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Doesn’t Get Much Better Than This? 27 28 GPT-5, a new release from OpenAI, is the latest product to suggest that 29 progress on large language models has stalled. 30 31 By [20]Cal Newport 32 August 12, 2025 33 Illustration by Shira Inbar 34 Save this story 35 Save this story 36 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 37 38 For this week’s Open Questions column, Cal Newport is filling in for Joshua 39 Rothman. 40 41 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 42 43 Much of the euphoria and dread swirling around today’s artificial-intelligence 44 technologies can be traced back to January, 2020, when a team of researchers at 45 OpenAI published a thirty-page [23]report titled “Scaling Laws for Neural 46 Language Models.” The team was led by the A.I. researcher Jared Kaplan, and 47 included Dario Amodei, who is now the C.E.O. of Anthropic. They investigated a 48 fairly nerdy question: What happens to the performance of language models when 49 you increase their size and the intensity of their training? 50 51 Back then, many machine-learning experts thought that, after they had reached a 52 certain size, language models would effectively start memorizing the answers to 53 their training questions, which would make them less useful once deployed. But 54 the OpenAI paper argued that these models would only get better as they grew, 55 and indeed that such improvements might follow a power law—an aggressive curve 56 that resembles a hockey stick. The implication: if you keep building larger 57 language models, and you train them on larger data sets, they’ll start to get 58 shockingly good. A few months after the paper, OpenAI seemed to validate the 59 scaling law by releasing GPT-3, which was ten times larger—and leaps and bounds 60 better—than its predecessor, GPT-2. 61 62 Suddenly, the theoretical idea of artificial general intelligence, which 63 performs as well as or better than humans on a wide variety of tasks, seemed 64 tantalizingly close. If the scaling law held, A.I. companies might achieve 65 A.G.I. by pouring more money and computing power into language models. Within a 66 year, [24]Sam Altman, the chief executive at OpenAI, published a blog post 67 titled “Moore’s Law for Everything,” which argued that A.I. will take over 68 “more and more of the work that people now do” and create unimaginable wealth 69 for the owners of capital. “This technological revolution is unstoppable,” he 70 wrote. “The world will change so rapidly and drastically that an equally 71 drastic change in policy will be needed to distribute this wealth and enable 72 more people to pursue the life they want.” 73 74 It’s hard to overstate how completely the A.I. community came to believe that 75 it would inevitably scale its way to A.G.I. In 2022, Gary Marcus, an A.I. 76 entrepreneur and an emeritus professor of psychology and neural science at 77 N.Y.U., pushed back on Kaplan’s paper, noting that “the so-called scaling laws 78 aren’t universal laws like gravity but rather mere observations that might not 79 hold forever.” The negative response was fierce and swift. “No other essay I 80 have ever written has been ridiculed by as many people, or as many famous 81 people, from Sam Altman and Greg Brockman to Yann LeCun and Elon Musk,” Marcus 82 later reflected. He recently told me that his remarks essentially 83 “excommunicated” him from the world of machine learning. Soon, ChatGPT would 84 reach a hundred million users faster than any digital service in history; in 85 March, 2023, OpenAI’s next release, GPT-4, vaulted so far up the scaling curve 86 that it inspired a Microsoft research paper titled “Sparks of Artificial 87 General Intelligence.” Over the following year, venture-capital spending on 88 A.I. jumped by eighty per cent. 89 90 After that, however, progress seemed to slow. OpenAI did not unveil a new 91 blockbuster model for more than two years, instead focussing on specialized 92 releases that became hard for the general public to follow. Some voices within 93 the industry began to wonder if the A.I. scaling law was starting to falter. 94 “The 2010s were the age of scaling, now we’re back in the age of wonder and 95 discovery once again,” Ilya Sutskever, one of the company’s founders, told 96 Reuters in November. “Everyone is looking for the next thing.” A 97 contemporaneous TechCrunch article summarized the general mood: “Everyone now 98 seems to be admitting you can’t just use more compute and more data while 99 pretraining large language models and expect them to turn into some sort of 100 all-knowing digital god.” But such observations were largely drowned out by the 101 headline-generating rhetoric of other A.I. leaders. “A.I. is starting to get 102 better than humans at almost all intellectual tasks,” Amodei recently told 103 Anderson Cooper. In an interview with Axios, he predicted that half of 104 entry-level white-collar jobs might be “wiped out” in the next one to five 105 years. This summer, both Altman and [25]Mark Zuckerberg, of Meta, claimed that 106 their companies were close to developing superintelligence. 