How do clusters of galaxies form?
What created this huge space bubble?
How do clusters of galaxies form?
What created this huge space bubble?
Dry thunderstorms that popped up over the Cascade Range on the evening of July 15, 2026, peppered Oregon and Washington with thousands of lightning strikes as they moved east across the states. By the following day, NASA satellites had begun to detect large numbers of wildland fires burning throughout central and eastern Oregon.
Though initially small, these blazes strengthened as they were fanned by gusty winds and raced through landscapes parched by extreme drought and baking in summer heat. When NASA’s Aqua satellite captured the image shown above on the afternoon of July 26, smoke poured northeast from dozens of large fires that had collectively charred hundreds of square miles. The fires produced a blanket of haze, prompting state officials to issue air quality advisories for eastern Oregon.
Many communities faced evacuation orders as more than 10,000 firefighters battled wildfires throughout the state. On the day the image was captured, the largest active fires were the Hay Creek Complex, Brewer, Big Grass, Akawa Butte, and Powder River fires. Several of these blazes were less than 5 percent contained, according to the National Interagency Fire Center. State officials invoked Oregon’s Emergency Conflagration Act to protect communities as they responded to particularly threatening fires such as the Shingle, Bench, Beachcomb, and Second Flat fires.
Government satellite data are part of a global system of observations used to track fire behavior and analyze emerging trends. Among the real-time wildfire monitoring tools that NASA makes available are FIRMS (Fire Information for Resource Management System), the Worldview browser, and the Fire Event Explorer.
As of July 27, 2026, fires in Oregon had burned more than one million acres, according to news outlets. Meanwhile, the National Interagency Fire Center reported that fires had burned more than 4 million acres across the United States. The 10-year average (2016-2025) for this point in the season is 3.4 million acres.
NASA Earth Observatory image by Michala Garrison, using MODIS data from NASA EOSDIS LANCE and GIBS/Worldview. Story by Adam Voiland.
Stay up-to-date with the latest content from NASA as we explore the universe and discover more about our home planet.
Dry, warm, and windy conditions across the U.S. Great Plains led to extreme fire activity in March 2026.
The blaze burned more than 150 square miles and swept through parts of a ski resort.
Firefighters are battling two destructive blazes in the southern part of the state as drought grips the U.S. Southeast.
The post Smoke Blankets Oregon appeared first on NASA Science.
We’re still not at zero homicides, but the decline is good. I usually post this on Friday, but some minor personal things got in the way, so I’m late. Anyway, as of Sunday 9pm, D.C. had reported two homicides this week, bringing the total for the year to 54*. Last year, during the same time period, we had 92 homicides, and in the surge year of 2023, there were 138 homicides during that time.
The good news is that other crime categories are trending downward too this week. We are still well on pace for another 33 percent drop in homicides for the third straight year–in fact, it looks like D.C.’s per capita homicide rate could be the lowest this year since 1900 (that’s not a typo).
Here’s to hoping that next week gets us back to a zero homicide week.
*Three of the 57 murders reported this year actually occurred in other years (e.g., a missing persons case from 2023 turned into a homicide case this year with new evidence).
Below I continue my survey of 50 essential western films. (Click here for part one.) I’m sharing these in chronological order. In this installment, I cover films released from 1955 through 1968.
It was a strange period for cowboy movies. The mass audience would soon abandon the genre, turning instead to space operas, disaster films, and superhero movies. The whole idiom sometimes now felt like a tired string of clichés—almost the antithesis of the free and untamed ethos that had originally made the Wild West so appealing.
But this shift in preferences also allowed—or even required—directors to take more chances when dealing with western themes. So in some ways, this end-of-an-era mentality produced many of the most innovative films in the genre’s history.
This film is as tightly constructed as a Greek tragedy, and even abides by the three Aristotelian unities of time, space, and action. The entire drama plays out over the course of a single day in Black Rock, a small desert outpost, where a World War II vet named John Macreedy (Spencer Tracy) attempts to repay a favor to a Japanese soldier who saved his life on the battlefield. But he finds himself unexpectedly caught up in a criminal investigation, with the entire town conspiring against him.
The film features five past or future Oscar winners—Tracy, Ernest Borgnine, Lee Marvin, Walter Brennan and Dean Jagger—who between them would eventually earn eight Academy Awards for acting. That’s essentially half of the cast, and it ensures a gripping ensemble performance rarely surpassed on screen.
