Posts Tagged ‘Technology’
“Adaptation and mitigation are two sides of the same coin. If mitigation is about preventing the unmanageable, adaptation is about managing the unavoidable.”*…
Adapting to climate change is quickly becoming part of everyday life. Nabig Chaudhry outlines seven trends we’re seeing for 2026 and beyond…
Within the climate and scientific communities, there’s growing concern about how quickly the world is approaching (and may exceed) 2°C of warming. 2024 was the first calendar year in which global average temperature exceeded 1.5°C above preindustrial levels. The impacts of rapid warming are becoming harder to miss: The climate is changing quickly almost everywhere, local and global climate risks are growing, progress on mitigation has become more politically constrained and uncertain, and many of our systems and policies aren’t prepared for the conditions ahead.
Growing climate risk is increasing the demand for new technologies, tools, strategies, and ways of thinking about climate adaptation. Since publishing our Insights on Climate Adaptation in 2025 report, the practice of climate adaptation has continued to develop, as more people, communities, organizations, and institutions work to understand and respond to climate risks.
People use different language to describe climate adaptation (including climate resilience), but the work centers on helping people, communities, and organizations manage the risks of a changing climate. Those activities are expanding, and we can already see signs. For example, new funding and investment vehicles are emerging, such as Tailwind Futures, and adaptation is receiving more dedicated space at major climate convenings, including The Adaptation Forum, a co-hosted gathering of thought leaders in the adaptation space during Climate Week NYC 2025.
In my role as Director of Climate Adaptation Research at Probable Futures and through my PhD program at the University of California, Berkeley, I speak with experts, read emerging research, and study adaptation developments every day. Through these conversations and insights, I’ve reflected on which adaptation trends are likely to emerge and strengthen…
Chaudhry npacks seven different trends; here, let me highlight two. The first is one that (Roughly) Daily has visited before, insurance…
Elevating insurance as a force in adaptation planning, policy, and behavior
Insurance is a valuable adaptation tool, as it can transfer risk, support recovery after climate shocks, and help signal where danger is increasing through premiums, deductibles, coverage limits, or insurer retreat. It can also shape incentives, because the way risk is priced can influence whether and how people and institutions reduce exposure, strengthen buildings, or avoid certain kinds of development.
As climate risks grow, damage to property and homes becomes more frequent and severe. Property owners are experiencing those shocks both physically (flooding, fire, wind damage, etc.) and financially as insurance markets adjust and recalibrate in response to changing probabilities and severities. Insurance markets have begun reflecting climate risk, and those changes are starting to influence where and how people build homes and infrastructure, where they invest in property, and where they choose to live.
A useful example of how insurance is beginning to influence adaptation efforts in the public sphere is Strengthen Alabama Homes, a program of the Alabama Department of Insurance. The program provides grants to help homeowners retrofit their homes and roofs to reduce wind damage from extreme winds and storms. Homeowners who participate can receive discounts on the wind portion of their homeowner’s insurance premium, which makes insurance not only a tool for recovery but also a tool for encouraging adaptation before exposure occurs.
Insurance pricing is one way climate risk is made visible, priced, and acted on through adaptation. I expect that insurance will increasingly influence adaptation planning, policy, and behavior, not only by helping people recover after climate shocks, but by shaping the choices people make before those shocks occur. The development of the insurance industry will therefore be an important factor in adaptation. If insurers become a source not only of risk pricing but also of risk information, adaptation guidance, and incentives to reduce risk, they could help more people act before losses occur. But that would require a meaningful shift in the role of insurance companies, from mainly pricing and transferring risk to also helping people reduce it…
The second goes to the contentious topic of geoengineering…
Expanding debate around the role of climate intervention
As warming continues, risks keep growing. We have more, clearer, worrisome signals that irreversible change, tipping points, and local climate changes so severe that adaptation is impractical if not impossible, are not far off. In response, people and institutions are starting new conversations about global-scale responses. One of those responses is climate intervention, sometimes called geoengineering.
Climate intervention generally refers to intentional efforts to alter Earth’s systems in order to counteract some of the effects of climate change. It can include approaches that remove carbon dioxide from the atmosphere, as well as approaches that reflect a portion of sunlight back into space, such as stratospheric aerosol injection.
