce duplication of work and make accountability easier. And, if the job cuts do happen, they may be about trimming overlapping roles rather than pulling back on AI altogether.- Ends
CNBC
11 hours ago
- Business
- CNBC
OpenAI CFO Sarah Friar: Biggest issue we face is being 'constantly under compute'
OpenAI CFO Sarah Friar joins 'Squawk Box' to discuss the launch of GPT-5, whether an AI bubble is forming, growing competition in AI, Microsoft partnership, the idea of tokenization, whether the company will go public in the future, industrial policy, and more.
Mint
12 hours ago
- Business
- Mint
Is an AI winter upon us? There seems a chill in the air
I wrote at the beginning of the year that Wall Street investors should brace for an 'AI winter' in 2025; not necessarily a slowdown in investment, and certainly not in hype from AI companies, but in tangible progress. Patience would be tested. Some recent events warrant revisiting the question: Is the AI winter upon us? GPT-5, the long-awaited new model from Sam Altman's OpenAI, was released earlier this month to a tepid reception. If it's a step toward artificial general intelligence (AGI), as the company repeatedly said it would be, it's a tiny one indeed. The model was so poorly received by some ChatGPT diehards that OpenAI was forced into an embarrassing rollback, making older models available again. Altman's claim that GPT-5 was like talking to a 'PhD-level" expert quickly became a joke. Also Read: Outrage over AI is pointless if we're clueless about AI models At the same time, CoreWeave, one of the few pure-play AI stocks, plummeted more than 25% last week after guidance that spooked investors: Revenue growth is expected to be enormously outpaced by capital expenditure increases. And its IPO lock-up was coming to an end, which didn't help either. And while it's hard to pin down just how beneficial AI has been (or will be) in the business world, one piece of research from McKinsey & Company should give everyone pause. While eight of out 10 companies surveyed said they were implementing Generative AI in their business, the consultancy group observed, just as many said there had been 'no significant bottom-line impact." Gulp. The reaction to GPT-5 in particular has longtime AI sceptics taking a victory lap. Author and journalist Brian Merchant noted that Altman seemed less willing to use the phrase 'artificial general intelligence' now that his latest AI tool is still so far from that. 'I think it's not a super useful term," Altman told CNBC. Merchant pointed out that it's a term Altman has used often, including in February on his personal blog. It has been handy in raising billions of dollars. Also Read: Siddharth Pai: Don't be naive, Agentic AI won't eliminate agency costs Altman has had some other telling things to say over the past few days. 'Are we in a phase where investors as a whole are overexcited about AI? My opinion is yes," he told a group of reporters last week. 'You should expect OpenAI to spend trillions of dollars," was another remark. My favourite, from a CNBC interview the week before, was: 'It's nice not to be public." I'll bet! Throughout this, I have been wondering how investors would have reacted to GPT-5 had OpenAI been a publicly traded company. I suspect at the very least, OpenAI would have had a CoreWeave-like week once investors looked at the user revolt, the backtrack-of-sorts on super-intelligence and the prediction that 'trillions" of more dollars would be needed. Altman said he was confident the company could invent a 'new kind of financial instrument for finance and compute" to fund its rapid industrial expansion. I guess we now know at least one book in the GPT-5 training data. Then again, the fallout hasn't extended to other stocks tied closely to OpenAI's fortunes, such as Microsoft or Nvidia. This suggests investor nerves have not yet been frayed. One argument is that the embarrassing viral failings, such as not being able to spell 'blueberry,' are trivial stunts that miss the bigger picture: GPT-5 is more sophisticated in picking the appropriate model for a task, which, though it seems unremarkable, is actually practical and useful. Another view is that AI capabilities have improved sufficiently and groundbreaking AI agents can go off and carry out certain tasks that are just in reach—and the return on AI investment will finally kick in at that point. Also Read: What OpenAI has up its sleeve: An AI gadget mightier than the sword? Or it may not, and this month will be seen as the beginning of something significant. I don't quite think we can call it an AI winter yet—but there's no question that a sudden chill is in the air. My takeaway from the GPT-5 launch has been that while AI companies can tout overall performance on various benchmarks, these are becoming increasingly less relevant. Impenetrable to anyone other than AI researchers, these scores mean little to the end user, be it the consumer or a CEO. What sets the narrative around AI progress (or lack thereof) is its practical application; it's here where all AI firms are still falling short. GPT-3.5 was around for months before the demonstration of ChatGPT wowed the world—until we all discovered its shortcomings, not through lab testing of millions of queries, but with our own eyes. AI agents might be the next 'ChatGPT moment,' if they do as promised. Call it the 'Blueberry Benchmark' of real-world usefulness. Better scores are needed urgently, or investors could be in for a frigid time. ©Bloomberg The author is Bloomberg Opinion's US technology columnist.