107 108 Then, last week, OpenAI finally released GPT-5, which many had hoped would 109 usher in the next significant leap in A.I. capabilities. Early reviewers found 110 some features to like. When a popular tech YouTuber, Mrwhosetheboss, asked it 111 to create a chess game that used Pokémon as pieces, he got a significantly 112 better result than when he used GPT-o4-mini-high, an industry-leading coding 113 model; he also discovered that GPT-5 could write a more effective script for 114 his YouTube channel than GPT-4o. Mrwhosetheboss was particularly enthusiastic 115 that GPT-5 will automatically route queries to a model suited for the task, 116 instead of requiring users to manually pick the model they want to try. Yet he 117 also learned that GPT-4o was clearly more successful at generating a YouTube 118 thumbnail and a birthday-party invitation—and he had no trouble inducing GPT-5 119 to make up fake facts. Within hours, users began expressing disappointment with 120 the new model on the r/ChatGPT subreddit. One post called it the “biggest piece 121 of garbage even as a paid user.” In an Ask Me Anything (A.M.A.) session, Altman 122 and other OpenAI engineers found themselves on the defensive, addressing 123 complaints. Marcus summarized the release as “overdue, overhyped and 124 underwhelming.” 125 126 In the aftermath of GPT-5’s launch, it has become more difficult to take 127 bombastic predictions about A.I. at face value, and the views of critics like 128 Marcus seem increasingly moderate. Such voices argue that this technology is 129 important, but not poised to drastically transform our lives. They challenge us 130 to consider a different vision for the near-future—one in which A.I. might not 131 get much better than this. 132 133 OpenAI didn’t want to wait nearly two and a half years to release GPT-5. 134 According to The Information, by the spring of 2024, Altman was telling 135 employees that their next major model, code-named Orion, would be significantly 136 better than GPT-4. By the fall, however, it became clear that the results were 137 disappointing. “While Orion’s performance ended up exceeding that of prior 138 models,” The Information reported in November, “the increase in quality was far 139 smaller compared with the jump between GPT-3 and GPT-4.” 140 141 Orion’s failure helped cement the creeping fear within the industry that the 142 A.I. scaling law wasn’t a law after all. If building ever-bigger models was 143 yielding diminishing returns, the tech companies would need a new strategy to 144 strengthen their A.I. products. They soon settled on what could be described as 145 “post-training improvements.” The leading large language models all go through 146 a process called pre-training in which they essentially digest the entire 147 internet to become smart. But it is also possible to refine models later, to 148 help them better make use of the knowledge and abilities they have absorbed. 149 One post-training technique is to apply a machine-learning tool, reinforcement 150 learning, to teach a pre-trained model to behave better on specific types of 151 tasks. Another enables a model to spend more computing time generating 152 responses to demanding queries. 153 154 A useful metaphor here is a car. Pre-training can be said to produce the 155 vehicle; post-training soups it up. In the scaling-law paper, Kaplan and his 156 co-authors predicted that as you expand the pre-training process you increase 157 the power of the cars you produce; if GPT-3 was a sedan, GPT-4 was a sports 158 car. Once this progression faltered, however, the industry turned its attention 159 to helping the cars that they’d already built to perform better. Post-training 160 techniques turned engineers into mechanics. 161 162 Tech leaders were quick to express a hope that a post-training approach would 163 improve their products as quickly as traditional scaling had. “We are seeing 164 the emergence of a new scaling law,” Satya Nadella, the C.E.O. of Microsoft, 165 said at a conference last fall. The venture capitalist Anjney Midha similarly 166 spoke of a “second era of scaling laws.” In December, OpenAI released o1, which 167 used post-training techniques to make the model better at step-by-step 168 reasoning and at writing computer code. Soon the company had unveiled o3-mini, 169 o3-mini-high, o4-mini, o4-mini-high, and o3-pro, each of which was souped up 170 with a bespoke combination of post-training techniques. 171 172 Other A.I. companies pursued a similar pivot. Anthropic experimented with 173 post-training improvements in a February release of Claude 3.7 Sonnet, and then 174 made them central to its Claude 4 family of models. [26]Elon Musk’s xAI 175 continued to chase a scaling strategy until its wintertime launch of Grok 3, 176 which was pre-trained on an astonishing 100,000 H100 G.P.U. chips—many times 177 the computational power that was reportedly used to train GPT-4. When Grok 3 178 failed to outperform its competitors significantly, the company embraced 179 post-training approaches to develop Grok 4. GPT-5 fits neatly into this 180 trajectory. It’s less a brand-new model than an attempt to refine recent 181 post-trained products and integrate them into a single package. 182 183 Has this post-training approach put us back on track toward something like 184 A.G.I.? OpenAI’s announcement for GPT-5 included more than two dozen charts and 185 graphs, on measures such as “Aider Polyglot Multi-language code editing” and 186 “ERQA Multimodal spatial reasoning,” to quantify how much the model outperforms 187 its predecessors. Some A.I. benchmarks capture useful advances. GPT-5 scored 188 higher than previous models on benchmarks focussed on programming, and early 189 reviews seemed to agree that it produces better code. New models also write in 190 a more natural and fluid way, and this is reflected in the benchmarks as well. 191 But these changes now feel narrow—more like the targeted improvements you’d 192 expect from a software update than like the broad expansion of capabilities in 193 earlier generative-A.I. breakthroughs. You didn’t need a bar chart to recognize 194 that GPT-4 had leaped ahead of anything that had come before. 