Its relationship to adaptation is uneasy, but important. If climate intervention is, at its core, an effort to manage the otherwise unmanageable risks of global climate change, then is it another tool for adapting to climate change, or is it something fundamentally different? There is no consensus, and there may never be, not least because global action will cause uneven responses locally. We don’t know much about the potential impacts of some climate interventions, how they could affect different regions unequally, or what long-term consequences they may have for Earth’s climate and natural systems.
There are good reasons to have informed conversations and do fundamental research on intervention. People with adaptation expertise can help explore, illuminate, and explain what climate intervention could mean for society and nature. There are also likely to be benefits for adaptation professionals to participate in these conversations and research projects. Even if climate intervention is never widely deployed, the debate itself may shape adaptation thinking, climate policy, research funding, public trust, and international governance.
Climate change requires people to consider risks and options, whether for mitigation, adaptation, or intervention. Treating strategies for managing the rate, pace, and impacts of climate change as distinct and separate is unlikely to lead to good outcomes. I am hopeful that there will be more collaboration across these new fields as society faces new challenges that have a common root cause. This may include more discussion about how these technologies should be governed, whether they should receive more investment, and whether climate intervention is a possible third leg alongside mitigation and adaptation…
Eminently worth reading in full: “The near-term future of climate adaptation: emerging trends.”
* U. N. Environmental Program
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As we prepare, we might recall (wistfully) that it was on this date in 1942 that Bing Crosby, with the Trotter Orchestra and the Darby Singers, recorded Irving Berlin’s song, “White Christmas.” According to the Guinness Book of World Records, this version is the best-selling single of all time with an excess of 50 million copies sold worldwide. (In fact, the version most often heard today is not the original. After frequent use, the master had become damaged, so on March 18, 1947, Crosby re-recorded the holiday hit.)
“Great inventions are never, and great discoveries are seldom, the work of any one mind. Every great invention is really an aggregation of minor inventions, or the final step of a progression. . It is not usually a creation, but a growth, as truly so as is the growth of the trees in the forest.”*…
Our old friend (and here and here) Brian Potter thinks deeply about scientific and technological advance. Here, he ponders the pace of progress…
In her book on the history of the laser, historian Joan Bromberg notes that the technological and scientific predecessors of the maser (which itself preceded the laser – two critical technologies whose developmental histories I sketched in this piece two months ago) were in place for decades before physicist Charles Townes had the insight to combine them…
… This sort of decades-long wait between when a technology first becomes possible, and when it actually appears, seems common, or at least seems like it might be common. I’ve previously written about why it took so long for wind power to be widely deployed after it became technologically possible, and people often idly speculate whether inventors in the Roman Empire could have built a steam engine, or why we waited so long to put wheels on luggage.
Knowing how long this gap between when an invention becomes possible, and when it actually appears, is useful, because it tells us something about the nature of technology and technological progress. What factors govern whether some new technology appears? How much does mere technical possibility matter, and how much do things like cross-pollination of knowledge, economic feasibility, and political factors contribute? Knowing more about how long it takes for an invention to appear once it becomes technically possible can help us answer these sorts of questions.
I wanted a better sense of how long it takes for some technology to appear once its necessary predecessors are in place. So I used AI to try and find out…
[Potter explains his method, then unpacks his results…]
We can clearly see a few trends on this graph. One is that for most inventions, the gap between when it could have been invented and when it was actually invented is not particularly large. Of the 166 inventions Claude estimated a date for, 107 of them (64%) had an “earliest plausible” date 50 years or less from the actual date, and 150 of them (90%) had an “earliest straightforward” date 50 years or less from the actual date. For more than half the inventions, the average earliest straightforward date of invention was 10 years or less from the actual date.
Conversely, there were a relatively small number of inventions where the gap between “could have been invented” and “was invented” was very large. 30 inventions (18%) had an average gap of more than 100 years between “earliest plausible” and actually invented, and eight inventions had a gap of more than 1000 years. You can see this clearly on a histogram, which shows a large bump of small time gaps, and a long tail of fewer, larger gaps.
The inventions with the longest period between “could have been invented” and “was invented” are below.