Together they create a slowly building tension finally resolved in a fiery desert confrontation. The widescreen CinemaScope technology is artfully employed, and André Previn makes full use of its stereophonic capability for his brash, electrifying score. This film has been unfairly forgotten, but rewards repeated viewings.
John Wayne was often criticized for playing the same role over and over. But that’s not entirely fair. As we’ve already seen with Red River (1948), he sometimes took parts that undercut the simple heroism of his public image, defying the standard formulas of the genre.
In The Searchers, Wayne plays a Civil War veteran on a mission to find his niece, abducted by Comanches. But he grows obsessed with vengeance and hatred—no doubt shocking moviegoers unready for this ignoble side of the Duke. Yet The Searchers undercuts its disruptive story lines with the sheer beauty of its delivery. Director John Ford, taking full advantage of Technicolor technology, never made a more visually stunning film.
Even from that first scene, when the cabin door opens, you know you’re in for a magical experience. It’s like that other Hollywood doorway leading to Oz, or the journey to Narnia from the back of a closet. You almost feel as if you’re leaving your seat in the cinema and entering a spectacle from a different time and place.
Links for you. Science:
Why AI will not speed up science (yet) (one issue not raised is will the possible improvements actually be AI per se, or just “computer”)
RNA quality control enables antibiotic tolerance
Ancient ape fossil in Egypt challenges the story of human origins
U.S. Measles Case Count Breaks Record
Even camels can’t cope: Africa’s ships of the desert hit by rising temperatures
Other:
Trump Administration Admits Canceling Grants to States That Did Not Vote for Him (impeach him. Impeach him now)
Crews preparing DC streets for Freedom 250 Grand Prix
The Hat Was the Message. At the Correspondents’ Dinner, Trump wore the punchline on his head: he has no intention of leaving.
Inside the Phone-Throwing Drama at the Troubled, Broke Democratic Party (the article does neglect to say that the party raised more money than under Perez when Democrats were out of power)
The collapse of the American law school financing model
Apple targets WWDC 2027 for smart glasses but is still deciding on the camera question
Teeing Off on the DSA 2025-26 Platform
Andrew Tate’s arrest causes trouble for Trump’s youngest son
Real-World Democratic Socialism
A Penn professor thought she’d sell 1000 copies of her ‘Odyssey’ translation. She sold more than a million this year alone, thanks to Christopher Nolan.
Barack Obama And The Lost Art Of Broad Empathy
How Cops Consolidated So Much Power Over Local Governments
What Does Olive Garden’s Never-Ending Pasta Have to Do With Voting Rights?
The Accidental Senator
What Are We Going To Talk About This Week?
What Happened When Flock Came to My Town
Life During Bari
A Court Reporter Submitted AI-Generated Errors in Official Court Transcript, Judge Says
Maine’s working class hated Platner’s working-class shtick
The Rise of Anti-Flock Influencers Who Make Things Up for Clout
Trump Orders Warnings Placed at ‘Inaccurate’ Smithsonian History Museum
New Fed research suggests far fewer Americans own homes than widely believed
The SpaceX Files: Now that the post-I.P.O. “quiet period” has expired, nearly all of SpaceX’s Wall Street underwriters have published their initial research reports on the stock’s prospects. And wouldn’t you know it, the verdict is unanimous: buy, buy, buy! (gift link)
AI isn’t boosting productivity, says Barclays
Katy Perry ‘appalled’ that White House used ‘Firework’ song in Iran war video
Yet another Israeli mass surveillance company:
Made by Israeli surveillance company Cognyte, the tech simulates a mobile phone tower, which forces nearby phones to connect to it. That enables cops to keep tabs on any phones in the vicinity whether they’re owned by a suspect in a case or not. Cognyte’s contract with the state of Texas reveals that the simulator, called FalcoNet, can be concealed within the vehicles, hidden in a backpack for on-foot missions or attached to a helicopter. It’s the same technology as the infamous Stingray, one of the original cell-site simulators made by defense giant L3Harris.
Why do I write? I write because I like the mental state created by writing. It calms and quiets me, but that is not why I write.
Why do I write? I write because I am curious and like to understand the world. Writing forces me to understand a thing, a person, a situation. I don’t think many humans do this, but I am addicted to it. But that is not why I write.
Why do I write? I write because I like helping. People are often the worst, but I have a desire to help. This internal contradiction is confusing. Humans who approach me looking for Rands, but find Lopp, are disappointed and confused because Rands helped them and Lopp apparently couldn’t care less? Introvert. But helping is not why I write.
