2025-08-08 20:10:00
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
GPT-5 is here. Now what?
At long last, OpenAI has released GPT-5. The new system abandons the distinction between OpenAI’s flagship models and its o series of reasoning models, automatically routing user queries to a fast nonreasoning model or a slower reasoning version.
It is now available to everyone through the ChatGPT web interface—though nonpaying users may need to wait a few days to gain full access.
GPT-5 will furnish a more pleasant and seamless user experience. That’s not nothing, but it falls far short of the transformative AI future that Sam Altman has spent much of the past year hyping. Read the full story.
—Grace Huckins
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Donald Trump has called on Intel’s CEO to resign
He claims Lip-Bu Tan is “conflicted” by his business ties to China. (FT $)
+ Tan was already at odds with some of his board members before the intervention. (WSJ $)
+ But the CEO claims he’s got the full backing of his board presently. (Bloomberg $)
2 Wildfires are raging across the western US
And strong winds are rapidly spreading them across parched land. (WP $)
+ The fires have a devastating effect on human health. (The Guardian)
+ How AI can help spot wildfires. (MIT Technology Review)
3 Meta’s AI superintelligence team is growing
The new TBD Lab is currently working on the newest version of its Llama model. (WSJ $)
+ Meta has also been busy acquiring an AI audio firm. (The Information $)
+ Elsewhere, Tesla has disbanded its supercomputer team. (Bloomberg $)
4 A man suffered psychosis after ChatGPT suggested he take sodium bromide
The 60-year old ended up with bromism. (Ars Technica)
+ He’d been taking it for three months before he went to the ER. (The Independent)
+ AI companies have stopped warning you that their chatbots aren’t doctors. (MIT Technology Review)
5 Meet Silicon Valley’s AI Rationalists
The group’s influence has spread through tech giants and AI pioneers alike. (NYT $)
+ Inside effective altruism, where the far future counts a lot more than the present. (MIT Technology Review)
6 An CBP agent wore Meta smart glasses during an immigration raid
Signalling that law enforcement are interested in this technology. (404 Media)
7 The US military has a new use for Tesla Cybertrucks
Namely, aiming missiles at them. (The Verge)
+ It wants to learn how to destroy them if enemies start deploying them. (The Register)
8 South Korea will decide whether to let Google Maps work 
The decades-old debate could be laid to rest next week. (The Guardian)
+ The country has previously rejected Google’s requests on security grounds. (Reuters)
9 Instagram’s new location-sharing feature is here
It’s a bid to make the app more participatory and social. (Insider $)
+ It also looks a whole lot like Snap’s map. (Fast Company $)
10 These headphones could help you to focus 
Startup Neurable wants to track your brain activity to prevent you getting distracted. (Vox)
+ A new AI translation system for headphones clones multiple voices simultaneously. (MIT Technology Review)
Quote of the day
“I don’t think we should think of them as the ‘new Google’ yet.”
—Brian Chesky, Airbnb’s CEO, questions the hype swirling around AI agents’ capabilities, TechCrunch reports.
One more thing
The arrhythmia of our current age
Arrhythmia means the heart beats, but not in proper time—a critical rhythm of life suddenly going rogue and unpredictable. It’s frightening to experience, but what if it’s also a good metaphor for our current times? That a pulse once seemingly so steady is now less sure.
Perhaps this wobbliness might be extrapolated into a broader sense of life in the 2020s.
Maybe you feel it, too—that the world seems to have skipped more than a beat or two as demagogues rant and democracy shudders, hurricanes rage, and glaciers dissolve. We can’t stop watching tiny screens where influencers pitch products we don’t need alongside news about senseless wars that destroy, murder, and maim tens-of-thousands.
All the resulting anxiety has been hard on our hearts—literally and metaphorically. Read the full story.
—David Ewing Duncan
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)
+ This entertaining site tracks just how many cigarettes are smoked throughout famous films (and the ones which feature no smoking at all)
+ Check out the very best seaside amusement arcade games.
+ Next week promises a dazzling meteor shower—here’s how to best watch it.