There’re a few interesting trends observable here. Many of the longest-delayed inventions — the hypodermic needle, general anaesthetic, stethoscope — are medical inventions. (You could argue the surgical mask could be in this category as well). For the hypodermic needle, this probably needed to wait until the existence of some substance that needed to be injected (such as morphine, first synthesized in 1804), but for other medical inventions this possibly also reflects folks’ reluctance to do inventive-tinkering in a medical context. For general anaesthetic, for instance, the trial and error of getting the dose right was incredibly dangerous, and the inventor Hanaoka Seishu “crippled his mother and blinded his wife perfecting the dose.”
Several of the longest-awaited inventions are ones where the version in the list is an early, impractical version of the one that actually solved a problem. So the “dandy horse” — a two-wheeled, wooden vehicle that was a predecessor of the bicycle — could have been built in antiquity, but the dandy horse wasn’t particularly practical as a means of transportation, and actually useful bicycles had to wait for the improved manufacturing technology of the later 19th century. Likewise, the version of the ballpoint pen that Claude thinks could have been invented much earlier is John Loud’s 1888 version, but Loud’s pen worked poorly and wasn’t successful. Actually useful ballpoint pens are surprisingly difficult to manufacture (China famously couldn’t manufacture them until very recently), and credit for the “useful ballpoint pen” is usually given to Lazlo Biro in 1938. (Claude correctly notes that “useful” versions of both these inventions would need to wait until much later.) Judson’s early zipper and de Martinsville’s early sound-recording device are also examples of early, not-particularly-useful inventions.
Other inventions on this list seem like they might be a case of the surrounding social or technological conditions needing to be right for the invention to appear. So Otis’ elevator safety brake needed to wait until elevators were in higher demand, which probably didn’t occur until steam engines or some other similar power source came along (though maybe you could have water-driven elevators much earlier). Barbed wire perhaps needed to wait until enclosing very large areas of land for grazing became something people needed to do.
And some inventions seem like they might have been genuinely useful had someone thought of them earlier, and simply nobody did. Blanchard’s pattern-tracing lathe, Neilson’s hot blast, and the safety pin all seem like they fall into this category, though perhaps there were good reasons these didn’t appear earlier.
Going back to the scatterplot, the other obvious trend on this chart is that the gap between when an invention becomes possible and when it appears has narrowed over time. If we graph the average and median gaps for inventions by 20-year time periods, we can see that they have fallen over time.
For the 60 post-1900 inventions, every one has a “straightforward” invention date of 50 years or less than the actual date, and 75% of them have a straightforward date of 10 years or less before the actual date. Of the 30 inventions with a gap of more than 100 years between when they could have been invented and when they actually appeared, 29 of them were invented before 1900. So the process for creating new inventions seems to be getting more and more efficient — opportunities are getting noticed and exploited sooner and sooner, up through 1970 at least (which is when the list of major inventions extends to).
We can also look at how wait times vary by type of technology. The chart below shows average wait times by different categories, for both inventions overall and for just post-1900 inventions. We can see that medical inventions have the longest wait, while electronic inventions have the shortest wait…
… We can also look at what types of factors tend to be bottlenecks. For some inventions, the bottleneck is primarily scientific: the limiting factor for the transistor is the band theory of quantum mechanics, and the limiting factor for the radio was Hertz’s demonstration of electromagnetic waves. But for other inventions, it’s primarily technological: the turbojet had to wait not for some new physical theory, but until compressor technology and high-temperature steels appeared; likewise the airplane had to wait not for some novel theory of aerodynamics but until a light enough engine appeared. The chart below shows how often “science” or “technology” was the limiting factor for a given invention, for both inventions overall and post-1900 inventions.
In both cases, technology is the bottleneck far more often than science (though of course if you removed enough technological bottlenecks eventually you’d hit a scientific one, and vice versa).
There is of course only so much you can learn from this sort of exercise: at the end of the day, this is based on an AI’s best guess, not a thorough analysis of the various controlling factors by experts. But while I wouldn’t swear to its accuracy, I think the answers are probably mostly pretty good, and enough for us to draw some general (if tentative) conclusions about the nature of technological progress.
My main takeaway is that we mostly don’t wait all that long for new inventions. Since 1800 most inventions have appeared within a few decades of when it was possible to build them, and since 1900 these gaps been even narrower. It also seems likely that medical inventions are more likely to have long wait times than other types of inventions, and that the limiting factor for how early some new technology could appear is most likely to be technological, rather than scientific.