Why do I write? I deeply believe it is important for humans to build stuff. I think this is a primary reason we are on the planet, and I think that the ways a human can build are as varied as the humans who do the building.
Failure to build is abhorrent to me. Much of the anger on the planet right now is from humans who aren’t building, believing they are owed a free lunch because they showed up, or they know better, or some other poorly informed worldview they acquired without effort, without curiosity… just because they exist in their particular circumstances.
Writing is how I build. That’s why I write.
You will never do 150% of a thing if you do not have a version of these motivations. 15% effort means you have not found your reasons yet.
Perhaps I’m flattering myself or my country. But the language and mindset of great replacement and of race armies overrunning a primarily white, Christian country seems much more widespread, entrenched and pervasive across a wider swath of the political spectrum in the United Kingdom than it is here in the United States. I don’t really have an argument here or a even a question here. And this may seem like quite a lot to say given that the federal government here in the U.S. fairly openly propagandizes with white nationalist imagery and is more than a year into a campaign of mass deportation. But it is still my perception based on reading the language of statements from people across the political spectrum in the UK, the reaction to pogrom-like incidents in the UK and so forth.
It is also why I think there’s a majority negative response when Americans see the reality of “mass deportation” as opposed to intentionally vague statements about deporting undocumented violent criminals and the like — something that is basically impossible to oppose and has been U.S. policy under every administration for decades.
To the extent this is true, and my ability to compare these things in a systematic way is of course limited, I assume it is rooted in the fact that the U.S., for all its problems, for all its history of recurrent episodes of nativism and xenophobia, has experience with pluralism fairly deep in its currently and civic DNA. I also wonder the extent to which these differences can’t escape the history of colonialism — and this of course applies just as much to France. To a significant degree, mass migration into these countries is migration from former colonies, formerly subjugated peoples. And that has to shape societal responses in some basic way.
This short passage in an article (paywalled) in Haaretz focuses on a point that hasn’t been sufficiently visible in the US press. There’s been a decent amount of US coverage of the increasingly violent and predatory escalation in the West Bank, in which the government has both encouraged and allowed the most violent members of the settler community to accelerate their campaign of harassment, violence and expulsions of Palestinian families and communities. But this isn’t just a broader expression of Greater Israel maximalism, though it’s also very much that. There’s a nearer term goal. It’s also an electoral strategy, one aimed at hyper-mobilizing the settler parties in Netanyahu’s coalition and also creating a security crisis he hopes he will benefit from.
There are many on the Israeli side who want, and are actively seeking, a severe escalation in the West Bank. Their motives reflect two overlapping interests: the hope that growing violence will ultimately trigger a broader confrontation, allowing Israel to dismantle the Palestinian Authority and seize large parts of the West Bank by force; and the desire to manufacture a security emergency ahead of the October 27 election, pushing the Netanyahu government’s responsibility for the failures that led to the October 7 massacre off the political agenda.
Yesterday I was reading this piece from Status about the agreement between Paramount and the states suing the company on anti-trust grounds to halt the Time Warner merger for up to a year. (The piece is paywalled. But I think you can read it without a subscription by giving them your email.) Reporter Natalie Korach spoke to a handful of antitrust experts to evaluate what this agreed upon delay means, who it’s good or bad for and what it means for the future of the merger deal itself.
She didn’t find unanimity on the outlook. But the weight of opinion was definitely along the lines of this being not at all good news for Paramount or the merger. The Ellisons seem to have thought that buying off the Trump administration (largely in the form of promised friendly coverage and the destruction of CNN) was enough and that any state lawsuits would be tossed aside by judges. Agreeing to this deal, according to most of these experts, was simply the least bad option Paramount had out of a field of bad options and that the deal itself is in real danger. And it’s in danger not necessarily because Paramount will lose eventually on the merits but because mergers are a bit like sharks. They have to keep moving forward to survive. Delay makes everything harder – investors get skittish, synergies get more iffy, you have a longer period trying to work with people who might end up being colleagues or corporate competitors.
In any case, I was gratified to hear this because this deal is crooked and bad by really every possible measure. If you’re willing to give Status your email I’d recommend reading the piece because the details and nuances of the responses from antitrust experts are interesting and telling. But my point here isn’t to game that out or predict the future. As at least one expert put it, you have to weigh into this equation that Paramount seems to want this deal for more than purely financial reasons. They may be more committed to the detail than your average corporate behemoth because this is a) building a media juggernaut on the fly with dad’s money and b) operating on more pronounced ideological motives than usual. But what I really want to focus on is the role of the states.