+ Oh, to live an hour in the shoes of a Boston-based bee doctor 
2025-08-08 01:00:00
At long last, OpenAI has released GPT-5. The new system abandons the distinction between OpenAI’s flagship models and its o series of reasoning models, automatically routing user queries to a fast nonreasoning model or a slower reasoning version. It is now available to everyone through the ChatGPT web interface—though nonpaying users may need to wait a few days to gain full access to the new capabilities.
It’s tempting to compare GPT-5 with its explicit predecessor, GPT-4, but the more illuminating juxtaposition is with o1, OpenAI’s first reasoning model, which was released last year. In contrast to GPT-5’s broad release, o1 was initially available only to Plus and Team subscribers. Those users got access to a completely new kind of language model—one that would “reason” through its answers by generating additional text before providing a final response, enabling it to solve much more challenging problems than its nonreasoning counterparts.
Whereas o1 was a major technological advancement, GPT-5 is, above all else, a refined product. During a press briefing, Sam Altman compared GPT-5 to Apple’s Retina displays, and it’s an apt analogy, though perhaps not in the way that he intended. Much like an unprecedentedly crisp screen, GPT-5 will furnish a more pleasant and seamless user experience. That’s not nothing, but it falls far short of the transformative AI future that Altman has spent much of the past year hyping. In the briefing, Altman called GPT-5 “a significant step along the path to AGI,” or artificial general intelligence, and maybe he’s right—but if so, it’s a very small step.
Take the demo of the model’s abilities that OpenAI showed to MIT Technology Review in advance of its release. Yann Dubois, a post-training lead at OpenAI, asked GPT-5 to design a web application that would help his partner learn French so that she could communicate more easily with his family. The model did an admirable job of following his instructions and created an appealing, user-friendly app. But when I gave GPT-4o an almost identical prompt, it produced an app with exactly the same functionality. The only difference is that it wasn’t as aesthetically pleasing.
Some of the other user-experience improvements are more substantial. Having the model rather than the user choose whether to apply reasoning to each query removes a major pain point, especially for users who don’t follow LLM advancements closely.
And, according to Altman, GPT-5 reasons much faster than the o-series models. The fact that OpenAI is releasing it to nonpaying users suggests that it’s also less expensive for the company to run. That’s a big deal: Running powerful models cheaply and quickly is a tough problem, and solving it is key to reducing AI’s environmental impact.
OpenAI has also taken steps to mitigate hallucinations, which have been a persistent headache. OpenAI’s evaluations suggest that GPT-5 models are substantially less likely to make incorrect claims than their predecessor models, o3 and GPT-4o. If that advancement holds up to scrutiny, it could help pave the way for more reliable and trustworthy agents. “Hallucination can cause real safety and security issues,” says Dawn Song, a professor of computer science at UC Berkeley. For example, an agent that hallucinates software packages could download malicious code to a user’s device.
GPT-5 has achieved the state of the art on several benchmarks, including a test of agentic abilities and the coding evaluations SWE-Bench and Aider Polyglot. But according to Clémentine Fourrier, an AI researcher at the company HuggingFace, those evaluations are nearing saturation, which means that current models have achieved close to maximal performance.
“It’s basically like looking at the performance of a high schooler on middle-grade problems,” she says. “If the high schooler fails, it tells you something, but if it succeeds, it doesn’t tell you a lot.” Fourrier said she would be impressed if the system achieved a score of 80% or 85% on SWE-Bench—but it only managed a 74.9%.
Ultimately, the headline message from OpenAI is that GPT-5 feels better to use. “The vibes of this model are really good, and I think that people are really going to feel that, especially average people who haven’t been spending their time thinking about models,” said Nick Turley, the head of ChatGPT.
Vibes alone, however, won’t bring about the automated future that Altman has promised. Reasoning felt like a major step forward on the way to AGI. We’re still waiting for the next one.
2025-08-07 20:10:00
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Five ways that AI is learning to improve itself
Last week, Mark Zuckerberg declared that Meta aims to achieve smarter-than-human AI. He seems to have a recipe for achieving that goal, and the first ingredient is human talent: Zuckerberg has reportedly tried to lure top researchers to Meta Superintelligence Labs with nine-figure offers.
The second ingredient is AI itself. Zuckerberg recently said on an earnings call that Meta will focus on building self-improving AI—systems that can bootstrap themselves to higher and higher levels of performance. He hopes to tap into a very real trend. Here are five ways that AI is already making itself better.