195 196 Other benchmarks might not measure what they claim. Starting with the release 197 of o1, A.I. companies have touted progress on measures of step-by-step 198 reasoning. But in June Apple researchers released a paper titled “The Illusion 199 of Thinking,” which found that state-of-the-art “large reasoning models” 200 demonstrated “performance collapsing to zero” when the complexity of puzzles 201 was extended beyond a modest threshold. Reasoning models, which include 202 o3-mini, Claude 3.7 Sonnet’s “thinking” mode, and DeepSeek-R1, “still fail to 203 develop generalizable problem-solving capabilities,” the authors wrote. Last 204 week, researchers at Arizona State University reached an even blunter 205 conclusion: what A.I. companies call reasoning “is a brittle mirage that 206 vanishes when it is pushed beyond training distributions.” Beating these 207 benchmarks is different from, say, reasoning through the types of daily 208 problems we face in our jobs. “I don’t hear a lot of companies using A.I. 209 saying that 2025 models are a lot more useful to them than 2024 models, even 210 though the 2025 models perform better on benchmarks,” Marcus told me. 211 Post-training improvements don’t seem to be strengthening models as thoroughly 212 as scaling once did. A lot of utility can come from souping up your Camry, but 213 no amount of tweaking will turn it into a Ferrari. 214 215 I recently asked Marcus and two other skeptics to predict the impact of 216 generative A.I. on the economy in the coming years. “This is a 217 fifty-billion-dollar market, not a trillion-dollar market,” Ed Zitron, a 218 technology analyst who hosts the “Better Offline” podcast, told me. Marcus 219 agreed: “A fifty-billion-dollar market, maybe a hundred.” The linguistics 220 professor Emily Bender, who co-authored a well-known critique of early language 221 models, told me that “the impacts will depend on how many in the management 222 class fall for the hype from the people selling this tech, and retool their 223 workplaces around it.” She added, “The more this happens, the worse off 224 everyone will be.” Such views have been portrayed as unrealistic—Nate Silver 225 once replied to an Ed Zitron tweet by writing, “old man yells at cloud 226 vibes”—while we readily accepted the grandiose visions of tech C.E.O.s. Maybe 227 that’s starting to change. 228 229 If these moderate views of A.I. are right, then in the next few years A.I. 230 tools will make steady but gradual advances. Many people will use A.I. on a 231 regular but limited basis, whether to look up information or to speed up 232 certain annoying tasks, such as summarizing a report or writing the rough draft 233 of an event agenda. Certain fields, like programming and academia, will change 234 dramatically. A minority of professions, such as voice acting and social-media 235 copywriting, might essentially disappear. But A.I. may not massively disrupt 236 the job market, and more hyperbolic ideas like superintelligence may come to 237 seem unserious. 238 239 Continuing to buy into the A.I. hype might bring its own perils. In a [27] 240 recent article, Zitron pointed out that about thirty-five per cent of U.S. 241 stock-market value—and therefore a large share of many retirement portfolios—is 242 currently tied up in the so-called Magnificent Seven technology companies. 243 According to Zitron’s analysis, these firms spent five hundred and sixty 244 billion dollars on A.I.-related capital expenditures in the past eighteen 245 months, while their A.I. revenues were only about thirty-five billion. “When 246 you look at these numbers, you feel insane,” Zitron told me. 247 248 Even the figures we might call A.I. moderates, however, don’t think the public 249 should let its guard down. Marcus believes that we were misguided to place so 250 much emphasis on generative A.I., but he also thinks that, with new techniques, 251 A.G.I. could still be attainable as early as the twenty-thirties. Even if 252 language models never automate our jobs, the renewed interest and investment in 253 A.I. might lead toward more complicated solutions, which could. In the 254 meantime, we should use this reprieve to prepare for disruptions that might 255 still loom—by crafting effective A.I. regulations, for example, and by 256 developing the nascent field of digital ethics. 257 258 The appendices of the scaling-law paper, from 2020, included a section called 259 “Caveats,” which subsequent coverage tended to miss. “At present we do not have 260 a solid theoretical understanding for any of our proposed scaling laws,” the 261 authors wrote. “The scaling relations with model size and compute are 262 especially mysterious.” In practice, the scaling laws worked until they didn’t. 263 The whole enterprise of teaching computers to think remains mysterious. We 264 should proceed with less hubris and more care. ♦ 265 266 An earlier version of this article included an inaccurate transcription of Greg 267 Brockman’s name. 268 269 New Yorker Favorites 270 271 • A professor claimed to be Native American. 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