States banding together for major lawsuits is not new. It’s something Attorneys General often do. The 1998 Tobacco settlement is a key example. And it is relevant to our current moment inasmuch as the states ending up playing a role because the federal government would not. There was a growing national consensus but reform was blocked at the federal level. So there’s some analog here to the extent that the story here is that the federal government was corrupt and could be bought off but Paramount apparently didn’t give enough thought to the existence of other sovereigns who could do what the federal government refused to.
So no, this isn’t a totally new thing under the sun. But it is an example to be emulated, built upon, brainstormed upon. As we’ve discussed, while federal law is supreme, the states are also sovereign powers and they wield executive power. There are numerous gaps and lacunae where federal and state authority are on fairly equal terms. This is an example of working through the courts. But there are many others where states have the power of non-compliance or competing sovereign powers. Critically, much of Trump’s current corruption is based on the simple refusal to act. Numerous laws are simply not being enforced. And since they’re not enforced the laws might as well not exist. But that is more a refusal to act than any ability to make criminals immune or overturn the laws themselves. And the power of that refusal diminishes greatly if there is another sovereign authority which can act on its own. In many cases, states can do just that, either individually or as a group.
Blue Origin will use NASA’s Stennis Space Center to test New Glenn upper stages as it rebuilds its Cape Canaveral launch complex.
The post Blue Origin to test New Glenn upper stages at Stennis appeared first on SpaceNews.
New Kodiak facility will support up to 18 suborbital missions for military testing
The post Rocket Lab to open Alaska launch site under $266 million Space Force deal appeared first on SpaceNews.
When we talk about stopping an arms race in space, people usually say that we need treaties that every state must follow or agreements that states can choose to follow. […]
The post AI in orbit is a double-edged sword. Here’s how to keep it from cutting space cooperation apart appeared first on SpaceNews.
Amazon has filed plans with the Federal Communications Commission for a constellation of more than 5,100 satellites that would provide direct-to-device (D2D) services using Globalstar spectrum.
The post Amazon files application for direct-to-device satellite constellation appeared first on SpaceNews.
Join the search for the elusive cassowary, a big bird that plays an outsized role in maintaining its rainforest home
- by Aeon Video
Black philosophers now feature on Western curricula, but the European tradition diminishes their radicalism into reassurance
- by Takeshi Morisato
The pinkish orangey hues of Antarctica’s Danger Islands aren’t due to minerals in the rocks or lichens covering them. Instead, the color comes from the islands’ iconic black and white inhabitants.
Dense colonies of Adélie penguins on Antarctica’s islands and coasts produce large quantities of guano, extensive enough to be seen in satellite images. The color of the droppings can even provide clues into the penguins’ diet, since krill tints the guano pink, giving researchers a unique way to monitor penguin populations.
Analyzing three decades of Landsat images, scientists found that Adélie penguin diets vary around the Antarctic continent and are tied to sea ice conditions. In areas with less sea ice, the penguins have a krill-rich diet; in areas with more sea ice, they eat more fish, which is associated with healthier long-term population trends.
“Sea ice is such a hugely important factor in the Antarctic marine environment,” said Casey Youngflesh, an assistant professor at Clemson University in South Carolina who led the new study. “The structure of the region’s food web is changing in response.”
Adélie penguin colonies might be remote and often inaccessible, but the stark and treeless Antarctic environment provides a rare opportunity to study the entire population from space, Youngflesh said.
Previous research identified colonies based on satellite images of guano stains on rocky shores. Youngflesh and his colleagues visited the colonies to collect and analyze guano samples, allowing them to link specific color spectra observed by satellites to the balance of krill and fish in penguin diets.
Now, surveying more than 100 sites around the perimeter of Antarctica with Landsat images captured between 1984 and 2013, the scientists have connected penguin diets to the surrounding sea ice conditions during the breeding and nesting seasons.
Between November and February, colonies around the Antarctic Peninsula and West Antarctica, where temperatures were warmer and sea ice sparser, favored a krill-based diet. In East Antarctica, where sea ice cover was more extensive, colonies feasted on more fish—a higher-quality food resource and one that leads to better chick growth and survival. A given colony’s diet also changed year to year with shifting sea ice conditions.
Antarctic sea ice was relatively stable, or even increased in some regions, during the years of the study. However, since 2013 Antarctic sea ice has reached record lows, with decreasing sea ice predicted for the future. That could lead to Adélie penguins leaning more heavily on krill, Youngflesh said. But the demand for krill is increasing, he noted, due to fishing and rebounding populations of other predators like seals and whales.