—Grace Huckins
The greenhouse gases we’re not accounting for
Back in 2021, climate scientists noticed that levels of methane had soared in the atmosphere the previous year, rising at the fastest rate on record despite the global covid-19 lockdowns.
Researchers eventually spotted a clear pattern: Methane emissions had increased sharply across the tropics, where wetlands were growing wetter and warmer.
The findings offer one of the clearest cases so far where climate change itself is driving additional greenhouse-gas emissions from natural systems, triggering a feedback effect that threatens to produce more warming, more emissions, and on and on.
There’s now a major endeavor underway to better track and understand what’s going on. Read our story about it.
—James Temple
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 The Trump administration’s punishing new tariffs have come into effect
And prices are already climbing. (NYT $)
+ Economists fear the US economy is poised to shrink. (WP $)
+ Sweeping tariffs could threaten the US manufacturing rebound. (MIT Technology Review)
2 Sections of the US Constitution have been deleted online
Passages about Congress’ powers and citizens’ unlawful detention have been scrubbed from the US government’s website. (TechCrunch)
+ The Library of Congress blamed a coding error. (Ars Technica)
3 China is fighting a mosquito-borne virus
It’s deploying drones to search for standing water where the insects lay eggs. (AP News)
+ Chikungunya virus is rarely fatal, but can cause fever and joint pain. (CNN)
+ Authorities are taking a leaf out of their covid-fighting playbooks. (NYT $)
4 US federal agencies will have access to ChatGPT Enterprise
For the grand sum of $1 a year. (Ars Technica)
+ It won’t use workers’ data to train ChatGPT, apparently. (Bloomberg $)
+ The news comes after major AI firms were greenlit as federal vendors. (Engadget)
5 Chinese drug discovery startups are striking deals with Big Pharma
Western pharmaceutical giants are confident they can deliver. (Rest of World)
+ An AI-driven “factory of drugs” claims to have hit a big milestone. (MIT Technology Review)
6 Is it possible to build truly green AI data centers?
The tech industry appears pretty hooked on fossil fuels. (FT $)
+ We did the math on AI’s energy footprint. Here’s the story you haven’t heard. (MIT Technology Review)
7 The US is increasingly reliant on private companies for weather data
Experts are wary about losing access to vital tools. (Undark)
+ How US research cuts are threatening crucial climate data. (MIT Technology Review)
8 Genetic factors could contribute to the risk of developing chronic fatigue syndrome
It’s the first robust evidence that genetics play a role. (New Scientist $)
9 An experimental pill is showing weight-loss promise
Obese participants in Eli Lilly’s trial lost more than 12% of their body weight. (Wired $)
+ We’re learning more about what weight-loss drugs do to the body. (MIT Technology Review)
10 Finding a job online is a nightmare
Some companies are going back to basics to find the best recruits. (WSJ $)
Quote of the day
“We didn’t vote for ChatGPT.”
—Virginia Dignum, a professor of responsible artificial intelligence at Sweden’s Umeå University, criticizes the country’s prime minister, Ulf Kristersson for admitting he regularly consults AI tools, the Guardian reports.
One more thing
Why AI could eat quantum computing’s lunch
Tech companies have been funneling billions of dollars into quantum computers for years. The hope is that they’ll be a game changer for fields as diverse as finance, drug discovery, and logistics.
But while the field struggles with the realities of tricky quantum hardware, another challenger is making headway in some of these most promising use cases. AI is now being applied to fundamental physics, chemistry, and materials science in a way that suggests quantum computing’s purported home turf might not be so safe after all. Read the full story.
—Edd Gent
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)
+ A super rare edition of The Hobbit has sold for a record-breaking amount at auction.
+ How to find new music without relying on Spotify.
+ The forthcoming Social Network sequel is rumored to be getting a Succession shakeup.
+ If you own both a cat and a scanner, you know what you have to do.
2025-08-07 17:00:00
In the spring of 2021, climate scientists were stumped.
The global economy was just emerging from the covid-19 lockdowns, but for some reason the levels of methane—a greenhouse gas emitted mainly through agriculture and fossil-fuel production—had soared in the atmosphere the previous year, rising at the fastest rate on record.