On the (maybe suprisingly) quick– and quickening– pace of progress: “How Long Do We Wait for New Inventions?” from @constructionphysics.skystack.xyz
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As we analyze advance, we might send inventive birthday greetings to William Webster (W. W.) Hansen; he was born on this date in 1909. A physicist and one of the founders of the technology of microwave electronics, he had a central hand in the development of klystron technology (essential to high frequency amplification, thus central to microwave technology, radar, and UHF television transmission), and linear accelerators (he led the development of SLAC), and along with the Varian brothers and Edward Ginzton, co-founded Varian Associates (in 1948)–one of the first high-tech companies in Silicon Valley.
“Do not fold, spindle, or mutilate”*…
Punched cards have a long history in machine control (dating back to Jacquard) and computing (starting with Babbage‘s Difference Engine), but it was Herman Hollerith who brought them into modern computation in the late 1880s… where punch cards remained for about 100 years. From the Smithsonian’s American History Museum…
In the late 1880s, American engineer Herman Hollerith saw a railroad punch card when he was trying to figure out new ways of compiling statistical information for the U.S. Census. His first punch card, like those used on railways, only had holes along the edges. The meaning of each hole was indicated on the card. By the time Hollerith tabulating equipment was used in the 1890 U.S. Census, holes were scattered across the cards, although their meaning was not indicated on it.
Hollerith and his employees at the Tabulating Machine Company in Washington, D.C. soon developed punched cards for use in compiling information for commercial enterprises such as railroads. They and staff of the U.S. Census Bureau prepared improved machines—these devices are shown in the object group on tabulating equipment. By the 1920s, the United States had two major manufacturers of punch card equipment, International Business Machines (the descendent of the Tabulating Machine Company) and Remington Rand (the descendent of Powers Accounting Machine Company established by Russian emigré and former Census Bureau employee James Powers). Each manufacturer developed a distinctive standard punch card. IBM cards had eighty columns of rectangular holes while those of Remington Rand had ninety columns of circular holes. Tabulating machines were widely used in both government and commerce, with cards designed to meet the needs of customers. For example, checks issued by the U.S. government often came on punch cards.
When IBM and Remington Rand began selling electronic computers in the years following World War II, punch cards became the preferred method of entering data and programs onto them. They also were used in later minicomputers and some early desktop calculators. Punch cards surviving in the Smithsonian collections reflect the widespread use of computers – they announced scores on standardized tests, served as a library cards, were part of the proof of mathematical theorems, and kept medical records. Some are printed with the names of users, from university computer centers and computer clubs to the Library of Congress to Bell Laboratories…
Browse the collection: “Punch Cards for Data Processing“
See also: here, here, and here.
* Ubiquitous warning on punch cards:
… in the 1950s, after the invention of the computer and its widespread business use, that everyone began to see punch cards. Companies sent punch cards out with bills: the telephone company, utility companies, and even department stores realized that they could save a step in their billing process, as well as making it easier for them to process the returned check, by using the cards themselves as the bills. By the 1960s, punch cards were familiar, everyday objects.
While company employees could be trusted to take care of the cards, the person in the street could not. Warnings were necessary. In the 1930s the University of Iowa used cards for student registration; on each card was printed “Do not fold or bend this card.” Cards reproduced in an IBM sales brochure of the 1930s read “Do not fold, tear, or mutilate this card” and “Do not fold tear or destroy.” I’m not sure when the canonical “Do not fold, spindle, or mutilate” first appeared; it’s one of those traditions whose author and origin is lost in the mists of time. Let’s consider the words one at a time, stop and take them seriously…
– “A Cultural History of the Punch Card” (from 1991; eminently worth reading in full)
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As we contemplate chads (of which, punch cards produced a gracious plenty), we might spare a thought for Gerald Hawkins; he died on this date in 2003. An astronomer and author, he was best known for his work in archaeoastronomy— most of all, for his 1965 book, Stonehenge Decoded. In the early 1960s, Hawkins had used punch cards to load data modeling sun and moon movements onto magnetic tapes, then into an IBM 7090. The results led him to conclude, as the book argues, that the features at the monument were arranged in such a way as to predict a variety of astronomical events– that Stonehenge was a giant prehistoric observatory and computer. While some archaeologists are hesitant to accept Hawkins’ theories, many archaeoastronomers have built upon his work. More widely, scholars accept that the importance of astronomical alignment and large complexes being planned and constructed to fulfill cosmology has been demonstrated at other prehistoric sites, such as the Snake Mound and Cahokia in the U.S.