The researchers are now developing computer models to use Landsat 8 and Landsat 9 data to continue tracking penguin diet and how the population responds to environmental change.
NASA Earth Observatory image by Michala Garrison, using Landsat data from the U.S. Geological Survey. Drone image by Thomas Sayre-McCord (Woods Hole Oceanographic Institution/MIT). Story by Kate Ramsayer.
Stay up-to-date with the latest content from NASA as we explore the universe and discover more about our home planet.
Patches of open water in the region contributed to low sea ice extent across the Arctic in March 2026, which…
Scientists relied on satellite data to understand how the Antarctic glacier lost so much ice so rapidly.
A network of meltwater lakes and drainage channels made an Antarctic ice shelf known for its blue ice areas even…
The post Pink Penguin Guano Provides Diet Clues appeared first on NASA Science.
Links for you. Science:
Tulsi, Trump, and the War on Fauci – Part 1: The Smear Campaign
Orcas Are Kerploding Sunfish Into A Thousand Pieces
Tulsi, Trump, and the War on Fauci – Part 2: The ‘Lab Leak’ Obsession
Snake in Brazil 80 Million Years Ago Lived in Burrow
Other:
We used to be able to buy things in this country. Locking up toothpaste was a warning sign that our executive class was crazy
What Is ‘Narcissistic Collapse’? Experts See Familiar Hallmarks In Trump’s Rants (I’ve been saying this for a decade now…)
‘I’m over the moon’: New York’s small business owners rejoice as Mamdani cuts red tape
Presidenting
Donald Trump Doesn’t Want Nursing Home Residents To Vote
“Personal Toll”
How To Divide And Conquer The Billionaire Class
For Better Or Worse, ‘The Odyssey’ Is Peak Christopher Nolan
What Polls Do and Don’t Tell You
Colson Whitehead Talks About Embracing Change, Pulling Off A Heist, And Finding A Good Coffee Table
Michigan Battles Massive Cyclosporiasis Outbreak As State Reels From Trump Cuts
‘Why aren’t they listening?’: For many Black residents, this Blue Hill Avenue transit plan repeats old problems
Fired ‘60 Minutes’ Reporter Says CBS Bosses Asked Her To Cover News ‘That Never Happened’
How the lighthearted social media trend of ‘teen takeovers’ took a serious turn
Trump Orders New Signs Outside Smithsonian Claiming Some Exhibits Are Inaccurate
“[I]ntelligence, that counterentropic conjoined twin of information, must become the most powerful force in the universe, the energy to which all other physical laws must eventually kneel…Intelligence was destiny, manifest.” — Ian McDonald, “Verthandi’s Ring”
Not a lot of people expected that AI would come for the mathematicians before it came for the truck drivers, but it did. The other day, an AI model disproved the Jacobian Conjecture — an 87-year-old open problem that human mathematicians had struggled to solve. The greatest living human mathematician, Terence Tao, turned to AI to help him understand the solution. Around the same time, AI solved a very important open question in quantum cryptography. Solving Erdos problems has now become almost child’s play for the best AI models. And this is the worst AI will ever be at math. Model capabilities, and the amount of compute available, both continue to increase at rapid rates. (Meanwhile, long-distance trucking employment is slightly higher than it was a decade ago.)
I don’t expect mathematicians to actually lose their jobs en masse, of course.1 But it’s becoming clearer and clearer that humankind has invented machines that are smarter than we are. Intelligence isn’t defined for machines the same way it is for humans — AI’s capabilities are spiky in different ways than ours — but it’s undeniable that the technology is improving rapidly in every domain of cognitive capability. It’s still possible to find some mental tasks that humans are better than machines at, but those final advantages tend to disappear almost as quickly as we can identify them. “AGI”, or “ASI”, or whatever you want to call it, is certainly here.