Researchers around the world set to work unraveling the mystery, reviewing readings from satellites, aircraft, and greenhouse-gas monitoring stations. They eventually spotted a clear pattern: Methane emissions had increased sharply across the tropics, where wetlands were growing wetter and warmer.
That created the ideal conditions for microbes that thrive in anaerobic muck, which gobbled up more of the carbon-rich organic matter and spat out more methane as a by-product. (Reduced pollution from nitrogen oxides, which help to break down methane in the atmosphere, also likely played a substantial role.)
The findings offer one of the clearest cases so far where climate change itself is driving additional greenhouse-gas emissions from natural systems, triggering a feedback effect that threatens to produce more warming, more emissions, and on and on.
There are numerous additional ways this is happening or soon could, including wildfires and thawing permafrost. These are major emissions sources that aren’t included in the commitments nations have made under the Paris climate agreement—and climate risks that largely aren’t accounted for in the UN Intergovernmental Panel on Climate Change’s most recent warming scenarios.
Spark Climate Solutions (not to be confused with this newsletter) hopes to change that.
The San Francisco nonprofit is launching what’s known as a model intercomparison project, in which different research teams run the same set of experiments on different models across a variety of emissions scenarios to determine how climate change could play out. This one would specifically explore how a range of climate feedback effects could propel additional warming, additional emissions, and additional types of feedback.
“These increased emissions from natural sources add to human emissions and amplify climate change,” says Phil Duffy, chief scientist at Spark Climate Solutions, who previously served as climate science advisor to President Joe Biden. “And if you don’t look at all of them together, you can’t quantify the strength of that feedback effect.”
Other participants in the effort will include scientists at the Environmental Defense Fund, Stanford University, the Woodwell Climate Research Center, and other institutions in Europe and Australia, according to Spark Climate Solutions.
The nonprofit hopes to publish the findings in time for them to be incorporated into the UN climate panel’s seventh major assessment report, which is just getting underway, to help ensure that these dangers are more fully represented. That, in turn, would give nations a more accurate sense of the world’s carbon budgets, or the quantity of greenhouse gases they can produce before the planet reaches temperatures 1.5 °C or 2 °C over preindustrial levels.
But one thing is already clear: Since the current scenarios don’t fully account for these feedback effects, the world will almost certainly warm faster than is now forecast, which underscores the importance of carrying out this exercise.
Scientists at EDF, Woodwell and other institutions found that fires in the world’s northernmost forests, thawing permafrost and warming tropical wetlands could together push the planet beyond 2 °C years faster, eliminating up to a quarter of the time left before the world passes the core goal of the Paris agreement, in a paper under review.
Earlier this year, Spark Climate Solutions set up a broader program to advance research and awareness of what’s known as warming-induced emissions, which will launch additional collaborations similar to the modeling intercomparison project.
The goal of the program and the research project is “to really mainstream the inclusion of this topic in climate science and climate policy, and to drive research around climate solutions,” says Ben Poulter, who leads the program at Spark Climate Solutions and was previously a scientist at the NASA Goddard Space Flight Center.
Spark notes that warming temperatures could also release more carbon dioxide from the oceans, in a process known as outgassing; additional carbon dioxide and nitrous oxide, a potent greenhouse gas that also depletes the protective ozone layer, from farmland; more carbon dioxide and methane from wildfires; and still more of all three of these gases as permafrost thaws.
The ground remains frozen year round across a vast expanse of the Northern Hemisphere, creating a frosty underground storehouse from Alaska to Siberia that’s packed with twice as much carbon as the atmosphere.
But as it thaws, it starts to decompose and release greenhouse gases, says Susan Natali, an Arctic climate scientist focused on permafrost at Woodwell. A study published in Nature in January noted that 30% of the world’s Arctic–Boreal Zone has already flipped from a carbon sink to a carbon source, when wildfires, thawing permafrost and other factors are taken into account.
Despite these increasing risks, only a minority of the models that fed into the UN climate panel’s last major report incorporated the feedback effects of thawing permafrost. And the emissions risks still weren’t fully accounted for because these ecosystems are difficult to monitor and model, Natali says.
Among the complexities: Wildfires, which are themselves hard to predict, can accelerate thawing. It’s also hard to foresee which regions will grow drier or wetter, which determines whether they release mostly methane or carbon dioxide—and those gases have very different warming effects over different time periods. There are counterbalancing effects that must be taken into account as well—for instance, as carbon-absorbing plants replace ice and snow in certain areas.