“Gambling is a tax on ignorance”*…
And as Einstein observed, “two things are infinite: the universe and human stupidity; and I’m not sure about the universe.”
Gambling– and related specualtive investments– have always been, for the vast majority of punters, a sucker’s bet. But, as Paul Kedrosky explains, the growing prevalence of AI and the emergence of prediction markets have amplified that painful reality…
The return skew in prediction markets’ returns is startling. It is partly a function of their nature, but also of vibe-coding script kiddies attacking every market anomaly as quickly as it arises. Check a recent WSJ article for examples.
The same dynamic is now spreading across retail-dominated markets. A driver is how AI lowers the cost of systematic exploitation and exploration to near zero. What used to require infrastructure, data pipelines, and bearded quants is now accessible via off-the-shelf models, APIs, and loosely stitched “agent” workflows doing … stuff that even their users don’t fully understand.
The result isn’t democratization of returns. It is wider participation, of a sort, alongside the rapid re-concentration of profits. A small subset of users—those willing to iterate fastest, monitor continuously, and deploy capital programmatically—capture gains, with everyone else just liquidity.
They scrape sentiment, parse new information, and reprice positions in seconds, compressing the half-life of mispricings. That doesn’t eliminate inefficiency, but changes who harvests it. The edge shifts from insight to speed, coverage, and execution discipline—areas where even modest automation compounds quickly, and edges disappear overnight.
Prediction markets are simply the cleanest expression of this trend because they combine thin liquidity, discrete outcomes, and high retail participation. But the same pattern is visible in options flow, single-stock volatility events, and even online poker, which AI increasingly dominates.
As AI tools continue to scale, expect this to get worse: a small cohort running semi-automated strategies extracting semi-consistent edge, and a much larger base supplying them returns. Under the pressure of AI prevalance, markets don’t flatten, the return gradient steepens to a cliff…
Fewer and fewer winners take more and more of the pot. The mechanics of concentration: “AI is Eating Markets” from @paulkedrosky.com.
* Warren Buffett
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As we contemplate concentration, we might note that today is Mother’s Day. As noted yesterday, the observance became official on that date in 1914. But the quest to honor moms began a good bit earlier. On this date in 1908, Anna Jarvis held a memorial for her mother at St. Andrew’s Methodist Church in Grafton, West Virginia, the location of the International Mother’s Day Shrine. But her quest to create Mother’s Day had begun three years earlier when her mother Ann, a lifelong activist, died.
Ann had tried to start a “Mother’s Remembrance Day” in the mid-19th century. On her passing, Anna enlisted the support of retailer extraordinaire John Wanamaker, who knew a merchandising opportunity when he saw one, and who hosted the first Mother’s Day ceremonies in his Philadelphia emporium’s auditorium. In 1912, Anna trademarked the phrases “second Sunday in May” and “Mother’s Day”, and created the Mother’s Day International Association. By 1914, she and Wanamaker had built sufficient support in Congress to score the Congressional Resolution noted yesterday. (President Wilson, who was by current accounts uninterested in the move– distracted as he was by the beginnings of his ultimately unsuccessful effort to keep the U.S. out of the troubles in Europe that became World War I– nonetheless knew better than to take a stand against moms.)
“The danger of the past was that men became slaves. The danger of the future is that man may become robots.”*…
… which might be the same thing?
As more and more folks are fearing obsolescence (if not, indeed, subjugation) by emerging technology, Matthew Wills reminds us that this fear– especially as embodied in androids– has a long (and dark) history here in the U.S…
Our word “robot” comes from Karel Čapek’s 1921 play R.U.R. In it, historian of robots Dustin A. Abnet explains, Čapek repurposed the Czech word for “drudgery” or “servitude” to refer to the artificial workers produced by the play’s Rossumovi Univerzální Roboti (Rossum’s Universal Robots) company. [See also here.] Created from synthetic organic material, and thus more android than mechanical, these worker-roboti ultimately overthrow their human masters.