And yet…the world remains much the same. In lots of sci-fi books, as soon as artificial superintelligence arrives, it bootstraps itself to even more godlike intelligence in an explosive “singularity” that rapidly transforms the entire physical universe. Lots of people, especially “AI safety” and “effective altruist” types, expected things to play out basically the same way in reality. But looking around, not much has changed since we entered the intelligence explosion. There’s a huge data center boom, and most people use AI on a daily basis, but we still live basically the same lives — driving to work or taking the train, sitting in front of a computer, scrolling on our phones, collecting a paycheck. People are staying in their jobs longer, but employment hasn’t been disrupted in a significant way:
Meanwhile, we’ve had decently robust productivity growth, but nothing really amazing:
A lot of people I know are surprised by this. Ruxandra Teslo writes:
Walking around the world today one might notice that it is weirdly unchanged…To many, this is surprising. Just the other day I was at a conference where someone remarked that if he could have seen today’s AI capabilities a few years ago, he would have been astonished — and would have assumed the world by now would look far more transformed, with much higher GDP growth.
And Clifford Sosin writes:2
Superintelligence arrived. You probably didn’t notice, because it turned out to be kind of incremental…Don’t believe me? Run the test. Talk to Fable 5 for an hour, then talk to your ten smartest friends. Which one is smarter? Don’t worry, they won’t be offended…We were told to expect something bigger. Once machines crossed some line, the system’s IQ would climb to heights we couldn’t follow, and we’d be sharing the planet with something that designs warp drives and thinks thoughts as far past us as mine are past my dog…What we got is a tool that writes excellent code, beats people at a startling range of tasks, and will clearly reshape the economy. It’s also, somehow, incremental. No takeoff. No explosion. That’s the strange part.
Teslo blames bottlenecks — governance and other “frictions” — for the slow economic impact. But some others are advancing a more radical hypothesis3 — that intelligence itself is subject to diminishing returns.
One of these is Francois Chollet, an AI researcher who specializes in measuring AI’s capabilities. In a highly controversial series of tweets back in March, he conjectured that intelligence might be subject to diminishing returns:
One of the biggest misconceptions people have about intelligence is seeing it as some kind of unbounded scalar stat, like height. "Future AI will have 10,000 IQ", that sort of thing. Intelligence is a conversion ratio, with an optimality bound. Increasing intelligence is not so much like "making the tower taller", it's more like "making the ball rounder". At some point it's already pretty damn spherical and any improvement is marginal.
Now of course smart humans aren't quite at the optimal bound yet on an individual level, and machines will have many advantages besides intelligence -- mostly the removal of biological bottlenecks: greater processing speed, unlimited working memory, unlimited memory with perfect recall... but these are mostly things humans can also access through externalized cognitive tools.
In fact, this is a possibility I myself had raised in a post a year earlier:
It seems possible that humans are simply incredibly specialized in a few types of cognitive tasks — extracting patterns from sparse data, synthesizing various patterns into “intuition” and “judgement”, and communicating those patterns in language — and that we’ve basically approached the theoretical maximum in those narrow areas…That would explain why AI has gotten much better at things like math and coding and forecasting over the last year, but why the basic chatbot interface doesn’t seem much more “intelligent”. It would also explain why when you talk to Terence Tao about math, it’s like talking to a superhuman, but when you talk to him about where to get lunch or which movies are the best, he’ll just sound like a fairly smart normal dude. AI will eventually get better than Tao at math…but it may never get much better than the most thoughtful, eloquent humans at deciding where to get lunch or recommending movies. It may simply not be mathematically possible to get much better than we already are at that sort of thing.
Why would intelligence top out like this? Well, if we think of intelligence as the ability to extract information from data, then even an infinitely advanced model endowed with infinite compute will be limited by the fact that there’s a limited amount of information that can be extracted from the data.
For one thing, data itself is in limited supply. You can’t transform the world unless you can (in some generalized sense) understand it, and you can’t understand the world unless you can measure it, and our ability to measure the world is inherently limited and finite.
This is basically the hypothesis advanced by Arvind Narayanan and Sayash Kapoor:
We think there are relatively few real-world cognitive tasks in which human limitations are so telling that AI is able to blow past human performance (as AI does in chess). In many other areas, including some that are associated with prominent hopes and fears about AI performance, we think there is a high “irreducible error”—unavoidable error due to the inherent stochasticity of the phenomenon—and human performance is essentially near that limit….We predict that AI will not be able to meaningfully outperform trained humans (particularly teams of humans and especially if augmented with simple automated tools) at forecasting geopolitical events (say elections).[emphasis mine]
Sosin says something similar:
We hold a thin scatter of facts about the world, and intelligence or reasoning is whatever fills the space between them…Where the space between the facts behaves well, this is close to godlike…Coding, math and most administrative work are [like this]. What makes them easy is that they have relatively smooth solution spaces and are tractably verifiable…Most of what matters doesn't behave like that. The universe is mostly the emergent behavior of complex systems…The limit is contact with reality. A smarter reasoner fills the gaps between known facts in simple areas faster and better, but it doesn't produce new facts.