Natali says improving our understanding of these complex feedback effects is essential to understanding the dangers we face.
“It’s going to mean additional costs to human health, human life,” she says. “We want people to be safe—and it’s very hard to do that if you don’t know what’s coming and you’re not prepared for it.”
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
2025-08-06 23:14:12
Last week, Mark Zuckerberg declared that Meta is aiming to achieve smarter-than-human AI. He seems to have a recipe for achieving that goal, and the first ingredient is human talent: Zuckerberg has reportedly tried to lure top researchers to Meta Superintelligence Labs with nine-figure offers. The second ingredient is AI itself. Zuckerberg recently said on an earnings call that Meta Superintelligence Labs will be focused on building self-improving AI—systems that can bootstrap themselves to higher and higher levels of performance.
The possibility of self-improvement distinguishes AI from other revolutionary technologies. CRISPR can’t improve its own targeting of DNA sequences, and fusion reactors can’t figure out how to make the technology commercially viable. But LLMs can optimize the computer chips they run on, train other LLMs cheaply and efficiently, and perhaps even come up with original ideas for AI research. And they’ve already made some progress in all these domains.
According to Zuckerberg, AI self-improvement could bring about a world in which humans are liberated from workaday drudgery and can pursue their highest goals with the support of brilliant, hypereffective artificial companions. But self-improvement also creates a fundamental risk, according to Chris Painter, the policy director at the AI research nonprofit METR. If AI accelerates the development of its own capabilities, he says, it could rapidly get better at hacking, designing weapons, and manipulating people. Some researchers even speculate that this positive feedback cycle could lead to an “intelligence explosion,” in which AI rapidly launches itself far beyond the level of human capabilities.
But you don’t have to be a doomer to take the implications of self-improving AI seriously. OpenAI, Anthropic, and Google all include references to automated AI research in their AI safety frameworks, alongside more familiar risk categories such as chemical weapons and cybersecurity. “I think this is the fastest path to powerful AI,” says Jeff Clune, a professor of computer science at the University of British Columbia and senior research advisor at Google DeepMind. “It’s probably the most important thing we should be thinking about.”
By the same token, Clune says, automating AI research and development could have enormous upsides. On our own, we humans might not be able to think up the innovations and improvements that will allow AI to one day tackle prodigious problems like cancer and climate change.
For now, human ingenuity is still the primary engine of AI advancement; otherwise, Meta would hardly have made such exorbitant offers to attract researchers to its superintelligence lab. But AI is already contributing to its own development, and it’s set to take even more of a role in the years to come. Here are five ways that AI is making itself better.
Today, the most important contribution that LLMs make to AI development may also be the most banal. “The biggest thing is coding assistance,” says Tom Davidson, a senior research fellow at Forethought, an AI research nonprofit. Tools that help engineers write software more quickly, such as Claude Code and Cursor, appear popular across the AI industry: Google CEO Sundar Pichai claimed in October 2024 that a quarter of the company’s new code was generated by AI, and Anthropic recently documented a wide variety of ways that its employees use Claude Code. If engineers are more productive because of this coding assistance, they will be able to design, test, and deploy new AI systems more quickly.
But the productivity advantage that these tools confer remains uncertain: If engineers are spending large amounts of time correcting errors made by AI systems, they might not be getting any more work done, even if they are spending less of their time writing code manually. A recent study from METR found that developers take about 20% longer to complete tasks when using AI coding assistants, though Nate Rush, a member of METR’s technical staff who co-led the study, notes that it only examined extremely experienced developers working on large code bases. Its conclusions might not apply to AI researchers who write up quick scripts to run experiments.
Conducting a similar study within the frontier labs could help provide a much clearer picture of whether coding assistants are making AI researchers at the cutting edge more productive, Rush says—but that work hasn’t yet been undertaken. In the meantime, just taking software engineers’ word for it isn’t enough: The developers METR studied thought that the AI coding tools had made them work more efficiently, even though the tools had actually slowed them down substantially.