The play was a sensation in Europe, and then a year later, in America, though something was lost in translation. Čapek used robots to criticize soulless Fordism—the “standardization and regimentation” of American capitalism—and hence the US’s political and cultural power in Europe and around the world. (Other Europeans would conceive of the robot in the same way, notably director Fritz Lang and screenwriter Thea von Harbou in the 1927 German film Metropolis.)
But a funny thing happened to these robotic symbols of American capitalism by the mid-twentieth century. They were Americanized by American capitalism. Americans, as Abnet notes, “turned a figure that initially rebelled against the dehumanizing effects of Fordism into a tamed electro-mechanical slave holding aloft a global empire of consumerism.”
Nowhere was this more literal than in the Westinghouse Electric Company’s “simple remotely controlled mechanical men and women” used to advertise the company’s products from 1927 to 1940. “Technology did not have to run amok, Westinghouse’s robots suggested; it could instead become a tamed slave that empowered each individual consumer to become his or her own master.” In the American context, where the language of master and slave was rooted in racism, Westinghouse “connected robots to romanticized white myths about slavery.”
“Americans had always racialized robot-like creations,” continues Abnet, citing the first American automaton (a caricature of a Native American) and the “grotesque minstrel-like caricatures of Black and Asian bodies” that made up automatons in the late nineteenth century.
Westinghouse’s creations, named Herbert Televox, Karina Van Televox, Telelux, Rastus, Willie Vocalite, and Elektro, were promoted as docile domestic workers. Abnet quotes the New York Times’ science and technology editor extolling the benefits of the first of these “mechanical slaves” in 1927: “it obeys without the usual human arguing, impudence or procrastination.”
Rastus, Westinghouse’s Great Depression-era robot, was the most overtly racialized of these corporate robot slaves. Rastus was modeled on a minstrel show character: “black rubber ‘skin,’ overalls, a white shirt, and a pail hat.” In addition, “the robot had a ‘rich, baritone voice’ that would have been read as unmistakably black.” While “all of Westinghouse’s other robots told jokes…Rastus and its blackness were themselves the joke.”
In 1930, Westinghouse’s President explicitly expressed the prevailing white romanticism of slavery. In the company’s Electric Journal, he argued that without the exploitation of the “muscles of others,” there could be “no art, literature, science, leisure, or comfort for anyone.” Rastus’s “tamed black body,” stresses Abnet, “underscored the larger rhetoric of slavery that shaped the fantasy the company offered white consumers.”
“Ultimately, Westinghouse’s robots were not just about more efficiently accomplishing work or ensuring greater leisure time; they were a symbol that deployed racialized slavery in ways that could reassure white Americans of their own freedom, their own mastery over both technology and the bodies of others.”
Čapek’s robots had successfully rebelled, killing all but one human. In America, that couldn’t happen, at least according to the corporations selling the robot idea. But fear of a robot rebellion, like the fear of slave rebellion before the Civil War, remained. Abnet notes that the “most common robot story in American science fiction during the 1920s and 1930s told a story of white men, using their cunning, strength, and willpower to restore their authority over the robots who should be their slaves.” Movies, especially science fiction serials, often told the same story.
A century after R.U.R. and forty years after The Terminator, the uneasiness engendered by robots (and their droid, cyborg, replicant, and AI cousins) persists, reflecting longstanding concerns about labor, autonomy, and power…
Early automatons in the US evolved from symbols of revolt into racialized figures tied to labor and the legacy of slavery: “How America Racialized the Robot,” from @jstordaily.bsky.social.
* Erich Fromm, The Sane Society
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As we move on, we might recall that it was on this date in 1967 that Aretha Franklin’s up-tempo cover of Otis Redding’s “Respect” enter the Billboard Hot 100. It rose steadily over the next several weeks, hitting #1 in June, where it stayed for two weeks and won Franklin two Grammy Awards at the 1968 ceremony, including the first of eight consecutive Grammys for Best Female R&B Vocal Performance. An R&B classic, it has also become a protest anthem, thanks to its connections to both the civil rights movement of the 1960s and the second-wave feminist movement of the 1970s.
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