Sosin makes an important point here, which is that the limitations of intelligence aren’t necessarily about limited data. Even if we can keep on collecting infinite data, the cost of extracting additional information from that data might explode to infinity. This is the idea of chaos. Even in a deterministic universe where the present states of all particles are enough to perfectly determine the future, our ability to predict the future can be inherently limited; the tiniest infinitesimal error in our measurement of the present explodes into a huge error when we attempt to extrapolate even a small distance into the future.
So although we don’t know yet, it’s possible that humans were already hitting the point of diminishing returns with regards to individual cognitive capacity, and that superintelligent machines will never be as far beyond us as we are beyond dogs. But even if that’s true, I can think of at least three reasons why machine superintelligence could still deliver huge productivity gains.
The most obvious advantage that machine superintelligence confers is replicability. The number of human intelligences is fixed by the fertility rate, and we don’t know how to substantially boost that rate; in fact, it’s falling inexorably, and the human race is set to shrink.
AI isn’t bound by those limitations. By building more data centers with more compute, you can run more agents in parallel — essentially, you get more cognitive work. It’s not free, but nor is it limited. It’s good old physical capital; unlike human capital, you can just build more of it whenever you like, simply by reinvesting some portion of your economic output. Imagine if we suddenly discovered a way to manufacture more land in any city on the planet; this is similar.4
Of course, lots of tasks are physical ones, and for these you need physical machines — basically, robots. This is probably why so many AI people are now working on robotics, world models, physical AI, and so on. The roboticization of the world has a huge tailwind — the battery revolution, which allows energy to be stored and moved around much more easily. Conveniently, we got the physical tools to turn dumb matter into smart matter just as we also got the digital tools.
(It’s also worth noting that like the energy in batteries, the intelligence in robots is divisible. A robot the size of an ant can be remotely controlled by a data center the size of a football field. This is also a capability that human intelligence lacks.)
This doesn’t mean economic output will explode to infinity. But what it does mean is that humans will be able to use physical capital — GPUs and robots — to do more and more tasks at once, including many cognitive tasks that we used to do the hard way. In the long-run steady state, this should increase the capital-to-labor ratio of our society; each human will essentially leverage an army of intelligent machines. It’s basically another industrial revolution, and it has very little to do with whether artificial intelligence is smarter than human intelligence in any sort of head-to-head matchup.
The German company Zeiss makes the best glass on the planet. If one of the mirrors that Zeiss makes for ASML’s EUV chipmaking machines were the size of Germany, the biggest bump on that mirror would be just one millimeter high. Only a few other companies — and maybe no other company on Earth — can match that. Zeiss’ mirrors also have a number of other amazing properties, like not distorting much due to temperature changes.
How does Zeiss make glass this good? No one knows — not even the people at Zeiss. If the technology were capable of being written down on a blueprint, China would have hacked Zeiss and stolen it, the way Huawei hacked Cisco and Nortel. If the technology were capable of being explained by a former Zeiss employee, or even several former Zeiss employees, China would have paid those people many millions of dollars to spill the beans.
Zeiss’ technology basically can’t be stolen, because it’s tacit and distributed. It consists of a vast number of little tricks and techniques that a huge number of individual employees use on a daily basis. These people don’t always even realize all those little things they’re doing that make the glass come out so good. And each employee knows a different set of tricks and techniques. The knowledge exists at the level of the organization itself, and is thus very hard to steal or recreate.
This is true of lots of corporate technology. A big part of the reason China can cut off the supply of rare earths to the rest of the world any time it wants to is that other countries aren’t very good at refining rare earths. Rare earths are difficult to separate from each other in solutions; it takes a ton of little chemistry tricks to do it cheaply at scale. Chinese refiners have spent four decades building up those little tricks and techniques; American or Japanese refiners won’t simply be able to replicate their efficiency overnight, and so it’ll continue to cost much more to produce rare earths outside China.
Except in the age of AI, this might change. Suppose American rare earth refiners give their employees a bunch of equipment to record everything they do — smart glasses, gloves, and so on — in addition to sensors distributed throughout their plants. AI will be able to synthesize all that information and very rapidly suggest small ways to improve the production process. Many of those little experiments will fail; others will succeed and will quickly be adopted, allowing another round of experimentation and improvement to begin very quickly. Crucially, AI’s ability to do this doesn’t depend on its raw intelligence — only on its ability to handle huge amounts of data very quickly.