Writing code quickly isn’t that much of an advantage if you have to wait hours, days, or weeks for it to run. LLM training, in particular, is an agonizingly slow process, and the most sophisticated reasoning models can take many minutes to generate a single response. These delays are major bottlenecks for AI development, says Azalia Mirhoseini, an assistant professor of computer science at Stanford University and senior staff scientist at Google DeepMind. “If we can run AI faster, we can innovate more,” she says.
That’s why Mirhoseini has been using AI to optimize AI chips. Back in 2021, she and her collaborators at Google built a non-LLM AI system that could decide where to place various components on a computer chip to optimize efficiency. Although some other researchers failed to replicate the study’s results, Mirhoseini says that Nature investigated the paper and upheld the work’s validity—and she notes that Google has used the system’s designs for multiple generations of its custom AI chips.
More recently, Mirhoseini has applied LLMs to the problem of writing kernels, low-level functions that control how various operations, like matrix multiplication, are carried out in chips. She’s found that even general-purpose LLMs can, in some cases, write kernels that run faster than the human-designed versions.
Elsewhere at Google, scientists built a system that they used to optimize various parts of the company’s LLM infrastructure. The system, called AlphaEvolve, prompts Google’s Gemini LLM to write algorithms for solving some problem, evaluates those algorithms, and asks Gemini to improve on the most successful—and repeats that process several times. AlphaEvolve designed a new approach for running datacenters that saved 0.7% of Google’s computational resources, made further improvements to Google’s custom chip design, and designed a new kernel that sped up Gemini’s training by 1%.
That might sound like a small improvement, but at a huge company like Google it equates to enormous savings of time, money, and energy. And Matej Balog, a staff research scientist at Google DeepMind who led the AlphaEvolve project, says that he and his team tested the system on only a small component of Gemini’s overall training pipeline. Applying it more broadly, he says, could lead to more savings.
LLMs are famously data hungry, and training them is costly at every stage. In some specific domains—unusual programming languages, for example—real-world data is too scarce to train LLMs effectively. Reinforcement learning with human feedback, a technique in which humans score LLM responses to prompts and the LLMs are then trained using those scores, has been key to creating models that behave in line with human standards and preferences, but obtaining human feedback is slow and expensive.
Increasingly, LLMs are being used to fill in the gaps. If prompted with plenty of examples, LLMs can generate plausible synthetic data in domains in which they haven’t been trained, and that synthetic data can then be used for training. LLMs can also be used effectively for reinforcement learning: In an approach called “LLM as a judge,” LLMs, rather than humans, are used to score the outputs of models that are being trained. That approach is key to the influential “Constitutional AI” framework proposed by Anthropic researchers in 2022, in which one LLM is trained to be less harmful based on feedback from another LLM.
Data scarcity is a particularly acute problem for AI agents. Effective agents need to be able to carry out multistep plans to accomplish particular tasks, but examples of successful step-by-step task completion are scarce online, and using humans to generate new examples would be pricey. To overcome this limitation, Stanford’s Mirhoseini and her colleagues have recently piloted a technique in which an LLM agent generates a possible step-by-step approach to a given problem, an LLM judge evaluates whether each step is valid, and then a new LLM agent is trained on those steps. “You’re not limited by data anymore, because the model can just arbitrarily generate more and more experiences,” Mirhoseini says.
One area where LLMs haven’t yet made major contributions is in the design of LLMs themselves. Today’s LLMs are all based on a neural-network structure called a transformer, which was proposed by human researchers in 2017, and the notable improvements that have since been made to the architecture were also human-designed.
But the rise of LLM agents has created an entirely new design universe to explore. Agents need tools to interact with the outside world and instructions for how to use them, and optimizing those tools and instructions is essential to producing effective agents. “Humans haven’t spent as much time mapping out all these ideas, so there’s a lot more low-hanging fruit,” Clune says. “It’s easier to just create an AI system to go pick it.”
Together with researchers at the startup Sakana AI, Clune created a system called a “Darwin Gödel Machine”: an LLM agent that can iteratively modify its prompts, tools, and other aspects of its code to improve its own task performance. Not only did the Darwin Gödel Machine achieve higher task scores through modifying itself, but as it evolved, it also managed to find new modifications that its original version wouldn’t have been able to discover. It had entered a true self-improvement loop.