In other words, in the age of AI, distributed tacit knowledge might not be nearly as big of a barrier to technological diffusion. This could improve economy-wide productivity, as lagging firms catch up to leading firms much more quickly. A more equal distribution of productivity would also make the economy more competitive, creating more surplus for consumers (though possibly reducing the incentive for firms to innovate, by making technology less excludable).
AI’s ability to quickly produce distributed tacit process knowledge might also supercharge productivity growth at the frontier. Imagine if any company could optimize any production process five times faster than today. The whole economy would speed up, as components got cheaper, turnaround times and product cycles got shorter, and scale-up got much faster.
And as with the previous example, improving the production of distributed tacit knowledge wouldn’t depend on AI’s raw intelligence. It would spring from AI’s ability to act like a computer — to interface directly with sensors, to handle lots of data, to perceive tiny details, and to do everything very very quickly.
For decades, researchers in the field of natural language processing tried to figure out the principles behind human linguistic communication. They made frustratingly little progress; the processes by which humans convey information to each other through words just don’t seem to obey simple laws, like the ones that govern electromagnetism or the circulatory system.
Then along came AI, and suddenly linguistic communication seemed like a solved problem. LLMs can reliably sound like a human being, even if we don’t understand how they manage to do it.
What if there are lots of other aspects of the Universe that work the same way — too complex to understand in terms of simple laws, but not so complex that they just dissolve into unknowable chaos? It’s possible that we can reliably control these complex phenomena with AI, even if we never reduce them to the kind of principles that we can teach a grad student in a textbook. In fact, I wrote an essay called “The Third Magic”, where I suggested that this might be equivalent to a whole new scientific revolution:
Another way of saying this is that there may be laws of the universe that humans can’t understand but AI can. I call these “cloud laws” — causal regularities that can be exploited by technology, but which are too diffuse and complex for an individual human being to either intuit or communicate.
Human language seems to obey cloud laws, so why not other phenomena too? Perhaps social sciences like economics, sociology, and political science obey similarly complex regularities, and AI can help us find them. Perhaps there are physical processes — plasma, or topological materials, or aerial turbulence, etc. — that obey cloud laws instead of chaos?
In other words, thanks to AI, we might be on the precipice of a new age of scientific advancement. And this won’t necessarily depend on how smart AI is in comparison to a single human; it’ll depend on its computer-like ability to hold huge amounts of data in its working memory and extract complex patterns from that data.
If this turns out to be true, it means Francois Chollet is wrong. Chollet hypothesizes that groups of humans, using pre-AI computing tools, can approach AI’s level of scientific competence. But human collaboration is bottlenecked — it’s limited by our ability to intuit patterns at an individual level, and to communicate these patterns from one individual to another. AI, being a computer, just doesn’t have this sort of limitation; it can work with vast, diffuse patterns without having to break them into pieces or simplify them in order to compress them into the tiny pipelines of person-to-person explanation.
So even if AI never gets much better than humans at the kind of science that humans have done heretofore, it might open up whole realms of scientific discovery that have previously been totally inaccessible to even the largest groups of the smartest humans. If much of the Universe turns out to be ruled by cloud laws, we could be on the precipice of a scientific renaissance.
The common thread in all three of these examples is that AI may revolutionize productivity not by being much smarter than a single individual human — not by simply solving harder and harder math problems — but by marrying human-style intelligence to the vast, inhuman capabilities of computers. We could simply be thinking about the benefits of intelligence wrong — arrogantly privileging the kind of mental tasks we humans happen to do especially well, while ignoring the value of the tasks we do poorly.
Why? Several reasons. Tenure still exists. Humans who can do math at a high level will still be needed if we care about understanding the results that AI spits out. Human math teachers will still probably be valuable. And most importantly, humans will be needed to tell AI what kind of math we want it to solve, and why.
Or at least, prompts AI to write.
I think there is a widespread, tacit assumption among many intelligent humans that the quality that had made them stand out among their peers was the fundamental stuff of the Universe, the font of all value. I will write more about this at some point, but I think it helps explain why the idea that intelligence is just one production factor among many seems so unthinkable, heretical, and revolutionary to so many people in the tech industry.
Yes, I know we can reclaim land from the ocean. But the opportunities for this are fairly limited, and the cost is often very high.
Key ground stations in Spain operated by NASA and the European Space Agency have avoided serious damage from major wildfires in the country.
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What's connecting these two volcanoes?