Although LLMs are speeding up numerous parts of the LLM development pipeline, humans may still remain essential to AI research for quite a while. Many experts point to “research taste,” or the ability that the best scientists have to pick out promising new research questions and directions, as both a particular challenge for AI and a key ingredient in AI development.
But Clune says research taste might not be as much of a challenge for AI as some researchers think. He and Sakana AI researchers are working on an end-to-end system for AI research that they call the “AI Scientist.” It searches through the scientific literature to determine its own research question, runs experiments to answer that question, and then writes up its results.
One paper that it wrote earlier this year, in which it devised and tested a new training strategy aimed at making neural networks better at combining examples from their training data, was anonymously submitted to a workshop at the International Conference on Machine Learning, or ICML—one of the most prestigious conferences in the field—with the consent of the workshop organizers. The training strategy didn’t end up working, but the paper was scored highly enough by reviewers to qualify it for acceptance (it is worth noting that ICML workshops have lower standards for acceptance than the main conference). In another instance, Clune says, the AI Scientist came up with a research idea that was later independently proposed by a human researcher on X, where it attracted plenty of interest from other scientists.
“We are looking right now at the GPT-1 moment of the AI Scientist,” Clune says. “In a few short years, it is going to be writing papers that will be accepted at the top peer-reviewed conferences and journals in the world. It will be making novel scientific discoveries.”
With all this enthusiasm for AI self-improvement, it seems likely that in the coming months and years, the contributions AI makes to its own development will only multiply. To hear Mark Zuckerberg tell it, this could mean that superintelligent models, which exceed human capabilities in many domains, are just around the corner. In reality, though, the impact of self-improving AI is far from certain.
It’s notable that AlphaEvolve has sped up the training of its own core LLM system, Gemini—but that 1% speedup may not observably change the pace of Google’s AI advancements. “This is still a feedback loop that’s very slow,” says Balog, the AlphaEvolve researcher. “The training of Gemini takes a significant amount of time. So you can maybe see the exciting beginnings of this virtuous [cycle], but it’s still a very slow process.”
If each subsequent version of Gemini speeds up its own training by an additional 1%, those accelerations will compound. And because each successive generation will be more capable than the previous one, it should be able to achieve even greater training speedups—not to mention all the other ways it might devise to improve itself. Under such circumstances, proponents of superintelligence argue, an eventual intelligence explosion looks inevitable.
This conclusion, however, ignores a key observation: Innovation gets harder over time. In the early days of any scientific field, discoveries come fast and easy. There are plenty of obvious experiments to run and ideas to investigate, and none of them have been tried before. But as the science of deep learning matures, finding each additional improvement might require substantially more effort on the part of both humans and their AI collaborators. It’s possible that by the time AI systems attain human-level research abilities, humans or less-intelligent AI systems will already have plucked all the low-hanging fruit.
Determining the real-world impact of AI self-improvement, then, is a mighty challenge. To make matters worse, the AI systems that matter most for AI development—those being used inside frontier AI companies—are likely more advanced than those that have been released to the general public, so measuring o3’s capabilities might not be a great way to infer what’s happening inside OpenAI.
But external researchers are doing their best—by, for example, tracking the overall pace of AI development to determine whether or not that pace is accelerating. METR is monitoring advancements in AI abilities by measuring how long it takes humans to do tasks that cutting-edge systems can complete themselves. They’ve found that the length of tasks that AI systems can complete independently has, since the release of GPT-2 in 2019, doubled every seven months.
Since 2024, that doubling time has shortened to four months, which suggests that AI progress is indeed accelerating. There may be unglamorous reasons for that: Frontier AI labs are flush with investor cash, which they can spend on hiring new researchers and purchasing new hardware. But it’s entirely plausible that AI self-improvement could also be playing a role.
That’s just one indirect piece of evidence. But Davidson, the Forethought researcher, says there’s good reason to expect that AI will supercharge its own advancement, at least for a time. METR’s work suggests that the low-hanging-fruit effect isn’t slowing down human researchers today, or at least that increased investment is effectively counterbalancing any slowdown. If AI notably increases the productivity of those researchers, or even takes on some fraction of the research work itself, that balance will shift in favor of research acceleration.
“You would, I think, strongly expect that there’ll be a period when AI progress speeds up,” Davidson says. “The big question is how long it goes on for.”
2025-08-06 20:10:00
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
OpenAI has finally released open-weight language models
The news: OpenAI has finally released its first open-weight large language models since 2019’s GPT-2. Unlike the models available through OpenAI’s web interface, these new open models can be freely downloaded, run, and even modified on laptops and other local devices.
Why it matters: These releases re-establish OpenAI as a presence for users of open models. That’s particularly notable at a time when Meta, which had previously dominated the American open-model landscape with its Llama models, may be reorienting toward closed releases—and when Chinese open models are becoming more popular than their American competitors. Read the full story.
—Grace Huckins
MIT Technology Review Narrated: AI means the end of internet search as we’ve known it
The biggest change to the way search engines deliver information to us since the 1990s is happening right now. No more keyword searching. Instead, you can ask questions in natural language. And instead of links, you’ll increasingly be met with answers written by generative AI and based on live information from across the internet, delivered the same way.
Not everyone is excited for the change. Publishers are completely freaked out. And people are also worried about what these new LLM-powered results will mean for our fundamental shared reality.
This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Nvidia insists its AI chips don’t have a “kill switch”
After China’s Cyberspace Administration asked for security documentation. (CNBC)
+ The country’s ambitions to consolidate its chip giants aren’t going to plan. (FT $)
+ Two Chinese nationals have been charged with illegally shipping chips. (Reuters)
2 America’s new data centers are driving colossal electricity demand
And a handful of equipment makers are reaping the benefits. (FT $)
+ We did the math on AI’s energy footprint. Here’s the story you haven’t heard. (MIT Technology Review)
3 RFK Jr has cancelled close to $500 million in mRNA vaccine contracts
Which could leave us dangerously underprepared for a future pandemic. (Politico)
+ We’re losing a key insight into global health. (Vox)
+ How measuring vaccine hesitancy could help health professionals tackle it. (MIT Technology Review)
4 Uber has a sexual assault problem
Newly-unveiled records show it gathered far more sexual assault and misconduct reports than previously revealed. (NYT $)
5 A British politician created an AI clone of himself
And although it provoked a backlash, other MPs may follow his lead. (WP $)
+ A former CNN journalist has interviewed an AI version of a mass-shooting victim. (The Guardian)
6 xAI’s new Grok Imagine tool has a “spicy” mode
Which seems to be code for non-consensual porn images. (The Verge)
+ It’s already generated fake Taylor Swift nudes without being asked. (Ars Technica)
7 How does ChatGPT fare as a couple’s counselor?
It gets some stuff right. But it also gets some things really wrong. (NPR)
+ The AI relationship revolution is already here. (MIT Technology Review)
8 Syria’s refugees are returning to rebuild its tech industry
But sectarian violence and poor connectivity mean it’s an uphill battle. (Rest of World)
9 Sales of Ozempic have dropped
Rival Mounjaro seems to be more effective. (The Guardian)
+ We’re learning more about what weight-loss drugs do to the body. (MIT Technology Review)
10 Google Calendar rules college kids’ lives
They schedule everything from assignments to parties and hook ups. (WSJ $)
Quote of the day
“This is a bad day for science.”
—Scott Hensley, an immunologist at the University of Pennsylvania, criticizes the Department of Health and Human Services’ decision to cancel hundreds of millions of dollars in funding for mRNA vaccine projects, the New York Times reports.
One more thing
Future space food could be made from astronaut breath
The future of space food could be as simple—and weird—as a protein shake made with astronaut breath or a burger made from fungus.
For decades, astronauts have relied mostly on pre-packaged food during their forays off our planet. With missions beyond Earth orbit in sight, a NASA-led competition is hoping to change all that and usher in a new era of sustainable space food.
To solve the problem of feeding astronauts on long-duration missions, NASA asked companies to propose novel ways to develop sustainable foods for future missions. Around 200 rose to the challenge—creating nutritious (and outlandish) culinary creations in the process. Read the full story.
—Jonathan O’Callaghan
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)
+ There are a lot of funny cat videos out there but honestly, this is top-drawer.
+ Check out this adorable website where people share what they see in clouds.
+ Babe you’re glowing! No seriously, you literally are.
+ I loved watching this woman from London’s East End wax lyrical about the dawn of TV.
